Ecology

1.
Cain, M. L., Milligan, B. G. & Strand, A. E. Long-distance seed dispersal in plant populations. Am. J. Bot. 87, 1217–1227 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Cousens, R., Dytham, C. & Law, R. Dispersal in Plants: A Population Perspective 1st edn. (Oxford University Press, Oxford, 2008).
Google Scholar 

3.
Jordano, P. Fruits and frugivory. In Seeds: The Ecology of Regeneration in Plant Communities 2nd edn (ed. Fenner, M.) 125–166 (UK CAB International, Wallingford, 2000).
Google Scholar 

4.
Jordano, P., García, C., Godoy, J. A. & García-Castaño, J. L. Differential contribution of frugivores to complex seed dispersal patterns. PNAS 104, 3278–3282 (2007).
CAS  PubMed  Article  ADS  Google Scholar 

5.
Bueno, R. S. et al. Functional redundancy and complementarities of seed dispersal by the last neotropical megafrugivores. PLoS ONE 8, 0056252 (2013).
Article  ADS  CAS  Google Scholar 

6.
Pérez-Méndez, N., Jordano, P., García, C. & Valido, A. The signatures of Anthropocene defaunation: cascading effects of the seed dispersal collapse. Sci. Rep. 6, 24820 (2016).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

7.
Hamrick, J. L., Murawski, D. A. & Nason, J. D. The influence of seed dispersal mechanisms on the genetic structure of tropical tree populations. Vegetatio 107, 281–297 (1993).
Google Scholar 

8.
Mueller, T., Lenz, J., Caprano, T., Fiedler, W. & Böhning-Gaese, K. Large frugivorous birds facilitate functional connectivity of fragmented landscapes. J. Appl. Ecol. 51, 684–692 (2014).
Article  Google Scholar 

9.
Pérez-Méndez, N., Jordano, P. & Valido, A. Persisting in defaunated landscapes: reduced plant population connectivity after seed dispersal collapse. J. Ecol. 106, 936–947 (2018).
Article  Google Scholar 

10.
Schupp, E. W. Quantity, quality and the effectiveness of seed dispersal by animals. Vegetatio 107, 15–29 (1993).
Google Scholar 

11.
Schupp, E. W., Jordano, P. & Gómez, J. M. Seed dispersal effectiveness revisited: a conceptual review. New Phytol. 188, 333–353 (2010).
PubMed  Article  Google Scholar 

12.
Traveset, A. & Richardson, D. M. Mutualistic interactions and biological invasions. Annu. Rev. Ecol. Evol. Syst. 45, 89–113 (2014).
Article  Google Scholar 

13.
Herrera, C. M. Seed dispersal by vertebrates. In Plant—animal interactions, an evolutionary approach (eds Herrera, C. & Pellmyr, O.) 185–209 (Wiley, Oxford, 2002).
Google Scholar 

14.
Vidal, M. M., Pires, M. M. & Guimarães, J. P. R. Large vertebrates as the missing components of seed-dispersal networks. Biol. Conserv. 163, 42–48 (2013).
Article  Google Scholar 

15.
Moleón, M. et al. Rethinking megafauna. Proc. R. Soc. B 287, 20192643 (2020).
PubMed  Article  Google Scholar 

16.
Pires, M. M., Guimarães, P. R., Galetti, M. & Jordano, P. Pleistocene megafaunal extinctions and the functional loss of long-distance seed-dispersal services. Ecography 41, 153–163 (2018).
Article  Google Scholar 

17.
Chen, S. C. & Moles, A. T. A mammoth mouthful? A test of the idea that larger animals ingest larger seeds. Glob. Ecol. Biogeogr. 24, 1269–1280 (2015).
Article  Google Scholar 

18.
Dirzo, R. et al. Defaunation of the anthropocene. Science 345, 401–406 (2014).
CAS  PubMed  Article  ADS  Google Scholar 

19.
Galetti, M. et al. Functional extinction of birds drives rapid evolutionary changes in seed size. Science 340, 1086–1090 (2013).
CAS  PubMed  Article  ADS  Google Scholar 

20.
Pasitschniak-Arts, M. Ursus arctos. Mamm. Species 439, 1–10 (1993).
Article  Google Scholar 

21.
Steyaert, S. M. J. G., Endrestøl, A., Hacklaender, K., Swenson, J. E. & Zedrosser, A. The mating system of the brown bear Ursus arctos. Mamm. Rev. 42, 12–34 (2012).
Article  Google Scholar 

22.
Bojarska, K. & Selva, N. Spatial patterns in brown bears Ursus arctos diet: the role of geographical and environmental factors. Mamm. Rev. 42, 120–143 (2012).
Article  Google Scholar 

23.
Blanchard, B. N. Size and growth patterns of the Yellowstone grizzly bear. Bears Their Biol. Manag. 7, 99–107 (1987).
Article  Google Scholar 

24.
Palomero, G., Fernández-Gil, A. & Naves, J. Reproductive rates of brown bears in the Cantabrian Mountains, Spain. Bears Their Biol. Manag. 9, 129–132 (1997).
Article  Google Scholar 

25.
Welch, C. A., Keay, J., Kendall, K. C. & Robbins, C. T. Constraints on frugivory by bears. Ecology 78, 1105–1119 (1997).
Article  Google Scholar 

26.
Hilderbrand, G. V. et al. The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Can. J. Zool. 77, 132–138 (1999).
Article  Google Scholar 

27.
McLoughlin, P. D., Ferguson, S. H. & Messier, F. Intraspecific variation in home range overlap with habitat quality: a comparison among brown bear populations. Evol. Ecol. 14, 39–60 (2000).
Article  Google Scholar 

28.
Nomura, F. & Higashi, S. Effects of food distribution on the habitat usage of a female brown bear Ursus arctos yesoensis in a beech-forest zone of northernmost Japan. Ecol. Res. 15, 209–217 (2000).
Article  Google Scholar 

29.
Hertel, A. G. et al. Berry production drives bottom-up effects on body mass and reproductive success in an omnivore. Oikos 127, 197–207 (2017).
Article  Google Scholar 

30.
Zalewski, A. Geographical and seasonal variation in food habits and prey size of European pine martens. In Gilbert Martens and Fishers (Martes) in Human-Altered Environments (eds Harrison, D. J. & Fuller, A. K. P.) 77–98 (Springer, Boston, 2005).
Google Scholar 

31.
Soe, E. et al. Europe-wide biogeographical patterns in the diet of an ecologically and epidemiologically important mesopredator, the red fox Vulpes vulpes: a quantitative review. Mamm. Rev. 47, 198–211 (2017).
Article  Google Scholar 

32.
Jaroszewicz, B., Pirożnikow, E. & Sondej, I. Endozoochory by the guild of ungulates in Europe’s primeval forest. Forest Ecol. Manag. 305, 21–28 (2013).
Article  Google Scholar 

33.
Lundgren, E. J., Ramp, D., Ripple, W. J. & Wallach, A. D. Introduced megafauna are rewilding the Anthropocene. Ecography 41, 857–866 (2018).
Article  Google Scholar 

34.
Kowalczyk, R. et al. Foraging plasticity allows a large herbivore to persist in a sheltering forest habitat: DNA metabarcoding diet analysis of the European bison. Forest Ecol. Manag. 449, 117474 (2019).
Article  Google Scholar 

35.
Gebert, C. & Verheyden-Tixier, H. Variation of diet composition of red deer (Cervus elaphus L.) in Europe. Mamm. Rev. 31, 189–201 (2008).
Article  Google Scholar 

36.
Cosyns, E., Delporte, A., Lens, L. & Hoffmann, M. Germination success of temperate grassland species after gut passage through ungulate and rabbit guts. J. Ecol. 93, 353–361 (2005).
Article  Google Scholar 

37.
Albrecht, J. et al. Humans and climate change drove the Holocene decline of the brown bear. Sci. Rep. 7, 1–11 (2017).
CAS  Article  Google Scholar 

38.
Hertel, A. G. et al. Bears and berries: species-specific selective foraging on a patchily distributed food resource in a human-altered landscape. Behav. Ecol. Sociobiol. 70, 831–842 (2016).
PubMed  PubMed Central  Article  Google Scholar 

39.
Valido, A., Schaefer, H. M. & Jordano, P. Colour, design and reward: phenotypic integration of fleshy fruit displays. J. Evol. Biol. 24, 751–760 (2011).
CAS  PubMed  Article  Google Scholar 

40.
MacHutchon, A. G. & Wellwood, D. W. Grizzly bear food habits in the northern Yukon, Canada. Ursus 14, 225–235 (2003).
Google Scholar 

41.
Sato, Y., Mano, T. & Takatsuki, S. Stomach contents of brown bears Ursus arctos in Hokkaido, Japan. Wildl. Biol. 11, 133–144 (2005).
Article  Google Scholar 

42.
Lalleroni, A., Quenette, P.-Y., Daufresne, T., Pellerin, M. & Baltzinger, C. Exploring the potential of brown bear (Ursus arctos) as a long-distance seed disperser: a pilot study in South-Western Europe. Mammalia 81, 1–9 (2017).
Article  Google Scholar 

43.
Baldwin, R. A. & Bender, L. C. Foods and nutritional components of diets of black bear in Rocky Mountain National Park, Colorado. Can. J. Zool. 87, 1000–1008 (2009).
CAS  Article  Google Scholar 

44.
Koike, S. Long-term trends in food habits of Asiatic black bears in the Misaka Mountains on the Pacific coast of central Japan. Mamm. Biol. 75, 17–28 (2010).
Article  Google Scholar 

45.
Campos-Arceiz, A. & Blake, S. Megagardeners of the forest—the role of elephants in seed dispersal. Acta Oecol. 37, 542–553 (2011).
Article  ADS  Google Scholar 

46.
Willson, M. F. & Gende, S. M. Seed dispersal by brown bears, Ursus arctos, in southeastern Alaska. Can. Field-Nat. 118, 499–503 (2004).
Article  Google Scholar 

47.
Naoe, S. et al. Mountain-climbing bears protect cherry species from global warming through vertical seed dispersal. Curr. Biol. 26, 315–316 (2016).
Article  CAS  Google Scholar 

48.
Naoe, S. et al. Downhill seed dispersal by temperate mammals: a potential threat to plant escape from global warming. Sci. Rep. 9, 1–11 (2019).
CAS  Article  Google Scholar 

49.
McConkey, K. R. & O’Farrill, G. Loss of seed dispersal before the loss of seed dispersers. Biol. Conserv. 201, 38–49 (2016).
Article  Google Scholar 

50.
Skuban, M., Finďo, S. & Kajba, M. Human impacts on bear feeding habits and habitat selection in the Poľana Mountains, Slovakia. Eur. J. Wildl. Res. 62, 353–364 (2016).
Article  Google Scholar 

51.
Štofík, J., Merganič, J., Merganičová, K., Bučko, J. & Saniga, M. Brown bear winter feeding ecology in the area with supplementary feeding—Eastern Carpathians (Slovakia). Pol. J. Ecol. 64, 277–288 (2016).
Article  Google Scholar 

52.
Selva, N. et al. Supplementary ungulate feeding affects movement behavior of brown bears. Basic Appl. Ecol. 24, 68–76 (2017).
Article  Google Scholar 

53.
López-Bao, J. V. & González-Varo, J. P. Frugivory and spatial patterns of seed deposition by carnivorous mammals in anthropogenic landscapes: a multi-scale approach. PLoS ONE 6, e14569 (2011).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

54.
Traveset, A. & Willson, M. F. Effect of birds and bears on seed germination of fleshy-fruited plants in temperate rainforests of southeast Alaska. Oikos 80, 89–95 (1997).
Article  Google Scholar 

55.
Nowak, J. & Crone, E. E. It is good to be eaten by a bear: effects of ingestion on seed germination. Am. Midl. Nat. 167, 205–209 (2012).
Article  Google Scholar 

56.
Steyaert, S. M. J. G., Hertel, A. G. & Swenson, J. E. Endozoochory by brown bears stimulates germination in bilberry. Wildl. Biol. 2019, wlb.00573 (2019).
Article  Google Scholar 

57.
Samuels, I. A. & Levey, D. J. Effects of gut passage on seed germination: do experiments answer the questions they ask?. Funct. Ecol. 19, 365–368 (2005).
Article  Google Scholar 

58.
Valido, A. & Olesen, J. M. The importance of lizards as frugivores and seed dispersers. In Seed Dispersal: Theory and its Application in a Changing World (eds Dennis, A. J. et al.) 124–147 (CAB International, Wallingford, 2007).
Google Scholar 

59.
Traveset, A. Effect of seed passage through vertebrate frugivores’ guts on germination: a review. Perspect. Plant. Ecol. Syst. 1, 151–190 (1998).
Article  Google Scholar 

60.
Eriksson, O. & Fröborg, H. “Windows of opportunity” for recruitment in long-lived clonal plants: experimental studies of seedling establishment in Vaccinium shrubs. Can J. Bot. 74, 1369–1374 (1996).
Article  Google Scholar 

61.
Jansen, P. A. et al. Thieving rodents as substitute dispersers of megafaunal seeds. PNAS 109, 12610–12615 (2012).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

62.
Koike, S. et al. Seed removal and survival in Asiatic black bears Ursus thibetanus scats: effect of rodents as secondary seed dispersers. Wildlife Biol. 18, 24–34 (2012).
Article  Google Scholar 

63.
Bartoń, K. A., Zwijacz-Kozica, T., Zięba, F., Sergiel, A. & Selva, N. Bears without borders: long-distance movement in human-dominated landscapes. Glob. Ecol. Conserv. 17, e00541 (2019).
Article  Google Scholar 

64.
Willson, M. F. & Traveset, A. The ecology of seed dispersal. In Seeds: The Ecology of Regeneration in Plant Communities 2nd edn (ed. Fenner, M.) 85–111 (CAB International, Wallingford, 2000).
Google Scholar 

65.
Elfström, M., Støen, O.-G., Zedrosser, A., Warrington, I. & Swenson, J. E. Gut retention times in captive brown bears Ursus arctos. Wildl. Biol. 19, 317–324 (2013).
Article  Google Scholar 

66.
Koike, S. et al. Estimate of the seed shadow created by the Asiatic black bear Ursus thibetanus and its characteristics as a seed disperser in Japanese cool-temperate forest. Oikos 120, 280–290 (2010).
Article  Google Scholar 

67.
Hickey, J. R., Flynn, R. W., Buskirk, S. W., Gerow, K. G. & Willson, M. F. An evaluation of a mammalian predator, Martes americana, as a disperser of seeds. Oikos 87, 499–508 (1999).
Article  Google Scholar 

68.
Terakawa, M., Isagi, Y., Matsui, K. & Yumoto, T. Microsatellite analysis of the maternal origin of Myrica rubra seeds in the feces of Japanese macaques. Ecol. Res. 24, 663–670 (2009).
CAS  Article  Google Scholar 

69.
González-Varo, J. P., López-Bao, J. V. & Guitián, J. Functional diversity among seed dispersal kernels generated by carnivorous mammals. J. Anim. Ecol. 82, 562–571 (2013).
PubMed  Article  Google Scholar 

70.
Tsuji, Y., Okumura, T., Kitahara, M. & Jiang, Z. Estimated seed shadow generated by Japanese martens (Martes melampus): comparison with forest-dwelling animals in Japan. Zool. Sci. 33, 352–357 (2016).
Article  Google Scholar 

71.
Santini, L. et al. Ecological correlates of dispersal distance in terrestrial mammals. Hystrix 24, 181–186 (2013).
Google Scholar 

72.
Bunney, K., Bond, W. J. & Henley, M. Seed dispersal kernel of the largest surviving megaherbivore—the African savanna elephant. Biotropica 49, 395–401 (2017).
Article  Google Scholar 

73.
Galetti, et al. Ecological and evolutionary legacy of megafauna extinctions. Biol. Rev. 93, 845–862 (2018).
PubMed  Article  Google Scholar 

74.
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).
Article  Google Scholar 

75.
Nin, S., Petrucci, W. A., Del Bubba, M., Ancillotti, C. & Giordani, E. Effects of environmental factors on seed germination and seedling establishment in bilberry (Vaccinium myrtillus L.). Sci. Hortic. 226, 241–249 (2017).
Article  Google Scholar 

76.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

77.
Oksanen, J. et al. Vegan package: community ecology package. R package version 2.5–6 (2019).

78.
Silva, L. J. D. & Medeiros, A. D. D. SeedCalc, a new automated R software tool for germination and seedling length data processing. J. Seed. Sci. 41, 250–257 (2019).
Article  Google Scholar 

79.
R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2017).

80.
South, A. rworldmap: a new R package for mapping global data. R J. 3, 35–43 (2011).
Article  Google Scholar 

81.
IUCN SSC Bear Specialist Group. Ursus arctos. The IUCN Red List of Threatened Species. Version 2017-3 (2017). http://www.iucnredlist.org (Downloaded in May 2020). More

1.
Paupy, C., Delatte, H., Bagny, L., Corbel, V. & Fontenille, D. Aedes albopictus, an arbovirus vector: From the darkness to the light. Microbes Infect. 11, 1177–1185 (2009).
CAS  PubMed  Article  Google Scholar 
2.
Scholte, E. J. & Schaffner, F. Waiting for the tiger: Establishment and spread of the Aedes albopictus mosquito in Europe. In Emerging Pests and Vector-Borne Diseases in Europe (eds Takken, W. & Knols, B. G. J.) 241–260 (Wageningen Academic Publishers, Wageningen, 2007).
Google Scholar 

3.
Kraemer, M. U. G. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863. https://doi.org/10.1038/s41564-019-0376-y (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Kuhlisch, C., Kampen, H. & Walther, D. The Asian tiger mosquito Aedes albopictus (Diptera: Culicidae) in Central Germany: Surveillance in its northernmost distribution area. Acta Trop. 188, 78–85 (2018).
PubMed  Article  PubMed Central  Google Scholar 

5.
Kampen, H. & Walther, D. Vector potential of mosquito species (Diptera: Culicidae) occurring in Central Europe. In Mosquito-borne Diseases: Implications for Public Health, Parasitol. Res. Monogr. Vol. 10 (eds Benelli, G. & Mehlhorn, H.) 41–68 (Springer, Heidelberg, 2018).
Google Scholar 

6.
Kampen, H., Schuhbauer, A. & Walther, D. Emerging mosquito species in Germany—A synopsis after 6 years of mosquito monitoring (2011–2016). Parasitol. Res. 116, 3253–3263 (2017).
PubMed  Article  PubMed Central  Google Scholar 

7.
Ziegler, U. et al. West Nile virus epidemic in Germany triggered by epizootic emergence, 2019. Viruses 12, 448. https://doi.org/10.3390/v12040448 (2020).
Article  PubMed Central  Google Scholar 

8.
Sullivan, B. L. et al. The eBird enterprise: An integrated approach to development and application of citizen science. Biol. Conserv. 169, 31–40 (2014).
Article  Google Scholar 

9.
Oltra, A., Palmer, J. R. B. & Bartumeus, F. AtrapaelTigre.com: Enlisting citizen-scientists in the war on tiger mosquitoes. In European Handbook of Crowdsourced Geographic Information (eds Capineri, C. et al.) 295–308 (Ubiquity Press, London, 2016).
Google Scholar 

10.
Heigl, F., Horvath, K., Laaha, G. & Zaller, J. G. Amphibian and reptile road-kills on tertiary roads in relation to landscape structure: Using a citizen science approach with open-access land cover data. BMC Ecol. 17, 24. https://doi.org/10.1186/s12898-017-0134-z (2017).
Article  PubMed  PubMed Central  Google Scholar 

11.
Walther, D. & Kampen, H. The citizen science project “Mueckenatlas” helps monitor the distribution and spread of invasive mosquito species in Germany. J. Med. Entomol. 54, 1790–1794 (2017).
PubMed  PubMed Central  Article  Google Scholar 

12.
Pocock, M. J. O., Roy, H. E., Fox, R., Ellis, W. N. & Botham, M. Citizen science and invasive alien species: Predicting the detection of the oak processionary moth Thaumetopoea processionea by moth recorders. Biol. Conserv. 208, 146–154 (2017).
Article  Google Scholar 

13.
Kampen, H., Kronefeld, M., Zielke, D. & Werner, D. Further specimens of the Asian tiger mosquito Aedes albopictus (Diptera, Culicidae) trapped in Southwest Germany. Parasitol. Res. 112, 905–907 (2013).
PubMed  Article  PubMed Central  Google Scholar 

14.
Kampen, H., Kuhlisch, C., Fröhlich, A., Scheuch, D. E. & Walther, D. Occurrence and spread of the invasive Asian bush mosquito Aedes japonicus japonicus (Diptera: Culicidae) in West and North Germany since detection in 2012 and 2013, respectively. PLoS ONE 11, e0167948. https://doi.org/10.1371/journal.pone.0167948 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Walther, D., Scheuch, D. E. & Kampen, H. The invasive Asian tiger mosquito Aedes albopictus (Diptera: Culicidae) in Germany: Local reproduction and overwintering. Acta Trop. 166, 186–192 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Werner, D. & Kampen, H. Aedes albopictus breeding in southern Germany, 2014. Parasitol. Res. 114, 831–834 (2015).
PubMed  Article  PubMed Central  Google Scholar 

17.
Zielke, D. E., Walther, D. & Kampen, H. Newly discovered population of Aedes japonicus japonicus (Diptera: Culicidae) in upper Bavaria, Germany, and Salzburg, Austria, is closely related to the Austrian/Slovenian bush mosquito population. Parasit. Vectors 9, 163. https://doi.org/10.1186/s13071-016-1447-z (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

18.
Kampen, H., Jansen, S., Schmidt-Chanasit, J. & Walther, D. Indoor development of Aedes aegypti in Germany, 2016. Euro Surveill. 21, 30407. https://doi.org/10.2807/1560-7917.ES.2016.21.47.30407 (2016).
Article  PubMed  PubMed Central  Google Scholar 

19.
Werner, D., Zielke, D. E. & Kampen, H. First record of Aedes koreicus (Diptera: Culicidae) in Germany. Parasitol. Res. 115, 1331–1334 (2016).
PubMed  Article  Google Scholar 

20.
Kampen, H., Kronefeld, M., Zielke, D. & Werner, D. Three rarely encountered and one new Culiseta species (Diptera: Culicidae) in Germany. J. Eur. Mosq. Control Assoc. 31, 36–39 (2013).
Google Scholar 

21.
Kampen, H., Kronefeld, M., Zielke, D. & Werner, D. Some new, rare and less frequent mosquito species (Diptera, Culicidae) recently collected in Germany. Mitt. Dtsch. Ges. Allg. Angew. Ent. 19, 123–130 (2014).
Google Scholar 

22.
Isaac, N. J. B. et al. Statistics for citizen science: Extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060 (2014).
Article  Google Scholar 

23.
Kuhlisch, C., Kampen, H. & Werner, D. On the distribution and ecology of Culiseta (Culicella) ochroptera (Peus) (Diptera: Culicidae) in Germany. Zootaxa 4576, 544–558 (2019).
Article  Google Scholar 

24.
Heym, E. C., Schröder, J., Kampen, H. & Walther, D. The nuisance mosquito Anopheles plumbeus (Stephens, 1828) in Germany—A questionnaire survey may help support surveillance and control. Front. Public Health 5, 278. https://doi.org/10.3389/fpubh.2017.00278 (2017).
Article  PubMed  PubMed Central  Google Scholar 

25.
Zielke, D. Population genetics and distribution of the invasive mosquito Aedes japonicus japonicus (Diptera: Culicidae) in Germany and Europe (Ph.D. thesis, University of Greifswald, 2015).

26.
Kerkow, A. et al. What makes the Asian bush mosquito Aedes japonicus japonicus feel comfortable in Germany? A fuzzy modelling approach. Parasit. Vectors 12, 106. https://doi.org/10.1186/s13071-019-3368-0 (2019).
Article  PubMed  PubMed Central  Google Scholar 

27.
Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour. Sci. Rep. 6, 33051. https://doi.org/10.1038/srep33051 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

28.
Seymour, V. & Haklay, M. Exploring engagement characteristics and behaviours of environmental volunteers. Citiz. Sci. Theory Pract. 2, 5. https://doi.org/10.5334/cstp.66 (2017).
Article  Google Scholar 

29.
Mair, L. & Ruete, A. Explaining spatial variation in the recording effort of citizen science data across multiple taxa. PLoS ONE 11, e0147796. https://doi.org/10.1371/journal.pone.0147796 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

30.
Tiago, P., Ceia-Hasse, A., Marques, T. A., Capinha, C. & Pereira, H. M. Spatial distribution of citizen science casuistic observations for different taxonomic groups. Sci. Rep. 7, 12832. https://doi.org/10.1038/s41598-017-13130-8 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

31.
Chandler, M. et al. Contributions to publications and management plans from 7 years of citizen science: Use of a novel evaluation tool on Earthwatch-supported projects. Biol. Conserv. 208, 163–173 (2017).
Article  Google Scholar 

32.
Kelling, S. et al. Taking a “Big Data” approach to data quality in a citizen science project. Ambio 44, 601–611 (2015).
PubMed  PubMed Central  Article  Google Scholar 

33.
Becker, N. et al. Mosquitoes and Their Control (Springer, Heidelberg, 2010).
Google Scholar 

34.
Schaffner, F. et al. The Mosquitoes of Europe. An Identification and Training Programme (CD-Rom) (IRD Éditions & EID Méditerrannée, Montpellier, 2001).
Google Scholar 

35.
Heym, E. C., Kampen, H. & Walther, D. Mosquito species composition and phenology (Diptera, Culicidae) in two German zoological gardens imply different risks of mosquito-borne pathogen transmission. J. Vector Ecol. 43, 80–88 (2018).
PubMed  Article  PubMed Central  Google Scholar 

36.
European Union, Copernicus Land Monitoring Service. (European Environment Agency (EEA), 2012).

37.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).
Google Scholar 

38.
Tennekes, M. treemap: Treemap Visualization. R package version 2.4-2 (2017).

39.
Comtois, D. summarytools: Tools to Quickly and Neatly Summarize Data. R package version 0.9.3 (2019).

40.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018).
Google Scholar 

41.
Alender, B. Understanding volunteer motivations to participate in citizen science projects: A deeper look at water quality monitoring. J. Sci. Commun. 15, A04. https://doi.org/10.22323/2.15030204 (2016).
Article  Google Scholar 

42.
Domroese, M. C. & Johnson, E. A. Why watch bees? Motivations of citizen science volunteers in the Great Pollinator Project. Biol. Conserv. 208, 40–47 (2017).
Article  Google Scholar 

43.
Geoghegan, H., Dyke, A., Pateman, R., West, S. & Everett, G. Understanding Motivations for Citizen Science. Final report on behalf of UKEOF (University of Reading, Stockholm Environment Institute (University of York) and University of the West of England, 2016).

44.
Land-Zandstra, A. M., Devilee, J. L., Snik, F., Buurmeijer, F. & van den Broek, J. M. Citizen science on a smartphone: Participants’ motivations and learning. Public Underst. Sci. 25, 45–60 (2016).
PubMed  Article  PubMed Central  Google Scholar 

45.
GeoBasis-DE/BKG. Bundesamt für Kartographie und Geodäsie. WFS service. http://sg.geodatenzentrum.de/wfs_dlm250_inspire?request=GetCapabilities&service=wfs (2019).

46.
Statistisches Bundesamt, Wiesbaden. https://ergebnisse.zensus2011.de/ (2015).

47.
Deutscher Wetterdienst (German Weather Service, single values averaged). https://opendata.dwd.de/climate_environment/ (2020).

48.
Pebesma, E. Simple Features for R: Standardized support for spatial vector data. R J. 10, 439–446. https://doi.org/10.32614/rj-2018-009 (2018).
Article  Google Scholar 

49.
Cheng, J., Karambelkar, B. & Xie, Y. leaflet: Create Interactive Web Maps with the JavaScript ‘Leaflet’ Library. R package version 2.0.3 (2019).

50.
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 2.8-19 (2019).

51.
Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4-3 (2019).

52.
Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC Press, Boca Raton, 2015).
Google Scholar 

53.
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2019).
Google Scholar 

54.
Kleiber, C. & Zeileis, A. countreg: Count Data Regression. R package version 0.2-1 (2016).

55.
Barton, K. MuMIn: Multi-model Inference. R package version 1.43.6 (2019).

56.
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, New York, 2002).
Google Scholar 

57.
Zeileis, A., Kleiber, C. & Jackman, S. Regression models for count data in R. J. Stat. Softw. https://doi.org/10.18637/jss.v027.i08 (2008).
Article  Google Scholar 

58.
Bertone, M. A. et al. Arthropods of the great indoors: Characterizing diversity inside urban and suburban homes. PeerJ 4, e1582. https://doi.org/10.7717/peerj.1582 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

59.
Epps, M. J., Menninger, H. L., LaSala, N. & Dunn, R. R. Too big to be noticed: Cryptic invasion of Asian camel crickets in North American houses. PeerJ 2, e523. https://doi.org/10.7717/peerj.523 (2014).
Article  PubMed  PubMed Central  Google Scholar 

60.
Dunn, R. R. & Beasley, D. E. Democratizing evolutionary biology, lessons from insects. Curr. Opin. Insect Sci. 18, 89–92 (2016).
PubMed  Article  PubMed Central  Google Scholar 

61.
Hamer, S. A., Curtis-Robles, R. & Hamer, G. L. Contributions of citizen scientists to arthropod vector data in the age of digital epidemiology. Curr. Opin. Insect Sci. 28, 98–104 (2018).
PubMed  Article  PubMed Central  Google Scholar 

62.
Freitag, H., Pangantihon, C. V. & Njunjic, I. Three new species of Grouvellinus Champion, 1923 from Maliau Basin, Sabah, Borneo, discovered by citizen scientists during the first Taxon Expedition (Insecta, Coleoptera, Elmidae). ZooKeys 754, 1–21 (2018).
Article  Google Scholar 

63.
Higa, M. et al. Mapping large-scale bird distributions using occupancy models and citizen data with spatially biased sampling effort. Divers. Distrib. 21, 46–54 (2015).
Article  Google Scholar 

64.
Caputo, B. et al. ZanzaMapp: A scalable citizen science tool to monitor perception of mosquito abundance and nuisance in Italy and beyond. Int. J. Environ. Res. Public Health 17, 7872 (2020).
PubMed Central  Article  Google Scholar 

65.
Curtis-Robles, R., Wozniak, E. J., Auckland, L. D., Hamer, G. L. & Hamer, S. A. Combining public health education and disease ecology research: Using citizen science to assess Chagas disease entomological risk in Texas. PLoS Negl. Trop. Dis. 9, e0004235. https://doi.org/10.1371/journal.pntd.0004235 (2015).
Article  PubMed  PubMed Central  Google Scholar 

66.
Soroye, P., Ahmed, N. & Kerr, J. T. Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research. Glob. Change Biol. 24, 5281–5291 (2018).
ADS  Article  Google Scholar 

67.
Statistisches Bundesamt. Bevölkerungsdichte (Einwohner je km2) in Deutschland nach Bundesländern zum 31. Dezember 2019 (Statista GmbH, 2020).

68.
Newman, G. et al. Leveraging the power of place in citizen science for effective conservation decision making. Biol. Conserv. 208, 55–64 (2017).
Article  Google Scholar 

69.
Becker, N. Microbial control of mosquitoes: Management of the upper Rhine mosquito population as a model programme. Parasitol. Today 13, 485–487 (1997).
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Peus, F. Beiträge zur Faunistik und Ökologie der einheimischen Culiciden. I. Teil. Zeitschr. Desinfekt. 21(76–81), 92–98 (1929).
Google Scholar 

71.
Vezzani, D. Artificial container-breeding mosquitoes and cemeteries: A perfect match. Trop. Med. Int. Health 12, 299–313 (2007).
PubMed  Article  Google Scholar 

72.
Scharnweber, T. et al. Drought matters—declining precipitation influences growth of Fagus sylvatica L. and Quercus robur L. in north-eastern Germany. Forest Ecol. Manag. 262, 947–961 (2011).
Article  Google Scholar 

73.
Oedekoven, C. S. et al. Attributing changes in the distribution of species abundance to weather variables using the example of British breeding birds. Methods Ecol. Evol. 8, 1690–1702 (2017).
Article  Google Scholar 

74.
Catlin-Groves, C. L. The citizen science landscape: From volunteers to citizen sensors and beyond. Int. J. Zool. 2012, 349630 (2012).
Article  Google Scholar 

75.
Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179 (2019).
PubMed  PubMed Central  Article  Google Scholar 

76.
Weiser, E. L. et al. Balancing sampling intensity against spatial coverage for a community science monitoring programme. J. Appl. Ecol. 56, 2252–2263 (2019).
Article  Google Scholar 

77.
Mwangungulu, S. P. et al. Crowdsourcing vector surveillance: Using community knowledge and experiences to predict densities and distribution of outdoor-biting mosquitoes in rural Tanzania. PLoS ONE 11, e0156388. https://doi.org/10.1371/journal.pone.0156388 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

78.
Eritja, R. et al. First detection of Aedes japonicus in Spain: An unexpected finding triggered by citizen science. Parasit. Vectors 12, 53. https://doi.org/10.1186/s13071-019-3317-y (2019).
Article  PubMed  PubMed Central  Google Scholar  More

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