Ecology

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1.Mcguire, A. D. et al. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 79, 523–555 (2009). Details Arctic changes under RCP scenarios using a multi-model approach forecasting vegetation offsets of some carbon emissions.
Google Scholar 
2.Brandt, J. P. The extent of the North American boreal zone. Environ. Rev. 17, 101–161 (2009).
Google Scholar 
3.Chadburn, S. et al. Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models. Biogeosciences 14, 5143–5169 (2017).CAS 

Google Scholar 
4.Karjalainen, O. et al. Data descriptor: circumpolar permafrost maps and geohazard indices for near-future infrastructure risk assessments. Sci. Data 6, 190037 (2019).
Google Scholar 
5.Hjort, J. et al. Degrading permafrost puts Arctic infrastructure at risk by mid-century. Nat. Commun. 9, 5147 (2018).CAS 

Google Scholar 
6.Abramov, A., Vishnivetskaya, T. & Rivkina, E. Are permafrost microorganisms as old as permafrost? FEMS Microbiol. Ecol. 97, fiaa260 (2021).CAS 

Google Scholar 
7.Ricketts, M. P. et al. The effects of warming and soil chemistry on bacterial community structure in Arctic tundra soils. Soil Biol. Biochem. 148, 107882 (2020).CAS 

Google Scholar 
8.Hultman, J. et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208–212 (2015).CAS 

Google Scholar 
9.Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020). Seminal paper that identifies abrupt permafrost thaw as an important mechanism in rapid Arctic change.CAS 

Google Scholar 
10.Nikrad, M. P., Kerkhof, L. J. & Aggblom, M. M. The subzero microbiome: microbial activity in frozen and thawing soils. FEMS Microbiol. Ecol. 92, fiw081 (2016).
Google Scholar 
11.Turetsky, M. R. et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–24 (2019).CAS 

Google Scholar 
12.Wild, B. et al. Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proc. Natl Acad. Sci. USA 116, 10280–10285 (2019).CAS 

Google Scholar 
13.Anthony, K. W. et al. 21st-century modeled permafrost carbon emissions accelerated by abrupt thaw beneath lakes. Nat. Commun. 9, 3262 (2018).
Google Scholar 
14.Schaefer, K., Lantuit, H., Romanovsky, V. E., Schuur, E. A. G. & Witt, R. The impact of the permafrost carbon feedback on global climate. Environ. Res. Lett. 9, 085003 (2014).CAS 

Google Scholar 
15.Hong, E., Perkins, R. & Trainor, S. Thaw settlement hazard of permafrost related to climate warming in Alaska. Arctic 67, 93–103 (2014).
Google Scholar 
16.Trofimenko, Y. V., Evgenev, G. I. & Shashina, E. V. Functional loss risks of highways in permafrost areas due to climate change. Procedia Eng. 189, 258–264 (2017).
Google Scholar 
17.Wurzbacher, C., Nilsson, R. H., Rautio, M. & Peura, S. Poorly known microbial taxa dominate the microbiome of permafrost thaw ponds. ISME J. 11, 1938–1941 (2017).
Google Scholar 
18.Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018).CAS 

Google Scholar 
19.Gross, M. Permafrost thaw releases problems. Curr. Biol. 29, R39–R41 (2019).CAS 

Google Scholar 
20.Walsh, M. G., De Smalen, A. W. & Mor, S. M. Climatic influence on anthrax suitability in warming northern latitudes. Sci. Rep. 8, 9269 (2018).
Google Scholar 
21.Zolkos, S. et al. Mercury export from Arctic great rivers. Environ. Sci. Technol. 54, 4140–4148 (2020).CAS 

Google Scholar 
22.Ewing, S. A. et al. Uranium isotopes and dissolved organic carbon in loess permafrost: modeling the age of ancient ice. Geochim. Cosmochim. Acta 152, 143–165 (2015).CAS 

Google Scholar 
23.Eriksson, M. On Weapons Plutonium in the Arctic Environment (Thule, Greenland). PhD thesis, Lund Univ. (2002).24.Colgan, W. et al. The abandoned ice sheet base at Camp Century, Greenland, in a warming climate. Geophys. Res. Lett. 43, 8091–8096 (2016).
Google Scholar 
25.Anisimov, O., Kokorev, V. & Zhiltcova, Y. Arctic ecosystems and their services under changing climate: predictive-modeling assessment. Geogr. Rev. 107, 108–124 (2017).
Google Scholar 
26.Pelletier, M., Allard, M. & Levesque, E. Ecosystem changes across a gradient of permafrost degradation in subarctic Québec (Tasiapik Valley, Nunavik, Canada). Arct. Sci. 5, 1–26 (2019).
Google Scholar 
27.Perryman, C. R. et al. Heavy metals in the Arctic: distribution and enrichment of five metals in Alaskan soils. PLoS ONE 15, e0233297 (2020).CAS 

Google Scholar 
28.Gilichinsky, D. A. & Rivkina, E. M. Permafrost microbiology. Encycl. Earth Sci. Ser. 6, 726–732 (1995). Details the (at the time) emergent field of permafrost microbiology, extremophilic species and future prospects for emergent microbes.
Google Scholar 
29.Steven, B., Léveillé, R., Pollard, W. H. & Whyte, L. G. Microbial ecology and biodiversity in permafrost. Extremophiles 10, 259–267 (2006).
Google Scholar 
30.Voigt, C. et al. Warming of subarctic tundra increases emissions of all three important greenhouse gases—carbon dioxide, methane, and nitrous oxide. Glob. Change Biol. 23, 3121–3138 (2017).
Google Scholar 
31.Mackelprang, R., Saleska, S. R., Jacobsen, C. S., Jansson, J. K. & Taş, N. Permafrost meta-omics and climate change. Annu. Rev. Earth Planet. Sci. 44, 439–462 (2016).CAS 

Google Scholar 
32.Graham, D. E. et al. Microbes in thawing permafrost: the unknown variable in the climate change equation. ISME J. 6, 709–712 (2012).CAS 

Google Scholar 
33.Abbott, B. W. et al. Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment. Environ. Res. Lett. 11, 034014 (2016).
Google Scholar 
34.Ren, J. et al. Biomagnification of persistent organic pollutants along a high-altitude aquatic food chain in the Tibetan Plateau: processes and mechanisms. Environ. Pollut. https://doi.org/10.1016/j.envpol.2016.10.019 (2016).35.Dean, J. F. et al. Abundant pre-industrial carbon detected in Canadian Arctic headwaters: implications for the permafrost carbon feedback. Environ. Res. Lett. 13, 34024 (2018).
Google Scholar 
36.Jeffries, M. O., Overland, J. E. & Perovich, D. K. The Arctic shifts to a new normal. Phys. Today 66, 35–40 (2013).
Google Scholar 
37.El-Sayed, A. & Kamel, M. Future threat from the past. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-020-11234-9 (2020).38.Houwenhuyse, S., Macke, E., Reyserhove, L., Bulteel, L. & Decaestecker, E. Back to the future in a petri dish: origin and impact of resurrected microbes in natural populations. Evol. Appl. 11, 29–41 (2018).
Google Scholar 
39.Miner, K. R. et al. Organochlorine pollutants within a polythermal glacier in the Interior Eastern Alaska Range. Water 10, 1157 (2018).
Google Scholar 
40.Li, F. et al. Arctic sea-ice loss intensifies aerosol transport to the Tibetan Plateau. Nat. Clim. Change 10, 1037–1044 (2020).CAS 

Google Scholar 
41.Eriksson, M., Lindahl, P., Roos, P., Dahlgaard, H. & Holm, E. U, Pu, and Am nuclear signatures of the thule hydrogen bomb debris. Environ. Sci. Technol. 42, 4717–4722 (2008).CAS 

Google Scholar 
42.Lind, O. C. et al. Characterization of U/Pu particles originating from the nuclear weapon accidents at Palomares, Spain, 1966 and Thule, Greenland, 1968. Sci. Total Environ. 376, 294–305 (2007).CAS 

Google Scholar 
43.Slemmons, K. E. H., Saros, J. E. & Simon, K. The influence of glacial meltwater on alpine aquatic ecosystems: a review. Environ. Sci. Process. Impacts 15, 1794 (2013).CAS 

Google Scholar 
44.Bidleman, T. F., Jantunen, L. M., Kurt-Karakus, P. B. & Wong, F. Chiral persistent organic pollutants as tracers of atmospheric sources and fate: review and prospects for investigating climate change influences. Atmos. Pollut. Res. 3, 371–382 (2012).CAS 

Google Scholar 
45.Chen, M. et al. Release of perfluoroalkyl substances from melting glacier of the Tibetan Plateau: insights into the impact of global warming on the cycling of emerging pollutants. J. Geophys. Res. Atmos. 124, 7442–7456 (2019).
Google Scholar 
46.Goodman, S. & Kertysova, K. The Nuclearisation of the Russian Arctic: New Reactors, New Risks (European Leadership Network, 2020); https://www.europeanleadershipnetwork.org/wp-content/uploads/2020/06/The-nuclearisation-of-the-Russian-Arctic-2.pdf47.Byrne, S. et al. Persistent organochlorine pesticide exposure related to a formerly used defense site on St. Lawrence Island, Alaska: data from sentinel fish and human sera. Toxicol. Environ. Health 78, 37–54 (2015).
Google Scholar 
48.The National Academies of Sciences Understanding and Responding to Global Health Security Risks from Microbial Threats in the Arctic (National Academies Press, 2020); https://doi.org/10.17226/2588749.Edwards, A. et al. Microbial genomics amidst the Arctic crisis. Microb. Genom. 6, e000375 (2020). Catalogues known genomic diversity, evolution dynamics and environment of Arctic microbes.
Google Scholar 
50.Botnen, S. S., Mundra, S., Kauserud, H. & Eidesen, P. B. Glacier retreat in the high Arctic: opportunity or threat for ectomycorrhizal diversity? FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiaa171 (2020).51.Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).CAS 

Google Scholar 
52.Ward, C. P., Nalven, S. G., Crump, B. C., Kling, G. W. & Cory, R. M. Photochemical alteration of organic carbon draining permafrost soils shifts microbial metabolic pathways and stimulates respiration. Nat. Commun. 8, 772 (2017).
Google Scholar 
53.Taş, N. et al. Landscape topography structures the soil microbiome in Arctic polygonal tundra. Nat. Commun. 9, 777 (2018).
Google Scholar 
54.Price, P. B. Microbial genesis, life and death in glacial ice. Can. J. Microbiol. 55, 1–11 (2009).CAS 

Google Scholar 
55.Niederberger, T. D. et al. Microbial characterization of a subzero, hypersaline methane seep in the Canadian high Arctic. ISME J. 4, 1326–1339 (2010).CAS 

Google Scholar 
56.Malavin, S., Shmakova, L., Claverie, J. M. & Rivkina, E. Frozen Zoo: a collection of permafrost samples containing viable protists and their viruses. Biodivers. Data J. 8, e51586 (2020).
Google Scholar 
57.Gilichinsky, D., Rivkina, E., Shcherbakova, V., Laurinavichuis, K. & Tiedje, J. Supercooled water brines within permafrost—an unknown ecological niche for microorganisms: a model for astrobiology. Astrobiology 3, 331–341 (2003).CAS 

Google Scholar 
58.Legendre, M. et al. Thirty-thousand-year-old distant relative of giant icosahedral DNA viruses with a pandoravirus morphology. Proc. Natl Acad. Sci. USA 111, 4274–4279 (2014).CAS 

Google Scholar 
59.Legendre, M. et al. In-depth study of Mollivirus sibericum, a new 30,000-yold giant virus infecting Acanthamoeba. Proc. Natl Acad. Sci. USA 112, E5327–E5335 (2015).CAS 

Google Scholar 
60.MacKelprang, R. et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368–371 (2011). Uses deep metagenomic sequencing to map the impacts of thaw on the Arctic microbial community structure and genomics.CAS 

Google Scholar 
61.Mühlemann, B. et al. Diverse variola virus (smallpox) strains were widespread in northern Europe in the Viking age. Science 369, eaaw8977 (2020).
Google Scholar 
62.Ng, T. F. F. et al. Preservation of viral genomes in 700-y-old caribou feces from a subarctic ice patch. Proc. Natl Acad. Sci. USA 111, 16842–16847 (2014).
Google Scholar 
63.Shmakova, L. et al. A living bdelloid rotifer from 24,000-year-old Arctic permafrost. Curr. Biol. 31, PR712–R713 (2021).
Google Scholar 
64.Siliakus, M. F., van der Oost, J. & Kengen, S. W. M. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure. Extremophiles 21, 651–670 (2017).CAS 

Google Scholar 
65.Edwards, A. Coming in from the cold: potential microbial threats from the terrestrial cryosphere. Front. Earth Sci. 3, 12 (2015).
Google Scholar 
66.Mackelprang, R. et al. Microbial survival strategies in ancient permafrost: insights from metagenomics. ISME J. 11, 2305–2318 (2017).CAS 

Google Scholar 
67.Bale, N. J. et al. Fatty acid and hopanoid adaption to cold in the methanotroph Methylovulum psychrotolerans. Front. Microbiol. 10, 589 (2019).
Google Scholar 
68.Johnson, S. S. et al. Ancient bacteria show evidence of DNA repair. Proc. Natl Acad. Sci. USA 104, 14401–14405 (2007).CAS 

Google Scholar 
69.Ji, M. et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature 552, 400–403 (2017).CAS 

Google Scholar 
70.Burkert, A., Douglas, T. A., Waldrop, M. P. & Mackelprang, R. Changes in the active, dead, and dormant microbial community structure across a Pleistocene permafrost chronosequence. Appl. Environ. Microbiol. 85, e02646–18 (2019).CAS 

Google Scholar 
71.Colangelo-Lillis, J., Eicken, H., Carpenter, S. D. & Deming, J. W. Evidence for marine origin and microbial-viral habitability of subzero hypersaline aqueous inclusions within permafrost near Barrow, Alaska. FEMS Microbiol. Ecol. 92, fiw053 (2016).CAS 

Google Scholar 
72.Boetius, A., Anesio, A. M., Deming, J. W., Mikucki, J. A. & Rapp, J. Z. Microbial ecology of the cryosphere: sea ice and glacial habitats. Nat. Rev. Microbiol. 13, 677–690 (2015).CAS 

Google Scholar 
73.Zhong, Z.-P. et al. Viral ecogenomics of Arctic cryopeg brine and sea ice. mSystems https://doi.org/10.1128/mSystems.00246-20 (2020).74.Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nat. Microbiol. 6, 246–256 (2021).CAS 

Google Scholar 
75.Aslam, S. N., Huber, C., Asimakopoulos, A. G., Steinnes, E. & Mikkelsen, Ø. Trace elements and polychlorinated biphenyls (PCBs) in terrestrial compartments of Svalbard, Norwegian Arctic. Sci. Total Environ. 685, 1127–1138 (2019).CAS 

Google Scholar 
76.Winiger, P. et al. Source apportionment of circum-Arctic atmospheric black carbon from isotopes and modeling. Sci. Adv. 5, eaau8052 (2019).CAS 

Google Scholar 
77.Villa, S., Migliorati, S., Monti, G. S., Holoubek, I. & Vighi, M. Risk of POP mixtures on the Arctic food chain. Environ. Toxicol. Chem. 36, 1181–1192 (2017).CAS 

Google Scholar 
78.Ma, J., Hung, H., Tian, C. & Kallenborn, R. Revolatilization of persistent organic pollutants in the Arctic induced by climate change. Nat. Clim. Change 1, 255–260 (2011).CAS 

Google Scholar 
79.Ji, X., Abakumov, E. & Polyakov, V. Assessments of pollution status and human health risk of heavy metals in permafrost-affected soils and lichens: a case-study in Yamal Peninsula, Russia Arctic. Hum. Ecol. Risk Assess. 25, 2142–2159 (2019).CAS 

Google Scholar 
80.Mu, C. et al. Carbon and mercury export from the Arctic rivers and response to permafrost degradation. Water Res. 161, 54–60 (2019).CAS 

Google Scholar 
81.Brown, T. M., Macdonald, R. W., Muir, D. C. G. & Letcher, R. J. The distribution and trends of persistent organic pollutants and mercury in marine mammals from Canada’s eastern Arctic. Sci. Total Environ. 618, 500–517 (2018).CAS 

Google Scholar 
82.Ferrario, C., Finizio, A. & Villa, S. Legacy and emerging contaminants in meltwater of three alpine glaciers. Sci. Total Environ. 574, 350–357 (2017).CAS 

Google Scholar 
83.Miner, K. R., Bogdal, C., Pavlova, P. A., Steinlin, C. & Kreutz, K. J. Quantitative screening level assessment of human risk from PCB in glacial meltwater: Silvretta Glacier, Swiss Alps. Ecotoxicol. Environ. Saf. 166, 251–258 (2018).CAS 

Google Scholar 
84.Octaviani, M., Stemmler, I., Lammel, G. & Graf, H. F. Atmospheric transport of persistent organic pollutants to and from the Arctic under present-day and future climate. Environ. Sci. Technol. 49, 3593–3602 (2015).CAS 

Google Scholar 
85.Nielsen, S. P., Iosjpe, M. & Strand, P. Collective doses to man from dumping of radioactive waste in the Arctic seas. Sci. Total Environ. 202, 135–146 (1997).CAS 

Google Scholar 
86.Eickmeyer, D. C. et al. Interactions of polychlorinated biphenyls and organochlorine pesticides with sedimentary organic matter of retrogressive thaw slump-affected lakes in the tundra uplands adjacent to the Mackenzie Delta, NT, Canada. J. Geophys. Res. G Biogeosci. 121, 411–421 (2016).CAS 

Google Scholar 
87.St Pierre, K. A. et al. Unprecedented increases in total and methyl mercury concentrations downstream of retrogressive thaw slumps in the western Canadian Arctic. Environ. Sci. Technol. 52, 14099–14109 (2018).
Google Scholar 
88.Birnbaum, L. S. When environmental chemicals act like uncontrolled medicine. Trends Endocrinol. Metab. 24, 321–323 (2013).CAS 

Google Scholar 
89.Potapowicz, J., Szumińska, D., Szopińska, M. & Polkowska, Ż. The influence of global climate change on the environmental fate of anthropogenic pollution released from the permafrost: part I. Case study of Antarctica. Sci. Total Environ. 651, 1534–1548 (2019).CAS 

Google Scholar 
90.Kim, K.-S. et al. Associations of organochlorine pesticides and polychlorinated biphenyls in visceral vs. subcutaneous adipose tissue with type 2 diabetes and insulin resistance. Chemosphere 94, 151–157 (2014).CAS 

Google Scholar 
91.Knutsen, H. K. et al. Risk to human health related to the presence of perfluorooctane sulfonic acid and perfluorooctanoic acid in food. EFSA J. 16, e05194 (2018).
Google Scholar 
92.Iszatt, N. et al. Prenatal and postnatal exposure to persistent organic pollutants and infant growth: a pooled analysis of seven European birth cohorts. Environ. Health Perspect. 123, 730–736 (2015).CAS 

Google Scholar 
93.Nadal, M., Marquès, M., Mari, M. & Domingo, J. L. Climate change and environmental concentrations of POPs: a review. Environ. Res. 143, 177–185 (2015).CAS 

Google Scholar 
94.Toxicological Profile for Lead (Agency for Toxic Substances and Disease Registry, 2020); https://www.atsdr.cdc.gov/toxprofiles/tp13.pdf95.Toxicological Profile for Mercury (Agency for Toxic Substances and Disease Registry, 1999); https://www.atsdr.cdc.gov/ToxProfiles/tp46.pdf96.Toxicological Profile for Cadmium (Agency for Toxic Substances and Disease Registry, 2012); https://www.atsdr.cdc.gov/toxprofiles/tp5.pdf97.Halbach, K., Mikkelsen, Ø., Berg, T. & Steinnes, E. The presence of mercury and other trace metals in surface soils in the Norwegian Arctic. Chemosphere 188, 567–574 (2017).CAS 

Google Scholar 
98.Miner, K. R. et al. Legacy organochlorine pollutants in glacial watersheds: a review. Environ. Sci. Process. Impacts 19, 1474–1483 (2017).CAS 

Google Scholar 
99.Jamieson, H. E. The legacy of arsenic contamination from mining and processing refractory gold ore at Giant Mine, Yellowknife, Northwest Territories, Canada. Rev. Mineral. Geochem. 79, 533–551 (2014).
Google Scholar 
100.Tolvanen, A. et al. Mining in the Arctic environment—a review from ecological, socioeconomic and legal perspectives. J. Environ. Manag. 233, 832–844 (2019).
Google Scholar 
101.Liu, X., Jiang, S., Zhang, P. & Xu, L. Effect of recent climate change on Arctic Pb pollution: a comparative study of historical records in lake and peat sediments. Environ. Pollut. 160, 161–168 (2012).CAS 

Google Scholar 
102.Antcibor, I. et al. Trace metal distribution in pristine permafrost-affected soils of the Lena River delta and its hinterland, northern Siberia, Russia. Biogeosciences 11, 1–15 (2014).
Google Scholar 
103.Lim, A. G. et al. A revised pan-Arctic permafrost soil Hg pool based on western Siberian peat Hg and carbon observations. Biogeosciences 17, 3083–3097 (2020).CAS 

Google Scholar 
104.Schuster, P. F. et al. Permafrost stores a globally significant amount of mercury. Geophys. Res. Lett. 45, 1463–1471 (2018).CAS 

Google Scholar 
105.Schaefer, K. et al. Potential impacts of mercury released from thawing permafrost. Nat. Commun. 11, 4650 (2020). Estimates future releases of mercury from the permafrost from present to 2300, under RCP scenarios.CAS 

Google Scholar 
106.Jiskra, M. E., Sonke, J., Agnan, Y., Helmig, D. & Obrist, D. Insights from mercury stable isotopes on terrestrial-atmosphere exchange of Hg(0) in the Arctic tundra. Biogeosciences 16, 4051–4064 (2019).CAS 

Google Scholar 
107.Blais, J. M. et al. Arctic seabirds transport marine-derived contaminants. Science 309, 445 (2005).CAS 

Google Scholar 
108.Brimble, S. K. et al. High Arctic ponds receiving biotransported nutrients from a nearby seabird colony are also subject to potentially toxic loadings of arsenic, cadmium, and zinc. Environ. Toxicol. Chem. 28, 2426–2433 (2009).CAS 

Google Scholar 
109.Michelutti, N. et al. Trophic position influences the efficacy of seabirds as metal biovectors. Proc. Natl Acad. Sci. USA 107, 10543–10548 (2010).CAS 

Google Scholar 
110.Mallory, M. L. & Braune, B. M. Tracking contaminants in seabirds of Arctic Canada: temporal and spatial insights. Mar. Pollut. Bull. 64, 1475–1484 (2012).CAS 

Google Scholar 
111.Lehnherr, I. Methylmercury biogeochemistry: a review with special reference to Arctic aquatic ecosystems. Environ. Rev. 22, 229–243 (2014).CAS 

Google Scholar 
112.Steinlin, C. et al. A temperate alpine glacier as a reservoir of polychlorinated biphenyls: model results of incorporation, transport, and release. Environ. Sci. Technol. 50, 5572–5579 (2016).CAS 

Google Scholar 
113.Pavlova, P. A., Schmid, P., Zennegg, M., Bogdal, C. & Schwikowski, M. Trace analysis of hydrophobic micropollutants in aqueous samples using capillary traps. Chemosphere 106, 51–56 (2014).CAS 

Google Scholar 
114.Blais, J. M. et al. Melting glaciers: a major source of persistent organochlorines to subalpine Bow Lake in Banff National Park, Canada. Ambio 30, 410–415 (2001).CAS 

Google Scholar 
115.Lafrenière, M. J., Blais, J. M., Sharp, M. J. & Schindler, D. W. Organochlorine pesticide and polychlorinated biphenyl concentrations in snow, snowmelt, and runoff at Bow Lake, Alberta. Environ. Sci. Technol. 40, 4909–4915 (2006).
Google Scholar 
116.Elliott, J. E. et al. Factors influencing legacy pollutant accumulation in alpine osprey: biology, topography, or melting glaciers? Environ. Sci. Technol. 46, 9681–9689 (2012).CAS 

Google Scholar 
117.Walters, D. M. et al. Trophic magnification of organic chemicals: a global synthesis. Environ. Sci. Technol. 50, 4650–4658 (2016).CAS 

Google Scholar 
118.Miner, K. R., Wayant, N. & Ward, H. Preventing chemical release in hurricanes. Science 362, 166 (2018).CAS 

Google Scholar 
119.Quadroni, S. & Bettinetti, R. Health risk assessment for the consumption of fresh and preserved fish (Alosa agone) from Lago di Como (northern Italy). Environ. Res. 156, 571–578 (2017).CAS 

Google Scholar 
120.Mangano, M. C., Sarà, G. & Corsolini, S. Monitoring of persistent organic pollutants in the polar regions: knowledge gaps & gluts through evidence mapping. Chemosphere 172, 37–45 (2017).CAS 

Google Scholar 
121.Villa, S., Vighi, M., Maggi, V., Finizio, A. & Bolzacchini, E. Historical trends of organochlorine pesticides in an alpine glacier. J. Atmos. Chem. 46, 295–311 (2003).CAS 

Google Scholar 
122.Garmash, O. et al. Deposition history of polychlorinated biphenyls to the Lomonosovfonna glacier, Svalbard: a 209 congener analysis. Environ. Sci. Technol. 47, 12064–12072 (2013).CAS 

Google Scholar 
123.Bizzotto, E. C., Villa, S., Vaj, C. & Vighi, M. Comparison of glacial and non-glacial-fed streams to evaluate the loading of persistent organic pollutants through seasonal snow/ice melt. Chemosphere 74, 924–930 (2009).CAS 

Google Scholar 
124.Villa, S., Negrelli, C., Finizio, A., Flora, O. & Vighi, M. Organochlorine compounds in ice melt water from Italian alpine rivers. Ecotoxicol. Environ. Saf. 63, 84–90 (2006).CAS 

Google Scholar 
125.Miner, K. R. et al. A screening-level approach to quantifying risk from glacial release of organochlorine pollutants in the Alaskan Arctic. J. Expo. Sci. Environ. Epidemiol. 29, 293–301 (2018). Develops the first human risk assessment of glacially stored pollutants in the Arctic.
Google Scholar 
126.Czub, G. & McLachlan, M. S. A food chain model to predict the levels of lipophilic organic contaminants in humans. Environ. Toxicol. Chem. 23, 2356–2366 (2004).CAS 

Google Scholar 
127.Wang, X., Gong, P., Wang, C., Ren, J. & Yao, T. A review of current knowledge and future prospects regarding persistent organic pollutants over the Tibetan Plateau. Sci. Total Environ. 573, 139–154 (2016).CAS 

Google Scholar 
128.Desforges, J. P. et al. Predicting global killer whale population collapse from PCB pollution. Science 361, 1373–1376 (2018).CAS 

Google Scholar 
129.Macdonald, R. W. et al. Contaminants in the Canadian Arctic: 5 years of progress in understanding sources, occurrence and pathways. Sci. Total Environ. 254, 93–234 (2000).CAS 

Google Scholar 
130.Pavlova, P. A. et al. Polychlorinated biphenyls in a temperate alpine glacier: 1. Effect of percolating meltwater on their distribution in glacier ice. Environ. Sci. Technol. 49, 14085–14091 (2015).CAS 

Google Scholar 
131.Wania, F., Westgate, J. N., Technol, E. S. & Asap, A. On the mechanism of mountain cold-trapping of organic chemicals. Environ. Sci. Technol. 42, 9092–9098 (2008).CAS 

Google Scholar 
132.Strand, P. & Cooke, A. Environmental Radioactivity in the Arctic (Scientific Committee of the Environmental Radioactivity in the Arctic, 1995).133.Wright, S. M. et al. Spatial variation in the vulnerability of Norwegian Arctic counties to radiocaesium deposition. Sci. Total Environ. 202, 173–184 (1997).CAS 

Google Scholar 
134.Mitchell, P. I., León Vintró, L., Dahlgaard, H., Gascó, C. & Sánchez-Cabeza, J. A. Perturbation in the 240Pu/239Pu global fallout ratio in local sediments following the nuclear accidents at Thule (Greenland) and Palomares (Spain). Sci. Total Environ. 202, 147–153 (1997).CAS 

Google Scholar 
135.Khalturin, V. I., Rautian, T. G., Richards, P. G. & Leith, W. S. A review of nuclear testing by the Soviet Union at Novaya Zemlya, 1955–1990. Sci. Glob. Secur. 13, 1–42 (2005). Reviews the Novaya Zemlya nuclear testing site history, nuclear releases and posits environmental distribution.
Google Scholar 
136.Travkina, A. V. et al. Monitoring the environmental contamination of Kara Sea and shallow bays of Novaya Zemlya. J. Radioanal. Nucl. Chem. 311, 1673–1680 (2017).CAS 

Google Scholar 
137.Skorve, J. The environment of the nuclear test sites on Novaya Zemlya. Sci. Total Environ. 202, 167–172 (1997).CAS 

Google Scholar 
138.Sarkisov, A. A. The question of clean-up of radioactive contamination in the Arctic region. Her. Russ. Acad. Sci. 89, 7–22 (2019).
Google Scholar 
139.Pogrebov, V. B., Fokin, S. I., Galtsova, V. V. & Ivanov, G. I. Benthic communities as influenced by nuclear testing and radioactive waste disposal off Novaya Zemlya in the Russian Arctic. Mar. Pollut. Bull. 35, 333–339 (1997).CAS 

Google Scholar 
140.Miroshnikov, A. Y. et al. Radioecological investigations on the northern Novaya Zemlya Archipelago. Oceanology 57, 204–214 (2017).
Google Scholar 
141.Salbu, B. et al. Radioactive contamination from dumped nuclear waste in the Kara Sea—results from the joint Russian-Norwegian expeditions in 1992-1994. Sci. Total Environ. 202, 185–198 (1997).CAS 

Google Scholar 
142.Oughton, D. H., Børretzen, P., Salbu, B. & Tronstad, E. Mobilisation of 137Cs and 90Sr from sediments: potential sources to Arctic waters. Sci. Total Environ. 202, 155–165 (1997).CAS 

Google Scholar 
143.Faria, S. H., Weikusat, I. & Azuma, N. The microstructure of polar ice. Part I: highlights from ice core research. J. Struct. Geol. 61, 2–20 (2014).
Google Scholar 
144.Karlsson, N. B. et al. Ice-penetrating radar survey of the subsurface debris field at Camp Century, Greenland. Cold Reg. Sci. Technol. 165, 102788 (2019). The most recent ice-penetrating radar survey of Camp Century, Greenland, characterizing the location and concentration of wastes.
Google Scholar 
145.Vandecrux, B., Colgan, W. T., Solgaard, A., Steffensen, J. P. & Karlsson, N. B. Firn evolution at Camp Century, Greenland: 1966-2100. Front. Earth Sci. 9, 578978 (2021).
Google Scholar 
146.Vila, E., Hornero-Méndez, D., Azziz, G., Lareo, C. & Saravia, V. Carotenoids from heterotrophic bacteria isolated from Fildes Peninsula, King George Island, Antarctica. Biotechnol. Rep. 21, e00306 (2019).
Google Scholar 
147.Chaudhary, D. K., Kim, D. U., Kim, D. & Kim, J. Flavobacterium petrolei sp. nov., a novel psychrophilic, diesel-degrading bacterium isolated from oil-contaminated Arctic soil. Sci. Rep. 9, 4134 (2019).
Google Scholar 
148.de Gouw, J. A. et al. Daily satellite observations of methane from oil and gas production regions in the United States. Sci. Rep. 10, 1379 (2020).
Google Scholar 
149.Girardot, F. et al. Bacterial diversity on an abandoned, industrial wasteland contaminated by polychlorinated biphenyls, dioxins, furans and trace metals. Sci. Total Environ. 748, 141242 (2020).CAS 

Google Scholar 
150.Price, P. B. Microbial life in glacial ice and implications for a cold origin of life. FEMS Microbiol. Ecol. 59, 217–231 (2007).CAS 

Google Scholar 
151.Schütte, U. M. E. et al. Effect of permafrost thaw on plant and soil fungal community in a boreal forest: does fungal community change mediate plant productivity response? J. Ecol. 107, 1737–1752 (2019).
Google Scholar 
152.Jensen, P. E., Hennessy, T. W. & Kallenborn, R. Water, sanitation, pollution, and health in the Arctic. Environ. Sci. Pollut. Res. 25, 32827–32830 (2018).
Google Scholar 
153.Ewing, S. A. et al. Long-term anoxia and release of ancient, labile carbon upon thaw of Pleistocene permafrost. Geophys. Res. Lett. 42, 10730–10738 (2015).CAS 

Google Scholar 
154.Elder, C. D. et al. Seasonal sources of whole-lake CH4 and CO2 emissions from interior Alaskan thermokarst lakes. J. Geophys. Res. Biogeosci. 124, 1209–1229 (2019).CAS 

Google Scholar 
155.Jansen, E. et al. Past perspectives on the present era of abrupt Arctic climate change. Nat. Clim. Change 10, 714–721 (2020).
Google Scholar 
156.Nellier, Y.-M. et al. Mass budget in two high altitude lakes reveals their role as atmospheric PCB sinks. Sci. Total Environ. 511, 203–213 (2015).CAS 

Google Scholar 
157.Garnett, J. et al. Mechanistic insight into the uptake and fate of persistent organic pollutants in sea ice. Environ. Sci. Technol. 53, 6757–6764 (2019).CAS 

Google Scholar 
158.Kortenkamp, A. & Faust, M. Regulate to reduce chemical mixture risk. Science 361, 224–226 (2018).CAS 

Google Scholar 
159.Kirchgeorg, T. et al. Seasonal accumulation of persistent organic pollutants on a high altitude glacier in the eastern Alps. Environ. Pollut. 218, 804–812 (2016).CAS 

Google Scholar 
160.Weil, T. et al. Legal immigrants: invasion of alien microbial communities during winter occurring desert dust storms. Microbiome 5, 32 (2017).
Google Scholar 
161.Li, J. et al. Evidence for persistent organic pollutants released from melting glacier in the central Tibetan Plateau, China. Environ. Pollut. 220, 178–185 (2017).CAS 

Google Scholar 
162.Walvoord, M. A., Voss, C. I., Ebel, B. A. & Minsley, B. J. Development of perennial thaw zones in boreal hillslopes enhances potential mobilization of permafrost carbon. Environ. Res. Lett. 14, 015003 (2019).CAS 

Google Scholar 
163.Mogrovejo, D. C. et al. Prevalence of antimicrobial resistance and hemolytic phenotypes in culturable Arctic bacteria. Front. Microbiol. 11, 570 (2020).
Google Scholar 
164.Friedman, C. L. & Selin, N. E. Long-range atmospheric transport of polycyclic aromatic hydrocarbons: a global 3-D model analysis including evaluation of Arctic sources. Environ. Sci. Technol. 46, 9501–9510 (2012).CAS 

Google Scholar 
165.Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).
Google Scholar 
166.Vonk, J. E. et al. Reviews and syntheses: effects of permafrost thaw on Arctic aquatic ecosystems. Biogeosciences 12, 7129–7167 (2015).CAS 

Google Scholar 
167.MacInnis, J. J. et al. Fate and transport of perfluoroalkyl substances from snowpacks into a lake in the high Arctic of Canada. Environ. Sci. Technol. 53, 10753–10762 (2019).CAS 

Google Scholar 
168.Yeung, L. W. Y. et al. Vertical profiles, sources, and transport of PFASs in the Arctic Ocean. Environ. Sci. Technol. 51, 6735–6744 (2017).CAS 

Google Scholar 
169.Colatriano, D. et al. Genomic evidence for the degradation of terrestrial organic matter by pelagic Arctic Ocean Chloroflexi bacteria. Commun. Biol. 1, 90 (2018).
Google Scholar 
170.Commane, R. et al. Carbon dioxide sources from Alaska driven by increasing early winter respiration from Arctic tundra. Proc. Natl Acad. Sci. USA 114, 5361–5366 (2017).CAS 

Google Scholar 
171.Hartmann, M. et al. Variation of ice nucleating particles in the European Arctic over the last centuries. Geophys. Res. Lett. https://doi.org/10.1029/2019GL082311 (2019).172.Murray, B. J., Carslaw, K. S. & Field, P. R. Opinion: cloud-phase climate feedback and the importance of ice-nucleating particles. Atmos. Chem. Phys. 21, 665–679 (2021).CAS 

Google Scholar 
173.Joyce, R. E. et al. Biological ice-nucleating particles deposited year-round in subtropical precipitation. Appl. Environ. Microbiol. 85, e01567-19 (2019).
Google Scholar 
174.Yumashev, D., van Hussen, K., Gille, J. & Whiteman, G. Towards a balanced view of Arctic shipping: estimating economic impacts of emissions from increased traffic on the Northern Sea Route. Clim. Change 143, 143–155 (2017).CAS 

Google Scholar 
175.Ramage, J. et al. Population living on permafrost in the Arctic. Popul. Environ. https://doi.org/10.1007/s11111-020-00370-6 (2021).176.Bartsch, A., Pointner, G., Ingeman-Nielsen, T. & Lu, W. Towards circumpolar mapping of Arctic settlements and infrastructure based on Sentinel-1 and Sentinel-2. Remote Sens. 12, 2368 (2020).
Google Scholar 
177.Dewailly, E. Canadian Inuit and the Arctic dilemma. Oceanography 19, 88–89 (2006).
Google Scholar 
178.Plaza, C. et al. Direct observation of permafrost degradation and rapid soil carbon loss in tundra. Nat. Geosci. 12, 627–631 (2019).CAS 

Google Scholar 
179.Kashuba, E. et al. Ancient permafrost staphylococci carry antibiotic resistance genes. Microb. Ecol. Health Dis. https://doi.org/10.1080/16512235.2017.1345574 (2017).180.Dcosta, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).CAS 

Google Scholar 
181.Perron, G. G. et al. Functional characterization of bacteria isolated from ancient Arctic soil exposes diverse resistance mechanisms to modern antibiotics. PLoS ONE 10, e0069533 (2015).
Google Scholar 
182.Gilichinsky, D. et al. in Psychrophiles: From Biodiversity to Biotechnology (eds Margesin, R. et al.) 83–102 (Springer-Verlag, 2008).183.Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).CAS 

Google Scholar 
184.Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).CAS 

Google Scholar 
185.Taubenberger, J. K. et al. Reconstruction of the 1918 influenza virus: unexpected rewards from the past. mBio 3, e00201–12 (2012).
Google Scholar 
186.Jordan, D., Tumpey, T. & Jester, B. The Deadliest Flu: The Complete Story of the Discovery and Reconstruction of the 1918 Pandemic Virus (US Center for Disease Control, 2019).187.Tumpey, T. M. et al. Characterization of the reconstructed 1918 Spanish influenza pandemic virus. Science 310, 77–80 (2005).CAS 

Google Scholar 
188.Revich, B., Tokarevich, N. & Parkinson, A. J. Climate change and zoonotic infections in the Russian Arctic. Int. J. Circumpolar Health 71, 18792 (2012).
Google Scholar 
189.Waits, A., Emelyanova, A., Oksanen, A., Abass, K. & Rautio, A. Human infectious diseases and the changing climate in the Arctic. Environ. Int. 121, 703–713 (2018).
Google Scholar 
190.Hueffer, K., Drown, D., Romanovsky, V. & Hennessy, T. Factors contributing to anthrax outbreaks in the circumpolar north. Ecohealth 17, 174–180 (2020).
Google Scholar 
191.Springer, Y. P. et al. Novel Orthopoxvirus infection in an Alaska resident. Clin. Infect. Dis. 64, 1737–1741 (2017).
Google Scholar 
192.Mackay, D. Multimedia Environmental Models (CRC Press, 2001).193.Mackay, D., Celsie, A. K. D., Powell, D. E. & Parnis, J. M. Bioconcentration, bioaccumulation, biomagnification and trophic magnification: a modelling perspective. Environ. Sci. Process. Impacts 20, 72–85 (2018).CAS 

Google Scholar 
194.Vizcaino, E., Grimalt, J. O., Fernandez-Somoano, A. & Tardon, A. Transport of persistent organic pollutants across the human placenta. Environ. Int. 65, 107–115 (2014).CAS 

Google Scholar 
195.Costa, O. et al. First-trimester maternal concentrations of polyfluoroalkyl substances and fetal growth throughout pregnancy. Environ. Int. https://doi.org/10.1016/j.envint.2019.05.024 (2019).196.Adetona, O. et al. Concentrations of select persistent organic pollutants across pregnancy trimesters in maternal and in cord serum in Trujillo, Peru. Chemosphere 91, 1426–1433 (2013).CAS 

Google Scholar 
197.Toxicological Profile for Plutonium (Agency for Toxic Substances and Disease Registry, 2010); https://www.atsdr.cdc.gov/toxprofiles/tp143.pdf198.Toxicological Profile for Cesium (Agency for Toxic Substances and Disease Registry, 2004); https://www.atsdr.cdc.gov/toxprofiles/tp157.pdf199.Serikova, S. et al. High carbon emissions from thermokarst lakes of western Siberia. Nat. Commun. 10, 1552 (2019).CAS 

Google Scholar 
200.Swingedouw, D. et al. Early warning from space for a few key tipping points in physical, biological, and social-ecological systems. Surv. Geophys. https://doi.org/10.1007/s10712-020-09604-6 (2020).201.Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a high Arctic environment. Nat. Commun. 10, 1329 (2019).
Google Scholar 
202.Tank, S. E. et al. Landscape matters: predicting the biogeochemical effects of permafrost thaw on aquatic networks with a state factor approach. Permafr. Periglac. Process. https://doi.org/10.1002/ppp.2057 (2020).203.Feng, J. et al. Warming-induced permafrost thaw exacerbates tundra soil carbon decomposition mediated by microbial community. Microbiome 8, 3 (2020).
Google Scholar 
204.Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2015).
Google Scholar 
205.Donald, D. B. et al. Delayed deposition of organochlorine pesticides at a temperate glacier. Environ. Sci. Technol. 33, 1794–1798 (1999).CAS 

Google Scholar 
206.Hermanson, M. H. et al. Current-use and legacy pesticide history in the Austfonna ice cap, Svalbard, Norway. Environ. Sci. Technol. 39, 8163–8169 (2005).CAS 

Google Scholar 
207.Salvadó, J. A., Sobek, A., Carrizo, D. & Gustafsson, Ö. Observation-based assessment of PBDE loads in Arctic ocean waters. Environ. Sci. Technol. 50, 2236–2245 (2016).
Google Scholar 
208.Vecchiato, M. et al. The great acceleration of fragrances and PAHs archived in an ice core from Elbrus, Caucasus. Sci. Rep. 10, 10661 (2020).CAS 

Google Scholar 
209.Miteva, V., Teacher, C., Sowers, T. & Brenchley, J. Comparison of the microbial diversity at different depths of the GISP2 Greenland ice core in relationship to deposition climates. Environ. Microbiol. 11, 640–656 (2009).CAS 

Google Scholar  More

Our measure of interest was whether females ate more of the flake items (i.e., cheated less quickly) when the male had perceptual access than when he did not. Consistent with our predictions, females ate fewer flake items in the male not visible condition (Mean = 3.6 items, SD = 3.2) than in the male visible condition (Mean = 4.5, SD = 3.1; Fig. 2A; for flake items eaten by pairs see Supplementary Fig. S1). Additionally, females tended to cheat less over rounds (Supplementary Fig. S2). Indeed, the number of flake items eaten was predicted by both conditions (LRT, X21 = 8.13, p = 0.004; Fig. 2A; Supplementary Table S1) and round (LRT, X21 = 12.46, p  More

1.Aluja, M. Fruit fly (Diptera: Tephritidae) research in Latin America: myths, realities and dreams. Soc. Entomol. Bras. 28, 565–594 (1999).Article 

Google Scholar 
2.Weldon, C. W., Yap, S. & Taylor, P. W. Desiccation resistance of wild and mass-reared Bactrocera tryoni (Diptera: Tephritidae). Bull. Entomol. Res. 103, 690–699 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Weldon, C. W., Boardman, L., Marlin, D. & Terblanche, J. S. Physiological mechanisms of dehydration tolerance contribute to the invasion potential of Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) relative to its less widely distributed congeners. Front. Zool. 13, 15 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Weldon, C. W., Díaz-Fleischer, F. & Pérez-Staples, D. in Area-Wide Management of Fruit Fly Pests (eds. Pérez-Staples, D. et al.) 27–43 (CRC Press, 2020).5.Malacrida, A. R. et al. Globalization and fruit fly invasion and expansion: the medfly paradigm. Genetica 131, 1–9 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Diamantidis, A. D., Carey, J. R., Nakas, C. T. & Papadopoulos, N. T. Ancestral populations perform better in a novel environment: domestication of Mediterranean fruit fly populations from five global regions. Biol. J. Linn. Soc. 102, 334–345 (2011).Article 

Google Scholar 
7.Diamantidis, A. D. et al. Life history evolution in a globally invading tephritid: patterns of survival and reproduction in medflies from six world regions. Biol. J. Linn. Soc. 97, 106–117 (2009).Article 

Google Scholar 
8.Papadopoulos, N. T., Plant, R. E. & Carey, J. R. From trickle to flood: the large-scale, cryptic invasion of California by tropical fruit flies. Proc. R. Soc. Biol. Sci. Ser. B 280, 20131466 (2013).Article 

Google Scholar 
9.EUPHRESCO, project FLY_DETECT. Development and implementation of early detection tools and effective management strategies for invasive non-European and other selected fruit fly species of economic importance (FLY DETECT). Final report. https://doi.org/10.5281/zenodo.3732297. (2020)10.FSA PLH Panel, (EFSA Panel on Plant Health). Pest categorisation of non-EU Tephritidae. EFSA J. 18, e05931 (2020).
Google Scholar 
11.Carey, J. R. The Mediterranean fruit fly (Ceratitis capitata). Am. Entomol. 56, 158–163 (2010).Article 

Google Scholar 
12.Gutierrez, A. P. Applied Population Ecology: A Supply-Demand Approach. (Wiley, 1996).13.Sinclair, T. R. & Seligman, N. G. Crop modeling: from infancy to maturity. Agron. J. 88, 698–704 (1996).Article 

Google Scholar 
14.Gutierrez, A. P. & Ponti, L. Eradication of invasive species: why the biology matters. Environ. Entomol. 42, 395–411 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Asplen, M. K. et al. Invasion biology of spotted wing Drosophila (Drosophila suzukii): a global perspective and future priorities. J. Pest Sci. 88, 469–494 (2015).Article 

Google Scholar 
16.Neteler, M., Bowman, M. H., Landa, M. & Metz, M. GRASS GIS: a multi-purpose Open Source GIS. Environ. Model. Softw. 31, 124–130 (2012).Article 

Google Scholar 
17.Ekesi, S., Mohamed, S. & Meyer, M. D. Fruit Fly Research and Development in Africa—Towards a Sustainable Management Strategy to Improve Horticulture. (Springer, 2016).18.Vera, M. T., Rodriguez, R., Segura, D. F., Cladera, J. L. & Sutherst, R. W. Potential geographical distribution of the Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), with emphasis on Argentina and Australia. Environ. Entomol. 31, 1009–1022 (2002).Article 

Google Scholar 
19.De Meyer, M., Robertson, M. P., Peterson, A. T. & Mansell, M. W. Ecological niches and potential geographical distributions of Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa). J. Biogeogr. 35, 270–281 (2008).Article 

Google Scholar 
20.Tuel, A. & Eltahir, E. A. B. Why is the Mediterranean a climate change hot spot? J. Clim. 33, 5829–5843 (2020).Article 

Google Scholar 
21.Gaston, K. J. Geographic range limits: achieving synthesis. Proc. R. Soc. Biol. Sci. Ser. B 276, 1395–1406 (2009).Article 

Google Scholar 
22.IPCC, Intergovernmental Panel on Climate Change. Climate change 2014: Impacts, Adaptation, and Vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2014).23.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 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Ponti, L. et al. Biological invasion risk assessment of Tuta absoluta: mechanistic versus correlative methods. Biol. Invasions (in press).25.Carey, J. R., Papadopoulos, N. & Plant, R. The 30‐year debate on a multi‐billion‐dollar threat: tephritid fruit fly establishment in California. Am. Entomol. 63, 100–113 (2017).Article 

Google Scholar 
26.Gutierrez, A. P., Ponti, L. & Gilioli, G. Comments on the concept of ultra-low, cryptic tropical fruit fly populations. Proc. R. Soc. B Biol. Sci. 281, 20132825 (2014).Article 

Google Scholar 
27.McInnis, D. O. et al. Can polyphagous invasive tephritid pest populations escape detection for years under favorable climatic and host conditions? Am. Entomol. 63, 89–99 (2017).Article 

Google Scholar 
28.Barr, N. B. et al. Genetic diversity of Bactrocera dorsalis (Diptera: Tephritidae) on the Hawaiian islands: implications for an introduction pathway into California. J. Econ. Entomol. 107, 1946–1958 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Davies, N., Villablanca, F. X. & Roderick, G. K. Bioinvasions of the medfly Ceratitis capitata: source estimation using DNA sequences at multiple intron loci. Genetics 153, 351–360 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Meixner, M. D., McPheron, B. A., Silva, J. G., Gasparich, G. E. & Sheppard, W. S. The Mediterranean fruit fly in California: evidence for multiple introductions and persistent populations based on microsatellite and mitochondrial DNA variability. Mol. Ecol. Notes 11, 891–899 (2002).CAS 
Article 

Google Scholar 
31.Gutierrez, A. P., Ponti, L. & Cossu, Q. A. Effects of climate warming on olive and olive fly (Bactrocera oleae (Gmelin)) in California and Italy. Clim. Change 95, 195–217 (2009).Article 

Google Scholar 
32.Johnson, M. W. et al. High temperature affects olive fruit fly populations in California’s Central Valley. Calif. Agric. 65, 29–33 (2011).Article 

Google Scholar 
33.Gutierrez, A. P., Ponti, L. & Dalton, D. T. Analysis of the invasiveness of spotted wing Drosophila (Drosophila suzukii) in North America, Europe, and the Mediterranean Basin. Biol. Invasions 18, 3647–3663 (2016).Article 

Google Scholar 
34.Ponti, L., Gutierrez, A. P., Ruti, P. M. & Dell’Aquila, A. Fine-scale ecological and economic assessment of climate change on olive in the Mediterranean Basin reveals winners and losers. Proc. Natl Acad. Sci. USA 111, 5598–5603 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Andrewartha, H. G. & Birch, L. C. The Distribution and Abundance of Animals. (The University of Chicago Press, 1954).36.Huffaker, C. B. & Messenger, P. S. Theory and Practice of Biological Control. (Academic Press, 1976).37.Palladino, P. Defining ecology: ecological theories, mathematical models, and applied biology in the 1960s and 1970s. J. Hist. Biol. 24, 223–243 (1991).Article 

Google Scholar 
38.Dormann, C. F., Fründ, J. & Schaefer, H. M. Identifying causes of patterns in ecological networks: opportunities and limitations. Annu. Rev. Ecol. Evol. Syst. 48, 559–584 (2017).Article 

Google Scholar 
39.Evans, M. R. Modelling ecological systems in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 367, 181–190 (2012).Article 

Google Scholar 
40.Jørgensen, S. E., Nielsen, S. N. & Fath, B. D. Recent progress in systems ecology. Ecol. Model. 319, 112–118 (2016).Article 

Google Scholar 
41.FSA PLH Panel, (EFSA Panel on Plant Health). Pest categorisation of non-EU Tephritidae. EFSA J. 18, e05931 (2020).
Google Scholar 
42.Messenger, P. S. & van den Bosch, R. in Biological Control (ed. Huffaker, C. B.) 511 (Plenum/Rosetta Press, 1969).43.Grout, T. G. & Stoltz, K. C. Developmental rates at constant temperatures of three economically important Ceratitis spp. (Diptera: Tephritidae) from southern Africa. Environ. Entomol. 36, 1310–1317 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Papanastasiou, S. A., Nestel, D., Diamantidis, A. D., Nakas, C. T. & Papadopoulos, N. T. Physiological and biological patterns of a highland and a coastal population of the European cherry fruit fly during diapause. J. Insect Physiol. 57, 83–93 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Müller, H. G., Wu, S., Diamantidis, A. D., Papadopoulos, N. T. & Carey, J. R. Reproduction is adapted to survival characteristics across geographically isolated medfly populations. Proc. R. Soc. Biol. Sci. Ser. B 276, 4409–4416 (2009).Article 

Google Scholar 
46.Wang, J., Zeng, L. & Han, Z. An assessment of cold hardiness and biochemical adaptations for cold tolerance among different geographic populations of the Bactrocera dorsalis (Diptera: Tephritidae) in China. J. Insect Sci. Ludhiana 14, 292 (2014).47.Aluja, M. et al. Nonhost status of Citrus sinensis cultivar Valencia and C. paradisi cultivar Ruby Red to Mexican Anastrepha fraterculus (Diptera: Tephritidae). J. Econ. Entomol. 96, 1693–1703 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Dupuis, J. R., Ruiz‐Arce, R., Barr, N. B., Thomas, D. B. & Geib, S. M. Range‐wide population genomics of the Mexican fruit fly: toward development of pathway analysis tools. Evol. Appl. 12, 1641–1660 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Ricalde, M. P., Nava, D. E., Loeck, A. E. & Donatti, M. G. Temperature-dependent development and survival of Brazilian populations of the Mediterranean fruit fly, Ceratitis capitata, from tropical, subtropical and temperate regions. J. Insect Sci. 12, 33 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Duyck, P. F. & Quilici, S. Survival and development of different life stages of three Ceratitis spp. (Diptera: Tephritidae) reared at five constant temperatures. Bull. Entomol. Res. 92, 461–469 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Gutierrez, A. P. & Regev, U. The bioeconomics of tritrophic systems: applications to invasive species. Ecol. Econ. 52, 383–396 (2005).Article 

Google Scholar 
53.Gutierrez, A. P. & Ponti, L. The new world screwworm: prospective distribution and role of weather in eradication. Agric. Entomol. 16, 158–173 (2014).Article 

Google Scholar 
54.Gutierrez, A. P., Ponti, L. & Arias, P. A. Deconstructing the eradication of new world screwworm in North America: retrospective analysis and climate warming effects. Med. Vet. Entomol. 33, 282–295 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Egartner, A. & Lethmayer, C. Invasive fruit flies of economic importance in Austria – monitoring activities 2016. IOBCWPRS Bull. 123, 45–49 (2017).
Google Scholar 
56.Nugnes, F., Russo, E., Viggiani, G. & Bernardo, U. First record of an invasive fruit fly belonging to Bactrocera dorsalis complex (Diptera: Tephritidae) in Europe. Insects 9, 182 (2018).PubMed Central 
Article 

Google Scholar 
57.Liebhold, A. M. et al. Eradication of invading insect populations: from concepts to applications. Annu. Rev. Entomol. 61, 335–352 (2016).58.Tobin, P. C. et al. Determinants of successful arthropod eradication programs. Biol. Invasions 16, 401–414 (2014).Article 

Google Scholar 
59.Gilbert, N., Gutierrez, A. P., Frazer, B. D. & Jones, R. E. Ecological Relationships. (W.H. Freeman and Co., 1976).60.Gutierrez, A. P. Applied Population Ecology: A Supply-Demand Approach (Wiley, 1996).61.Gutierrez, A. P. The physiological basis of ratio-dependent predator-prey theory: the metabolic pool model as a paradigm. Ecology 73, 1552–1563 (1992).Article 

Google Scholar 
62.Gutierrez, A. P., Mills, N. J., Schreiber, S. J. & Ellis, C. K. A physiologically based tritrophic perspective on bottom-up-top-down regulation of populations. Ecology 75, 2227–2242 (1994).Article 

Google Scholar 
63.Mills, N. J. & Gutierrez, A. P. in Theoretical Approaches to Biological Control (eds. Hawkins, B. A. & Cornell, V. H.) (Cambridge University Press, 1999).64.Barlow, N. D. in Theoretical Approaches to Biological Control (eds. Hawkins, B. A. & Cornell, H. V.) 43–70 (Cambridge University Press, 1999).65.Manetsch, T. J. Time-varying distributed delays and their use in aggregative models of large systems. IEEE Trans. Syst. Man Cybern. 6, 547–553 (1976).Article 

Google Scholar 
66.Buffoni, G. & Pasquali, S. Structured population dynamics: continuous size and discontinuous stage structures. J. Math. Biol. 54, 555–595 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
67.Di Cola, G., Gilioli, G. & Baumgärtner, J. in Ecological Entomology (eds. Huffaker, C. B. & Gutierrez, A. P.) (Wiley, 1999).68.Severini, M., Alilla, R., Pesolillo, S. & Baumgärtner, J. Fenologia della vite e della Lobesia botrana (Lep. Tortricidae) nella zona dei Castelli Romani. Riv. Ital. Agrometeorol. 3, 34–39 (2005).
Google Scholar 
69.Vansickle, J. Attrition in distributed delay models. IEEE Trans. Syst. Man Cybern. 7, 635–638 (1977).Article 

Google Scholar 
70.Wang, Y. H. & Gutierrez, A. P. An assessment of the use of stability analyses in population ecology. J. Anim. Ecol. 49, 435–452 (1980).Article 

Google Scholar 
71.Briére, J. F., Pracros, P., Le Roux, A. Y. & Pierre, J. S. A novel rate model of temperature-dependent development for arthropods. Environ. Entomol. 28, 22–29 (1999).Article 

Google Scholar 
72.Frazer, B. D. & Gilbert, N. Coccinellids and aphids: a quantitative study of the impact of adult ladybirds (Coleoptera: Coccinellidae) preying on field populations of pea aphids (Homoptera: Aphididae). J. Entomol. Soc. Br. Columbia 73, 33–56 (1976).
Google Scholar 
73.Gutierrez, A. P. & Baumgärtner, J. U. Multitrophic level models of predator-prey energetics: I. Age-specific energetics models—pea aphid Acyrthosiphon pisum (Homoptera: Aphididae) as an example. Can. Entomol. 116, 924–932 (1984).
Google Scholar 
74.Bieri, M., Baumgärtner, J., Bianchi, G., Delucchi, V. & von Arx, R. Development and fecundity of pea aphid (Acyrthosiphon pisum Harris) as affected by constant temperatures and by pea varieties. Mitteilungen Schweiz. Entomol. Ges. 56, 163–171 (1983).
Google Scholar 
75.Messenger, P. S. & Flitters, N. E. Effect of constant temperature environments on the egg stage of three species of Hawaiian fruit flies. Ann. Entomol. Soc. Am. 51, 109–119 (1958).Article 

Google Scholar 
76.Carey, J. R. Demography and population dynamics of the Mediterranean fruit fly. Ecol. Model. 16, 125–150 (1982).Article 

Google Scholar 
77.Muñiz, M. & Gil, A. Laboratory studies on isolated pairs of Ceratitis capitata—results obtained during the last three years in Spain. In: Cavalloro R (ed), Fruit flies of economic importance; Joint Ad-Hoc Meeting of the Commission of the European Communities and the International Organization for Biological and Integrated Control, Hamburg, West Germany, A.A. Balkema, Rotterdam, Netherlands; Boston, MA, USA, 125–128 (1984).78.Vargas, R. I., Walsh, W. A., Jang, E. B., Armstrong, J. W. & Kanehisa, D. T. Survival and development of immature stages of four Hawaiian fruit flies (Diptera: Tephritidae) reared at five constant temperatures. Ann. Entomol. Soc. Am. 89, 64–69 (1996).Article 

Google Scholar 
79.Vargas, R. I., Walsh, W. A., Kanehisa, D., Jang, E. B. & Armstrong, J. W. Demography of four Hawaiian fruit flies (Diptera: Tephritidae) reared at five constant temperatures. Ann. Entomol. Soc. Am. 90, 162–168 (1997).Article 

Google Scholar 
80.Vargas, R. I., Walsh, W. A., Kanehisa, D., Stark, J. D. & Nishida, T. Comparative demography of three Hawaiian fruit flies (Diptera:Tephritidae) at alternating temperatures. Ann. Entomol. Soc. Am. 93, 75–81 (2000).Article 

Google Scholar 
81.Delrio, G., Conti, B. & Corvetti, A. Effects of abiotic factors on Ceratitis capitata (Wied.) (Diptera: Tephritidae)—I. Egg development under constant temperatures. In Fruit Flies of Economic Importance 84. Proceedings of the CEC/IOBC “Ad-hoc Meeting” (ed. Cavalloro, R.) 133–139 (A.A. Balkema, 1984).82.Duyck, P. F., Sterlin, J. F. & Quilici, S. Survival and development of different life stages of Bactrocera zonata (Diptera: Tephritidae) reared at five constant temperatures compared to other fruit fly species. Bull. Entomol. Res. 94, 89–93 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Powell, M. R. Modeling the response of the Mediterranean fruit fly (Diptera:Tephritidae) to cold treatment. J. Econ. Entomol. 96, 300–310 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Shoukry, A. & Hafez, M. The biology of the Mediterranean fruit fly Ceratitis capitata. Entomol. Exp. Appl. 26, 33–39 (1979).Article 

Google Scholar 
85.Duyck, P. F., David, P. & Quilici, S. Climatic niche partitioning following successive invasions by fruit flies in La Réunion. J. Anim. Ecol. 75, 518–526 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
86.Dhillon, M. K., Singh, R., Naresh, J. S. & Sharma, H. C. The melon fruit fly, Bactrocera cucurbitae: a review of its biology and management. J. Insect Sci. Ludhiana 5, 40 (2005).CAS 

Google Scholar 
87.Messenger, P. S. & Flitters, N. E. Bioclimatic studies of three species of fruit flies in Hawaii. J. Econ. Entomol. 47, 756–765 (1954).Article 

Google Scholar 
88.Keck, C. B. Effect of temperature on development and activity of the melon fly. J. Econ. Entomol. 44, 1001–1002 (1951).Article 

Google Scholar 
89.Yang, P., Carey, J. R. & Dowell, R. V. Comparative demography of two cucurbit-attacking fruit flies, Bactrocera tau and B. cucurbitae (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 87, 538–545 (1994).Article 

Google Scholar 
90.Vayssières, J. F., Carel, Y., Coubes, M. & Duyck, P. F. Development of immature stages and comparative demography of two cucurbit-attacking fruit flies in Reunion Island: Bactrocera cucurbitae and Dacus ciliatus (Diptera Tephritidae). Environ. Entomol. 37, 307–314 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
91.Huang, Y. B. & Chi, H. Age-stage, two-sex life tables of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae) with a discussion on the problem of applying female age-specific life tables to insect populations. Insect Sci. 19, 263–273 (2012).Article 

Google Scholar 
92.Kandakoor, S. B., Chakravarthy, A. K., Rashmi, M. A. & Verghese, A. Effect of elevated carbon dioxide and temperature on biology of melon fruit fly, Bactrocera cucurbitae Coquillett (Tephritidae: Diptera). Afr. Entomol. 27, 36–42 (2019).Article 

Google Scholar 
93.Teruya, T. Effects of relative humidity during pupal development on subsequent eclosion and flight capability of the melon fly, Dacus cucurbitae Coquillett (Diptera:Tephiritidae). Appl. Entomol. Zool. 25, 521–523 (1990).Article 

Google Scholar 
94.Laskar, N. & Chatterjee, H. The effect of meteorological factors on the population dynamics of melon fly, Bactrocera cucurbitae (Coq.) (Diptera: Tephritidae) in the foot hills of Himalaya. J. Appl. Sci. Environ. Manag. 14, 53–58 (2010).95.Myers, S. W., Cancio-Martinez, E., Hallman, G. J., Fontenot, E. A. & Vreysen, M. J. B. Relative tolerance of six Bactrocera (Diptera: Tephritidae) species to phytosanitary cold treatment. J. Econ. Entomol. 109, 2341–2347 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
96.Zhou, S. H., Li, L., Zeng, B. & Fu, Y. G. Effects of short-term high-temperature conditions on oviposition and differential gene expression of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae. Int. J. Pest Manag. 66, 332–340 (2020).Article 
CAS 

Google Scholar 
97.Vargas, R. I. et al. Area-wide suppression of the Mediterranean fruit fly, Ceratitis capitata, and the Oriental fruit fly, Bactrocera dorsalis, in Kamuela, Hawaii. J. Insect Sci. 10, 135 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
98.Vargas, R. I. & Carey, J. R. Comparative survival and demographic statistics for wild Oriental fruit fly, Mediterranean fruit fly, and melon fly (Diptera: Tephritidae) on papaya. J. Econ. Entomol. 83, 1344–1349 (1990).Article 

Google Scholar 
99.Jang, E. B., Nagata, J. T., Chan, H. T. & Laidlaw, W. G. Thermal death kinetics in eggs and larvae of Bactrocera latifrons (Diptera: Tephritidae) and comparative thermotolerance to three other tephritid fruit fly species in Hawaii. J. Econ. Entomol. 92, 684–690 (1999).Article 

Google Scholar 
100.Xie, Q., Hou, B. & Zhang, R. Thermal responses of oriental fruit fly (diptera: tephritidae) late third instars: mortality, puparial morphology, and adult emerge. J. Econ. Entomol. 101, 736–741 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Armstrong, J. W., Tang, J. & Wang, S. Thermal death kinetics of Mediterranean, Malaysian, melon, and oriental fruit fly (Diptera: Tephritidae) eggs and third instars. J. Econ. Entomol. 102, 522–532 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
102.Choi, K. S., Samayoa, A. C., Hwang, S.-Y., Huang, Y.-B. & Ahn, J. J. Thermal effect on the fecundity and longevity of Bactrocera dorsalis adults and their improved oviposition model. PLOS ONE 15, e0235910 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
103.Shukla, R. P. & Prasad, V. G. Population fluctuations of the oriental fruit fly, Dacus dorsalis Hendel in relation to hosts and abiotic factors. Trop. Pest Manag. 31, 273–275 (1985).Article 

Google Scholar 
104.Hurtado, H. et al. Demography of three Mexican tephritids: Anastrepha ludens, A. obliqua and A. serpentina. Fla. Entomol. 71, 110–120 (1988).
Google Scholar 
105.Liedo, P., Carey, J. R., Celedonio, H. & Guillen, J. Size specific demography of three species of Anastrepha fruit flies. Entomol. Exp. Appl. 63, 135–142 (1992).Article 

Google Scholar 
106.Carey, J. R. et al. Biodemography of a long-lived tephritid: Reproduction and longevity in a large cohort of female Mexican fruit flies, Anastrepha ludens. Exp. Gerontol. 40, 793–800 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
107.Berrigan, D. A., Carey, J. R., Guillen, J. & Celedonio, H. Age and host effects on clutch size in the Mexican fruit fly, Anastrepha ludens. Entomol. Exp. Appl. 47, 73–80 (1988).Article 

Google Scholar 
108.Quintero‐Fong, L. et al. Demography of a genetic sexing strain of Anastrepha ludens (Diptera: Tephritidae): effects of selection based on mating performance. Agric. Entomol. 20, 1–8 (2018).Article 

Google Scholar 
109.Tejeda, M. T. et al. Reasons for success: rapid evolution for desiccation resistance and life-history changes in the polyphagous fly Anastrepha ludens. Evolution 70, 2583–2594 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
110.Darby, H. H. & Kapp, E. M. Observations on the thermal death points of Anatrepha ludens (Loew). US Dep. Agric. Tech. Bull. 400, 12445 (1933).111.Flitters, N. E. & Messenger, P. S. Effect of temperature and humidity on development and potential distribution of the Mexican fruit fly in the United States. U. S. Dep. Agric. Tech. Bull. 1330, 1–36 (1965).112.Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation. Agric. Meteorol. 200, 233–248 (2015).Article 

Google Scholar 
113.Rienecker, M. M. et al. MERRA: NASA’s Modern-Era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011).Article 

Google Scholar 
114.Dell’Aquila, A. et al. Effects of seasonal cycle fluctuations in an A1B scenario over the Euro-Mediterranean region. Clim. Res. 52, 135–157 (2012).Article 

Google Scholar 
115.Artale, V. et al. An atmosphere-ocean regional climate model for the Mediterranean area: assessment of a present climate simulation. Clim. Dyn. 35, 721–740 (2010).Article 

Google Scholar 
116.Giorgi, F. & Bi, X. Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations. Geophys. Res. Lett. 32, L21715 (2005).Article 

Google Scholar 
117.Gualdi, S. et al. The CIRCE simulations: regional climate change projections with realistic representation of the Mediterranean sea. Bull. Am. Meteorol. Soc. 94, 65–81 (2013).Article 

Google Scholar 
118.Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci. 16, 3309–3314 (2012).Article 

Google Scholar 
119.Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).Article 

Google Scholar 
120.Riahi, K. et al. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).CAS 
Article 

Google Scholar 
121.Rogelj, J., Meinshausen, M. & Knutti, R. Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat. Clim. Change 2, 248–253 (2012).Article 

Google Scholar 
122.GRASS Development Team. Geographic Resources Analysis Support System (GRASS) Software, Version 7.9.dev. (Open Source Geospatial Foundation. http://grass.osgeo.org, (2021).123.Gutierrez, A. P. & Ponti, L. in Invasive Species and Global Climate Change (eds. Ziska, L. H. & Dukes, J. S.) 271–288 (CABI Publishing, 2014).124.Ponti, L. et al. Bioeconomic analogies as a unifying paradigm for modeling agricultural systems under global change in the context of geographic information systems. Geophys. Res. Abstr. 21, 13677 (2019). EGU2019.
Google Scholar  More

In banana, bract opening behavior depends on the time of the day, the position of the bract, and sex of the flowers enclosed by the bract. Bract opening is a continuous process especially in the first bracts subtending female flowers of some genotypes; it starts in the evening and continues through the night (Table 1). In cases where bracts did not fully open, the process was halted early morning and resumed in the evening. It is therefore not obvious to judge whether such bracts have opened or not. However, opening is permanent as opposed to some plant species which open and close their flowers at specific times. Ssebuliba et al.16 considered East African Highland bananas ready for pollination when bracts were half way open with stigmas having a creamy white appearance. According to observations made in the current study, it can be said that bract lifting is indicative of flower opening thus pollination can start.Bract lift and bract roll seemed to be a response of a certain light quality6, the response time and speed are genotype dependent. Finger curling also seems to be triggered by the same factors that lead to bract opening. Bract opening and finger curling are likely to be a response of changes in turgor pressures in cells that lead to tissues being pushed in a given direction17. This was evident with upward movement of the inflorescence from the horizontal-pendent toward the horizontal position in the evening and downward movement towards the pendent position by mid-morning. These movements were genotype dependent and small, maximum oscillation was about 10˚. A similar pattern was observed for leaf folding to influence relative canopy cover18.Generally, bracts subtending female flower lifted and started rolling earlier than those subtending male flowers. However, male flowers ended opening before female flowers, resulting in faster bract opening for male flowers (Table 1 and t-test). This might be due to the smaller bract size of male flowers (Fig. 1) or an adaption for female flowers to find male flowers open with ready pollen. Consequently, the strategy ensures maximum pollination success and survival of the Musa spp. Studies have revealed that pollen viability reduces with time after flower opening1. This is in agreement that controlled pollination should be done between 07:00 and 10:00 h7. In comparison to lilies, some flowers were observed also to open starting at 17:00 h while others open during day. Both nocturnal and diurnal pollinators were found to be active flower visitors19. This implies that pollination in banana can start in the evening as long as bracts for parents in the cross of interest lift in time.In Musa itinerans, two nectar production peaks were found, that is between 08:00 to 12:00 h and 20:00 to 24:00 h20. This maybe a close depiction of what happens in edible bananas thus emphasizing the potential importance of diurnal and nocturnal pollinators. Bats, bees, and birds were found to be among the most important pollinators of bananas at Onne, Nigeria10. However, natural pollinators were not the main focus of the study though they are good indicators of when stigmas might be highly receptive. Since nectar quality and quantity varies between different agro-ecologies and seasons21, flower visitations and seed set are also expected to vary accordingly. Different agro-ecologies are also expected to experience variable BOTs due to variable solar radiation. Likewise the different growing seasons (rainy and dry) might also affect BOTs and therefore seed set22. However, a comparison of time from sunrise to beginning of bract lift of Musa AAA Cavendish cultivars in a glasshouse and M. basjoo in the garden in Belgium revealed no significant difference6. But comparison of bract curling time in Mchare in Arusha with short days and Cavendish cultivars in a glasshouse in Belgium with long days in summer, there was early curling in the glasshouse. However, bract lift time may be a better event to use for comparison than bract curling or rolling time.Bracts of both female and male flowers of different genotypes completed opening at different times and this may be partly the reason for variable pollen viability and stigma receptivity (Table 1). Female flowers that finish opening much earlier may set less seed compared to those that finish opening closer to the routine time of hand pollination between 07:00 and 10:00 h. Conversely, male flowers that are ready shortly before the time of hand pollination are expected to have higher pollen viability. This probably explains the high fertility of ‘Calcutta 4’ as it finished opening at 06:30 h. Some male flowers like those of Matooke finished opening as early as 21:54 h (Table 1) and are expected to have pollen with low viability at the time it is measured the next day.All observed inflorescences opened one female bract on the first day, increasing to multiple bracts opening on subsequent days (Fig. 2). One to three bracts subtending female flowers were observed to open per day from the second bract position of the inflorescence. The pattern of opening took on a hyperbolic shape with up to four bracts opening on the fourth day in the midsection of the inflorescence. For hand pollination, more clusters are therefore expected to be pollinated per day during bract opening in the mid-section of the inflorescence. The different clusters of female flowers that open on the same day are likely to have stigmas with varying receptivity. The darker appearance of stigmas of former clusters compared to creamy stigmas in latter clusters reflects higher receptivity in the latter2. This may explain why some clusters set more seed especially in the mid-section of a seemingly fertile inflorescence.Upon complete opening of female and transitional bracts, inflorescences went into a pause period before male flowers opened (Table 2). In additional to spatial separation of flowers, this is temporal separation to promote cross pollination in banana. However, temporal separation of male and female flowers is not very effective for genotypes that had less than 24 h of separation. With aid of crawling insects, self-pollination may happen between the last female cluster and the first male cluster as stigmas are likely to be receptive for more than one day. Once male flowers started opening, one bract opened per day and occasionally two bracts were observed to open on the same day. For highly fertile genotypes like ‘Calcutta 4’, ample pollen is produced to pollinate many female flowers. Male flowers are also produced throughout the inflorescence growth period which ensures constant supply of pollen especially for controlled hand pollination. Averages of bracts subtending male flowers opening per day could not be calculated as there were two to three observed bracts subtending male flowers for most genotypes.It appears that proximal bracts subtending female flowers are less stimulated to lift and roll compared to distal bracts subtending female flowers and all bracts subtending male flowers. This was revealed by low vigour of bract lift and the small angle of lift at 08:00 h especially in the first female flower cluster (Figs. 2, 3). The bract angle of lift increases from proximal to distal end and this has been linked to reduced fertility in proximal clusters2. But it may not be the case since highly female (in all clusters) and male fertile ‘Calcutta 4’ showed the same pattern as edible bananas. The high R2 for female bract roll scores compared to bracts subtending male flowers was a result of more bracts used to calculate averages for bracts subtending female flowers compared to bracts subtending male flowers (Fig. 3). For bracts subtending male flowers, two to three bracts were observed for most genotypes thus the first three data points were close to the trend line. Since the number of female clusters varies, reducing number of data points were used to calculate average bract lift angles in the distal end or larger inflorescences. Besides, bract lift angles of some clusters could not be measured because of obscurity or being in awkward positions. This led to the last two points being far off the trend line for angle of lift and hence a low R2.Flower opening time is said to be genetically and environmentally controlled, results from this study are in agreement since light had considerable influence on bract opening events (Tables 1, 3). Significant effects of temperature, solar radiation, and vapor pressure deficit on flower opening time have been observed in rice11. For Musa spp., only light has a significant relationship with BOT. However, there was early curling under long summer days in the glasshouse in Belgium compared to short days in Arusha field conditions6. This suggested a particular light signal for BOT in Musa spp. It is unclear why high light intensity led to early lift of bracts subtending male flowers and this calls for farther investigation. Since bracts subtending male flowers instinctively open later than bracts subtending female flowers, light intensity had less effect on the former bracts. The small sample size could have also had an impact on the results in the study, the light flush from the camera could have also affected the results. The extent of weather effects on BOT in banana need to be studied in field conditions of locations with significantly different day length for a more reliable conclusion. More

Study site, field survey, and in situ filtration
The field survey was performed in Tateyama Bay (34° 60′ N, 139° 48′ E), central Japan, in the proximity of the Kuroshio warm current facing the Pacific Ocean (Fig. 1). This area has many artificial reefs (ARs) created to improve fishing efficiency for fishers. Among the ARs, we focused on one high-rise steel AR (AR1), with a height of 30 m, where fish tended to aggregate (Fig. 1 and S1). Sampling stations were set up at the AR1 and at six linear distant points extending northeast and southwest. These stations were named E150, E500, E750, W150, W500, and W750, where “W” or “E” and the number of each station name represented northeast or southwest and distance in meters from the AR1, respectively (Table S1 and Fig. 1). Another station was set up at a second AR (AR2: 25 m height) 220 m from AR1 because we found AR2 by chance during the survey (Table S1 and Fig. 1), and it might affect the eDNA concentration at other stations.Figure 1(a) Location of sampling stations, cruise track, and a set net in Tateyama Bay. Gray areas indicate landmasses, a gray bold line indicates cruise track, and gray thin lines indicate depth contours with an interval of 20 m. The maps were created using ArcGIS Software 10.6.0.8321 by ESRI (https://www.esri.com/) based on the municipal boundary data of Japan (Esri Japan) and Global Map Japan (Geospatial information Authority of Japan) as well as the M7000-series isobath data set (Japan Hydrographic Association). A picture of the artificial reef (AR1) (b) taken one year after this survey (June 2019) and pictures of the dominant species, (c) splendid alfonsino (Beryx splendens), (d) chicken grunt (Parapristipoma trilineatum), (e) chub mackerel (Scomber japonicus), (f) red seabream (Pagrus major), and (g) jack mackerel (Trachurus japonicus). Photograph credits: (b) Nariaki Inoue, (c) Fumie Yamaguchi, (d, e, g) Yutaro Kawano, and (f) Masaaki Sato.Full size imageWe conducted water sampling at eight stations for eDNA analysis and performed an acoustic survey for estimating relative fish density using research vessel Takamaru (Japan Fisheries Research and Education Agency: FRA) on May 23, 2018. We started the echo sounder survey at the eastern part of the bay and continued it during the water sampling (Fig. 1). Although the echo sounder survey could not differentiate between fish species, we collected this data to assess the association between the estimated concentration of fish eDNA and the echo intensity measured by the echo sounder. Water sampling began at E750, then continued along the transect line to E150, AR1, W150, W500, W750, before going back to AR2. At each sampling station, we collected 10 L of seawater from both the middle and bottom layers by one cast of two Niskin water samplers (5L × 2 samples) and measured vertical profiles of water temperature and salinity with a conductivity-temperature-depth sensor (RINKO profiler, JFE Advantech Co., Ltd.). We subsampled 2L seawater from the 5 L seawater of Niskin sampler using measuring bottle and remaining 3 L seawater was used for pre-wash of measuring bottle and filtration devices. Two 2L samples were collected from two Niskin water samples, and then immediately filtered using a combination of Sterivex filter cartridges (nominal pore size = 0.45 μm; Merck Millipore) through an aspirator (i.e., the two filters were subsets of a single water collection) in a laboratory on the research vessel. After filtration (average time of 15 min), an outlet port of the filter cartridge was sealed with an outlet luer cap, 1.5 ml RNAlater (Thermo Fisher Scientific Inc., Waltham, MA) was injected into the cartridge using a filtered pipette tip to prevent eDNA degradation, and an inlet port was also sealed with an inlet luer cap14. The Niskin water samplers were bleached before each water collection using a commercial bleach solution while filtering devices (i.e., filter funnels and measuring cups used for filtration) were bleached after every filtration. We filtered 2L MilliQ water with a filter funnel and measuring cup as a field negative control to test for possible contamination. The filter cartridges were placed in a freezer immediately after filtration until eDNA extraction. In total we collected and filtered 32 eDNA samples (eight stations × two depth layers × two replicates). Disposable latex or nitrile gloves were worn during sampling and replaced between each sampling station.DNA extraction and purificationWorkspaces were sterilized prior to DNA extraction using 10% commercial bleach, and filter tip pipettes were used to safeguard against cross-contamination. Following the method developed by Miya et al.15, the eDNA was extracted and purified. Briefly, after removing RNAlater inside the cartridge using a centrifuge, proteinase-K solution was injected into the cartridge from the inlet port, and the port was re-capped with the inlet lure cap. The eDNA captured on the filter membrane was extracted by constant stirring of the cartridge at a speed of 20 rpm using a roller shaker placed in an incubator heated at 56 °C for 20 min. The eDNA extracts were transferred to a 2-ml tube from the inlet of the filter cartridges by centrifugation. The collected DNA was purified using a DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer’s protocol. After the purification, DNA was eluted using 100 μl of the elution buffer (buffer AE). All DNA extracts were frozen at − 20 °C until paired-end library preparation.Preparation of internal standard DNAsFive artificially designed and synthetic internal standard DNAs, which were similar but not identical to the region of any existing fish mitochondrial 12S rRNA, were included in the library preparation process to estimate the number of fish DNA copies [i.e., quantitative MiSeq sequencing (qMiseq)]7,16. They were designed to have the MiFish primer‐binding regions as those of known existing fishes and to have the conserved regions in the insert region. Variable regions in the insert region were replaced with random bases so that no known existing fish sequences had the same sequences as the standard sequences. The standard DNA size distribution of the library was estimated using an Agilent 2100 BioAnalyzer (Agilent, Santa Clara, CA, USA), and the concentration of double-stranded DNA of the library was quantified using a Qubit dsDNA HS assay kit and a Qubit fluorometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). Based on the quantification values obtained using the Qubit fluorometer, the copy number of the standard DNAs was adjusted as follows: Std. A (100 copies/µl), Std. B (50 copies/µl), Std. C (25 copies/µl), Std. D (12.5 copies/µl) and Std. E (2.5 copies/µl). Then, these standard DNAs were mixed.Paired-end library preparationTwo PCR‐level negative controls (i.e., each with and without internal standard DNAs) were employed for MiSeq run to monitor contamination during the experiments. The first-round PCR (1st PCR) was carried out with a 12-µl reaction volume containing 6.0 µl of 2 × KAPA HiFi HotStart ReadyMix (Roche, Basel, Switzerland), 0.7 µl of each primer (5 µM), 2.6 µl of sterilized distilled H2O, 1.0 µl of standard DNA mix and 1.0 µl of template. Note that the standard DNA mix was included for each sample. The final concentration of each primer was 0.3 µM. We used a mixture of the following four PCR primers modified from original MiFish primers16: MiFish-U-forward (5′-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT NNN NNG TCG GTA AAA CTC GTG CCA GC-3′) and MiFish-U-reverse (5′-GTG ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC TNN NNN CAT AGT GGG GTA TCT AAT CCC AGT TTG-3′), MiFish-E-forward (5′-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT NNN NNG TTG GTA AAT CTC GTG CCA GC-3′), and MiFish-E-reverse (5′-GTG ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC TNN NNN CAT AGT GGG GTA TCT AAT CCT AGT TTG-3′). These primer pairs co-amplify a hypervariable region of the fish mitochondrial 12S rRNA gene (around 172 bp) and append primer-binding sites (5′ ends of the sequences before five Ns) for sequencing at both ends of the amplicon. The five random bases were used to enhance cluster separation on the flow cells during initial base call calibrations on the MiSeq platform. The thermal cycle profile after an initial 3 min denaturation at 95 (^circ)C was as follows (35 cycles): denaturation at 98 (^circ)C for 20 s; annealing at 65 (^circ)C for 15 s; and extension at 72 (^circ)C for 15 s, with a final extension at the same temperature for 5 min. Eight replications were performed for the 1st PCR, and the replicates were pooled to minimize the PCR dropouts. The 1st PCR products from the eight tubes were pooled in a single 1.5-ml tube. Then, we sent the 1st PCR products to IDEA consultants, Inc. to outsource the following MiSeq sequencing processes. The pooled products were purified and size-selected for 200–400 bp using a SPRIselect (Beckman Coulter, Inc.) to remove dimers and monomers following the manufacturer’s protocol.The second-round PCR (2nd PCR) was carried out with a 24 µl reaction volume containing 12 µl of 2 × KAPA HiFi HotStart ReadyMix, 2.8 µl of each primer (5 µM), 4.4 µl of sterilized distilled H2O, and 2.0 µl of template. We used the following two primers to append the dual-index sequences (8 nucleotides indicated by Xs) and flowcell-binding sites for the MiSeq platform (5′ ends of the sequences before eight Xs): 2nd-PCR-forward (5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACX XXX XXX XAC ACT CTT TCC CTA CAC GAC GCT CTT CCG ATC T-3′); and 2nd- PCR-reverse (5′-CAA GCA GAA GAC GGC ATA CGA GAT XXX XXX XXG TGA CTG GAG TTC AGA CGT GTG CTC TTC CGA TCT-3′). The thermal cycle profile after an initial 3 min denaturation at 95 (^circ)C was as follows (12 cycles): denaturation at 98 (^circ)C for 20 s; combined annealing and extension at 72 (^circ)C for 15 s, with a final extension at 72 (^circ)C for 5 min. The concentration of each second PCR product was measured by quantitative PCR using TB Green Fast qPCR Mix (Takara inc.). Each sample was diluted to a fixed concentration and combined (i.e., one pooled 2nd PCR product that included all samples). The pooled 2nd PCR product was size-selected to approximately 370 bp using BluePippin (Sage Science). The size-selected library was purified using the Agencourt AMPure XP beads, adjusted to 4 nM by quantitative PCR using TB Green Fast qPCR Mix (Takara Bio Inc.), and sequenced on the MiSeq platform using a MiSeq v2 Reagent Kit (2 × 150 bp) (Illumina, Inc.).Data preprocessing and taxonomic assignmentThe raw MiSeq data were converted into FASTQ files using the bcl2fastq program provided by Illumina (bcl2fastq v2.18). The FASTQ files were then demultiplexed using the command implemented in Claident17. We adopted this process rather than using FASTQ files demultiplexed by the Illumina MiSeq default program in order to remove sequences with low-quality scores and PCR artifacts (chimeras).The processed reads were subjected to a BLASTN search against the full NCBI database. We excluded unique sequences of the following settings: the sequence belonged to organisms other than bony fishes, sharks, and rays; the sequence similarity between queries and the top BLASTN hit was  More

1.Hairston, N. G., Smith, F. E. & Lawrence, B. S. Community structure, population control, and competition. Am. Nat. 94, 421–425 (1960).Article 

Google Scholar 
2.Gross, P. Insect behavioral and morphological defenses against parasitoid. Annu. Rev. Entomol. 38, 251–273 (1993).Article 

Google Scholar 
3.Tylikinais, J. M., Tscharntke, T. & Klein, A. M. Diversity, ecosystem function and stability of parasitoid—host interactions across a tropical habitat gradient. Ecology 87, 3047–3057 (2006).Article 

Google Scholar 
4.Strand, M. R. & Obrycki, J. J. Host specificity of insect parasitoids and predators. Bioscience 46, 422–429 (1996).Article 

Google Scholar 
5.Dawkins, R. & Krebs, J. R. Arms races between and within species. Proc. R. Soc. Lond. B. 205, 489–511 (1979).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Kraaijeveld, A. R., van Alphen, J. J. M. & Godfray, H. C. J. The coevolution of host resistance and parasitoid virulence. Parasitology 116, 29–45 (1998).Article 

Google Scholar 
7.Jeffries, M. J. & Lawton, J. H. Enemy free space and the structure of ecological communities. Biol. J. Linn. Soc. 23, 269–286 (1984).Article 

Google Scholar 
8.Grosman, A. H. et al. No adaptation of a herbivore to a novel host but loss of adaptation to its native host. Sci. Rep.-UK 5, 16211 (2015).ADS 
CAS 
Article 

Google Scholar 
9.Diamond, S. E. & Kingsolver, J. G. Fitness consequences of host plant choice: A field experiment. Oikos 119, 542–550 (2010).Article 

Google Scholar 
10.Meijer, K., Schilthuizen, M., Beukeboom, L. & Smit, C. A review and meta-analysis of the enemy release hypothesis in plant–herbivorous insect systems. PeerJ 4, e2778 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Forbes, A. A., Powell, T. H., Stelinski, L. L., Smith, J. J. & Feder, J. L. Sequential sympatric speciation across trophic levels. Science 323, 776–779 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Grosman, A. H., Holtz, A. M., Pallini, A., Sabelis, M. W. & Janssen, A. Parasitoids follow herbivorous insects to a novel host plant, generalist predators less so. Entomol. Exp. Appl. 162, 261–271 (2017).Article 

Google Scholar 
13.Soler, R., Bezemer, T. M., Van Der Putten, W. H., Vet, L. E. & Harvey, J. A. Root herbivore effects on above-ground herbivore, parasitoid and hyperparasitoid performance via changes in plant quality. J. Anim. Ecol. 74, 1121–1130 (2005).Article 

Google Scholar 
14.Ode, P. J. Plant chemistry and natural enemy fitness: Effects on herbivore and natural enemy interactions. Annu. Rev. Entomol. 51, 163–185 (2006).CAS 
PubMed 
Article 

Google Scholar 
15.Thompson, J. N. Trade-offs in larval performance on normal and novel hosts. Entomol. Exp. Appl. 80, 133–139 (1996).Article 

Google Scholar 
16.Lucas, É., Coderre, D. & Brodeur, J. Intraguild predation among aphid predators: Characterization and influence of extraguild prey density. Ecology 79, 1084–1092 (1998).Article 

Google Scholar 
17.Henry, L. M., May, N., Acheampong, S., Gillespie, D. R. & Roitberg, B. D. Host-adapted parasitoids in biological control: Does source matter?. Ecol. Appl. 20, 242–250 (2010).PubMed 
Article 

Google Scholar 
18.Mackauer, M. Sexual size dimorphism in solitary parasitoid wasps: influence of host quality. Oikos 76, 265–272 (1996).Article 

Google Scholar 
19.Bezemer, T. M. & Mills, N. J. Clutch size decisions of a gregarious parasitoid under laboratory and feld conditions. Anim. Behav. 66, 1119–1128 (2003).Article 

Google Scholar 
20.Samková, A., Hadrava, J., Skuhrovec, J. & Janšta, P. Host population density and presence of predators as key factors influencing the number of gregarious parasitoid Anaphes flavipes offspring. Sci. Rep.-UK 9, 6081 (2019).ADS 
Article 
CAS 

Google Scholar 
21.Schmidt, J. M. & Smith, J. J. B. Correlations between body angles and substrate curvature in the parasitoid wasp Trichogramma minutum: a possible mechanism of host radius measurement. J. Exp. Biol. 125, 271–285 (1986).Article 

Google Scholar 
22.Boivin, G. & Baaren, J. The role of larval aggression and mobility in the transition between solitary and gregarious development in parasitoid wasps. Ecol. Lett. 3, 469–474 (2000).Article 

Google Scholar 
23.Mayhew, P. J. The evolution of gregariousness in parasitoid wasps. P. Roy. Soc. Lond. B. Bio. 265, 383–389 (1998).Article 

Google Scholar 
24.Pexton, J. J. & Mayhew, P. J. Competitive interactions between parasitoid larvae and the evolution of gregarious development. Oecologia 141, 179–190 (2004).ADS 
PubMed 
Article 

Google Scholar 
25.Harvey, P. H. & Partridge, L. Murderous mandibles and black holes in hymenopteran wasps. Nature 326, 128–129 (1987).ADS 
Article 

Google Scholar 
26.Godfray, H. C. J. The evolution of clutch size in parasitic wasps. Am. Nat. 129, 221–233 (1987).Article 

Google Scholar 
27.Rosenheim, J. A. Single-sex broods and the evolution of nonsiblicidal parasitoid wasps. Am. Nat. 141, 90–104 (1993).Article 

Google Scholar 
28.Mayhew, P. J. & van Alphen, J. J. Gregarious development in alysiine parasitoids evolved through a reduction in larval aggression. Anim. Behav. 58, 131–141 (1999).CAS 
PubMed 
Article 

Google Scholar 
29.Pexton, J. J. & Mayhew, P. J. Immobility: The key to family harmony?. Trends. Ecol. Evol. 16, 7–9 (2001).CAS 
PubMed 
Article 

Google Scholar 
30.Hamilton, W. D. Extraordinary sex ratios. Science 156, 477–488 (1967).ADS 
CAS 
PubMed 
Article 

Google Scholar 
31.Mayhew, P. J. & Hardy, I. C. Nonsiblicidal behavior and the evolution of clutch size in bethylid wasps. Am. Nat. 151, 409–424 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
32.Zaviezo, T. & Mills, N. Factors influencing the evolution of clutch size in a gregarious insect parasitoid. J. Anim. Ecol. 69, 1047–1057 (2000).Article 

Google Scholar 
33.Koppik, M., Tiel, A. & Hofmeister, T. S. Adaptive decision making or diferential mortality: What causes ofspring emergence in a gregarious parasitoid?. Entomol. Exp. Appl. 150, 208–216 (2014).Article 

Google Scholar 
34.Visser, M. E., Van Alphen, J. J. & Hemerik, L. Adaptive superparasitism and patch time allocation in solitary parasitoids: An ESS model. J. Anim. Ecol. 61, 93–101 (1992).Article 

Google Scholar 
35.Waage, J. K. & Ming, N. S. The reproductive strategy of a parasitic wasp: I. optimal progeny and sex allocation in Trichogramma evanescens. J. An. Ecol. 53, 401–415 (1984).Article 

Google Scholar 
36.Harvey, J. A., Poelman, E. H. & Tanaka, T. Intrinsic inter-and intraspecific competition in parasitoid wasps. Ann. Rev. Entomol. 58, 333–351 (2013).CAS 
Article 

Google Scholar 
37.Harvey, J. A., Bezemer, T. M., Gols, R., Nakamatsu, Y. & Tanaka, T. Comparing the physiological effects and function of larval feeding in closely-related endoparasitoids (Braconidae: Microgastrinae). Physiol. Entomol. 33, 217–225 (2008).Article 

Google Scholar 
38.Cloutier, C., Duperron, J., Tertuliano, M. & McNeil, J. N. Host instar, body size and fitness in the koinobiotic parasitoid Aphidius nigripes. Entomol. Exp. Appl. 97, 29–40 (2000).Article 

Google Scholar 
39.Bai, B., Luck, R. F., Forster, L., Stephens, B. & Janssen, J. M. The effect of host size on quality attributes of the egg parasitoid Trichogramma pretiosum. Entomol. Exp. Appl. 64, 37–48 (1992).Article 

Google Scholar 
40.Kazmer, D. J. & Luck, R. F. Field tests of the size-fitness hypothesis in the egg parasitoid Trichogramma Pretiosum. Ecology 76, 412–425 (1995).Article 

Google Scholar 
41.Samková, A., Hadrava, J., Skuhrovec, J. & Janšta, P. Reproductive strategy as a major factor determining female body size and fertility of a gregarious parasitoid. J. Appl. Entomol. 143, 441–450 (2019).Article 

Google Scholar 
42.Wei, K., Tang, Y. L., Wang, X. Y., Cao, L. M. & Yang, Z. Q. The developmental strategies and related profitability of an idiobiont ectoparasitoid Sclerodermus pupariae vary with host size. Ecol. Entomol. 39, 101–108 (2014).Article 

Google Scholar 
43.May, R. M., Hassell, M. P., Anderson, M. R. & Tonkyn, D. V. Density dependence in host-parasitoid models. J. Anim. Ecol. 50, 855–865 (1981).MathSciNet 
Article 

Google Scholar 
44.Hoddle, M. S., Van Driesche, R. G., Elkinton, J. S. & Sanderson, J. P. Discovery and utilization of Bemisia argentifolii patches by Eretmocerus eremicus and Encarsia formosa (Beltsville strain) in greenhouses. Entomol. Exp. Appl. 87, 15–28 (1998).Article 

Google Scholar 
45.Samková, A., Raska, J., Hadrava, J. & Skuhrovec, J. An intergenerational approach for prediction of parasitoid population dynamics. BioRxiv. https://doi.org/10.1101/2021.02.22.432341 (2021).Article 

Google Scholar 
46.Anderson, R. C. & Paschke, J. D. The biology and ecology of Anaphes flavipes (Hymenoptera: Mymaridae), an exotic egg parasite of the cereal leaf beetle. Ann. Entomol. Soc. Am. 61, 1–5 (1968).Article 

Google Scholar 
47.Klomp, H. & Teerink, B. J. The significance of oviposition rates in the egg parasite Trichogramma embryophagum Htg. Arch. Neerl. Zool. 17, 350–375 (1967).Article 

Google Scholar 
48.Waage, J. K. & Lane, J. A. The reproductive strategy of a parasitic wasp: II. Sex allocation and local mate competition in Trichogramma evanescens. J. Anim. Ecol. 53, 417–426 (1984).Article 

Google Scholar 
49.Dysart, R. J., Maltby, H. L. & Brunson, M. H. Larval parasites of Oulema melanopus in Europe and their colonization in the United States. Entomophaga 18, 133–167 (1973).Article 

Google Scholar 
50.Skuhrovec, J. et al. Insecticidal activity of two formulations of essential oils against the cereal leaf beetle. Acta Agr. Scand. 68, 489–495 (2018).CAS 

Google Scholar 
51.Jervis, M. A., Ellers, J. & Harvey, J. A. Resource acquisition, allocation, and utilization in parasitoid reproductive strategies. Annu. Rev. Entomol. 53, 361–385 (2008).CAS 
PubMed 
Article 

Google Scholar 
52.Vinson, S. B. & Iwantsch, G. F. Host suitability for insect parasitoids. Ann. Rev. Entomol. 25, 397–419 (1980).Article 

Google Scholar 
53.Mackauer, M., Sequeira, R. & Otto, M. Growth and development in parasitoid wasps adaptation to variable host resources. In Vertical Food Web Interactions 191–203 (Springer, 1997).Chapter 

Google Scholar 
54.Ode, P. J. Plant toxins and parasitoid trophic ecology. Curr. Opin. Insect sci. 32, 118–123 (2019).PubMed 
Article 

Google Scholar 
55.Cronin, J. T. & Abrahamson, W. G. Do parasitoids diversify in response to host-plant shifts by herbivorous insects?. Ecol. Entomol. 26, 347–355 (2001).Article 

Google Scholar 
56.Sarfraz, M., Dosdall, L. M. & Keddie, B. A. Host plant nutritional quality affects the performance of the parasitoid Diadegma insulare. Biol. Control. 51, 34–41 (2009).CAS 
Article 

Google Scholar 
57.Harvey, J. A. Factors affecting the evolution of development strategies in parasitoid wasps: The importance of functional constraints and incorporating complexity. Entomol. Exp. Appl. 117, 1–13 (2005).Article 

Google Scholar 
58.Cortesero, A. M. & Monge, J. P. Influence of pre-emergence experience on response to host and host plant odours in the larval parasitoid Eupelmus vuilleti. Entomol. Exp. Appl. 72, 281–288 (1994).Article 

Google Scholar 
59.Gandolfi, M., Mattiacci, L. & Dorn, S. Preimaginal learning determines adult response to chemical stimuli in a parasitic wasp. Proc. Roy. Soc. Lon. Series. B-Biol. Scien. 270, 2623–2629 (2003).Article 

Google Scholar 
60.Kester, K. M. & Barbosa, P. Postemergence learning in the insect parasitoid, Cotesia congregata (Say) (Hymenoptera: Braconidae). J. Insect Behav. 4, 727–742 (1991).Article 

Google Scholar 
61.Vet, L. E. & Groenewold, A. W. Semiochemicals and learning in parasitoids. J. Chem. Ecol. 16, 3119–3135 (1990).CAS 
PubMed 
Article 

Google Scholar 
62.Samková, A., Hadrava, J., Skuhrovec, J. & Janšta, P. Effect of adult feeding and timing of host exposure on the fertility and longevity of the parasitoid Anaphes flavipes. Entomol. Exp. Appl. 167, 932–938 (2019).Article 

Google Scholar 
63.Jervis, M. A., Heimpel, G. E., Ferns, P. N., Harvey, J. A. & Kidd, N. A. Life-history strategies in parasitoid wasps: A comparative analysis of ‘ovigeny’. J. Anim. Ecol. 70, 442–458 (2001).Article 

Google Scholar 
64.Bjorksten, T. A. & Hoffmann, A. A. Persistence of experience effects in the parasitoid Trichogramma nr. brassicae. Ecol. Entomol. 23, 110–117 (1998).Article 

Google Scholar 
65.Lentz, A. J. & Kester, K. M. Postemergence experience affects sex ratio allocation in a gregarious insect parasitoid. J. Insect. Behav. 21, 34–45 (2008).Article 

Google Scholar 
66.Nishida, R. Sequestration of defensive substances from plants by Lepidoptera. Annu. Rev. Entomol. 47, 57–92 (2002).CAS 
PubMed 
Article 

Google Scholar 
67.Zvereva, E. L. & Rank, N. E. Fly parasitoid Megaselia opacicornis uses defensive secretions of the leaf beetle Chrysomela lapponica to locate its host. Oecologia 140, 516–522 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
68.Roy, H. E., Handley, L. J. L., Schönrogge, K., Poland, R. L. & Purse, B. V. Can the enemy release hypothesis explain the success of invasive alien predators and parasitoids?. Biocontrol 56, 451–468 (2011).Article 

Google Scholar 
69.Snyder, W. E. & Ives, A. R. Interactions between specialist and generalist natural enemies: Parasitoids, predators, and pea aphid biocontrol. Ecology 84, 91–107 (2003).Article 

Google Scholar 
70.Polis, G. A., Myers, C. A. & Holt, R. D. The ecology and evolution of intraguild predation: Potential competitors that eat each other. Annu. Rev. Ecol. Syst. 20, 297–330 (1989).Article 

Google Scholar 
71.Nakashima, Y. & Senoo, N. Avoidance of ladybird trails by an aphid parasitoid Aphidius ervi: active period and efects of prior oviposition experience. Entomol. Exp. Appl. 109, 163–166 (2003).Article 

Google Scholar 
72.Samková, A., Raška, J., Hadrava, J., Skuhrovec, J. & Janšta, P. Female manipulation of offspring sex ratio in the gregarious parasitoid Anaphes flavipes from a new two-generation approach. BioRxiv https://doi.org/10.1101/2021.02.22.432331 (2021).Article 

Google Scholar 
73.Visser, M. E. The importance of being large: the relationship between size and fitness in females of the parasitoid Aphaereta minuta (Hymenoptera: Braconidae). J. Anim. Ecol. 63, 963–978 (1994).Article 

Google Scholar 
74.Banks, M. & Thomson, D. J. Lifetime mating success in the damselfly Coenagrion puella. Anim. Behav. 33, 1175–1183 (1985).Article 

Google Scholar 
75.Ellers, J. & Jervis, M. Body size and the timing of egg production in parasitoid wasps. Oikos 102, 164–172 (2003).Article 

Google Scholar 
76.Anderson, R. C. & Paschke, J. D. Additional Observations on the Biology of Anaphes flavipes (Hymenoptera: Mymaridae), with Special Reference to the Efects of Temperature and Superparasitism on Development. Ann. Entomol. Soc. Am. 62, 1316–1321 (1969).Article 

Google Scholar 
77.Bezděk, J. & Baselga, A. Revision of western Palaearctic species of the Oulema melanopus group, with description of two new species from Europe (Coleoptera: Chrysomelidae: Criocerinae). Acta. Ent. Mus. Nat. Pra. 55, 273–304 (2015).
Google Scholar 
78.R. Core Team R. A language and environment for statistical computing. R Foundation for Statistical Computing (R Core Team, 2020).
Google Scholar 
79.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw 67, 1–48. (2015) URL: https://CRAN.R-project.org/package=Hmisc.80.Harrell, F. E. Jr, Dupont, C., et mult. al. (2020) Hmisc: Harrell Miscellaneous. R package version 4.4–2. URL: https://CRAN.R-project.org/package=Hmisc.81.Signorell et mult. al. (2021). DescTools: Tools for descriptive statistics. R package version 0.99.40. URL: https://cran.r-project.org/package=DescTools. More

1.Andersson, M. B. Sexual Selection (Princeton University Press, 1994).Book 

Google Scholar 
2.Royle, N. J., Smiseth, P. T. & Kölliker, M. The Evolution of Parental Care (Oxford University Press, 2012).Book 

Google Scholar 
3.Herridge, E. J., Murray, R. L., Gwynne, D. T. & Bussière, L. F. Mating and parental sex roles, diversity in. Encycl. Evol. Biol. 2, 453–458 (2016).Article 

Google Scholar 
4.Kokko, H. & Jennions, M. D. Parental investment, sexual selection and sex ratios. J. Evol. Biol. 21, 919–948 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Schärer, L., Rowe, L. & Arnqvist, G. Anisogamy, chance and the evolution of sex roles. Trends Ecol. Evol. 27, 260–264 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Liker, A., Freckleton, R. P. & Székely, T. The evolution of sex roles in birds is related to adult sex ratio. Nat. Commun. 4, 1–6 (2013).Article 
CAS 

Google Scholar 
7.Jennions, M. D. & Fromhage, L. Not all sex ratios are equal: The Fisher condition, parental care and sexual selection. Philos. Trans. R. Soc. B Biol. Sci 372, 20160312 (2017).Article 

Google Scholar 
8.Darwin, C. The Descent Man, and Selection in Relation to Sex. John Murray, vol. ah-king (1871).9.Ah-King, M. & Ahnesjö, I. The ‘sex role’ concept: An overview and evaluation. Evol. Biol. 40, 461–470 (2013).Article 

Google Scholar 
10.Pizzari, T. & Bonduriansky, R. Sexual behaviour: Conflict, cooperation and co-evolution. In Social Behaviour: Genes, Ecology and Evolution (eds Szekely, T. et al.) (Cambridge University Press, 2010).
Google Scholar 
11.Trumbo, S. T. Patterns of parental care in invertebrates. Evol. Parent. Care 12, 62–81 (2012).
Google Scholar 
12.Balshine, S. Patterns of parental care in vertebrates. In The Evolution of Parental Care (eds Royle, N. et al.) 62–81 (Oxford University Press, 2012).Chapter 

Google Scholar 
13.Székely, T., Remeš, V., Freckleton, R. P. & Liker, A. Why care? Inferring the evolution of complex social behaviour. J. Evol. Biol. 26, 1381–1391 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Bateman, A. J. Intra-sexual selection in Drosophila. Heredity 2, 349–368 (1948).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
15.Snyder, B. F. & Gowaty, P. A. A reappraisal of Bateman’s classic study of intrasexual selection. Evolution 61, 2457–2468 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Gowaty, P. A., Kim, Y.-K. & Anderson, W. W. No evidence of sexual selection in a repetition of Bateman’s classic study of Drosophila melanogaster. Proc. Natl. Acad. Sci. 109, 11740–11745 (2012).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
17.Wade, M. J. Don’t Throw Bateman Out with the Bathwater!. Integr. Comp. Biol. 45, 945–951 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Dewsbury, D. A. The Darwin–Bateman paradigm in historical context. Integr. Comp. Biol. 45, 831–837 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Parker, G. A. The sexual cascade and the rise of pre-ejaculatory (Darwinian) sexual selection, sex roles, and sexual conflict. Cold Spring Harb. Lab. Press 6, a017509 (2014).Article 

Google Scholar 
20.Jones, A. G., Arguello, J. R. & Arnold, S. J. Validation of Bateman’s principles: A genetic study of sexual selection and mating patterns in the rough-skinned newt. Proc. R. Soc. B Biol. Sci. 269, 2533–2539 (2002).Article 

Google Scholar 
21.Collet, J. M., Dean, R. F., Worley, K., Richardson, D. S. & Pizzari, T. The measure and significance of Bateman’s principles. Proc. R. Soc. B Biol. Sci. 281, 20132973–20132973 (2014).Article 

Google Scholar 
22.Hoquet, T. Bateman (1948): Rise and fall of a paradigm?. Anim. Behav. https://doi.org/10.1016/j.anbehav.2019.12.008 (2019).Article 

Google Scholar 
23.Janicke, T., Häderer, I. K., Lajeunesse, M. J. & Anthes, N. Darwinian sex roles confirmed across the animal kingdom. Sci. Adv. 2, e1500983–e1500983 (2016).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
24.Tang-Martinez, Z. & Ryder, B. T. The problem with paradigms: Bateman’s worldview as a case study. Integr. Comp. Biol. 54, 821–830 (2005).Article 

Google Scholar 
25.Levitan, D. Does Bateman’s principle apply to broadcast-spawning organisms ? Egg traits Iifluence in situ fertilization rates among congeneric sea urchins. Evolution 52, 1043–1056 (1998).PubMed 

Google Scholar 
26.Drea, C. M. Bateman revisited: The reproductive tactics of female primates. Integr. Comp. Biol. 45, 915–923 (2005).PubMed 
Article 

Google Scholar 
27.Kokko, H. Should advertising parental care be honest?. Proc. R. Soc. B Biol. Sci. 265, 1871–1878 (1998).Article 

Google Scholar 
28.Remeš, V. & Matysioková, B. More ornamented females produce higher-quality offspring in a socially monogamous bird: An experimental study in the great tit (Parus major). Front. Zool. 10, 1–10 (2013).Article 

Google Scholar 
29.Hanschen, E. R., Herron, M. D., Wiens, J. J., Nozaki, H. & Michod, R. E. Multicellularity drives the evolution of sexual traits. Am. Nat. 192, E93–E105 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Queller, D. C. Why do females care more than males?. Proc. R. Soc. B Biol. Sci. 264, 1555–1557 (1997).Article 
ADS 

Google Scholar 
31.Alcock, J. Sexual selection and the mating behavior of solitary bees. in (eds. Brockmann, H. J. et al.) vol. 45 1–48 (Academic Press, 2013).32.Bjork, A. & Pitnick, S. Intensity of sexual selection along the anisogamy–isogamy continuum. Nature 441, 742–745 (2006).PubMed 
Article 
ADS 
CAS 
PubMed Central 

Google Scholar 
33.Kodric-Brown, A. & Brown, J. H. Anisogamy, sexual selection, and the evolution and maintenance of sex. Evol. Ecol. 1, 95–105 (1987).Article 

Google Scholar 
34.Schulte-Hostedde, A. I., Millar, J. S. & Gibbs, H. L. Sexual selection and mating patterns in a mammal with female-biased sexual size dimorphism. Behav. Ecol. 15, 351–356 (2004).Article 

Google Scholar 
35.Liker, A., Freckleton, R. P., Remeš, V. & Székely, T. Sex differences in parental care: Gametic investment, sexual selection, and social environment. Evolution 69, 2862–2875 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Bjork, A. & Pitnick, S. Intensity of sexual selection along the anisogamy-isogamy continuum. Nature 441, 742–745 (2006).PubMed 
Article 
ADS 
CAS 
PubMed Central 

Google Scholar 
37.Thomas, G. H. & Székely, T. Evolutionary pathways in shorebird breeding systems: Sexual conflict, parental care, and chick development. Evolution 59, 2222 (2006).Article 

Google Scholar 
38.Gonzalez-Voyer, A., Fitzpatrick, J. L. & Kolm, N. Sexual selection determines parental care patterns in cichlid fishes. Evolution 62, 2015–2026 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Garamszegi, L. Z. & Møller, A. P. Untested assumptions about within-species sample size and missing data in interspecific studies. Behav. Ecol. Sociobiol. 66, 1363–1373 (2012).Article 

Google Scholar 
40.Nakagawa, S. & Freckleton, R. P. Model averaging, missing data and multiple imputation: A case study for behavioural ecology. Behav. Ecol. Sociobiol. 65, 103–116 (2011).Article 

Google Scholar 
41.Nakagawa, S. & Freckleton, R. P. Missing inaction: The dangers of ignoring missing data. Trends Ecol. Evol. 23, 592–596 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Wiens, J. J. & Morrill, M. C. Missing data in phylogenetic analysis: Reconciling results from simulations and empirical data. Syst. Biol. 60, 719–731 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Apakupakul, K. & Rubenstein, D. R. Bateman’s principle is reversed in a cooperatively breeding bird. Biol. Lett. 11, 20150034 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Nakagawa, S. et al. Meta-analysis of variation: Ecological and evolutionary applications and beyond. Methods Ecol. Evol. 6, 143–152 (2015).Article 

Google Scholar 
45.Lajeunesse, M. Recovering missing data or partial data from studies: A survey of conversions and imputation for meta-analysis. Handb. Meta-Anal. Ecol. Evol. 195–206 (2013).46.Smith, R. J. Statistics of sexual size dimorphism. J. Hum. Evol. 36, 423–458 (1999).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Dunn, P. O., Whittingham, L. A. & Pitcher, T. E. Mating systems, sperm competition, and the evolution of sexual dimorphism in birds. Evolution 55, 161–175 (2001).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
48.Pérez-Barbería, F. J., Gordon, I. J. & Pagel, M. The origins of sexual dimorphism in body size in ungulates. Evolution 56, 1276–1285 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Weckerly, F. W. Sexual-size dimorphism: Influence of mass and mating systems in the most dimorphic mammals. J. Mammal. 79, 33–52 (1998).Article 

Google Scholar 
50.Székely, T., Reynolds, J. D. & Figuerola, J. Sexual size dimorphism in shorebirds, gulls, and alcids: The influence of sexual and natural selection. Evolution 54, 1404–1413 (2000).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Fairbairn, D. J., Blanckenhorn, W. U. & Székely, T. Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (Oxford University Press, 2007).Book 

Google Scholar 
52.Janicke, T. & Fromonteil, S. Sexual Selection and Sexual Size Dimorphism in Animals. (2021) https://doi.org/10.1101/2021.05.10.443408.53.De Lisle, S. P. Understanding the evolution of ecological sex differences: Integrating character displacement and the Darwin–Bateman paradigm. Evol. Lett. 3, 434–447 (2019).Article 

Google Scholar 
54.Harvey, P. H. & Clutton-Brock, T. H. Life history variation in primates. Evolution 39, 559–581 (1985).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Hedges, S. B., Dudley, J. & Kumar, S. TimeTree: A public knowledge-base of divergence times among organisms. Bioinformatics 22, 2971–2972 (2006).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
56.Martins, E. P. & Hansen, T. F. Phylogenies and the comparative method: A general approach to incorporating phylogenetic information into the analysis of interspecific data. Am. Nat. 149, 646–667 (1997).Article 

Google Scholar 
57.Pagel, M. Inferring evolutionary processes from molecular phylogenies. Zool. Scr. 98, 313–333 (1997).
Google Scholar 
58.Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: A test and review of evidence. Am. Nat. 160, 712–726 (2002).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
59.Cooper, N., Thomas, G. H., Venditti, C., Meade, A. & Freckleton, R. P. A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J. Linn. Soc. 118, 64–77 (2016).Article 

Google Scholar 
60.Orme, D. The caper package: Comparative analysis of phylogenetics and evolution in R. R Package Version 05(2), 1–36 (2013).
Google Scholar 
61.Penone, C. et al. Imputation of missing data in life-history trait datasets: Which approach performs the best?. Methods Ecol. Evol. 5, 1–10 (2014).Article 

Google Scholar 
62.Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: Fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

Google Scholar 
63.Goolsby, A. E. W., Bruggeman, J., Ane, C. & Goolsby, M. E. W. Package ‘ Rphylopars ’. (2016).64.Parker, G. A. Sexual selection and sexual conflict. In Sexual Selection and Reproductive Competition in Insects (eds Blum, M. S. & Blum, N. A.) (Academic Press, 1979).
Google Scholar 
65.Trivers, R. L. Social Evolution (Benjamin-Cummings Pub Co, 1985).
Google Scholar 
66.AlRashidi, M., Kosztolányi, A., Shobrak, M., Küpper, C. & Székely, T. Parental cooperation in an extreme hot environment: Natural behaviour and experimental evidence. Anim. Behav. 82, 235–243 (2011).Article 

Google Scholar 
67.Gwynne, D. T. & Simmons, L. W. Experimental reversal of courtship roles in an insect. Nature 346, 172–174 (1990).Article 
ADS 

Google Scholar 
68.Bonnet, X. et al. Sexual dimorphism in steppe tortoises (Testudo horsfieldii): Influence of the environment and sexual selection on body shape and mobility. Biol. J. Linn. Soc. 72, 357–372 (2001).Article 

Google Scholar 
69.Griskevicius, V. et al. The financial consequences of too many men: Sex ratio effects on saving, borrowing, and spending. J. Pers. Soc. Psychol. 102, 69–80 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Jirotkul, M. Operational sex ratio influences female preference and male-male competition in guppies. Anim. Behav. 58, 287–294 (1999).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
71.Liker, A., Freckleton, R. P. & Székely, T. Divorce and infidelity are associated with skewed adult sex ratios in birds. Curr. Biol. 24, 880–884 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
72.Schacht, R., Kramer, K. L., Székely, T. & Kappeler, P. M. Adult sex ratios and reproductive strategies: A critical re-examination of sex differences in human and animal societies. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 372, 20160309 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Székely, Á. & Székely, T. Human behaviour: Sex ratio and the city. Curr. Biol. 22, 684–685 (2012).Article 
CAS 

Google Scholar 
74.Székely, T., Liker, A., Freckleton, R. P., Fichtel, C. & Kappeler, P. M. Sex-biased survival predicts adult sex ratio variation in wild birds. Proc. R. Soc. B Biol. Sci. 281, 20140342–20140342 (2014).Article 

Google Scholar 
75.Grant, P. R. & Grant, B. R. Adult sex ratio influences mate choice in Darwin’s finches. Proc. Natl. Acad. Sci. U. S. A. 116, 12373–12382 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Procter, D. S., Moore, A. J. & Miller, C. W. The form of sexual selection arising from male-male competition depends on the presence of females in the social environment. J. Evol. Biol. 25, 803–812 (2012).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
77.Janicke, T. & Morrow, E. H. Operational sex ratio predicts the opportunity and direction of sexual selection across animals. Ecol. Lett. https://doi.org/10.1111/ele.12907 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
78.Wolf, K. N. et al. Age-dependent changes in sperm production, semen quality, and testicular volume in the black-footed ferret (Mustela nigripes). Biol. Reprod. 63, 179–187 (2000).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
79.Gasparini, C., Marino, I. A. M., Boschetto, C. & Pilastro, A. Effect of male age on sperm traits and sperm competition success in the guppy (Poecilia reticulata). J. Evol. Biol. 23, 124–135 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Chargé, R., Jalme, M. S., Lacroix, F., Cadet, A. & Sorci, G. Male health status, signalled by courtship display, reveals ejaculate quality and hatching success in a lekking species. J. Anim. Ecol. 79, 843–850 (2010).PubMed 
PubMed Central 

Google Scholar 
81.Ramirez, M. E. V., Le Pennec, M., Dorange, G., Devauchelle, N. & Nonnotte, G. Assessment of female gamete quality in the pacific oyster crassostrea gigas. Invertebr. Reprod. Dev. 36, 73–78 (1999).Article 

Google Scholar 
82.Berger, T. & Horner, C. M. In vivo exposure of female rats to toxicants may affect oocyte quality. Reprod. Toxicol. 17, 273–281 (2003).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
83.Dufour, J. J., Fahmy, M. H. & Minvielle, F. Seasonal changes in breeding activity, testicular size, testosterone concentration and seminal characteristics in rams with long or short breeding season. J. Anim. Sci. 58, 416–422 (1984).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
84.Gorman, M. R. & Zucker, I. Seasonal adaptations of siberian hamsters: II: Pattern of change in day length controls annual testicular and body weight rhythms. Biol. Reprod. 53, 116–125 (1995).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
85.Parker, G. A. & Begon, M. Optimal egg size and clutch size: Effects of environment and maternal Phenotype. Am. Nat. 128, 573–592 (1986).Article 

Google Scholar 
86.Boyce, M. S. & Perrins, C. M. Optimizing great tit clutch size in a fluctuating environment. Ecology 68, 142–153 (1987).Article 

Google Scholar 
87.Tallamy, D. W. Sexual selection and the evolution of exclusive paternal care in arthropods. Anim. Behav. 60, 559–567 (2000).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
88.Olson, V. A., Webb, T. J., Freckleton, R. P. & Székely, T. Are parental care trade-offs in shorebirds driven by parental investment or sexual selection?. J. Evol. Biol. 22, 672–682 (2009).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
89.Reynolds, J. D. & Székely, T. The evolution of parental care in shorebirds: Life histories, ecology, and sexual selection. Behav. Ecol. 8, 126–134 (1995).Article 

Google Scholar 
90.Balshine-Earn, S. & Earn, D. J. D. On the evolutionary pathway of parental care in mouth-brooding cichlid fish. Proc. R. Soc. B Biol. Sci. 265, 2217–2222 (1998).Article 

Google Scholar 
91.Ah-King, M., Kvarnemo, C. & Tullberg, B. S. The influence of territoriality and mating system on the evolution of male care: A phylogenetic study on fish. J. Evol. Biol. 18, 371–382 (2005).PubMed 
Article 
CAS 

Google Scholar 
92.Székely, T., Webb, J. N. & Cutchill, I. C. Mating patterns, sexual selection and parental care: An integrative approach. Vertebrate Mat. Syst. https://doi.org/10.1142/9789812793584_0008 (2000).Article 

Google Scholar 
93.Trivers, R. L. Parental investment and sexual selection. (1972).94.Keenleyside, M. H. A. Mate desertion in relation to adult sex ratio in the biparental cichlid fish Herotilapia multispinosa. Anim. Behav. 31, 683–688 (1983).Article 

Google Scholar 
95.Alonzo, S. H. Social and coevolutionary feedbacks between mating and parental investment. Trends Ecol. Evol. 25, 99–108 (2010).PubMed 
Article 

Google Scholar 
96.Houston, A. I., Székely, T. & McNamara, J. M. Conflict between parents over care. Trends Ecol. Evol. 20, 33–38 (2005).PubMed 
Article 

Google Scholar 
97.Clutton-Brock, T. H. The Evolution of Parental Care (Princeton University Press, 1991).Book 

Google Scholar 
98.Liker, A. & Szekely, T. Mortality costs of sexual selection and parental care in natural populations of birds. Evolution 59, 890–897 (2005).PubMed 
Article 

Google Scholar 
99.Emlen, S. T. Lek organization and mating strategies in the bullfrog. Behav. Ecol. Sociobiol. 1, 283–313 (1976).Article 

Google Scholar 
100.Weir, L. K., Grant, J. W. A. & Hutchings, J. A. The influence of operational sex ratio on the intensity of competition for mates. Am. Nat. 177, 167–176 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Orians, G. H. On the evolution of mating systems in birds and mammals. Am. Nat. 103, 589–603 (1969).Article 

Google Scholar 
102.Carmona-Isunza, M. C. et al. Adult sex ratio and operational sex ratio exhibit different temporal dynamics in the wild. Behav. Ecol. 28, 523–532 (2017).
Google Scholar 
103.Wikelski, M., Trillmich, F. & Jun, N. Body size and sexual size dimorphism in marine iguanas fluctuate as a result of opposing natural and sexual selection: An island comparison. Evolution 51, 922–936 (1997).PubMed 
Article 
PubMed Central 

Google Scholar 
104.Székely, T., Freckleton, R. P. & Reynolds, J. D. Sexual selection explains Rensch’s rule of size dimorphism in shorebirds. Proc. Natl. Acad. Sci. 101, 12224–12227 (2004).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
105.Kelly, C. D., Bussière, L. F. & Gwynne, D. T. Sexual selection for male mobility in a giant insect with female-biased size dimorphism. Am. Nat. 172, 417–423 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
106.Kotiaho, J., Alatalo, R. V., Mappes, J. & Parri, S. Sexual selection in a wolf spider: Male drumming activity, body size, and viability. Evolution 50, 1977 (1996).PubMed 
Article 
PubMed Central 

Google Scholar 
107.Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Projected losses of global mammal and bird ecological strategies. Nat. Commun. 10, 1–8 (2019).Article 
CAS 

Google Scholar 
108.Cooke, R. S. C., Bates, A. E. & Eigenbrod, F. Global trade-offs of functional redundancy and functional dispersion for birds and mammals. Glob. Ecol. Biogeogr. 28, 484–495 (2019).Article 

Google Scholar 
109.Bakewell, A. T., Davis, K. E., Freckleton, R. P., Isaac, N. J. B. & Mayhew, P. J. Comparing life histories across taxonomic groups in multiple dimensions: How mammal-like are insects?. Am. Nat. 195, 70–81 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
110.del Villalobos-Segura, M. C., García-Prieto, L. & Rico-Chávez, O. Effects of latitude, host body size, and host trophic guild on patterns of diversity of helminths associated with humans, wild and domestic mammals of Mexico. Int. J. Parasitol. Parasites Wildl. 13, 221–230 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
111.Pandit, P. S. et al. Predicting wildlife reservoirs and global vulnerability to zoonotic Flaviviruses. Nat. Commun. 9, 1–10 (2018).Article 
CAS 

Google Scholar 
112.Rapacciuolo, G. et al. Species diversity as a surrogate for conservation of phylogenetic and functional diversity in terrestrial vertebrates across the Americas. Nat. Ecol. Evol. 3, 53–61 (2019).Article 

Google Scholar 
113.Capdevila, P. et al. Longevity, body dimension and reproductive mode drive differences in aquatic versus terrestrial life-history strategies. Funct. Ecol. 34, 1613–1625 (2020).Article 

Google Scholar 
114.Ellington, E. H. et al. Using multiple imputation to estimate missing data in meta-regression. Methods Ecol. Evol. 6, 153–163 (2015).Article 

Google Scholar 
115.Pollock, L. J. et al. Protecting biodiversity (in all its complexity): New models and methods. Trends Ecol. Evol. 35, 1119–1128 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
116.Johnson, T. F., Isaac, N. J. B., Paviolo, A. & González-Suárez, M. Handling missing values in trait data. Glob. Ecol. Biogeogr. 30, 51–62 (2021).Article 

Google Scholar 
117.Onkelinx, T., Devos, K. & Quataert, P. Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision. J. Ornithol. 158, 603–615 (2017).Article 

Google Scholar  More