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    Exceptional longevity in northern peripheral populations of Wels catfish (Siluris glanis)

    Roff, D. A. The Evolution of Life Histories (Chapman & Hall, 1992).
    Google Scholar 
    Stearns, S. C. The Evolution of Life Histories (Oxford University Press, 1992).
    Google Scholar 
    Tibblin, P. et al. Evolutionary divergence of adult body size and juvenile growth in sympatric subpopulations of a top predator in aquatic ecosystems. Am. Nat. 186, 98–110 (2015).PubMed 

    Google Scholar 
    Voituron, Y., de Fraipont, M., Issartel, J., Guillaume, O. & Clobert, J. Extreme lifespan of the human fish (Proteus anguinus): A challenge for ageing mechanisms. Biol. Lett. 7, 105–107 (2011).PubMed 

    Google Scholar 
    Longhurst, A. Murphy’s law revisited: Longevity as a factor in recruitment to fish populations. Fish. Res. 56, 125–131 (2002).
    Google Scholar 
    Schaffer, W. M. Optimal reproductive effort in fluctuating environments. Am. Nat. 108, 783–790 (1974).
    Google Scholar 
    Beamish, R. J., McFarlane, G. A. & Benson, A. Longevity overfishing. Prog. Oceanogr. 68, 289–302 (2006).ADS 

    Google Scholar 
    Conti, B. Considerations on temperature, longevity and aging. Cell. Mol. Life Sci. 65, 1626–1630 (2008).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Inness, C. L. W. & Metcalfe, N. B. The impact of dietary restriction, intermittent feeding and compensatory growth on reproductive investment and lifespan in a short-lived fish. Proc. R. Soc. Lond. B Biol. Sci. 275, 1703–1708 (2008).
    Google Scholar 
    Liu, R. K. & Walford, R. L. Increased growth and life-span with lowered ambient temperature in the annual fish, Cynolebias adloffi. Nature 212, 1277–1278 (1966).ADS 

    Google Scholar 
    Trip, E. D., Clements, K. D., Raubenheimer, D. & Choat, J. H. Temperature-related variation in growth rate, size, maturation and life span in a marine herbivorous fish over a latitudinal gradient. J. Anim. Ecol. 83, 866–875 (2014).PubMed 

    Google Scholar 
    Munch, S. B. & Salinas, S. Latitudinal variation in lifespan within species is explained by the metabolic theory of ecology. Proc. Natl. Acad. Sci. U.S.A. 106, 13860–13864 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Britton, J. R., Pegg, J., Sedgwick, R. & Page, R. Investigating the catch returns and growth rate of wels catfish, Silurus glanis, using mark-recapture. Fish. Man. Ecol. 14, 263–268 (2007).
    Google Scholar 
    Hamel, M. J. et al. Range-wide age and growth characteristics of shovelnose sturgeon from mark–recapture data: Implications for conservation and management. Can. J. Fish. Aquat. Sci. 72, 71–82 (2015).
    Google Scholar 
    Hamel, M. J. et al. Using mark–recapture information to validate and assess age and growth of long-lived fish species. Can. J. Fish. Aquat. Sci. 71, 559–566 (2014).
    Google Scholar 
    Casale, P., Mazaris, A. D., Freggi, D., Vallini, C. & Argano, R. Growth rates and age at adult size of loggerhead sea turtles (Caretta caretta) in the Mediterranean Sea, estimated through capture-mark-recapture records. Sci. Mar. 73, 589–595 (2009).
    Google Scholar 
    IUCN (International Union for Conservation of Nature) 2008. Siluris glanis. The IUCN Red List of Threatened Species. Version 2021-3 (2010). https://www.iucnredlist.org. (Accessed 25 February 2021).Copp, G. H. et al. Voracious invader or benign feline? A review of the environmental biology of European catfish Silurus glanis in its native and introduced ranges. Fish. Fish. 10, 252–282 (2009).
    Google Scholar 
    Palm, S., Vinterstare, J., Nathanson, J. E., Triantafyllidis, A. & Petersson, E. Reduced genetic diversity and low effective size in peripheral northern European catfish Silurus glanis populations. J. Fish. Biol. 95, 1407–1421 (2019).PubMed 

    Google Scholar 
    Jensen, A., Lillie, M., Bergstrom, K., Larsson, P. & Hoglund, J. Whole genome sequencing reveals high differentiation, low levels of genetic diversity and short runs of homozygosity among Swedish wels catfish. Heredity 127, 79–91 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cucherousset, J. et al. Ecology, behaviour and management of the European catfish. Rev. Fish. Biol. Fish. 28, 177–190 (2017).
    Google Scholar 
    Kuzishchin, K. V., Gruzdeva, M. A. & Pavlov, D. S. Traits of biology of European Wels Catfish Silurus glanis from the Volga-Ahtuba water system, the Lower Volga. J. Ichthyol. 58, 833–844 (2019).
    Google Scholar 
    Alp, A., Kara, C., Üçkardeş, F., Carol, J. & García-Berthou, E. Age and growth of the European catfish (Silurus glanis) in a Turkish Reservoir and comparison with introduced populations. Rev. Fish. Biol. Fish. 21, 283–294 (2010).
    Google Scholar 
    Carol, J., Benejam, L. B. & García-Berthou, E. Growth and diet of European catfish (Silurus glanis) in early and late invasion stages. Fund. Appl. Limnol. 174, 317–328 (2009).
    Google Scholar 
    Severov, Y. A. Size–age structure, growth rate, and fishery of European Catfish Silurus glanis in the lower Kama Reservoir. J. Ichthyol. 60, 118–121 (2020).
    Google Scholar 
    Lessmark, O. Malprovfiske i Möckeln 2006. Länsstyrelsens rapportserie (2006).Lessmark, O. Malprovfiske i Möckeln 2007. Länsstyrelsens rapportserie (2007).Harka, A. Studies on the growth of the sheatfish (Silurus glanis L.) in River Tisza. Aquac. Hung. (Szarvas) 4, 135–144 (1984).
    Google Scholar 
    Edwards, J. E. et al. Advancing research for the management of long-lived species: A case study on the Greenland shark. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00087 (2019).Article 

    Google Scholar 
    Pikitch, E. K., Doukakis, P., Lauck, L., Chakrabarty, P. & Erickson, D. L. Status, trends and management of sturgeon and paddlefish fisheries. Fish. Fish. 6, 233–265 (2005).
    Google Scholar 
    Pironon, S. et al. Geographic variation in genetic and demographic performance: New insights from an old biogeographical paradigm. Biol. Rev. 92, 1877–1909 (2017).PubMed 

    Google Scholar 
    Antonovics, J., McKane, A. J. & Newman, T. J. Spatiotemporal dynamics in marginal populations. Am. Nat. 167, 16–27 (2006).CAS 
    PubMed 

    Google Scholar 
    Alp, A., Kara, C. & Büyükcapar, H. M. Reproductive biology in a Native European Catfish, Siluris glanis L., 1758, population in Menzelet Resevoir. Turk. J. Vet. Ani. Sci. 28, 613 (2004).
    Google Scholar 
    Boulêtreau, S. & Santoul, F. The end of the mythical giant catfish. Ecosphere 7(11), e01606. https://doi.org/10.1002/ecs2.1606 (2016).Article 

    Google Scholar 
    Bergmann, C. Ober die verhaltnisse der warmeokonomie der thiere zu ihrer grosse. Gottinger Studien 3, 595–708 (1847).
    Google Scholar 
    Blanck, A. & Lamouroux, N. Large-scale intraspecific variation in life-history traits of European freshwater fish. J. Biogeogr. 34, 862–875 (2007).
    Google Scholar 
    Charnov, E. L., Turner, T. F. & Winemiller, K. O. Reproductive constraints and the evolution of life histories with indeterminate growth. Proc. Natl. Acad. Sci. U.S.A. 98, 9460–9464 (2001).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ricklefs, R. E. Embryo development and ageing in birds and mammals. Proc. R. Soc. B 273, 2077–2082 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    Lee, W. S., Monaghan, P. & Metcalfe, N. B. Experimental demonstration of the growth rate-lifespan trade-off. Proc. R. Soc. B 280, 20122370 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Rennie, M. D., Kraft, C., Sprules, W. G. & Johnson, T. B. Factors affecting the growth and condition of lake whitefish (Coregonus clupeaformis). Can. J. Fish. Aquat. Sci. 66, 2096–2108 (2009).
    Google Scholar 
    Prats, J., Val, R., Armengol, J. & Dolz, J. Temporal variability in the thermal regime of the lower Ebro River (Spain) and alteration due to anthropogenic factors. J. Hydrol. 387, 105–118 (2010).ADS 

    Google Scholar 
    Kale, S. & Sönmez, A. Y. Climate change effects on annual streamflow of Filyos River (Turkey). J. Water Clim. Change 11, 420–433 (2020).
    Google Scholar 
    Britton, J. R., Cucherousset, J., Davies, G. D., Godard, M. J. & Copp, G. H. Non-native fishes and climate change: Predicting species responses to warming temperatures in a temperate region. Freshw. Biol. 55, 1130–1141 (2010).
    Google Scholar 
    Garcia, V. B., Lucifora, L. O. & Myers, R. A. The importance of habitat and life history to extinction risk in sharks, skates, rays and chimaeras. Proc. R. Soc. B 275, 83–89 (2008).PubMed 

    Google Scholar 
    Jackson, J. B. C. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
    PubMed 

    Google Scholar 
    Kuparinen, A. & Merilä, J. Detecting and managing fisheries-induced evolution. TREE 22, 652–659 (2007).PubMed 

    Google Scholar 
    Swedish University of Agricultural Sciences (SLU). National Data Host Lakes and Watercourses, and National Data Host Agricultural Land (Swedish University of Agricultural Sciences, 2021).
    Google Scholar 
    Emåförbundet. Vattenflöden och Nivåer (n.d.). http://www.eman.se/sv/vattenhushallning/vattenfloden-och-nivaer/historik/. (Accessed 12 May 2021)Fabens, A. J. Properties and fitting of the Von Bertalanffy growth curve. Growth 29, 265–289 (1965).CAS 
    PubMed 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/. (Accessed 13 April 2021)Bokor, Z. et al. Survival and growth rates of wels catfish (Siluris glanis Linnaeus, 1758) larvae originating from fertilization with cryopreserved or fresh sperm. J. Appl. Ichthyol. 31, 164–168 (2015).
    Google Scholar 
    du Sert, N. P. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18, e3000410 (2020).
    Google Scholar 
    Horoszewicz, L. & Backiel, T. Growth of Wels (Silurus glanis L.) in the Vistula river and the Zegrzyñski reservoir. Arch. Polish Fish. 11, 115–121 (2003).
    Google Scholar  More

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    Island biogeography and human practices drive ecological connectivity in mosquito species richness in the Lakshadweep Archipelago

    MacArthur, R. H. & Wilson, E. O. The theory of island biogeography (Princeton University Press, 1967).
    Google Scholar 
    MacArthur, R. H. & Wilson, E. O. An equilibrium theory of insular zoogeography. Evolution 17, 373–387 (1968).
    Google Scholar 
    Caraballo, H. Emergency department management of mosquito-borne illness: malaria, dengue, and west nile virus. Emerg. Med. Pract. 16(5), 1–2 (2014).MathSciNet 
    PubMed 

    Google Scholar 
    Rejmánková, E., Grieco, J., Achee, N., Roberts, DR. Ecology of larval habitats. In: Manguin S, editor. Anopheles mosquitoes: new insights into malaria vectors 9th. InTech; Rijeka: pp. 397–446. (2013).Sharma, M., Quader, S., Guttal, V. & Isvaran, K. The enemy of my enemy: multiple interacting selection pressures lead to unexpected anti-predator responses. Oecologia 192(1), 1–12 (2020).ADS 
    PubMed 

    Google Scholar 
    Yee, D. A., Kesavaraju, B. & Juliano, S. A. Interspecific differences in feeding behavior and survival under food-limited conditions for larval Aedes albopictus and Aedes aegypti (Diptera: Culicidae). Ann. Entomol. Soc. Am. 97, 720–728 (2006).
    Google Scholar 
    Messina, J. P. et al. The current and future global distribution and population at risk of dengue. Nat. Microbiol. 4, 1508–1515 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rose, N. H. et al. Climate and urbanization drive mosquito preference for humans. Curr. Biol. 30, 3570-3579.e6 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Day, J. F. Mosquito oviposition behavior and vector control. Insects 7(4), 65 (2016).PubMed Central 

    Google Scholar 
    McBride, C. S. Genes and odors underlying the recent evolution of mosquito preference for humans. Curr. Biol. 26, R41–R46 (2016).MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Southerst, R. W. Global change and human vulnerability to vector-borne diseases. Clin. Microbiol. Rev. 17, 136–173 (2004).
    Google Scholar 
    Vitousek, P. M. Nutrient cycling and limitation: Hawai‘i as a model system (Princeton University Press, 2004).
    Google Scholar 
    Grant, P. R. & Grant, B. R. How and why species multiply: the radiation of darwin’s finches (Princeton University Press, 2011).
    Google Scholar 
    Cliff, A. D. & Haggett, P. The epidemiological significance of islands. Health Place. 1, 199–209 (1995).
    Google Scholar 
    Arrhenius, O. Species and area. J. Ecol. 9(1), 95–99 (1921).
    Google Scholar 
    Preston, F. W. Time and space and the variation of species. Ecology 41(4), 611–627 (1960).
    Google Scholar 
    Rosenzweig, M. L. Species diversity in space and time (Cambridge University Press, 1995).
    Google Scholar 
    Drakare, S. et al. The imprint of the geographical, evolutionary and ecological context on species-area relationships. Ecol. Lett. 9: 215 227. (2006).Kotiaho, J., Kaitala, V., Komonen, A. & Päivinen, J. Predicting the risk of extinction from shared ecological characteristics. Proc. Natl. Acad. Sci. USA 102, 1963–1967 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bataille, A. et al. Natural colonization and adaptation of a mosquito species in Galápagos and its implications for disease threats to endemic wildlife. Proc. Nat. Acad. Sci. 106(25), 10230–10235 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sinka, M. E. et al. A new malaria vector in Africa: predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk. Proc. Nat. Acad. Sci. 117(40), 24900–24908 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Powell, J.R. Genetic variation in insect vectors: death of typology? Insects. 11;9(4):139. (2018).Whittaker, R. H. Communities and ecosystems (Macmillan, 1975).
    Google Scholar 
    Nekola, J. C. & White, P. S. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26, 867–878 (1999).
    Google Scholar 
    Green, J. L. et al. Spatial scaling of microbial eukaryote diversity. Nature 432, 747–750 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Horner-Devine, M. C., Lage, M. & Hughes, J. B. Bohannan BJ A taxa-area relationship for bacteria. Nature 432, 750–753 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Martiny, J, B. H., Eisen, J.A., Penn, K., Allison, S.D., Horner-Devine, M.C. Drivers of bacterial beta-diversity depend on spatial scale. Proc. Natl. Acad. Sci. USA 108(19):7850−4. (2011).Segre, H., Ron, R., de Malach, N., Henkin, Z., Mandel, M., Kadmon, R. Competitive exclusion, beta diversity, and deterministic vs. stochastic drivers of community assembly. Ecol. Lett., 17(11):1400−8. (2014).Ishtiaq, F. et al. Biogeographical patterns of blood parasite lineage diversity in avian hosts from southern Melanesian islands. J. Biogeogr. 37, 120–132 (2010).
    Google Scholar 
    Barrera, R., Amador, M. & MacKay, A. J. Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan. Puerto Rico. PLoS Negl. Trop. Dis. 5(12), e1378. https://doi.org/10.1371/journal.pntd.0001378 (2011).Article 
    PubMed 

    Google Scholar 
    Campbell, K. M., Lin, C. D., Iamsirithaworn, S. & Scott, T. W. The complex relationship between weather and dengue virus transmission in Thailand. Am. J. Trop. Med. Hyg. 89, 1066–1080. https://doi.org/10.4269/ajtmh.13-0321 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evans, M. V. et al. Microclimate and larval habitat density predict adult Aedes albopictus abundance in Urban Areas. Am. J. Trop. Med. Hyg. 101(2), 362–370 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Mustak, M. S. et al. The peopling of Lakshadweep Archipelago. Sci. Rep. 9, 6968 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sharma, S. K. & Hamzakoya, K. K. Geographical spread of Anopheles stephensi, vector of urban malaria, Aedes aegypti vector of Dengue/DHF, in the Arabian sea islands of Lakshadweep. India. Dengue Bull. 25, 88–91 (2001).
    Google Scholar 
    Sharma RS, Ali, MKS, Dhillon GPS. Epidemiological and entomological aspects of an outbreak of chikungunya in Lakshadweep islands, India, during 2007. Dengue Bull., 178–185 (2008).Subramaniam, H., Ramoo, H. & Sumanam, S. D. Filariasis survey in the Laccadive, minicoy and amindivi Islands. Madras state. Indian J. Malariol. 12, 115–127 (1958).CAS 
    PubMed 

    Google Scholar 
    Roy, R. G., Joy, C. T., Hussain, C. M. & Mohamed, I. K. Malaria in Lakshadweep Islands. Indian J. Med. Res. 67, 924–925 (1978).CAS 
    PubMed 

    Google Scholar 
    Ali, S. M. K. et al. Study on the ecoepidemiology of chikungunya in UT of Lakshadweep. J. Commun. Dis. 41(2), 81–92 (2009).
    Google Scholar 
    Samuel, P. P., Krishnamoorthi, R., Hamzakoya, K. K. & Aggarwal, C. S. Entomo-epidemiological investigations on chikungunya outbreak in the Lakshadweep Islands. Indian Ocean. Indian J. Med. Res. 129(4), 442–445 (2009).PubMed 

    Google Scholar 
    Jayalakshmi, K. & Mathiarasan, L. Prevalence of disease vectors in Lakshadweep Islands during post-monsoon season. J. Vector Borne Dis. 55, 189–196 (2018).
    Google Scholar 
    Su, C. L. et al. Molecular epidemiology of Japanese encephalitis virus in mosquitoes in Taiwan during 2005–2012. PLoS Negl. Trop. Dis. 8, e3122 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Muslim, A. et al. Armigeres subalbatus incriminated as a vector of zoonotic Brugia pahangi filariasis in suburban Kuala Lumpur. Peninsular Malaysia. Parasites Vectors 6, 219 (2013).PubMed 

    Google Scholar 
    Wilke, A. B. B. et al. Community composition and year-round abundance of vector species of mosquitoes make Miami-Dade County, Florida a receptive gateway for arbovirus entry to the United States. Sci. Rep. 9, 8732 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Medeiros-Sousa, A. R., Fernandes, A., Ceretti-Junior, W., Wilke, A. B. B. & Marrelli, M. T. Mosquitoes in urban green spaces: using an island biogeographic approach to identify drivers of species richness and composition. Sci. Rep. 7, 17826 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lum, J. K., Kaneko, A., Taleo, G., Amos, M. & Reiff, D. M. Genetic diversity and gene flow of humans, Plasmodium falciparum, and Anopheles farauti s.s. of Vanuatu. inferred malaria dispersal and implications for malaria control. Acta Trop. 103, 102–107 (2007).CAS 
    PubMed 

    Google Scholar 
    Marques, T. C. et al. Mosquito (Diptera: Culicidae) assemblages associated with Nidularium and Vriesea bromeliads in Serra do Mar, Atlantic Forest, Brazil. Parasites Vectors 5, 41 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Laporta, G. Z. & Sallum, M. A. M. Coexistence mechanisms at multiple scales in mosquito assemblages. BMC Ecol. 14, 30 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Koenraadt, C. J. & Takken, W. Cannibalism and predation among larvae of the Anopheles gambiae complex. Med. Vet. Entomol. 17(1), 61–66 (2003).CAS 
    PubMed 

    Google Scholar 
    Chathuranga, W. G. D., Karunaratne, S. H. P. P., Priyanka, W. A. & De Silva, P. Predator–prey interactions and the cannibalism of larvae of Armigeres subalbatus (Diptera: Culicidae). J. Asia-Pac. Entomol. 23, 124–131 (2020).
    Google Scholar 
    Focks, D. A. & Chadee, D. D. Pupal survey: an epidemiologically significant surveillance method for Aedes aegypti: an example using data from Trinidad. Am. J. Trop. Med. Hyg. 56(2), 159–167 (1997).CAS 
    PubMed 

    Google Scholar 
    Lounibos, L. P., Bargielowski, I., Carrasquilla, M. C. & Nishimura, N. Coexistence of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in Peninsular Florida two decades after competitive displacements. J. Med. Entomol. 53, 1385–1390 (2016).PubMed 

    Google Scholar 
    Juliano, S. A. Species interactions among larval mosquitoes: context dependence across habitat gradients. Annu. Rev. Entomol. 54, 37–56 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bargielowski, I.E., Lounibos, L.P., Carrasquilla, M.C. Evolution of resistance to satyrization through reproductive character displacement in populations of invasive dengue vectors. Proc. Natl. Acad. Sci. 19:110(8):2888–92. (2013).Chadee, D. D. Dengue cases and Aedes aegypti indices in Trinidad. West Indies. Acta Trop. 112(2), 174–180 (2009).CAS 
    PubMed 

    Google Scholar 
    XX. https://www.census2011.co.in/census/state/lakshadweep.htmlChristophers, S. R. The fauna of British India, including Ceylon and Burma; Diptera: Family Culicidae; Tribe Anophelini Vol. 4 (Taylor & Francis, 1933).
    Google Scholar 
    Barraud, P.J. The fauna of British India, including Ceylon and Burma. Diptera V. Family Culicidae. Tribes Megarhinini and Culicini. London: Taylor and Francis p. 463. (1934).Walther, B. A., Cotgreave, P., Price, R. D., Gregory, R. D. & Clayton, D. H. Sampling effort and parasite species richness. Parasitol. Today 11, 306–310 (1995).CAS 
    PubMed 

    Google Scholar 
    Chao, A. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).
    Google Scholar 
    Oksanen, J. et al. Vegan: community ecology package. R Package Version 2(10), 2013 (2015).
    Google Scholar 
    R Core Team. R Development Core Team. R A Lang. Environ. Stat. Comput. 55, 275–286 (2016).McFadden, D. Conditional logit analysis of qualitative choice behavior. Front. Econ. 1, 105–142 (1974).
    Google Scholar 
    Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).
    Google Scholar 
    Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monograph. 27, 325–349 (1957).
    Google Scholar 
    Sokal, R. R. & Rohlf, F. J. Biometry: the principles and practice of statistics in biological research 3rd edn. (Freeman, 1995).MATH 

    Google Scholar 
    Fortin, M. J. & Dale, M. R. T. Spatial analysis: a guide for ecologists 1–30 (Cambridge University Press, 2005).
    Google Scholar 
    Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. http://florianhartig.github.io/DHARMa/. (2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Google Scholar 
    World Health Organization, Guidelines for dengue surveillance and mosquito control. Western Pacific Education in Action Series No.8 (WHO, Geneva, 1995) More

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    A species diversity dataset of beetles by three passive acquisition methods in Tei Tong Tsai (Hong Kong)

    Study sitesThe sample site Tei Tong Tsai is located within the Island District (112°5’ E, 22°5’ N Hong Kong, China) and connected to Lantau Country Park. The rich woods in Tei Tong Tsai provide a suitable environment for insects to survive, with rich biodiversity. Weather records (Supplement 1) for May 2019 show that the highefst temperature was 27.2 °C, the lowest was 15.7 °C, the average was 21.7 °C; and the annual average rainfall was 297.8 mm. The suitable temperature and rainfall have created a suitable ecological environment and high biodiversity, establishing Tei Tong Tsai as a prime location for studying beetle diversity. In May 2019, a 13 sample sites were selected for beetle collection (Fig. 1). All latitude and longitude formats were converted to degrees, minutes, and seconds.Fig. 1Sampling points for the three passive acquisition methods used in the Tei Tong Tsai sampling site (indicated by red dots).Full size imageExperimental protocolIn this study, three passive collection methods were used for beetle collection. FIT is an efficient collecting method for insects with strong flying abilities and was first developed and used abroad14. MT and PT collect insects that are not strong flyers and live on the surface. A flight interception trap, a malaise trap, and 10 pitfall traps were set up to collect beetles in each sample site. Samples were selected to cover ecological environments at different longitudes, latitudes, altitudes, and distances from water sources. Reasonable sampling distances (depending on the terrain, with an interval between 100 and 200 m) were set up between sample sites to fully cover Tei Tong Tsai’s habitats. Due to the topography, the distance between the 10th and 11th sample points was about 350 m. The distance between two other close sample points were in the range of 100–200 m. All three traps were based on the original device to maximize the advantages and achieve better collection results.Collection devices. The flight interception trap (Fig. 2a) mainly comprises an interceptor screen (plastic net, PVC plastic glass, or plexiglas) and an insect specimen receiver (PVC), which is an efficient collection device for intercepting and collecting insects with strong flight ability. The detailed installation steps include the following: Firstly, punch two holes on the long side of the PVC screen with a hole puncher spaced about 30 cm apart; then, fix the screen to a bamboo pole with silk, install the specimen receiver, fix all three, bolt the rope, and fix it in the air with a thick rope (the sink is about 0.5–1 m from the ground). After installation, relevant drugs were placed inside the specimen receiver to poison the insects. The drugs used depend on the purpose of the study. For morphological studies, saline (5 mmol/L NaCl solution) or water with detergent is used. By contrast, DNA molecular studies use a mixture of 2% SDS (sodium dodecyl sulfate) and EDTA (ethylene diamine tetraacetic acid, 0.1 mol/L, PH = 8) or highly concentrated alcohol, which effectively controls the degradation of DNA. Currently, high-concentration alcohol, SDS and EDTA mixtures are commonly used. The device is widely applicable and can be installed in almost any habitat; however, it is best installed along the insects’ flight paths, including roads, rivers, or creeks between valleys. In this experiment, we improved this device by increasing the size of the water trough considering the actual situation of the sample site. Also, to properly conduct the molecular experiments, the reagents we used were a mixture of SDS and EDTA. Therefore, the improved device was more suitable for diverse habitats, and the insect species collected were abundant, reflecting good collection practices14.Fig. 2Three passive acquisition methods: (a) flight interception trap; (b) malaise trap; (c) pitfall trap.Full size imageMalaise traps (Fig. 2b) are large tent-like structures constructed from thin mesh. They are among the most commonly used static non-attractant insect traps and insect collection devices. Invented by Malaise (1937) and later improved upon by Townes and Sharkey, these traps are important tools for insect collection and monitoring worldwide15. The malaise trap used at the Tei Tong Tsai Country Park was the Townes type, which is generally set up in forest areas with rich habitats and relatively stable ground. The material is usually meshed mosquito netting fabricated into a tent-shaped insect interception field. The insects hit the net vertically, continue to fly upward, and are gradually led into the trap by the tilted top. The drug in the trap is usually anhydrous ethanol, which intercepts beetles with weak flying abilities16,17.The pitfall trap (Fig. 2c) is an effective method for capturing surface beetles; it is simple to use, easy to carry, and a common device for collection in the wild. The PT is created by digging a pit into the ground with the same depth as a wide-mouth plastic cup (20 cm high, 10 cm in diameter); The upper edge of the cup must be flushed with the soil surface, and a mixture of absolute ethanol is poured inside to collect flightless beetles14. About one-quarter of the way from the top, small holes are punched above the wide-mouth cup to prevent the loss of specimens from rainwater filling the cups. The 10 sets of traps in this experiment were not evenly distributed, but they were all in suitable habitats.Specimen samplingThe sampling site for this study was Tei Tong Tsai, and the sampling period was from 1st May to 28th May (2019). FIT, and PTs were collected once every two days. Due to the small number of beetles collected by MT, mt was collected only once. All beetles were picked out and arranged separately after collection, added to anhydrous ethanol, preserved, and labeled. The beetles collected by the three passive acquisition methods were picked according to morphological species.Specimen identificationThe taxonomic status for the family level of all samples was determined based on the relevant literature18,19,20,21. Relevant experts completed further identification (Supplement 2).All the specimens collected in this study are currently in the zoological museum of the Institute of Zoology, Chinese Academy of Sciences (Beijing, China).Specimen photographyBeetles were poured from the bottle and arranged separately according to the general species. Firstly, we used tweezers or a brush to place the beetles on unbreakable and unwrinkled paper (as far as possible with the backside upwards to keep them tight and neat, reducing the space left, and considering the label in the photograph). Simultaneously, we captured multiple photos according to the size and species of insect for the large specimens in the tube, adjusted the light near them to brighten the background, placed graph paper next to the beetles as a reference scale, then adjusted our Olympus camera settings to the appropriate photographing parameters. Finally, we inserted the photographed beetles and matching labels back into the tube and added anhydrous ethanol for preservation (Fig. 3). The labels were set in the photos as 2019 DTZ-FIT/MT/PTX-5XX-5XX (-N), in which 2019 represents the collection time, DTZ represents Tei Tong Tsai, FIT/MT/PT signifies the collection method, X represents the number of sampling points, 5XX-5XX represents sampling time, and N represents the photo number. If a sample site had many insects on the same date and required more than one photo, n was used to represent the number of photos. See the Supplement 3 for the complete document.Fig. 3Examples of beetles collected from three passive acquisition methods: overall photos of beetles collected by (a) FIT, (b) PT, and (c) MT. On the bottom right corner shows scale in each photo.Full size imageAfter the morphological data of the samples were collected, their Latin name and collection information were recorded in a table. Each passive acquisition method corresponded to a table, and each table was divided into 13 sheets according to 13 sampling points. The collection time was listed horizontally on each sheet, and the beetles’ species names were listed vertically (were named in the morphological species order as 1, 2, 3, …, N). The number of beetles was recorded in the corresponding position and the Supplement 4 file.Finally, data show the beetles’ biodiversity collected from each sampling site. Firstly, we summarized the data from each sampling point after completing the data statistics. Afterward, we counted the number of beetle individuals collected under the different passive acquisition methods at different points (Fig. 4). In Fig. 4, red, blue, and green represent the number of beetle individuals collected by MT, PT, and FIT, respectively. Fig. 4 shows that MT collected fewer beetles than FIT and PT. Secondly, the data of 13 sampling points in each collecting method were summarized to obtain the total number of families and species collected by each method (Fig. 5). A graph created in Excel 2016 displays the collection method as the horizontal coordinate and the number as the vertical coordinate. In the graph, red represents the number of families, and blue represents the number of species. Fig. 5 shows that FIT collected more beetle species and individuals than PT and MT, and MT collected the least. Thirdly, all data from the 13 sampling points and the three collection methods were summarized. The number of species collected in all families was counted. Families with more than ten species were selected (a total of 11 families) for data presentation (Fig. 6). Finally, a graphic was drawn in Excel 2016. Fig. 6 shows that the number of species in Staphylinidae, Curculionidae, and Chrysomelidae accounted for a large number, and the diversity was relatively high.Fig. 4Data table of numbers of individual beetles collected by different methods at 13 sampling points. The red, blue, and green columns represent the number of beetles collected by MT, PT, and FIT, respectively.Full size imageFig. 5The number of beetles collected by different passive acquisition methods. Horizontal coordinates represent collection methods. The red column and blue column represent the number of beetles collected on the family level and species level, respectively.Full size imageFig. 6Families with more than ten species (a total of 11 families) were selected for presentation. The sample sizes of each groups were also shown.Full size image More

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    Ranking threats to biodiversity and why it doesn’t matter

    The difficulties inherent in ranking global threats are due to them being context-dependent, which result from conditions and the nature of the threats themselves differing among locations, habitats, and taxa (Fig. 1). Current high-risk hotspots from habitat loss and overexploitation are primarily located in the tropics, whereas Europe is documented as a threat hotspot for pollution6. On islands, biological invasions mainly threaten biodiversity in the Pacific and Atlantic Oceans, while islands in the Indian Ocean and near the coasts of Asia are mostly threatened by overexploitation and agriculture3. Climate change affects species more at higher latitudes and altitudes because species are constrained by the physical environment (geographic barriers and mountain tops) to follow their optimal isotherms.Fig. 1: Divergence of global threat rankings across different references and international agencies.IPBES, WWF, and IUCN established global rankings of the five threats responsible for the current biodiversity crisis (B: central, yellow panel). However, the relative importance of each threat depends on the taxon, system, species’ characteristics, time, and/or the metric considered, resulting in divergences. Global biodiversity threats are represented by colors and symbols, given in the top panel. This figure encapsulates results combined from different studies detailed in Supplementary Table 1 with their associated references.Full size imageThe relative importance of threats also depends on the taxon considered. At the global scale, vertebrates are primarily threatened by habitat loss, overexploitation, and then biological invasions. But even within the vertebrates rankings differ — birds and mammals are mainly affected by overexploitation, while amphibians have a higher probability of succumbing to habitat loss6. Because of species-specific traits and adaptations, some species are likely to respond differently to global threats even within a clade. Large-bodied vertebrates are more likely to be threatened by overexploitation, whereas small-bodied vertebrates are more prone to habitat loss or pollution (Fig. 1). Threat ranking also depends on the habitat under consideration. Marine mammals are more threatened by overexploitation and pollution than terrestrial mammals for which habitat loss is the primary threat (Fig. 1). On islands, habitat loss is secondary to the pressures of biological invasions in freshwater systems, but the former is more important for terrestrial vertebrates and plants3. Another source of uncertainty is that most studies examining threats are based on well-studied taxa such as terrestrial vertebrates, which only represent a small subset of the tree of life. For instance, only 0.2% of fungi, 1.7% of invertebrates, and 10% of described plants are assessed in the IUCN update of 20197, potentially underestimating the intensity of some threats and biasing conservation priorities for these groups. Similarly, there is a bias of research effort towards regions with high-income countries, while research from low or middle-income countries is generally underrepresented8. This may give the false impression of absence of threats in some regions of the world.Likewise, period-specific global threat ranks are subject to the vagaries of temporal dynamics (Fig. 1). However, distinguishing past, current, and future threats is essential for current or future conservation interventions. Historically, overexploitation caused most of the Pleistocene megafauna extinctions, likely exacerbated by climate change. As agricultural practices intensified, habitat loss played a major role in extinctions. As humans later colonized islands, biological invasions caused the extinction of hundreds of species worldwide3. In contrast, climate change is only predicted to become major in the near future9. In fact, the effects of recent threats might be masked by delayed species’ responses, especially in under-studied regions, resulting in a large extinction debt. For instance, the severity of biological invasions often causes native species to decline rapidly to local extinction, while other threats such as habitat loss might affect species more slowly. In both cases, the eventual extinctions are ultimately if similar magnitude. More

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    A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020

    Cox, P. & Jones, C. Climate change – Illuminating the modern dance of climate and CO2. Science 321, 1642–1644 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gilmanov, T. G. et al. Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements. Glob. Biogeochem. Cycle 17, 1071 (2003).ADS 
    Article 
    CAS 

    Google Scholar 
    Running, S. W. Climate change – Ecosystem disturbance, carbon, and climate. Science 321, 652–653 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sun, Z. et al. Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015. Ecol. Inform. 46, 156–165 (2018).CAS 
    Article 

    Google Scholar 
    Running, S. W. et al. A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens. Environ. 70, 108–127 (1999).ADS 
    Article 

    Google Scholar 
    Madani, N. et al. The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska. J. Geophys. Res.-Biogeosci. 126, e2020JG006078 (2021).ADS 
    Article 

    Google Scholar 
    Morales, P. et al. Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Glob. Change Biol. 11, 2211–2233 (2005).ADS 
    Article 

    Google Scholar 
    Tramontana, G., Ichii, K., Camps-Valls, G., Tomelleri, E. & Papale, D. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data. Remote Sens. Environ. 168, 360–373 (2015).ADS 
    Article 

    Google Scholar 
    Canadell, J. G. et al. Carbon metabolism of the terrestrial biosphere: A multitechnique approach for improved understanding. Ecosystems 3, 115–130 (2000).CAS 
    Article 

    Google Scholar 
    Fletcher, B. J. et al. Photosynthesis and productivity in heterogeneous arctic tundra: consequences for ecosystem function of mixing vegetation types at stand edges. J. Ecol. 100, 441–451 (2012).CAS 
    Article 

    Google Scholar 
    Liu, L., Guan, L. & Liu, X. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence. Agr. Forest Meteorol. 232, 1–9 (2017).ADS 
    Article 

    Google Scholar 
    Xu, X. et al. Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data. J. Environ. Manage. 246, 605–616 (2019).PubMed 
    Article 

    Google Scholar 
    Baldocchi, D. D. How eddy covariance flux measurements have contributed to our understanding of Global Change Biology. Glob. Change Biol. 26, 242–260 (2020).ADS 
    Article 

    Google Scholar 
    He, L., Chen, J. M., Liu, J., Belair, S. & Luo, X. Assessment of SMAP soil moisture for global simulation of gross primary production. J. Geophys. Res.-Biogeosci. 122, 1549–1563 (2017).Article 

    Google Scholar 
    Wang, S., Ibrom, A., Bauer-Gottwein, P. & Garcia, M. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agr. Forest Meteorol. 248, 479–493 (2018).ADS 
    Article 

    Google Scholar 
    Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Yu, G., Fu, Y., Sun, X., Wen, X. & Zhang, L. Recent progress and future directions of ChinaFLUX. Sci. China Ser. D-Earth Sci. 49, 1–23 (2006).ADS 
    Article 

    Google Scholar 
    McCallum, I. et al. Improved light and temperature responses for light-use-efficiency-based GPP models. Biogeosciences 10, 6577–6590 (2013).ADS 
    Article 

    Google Scholar 
    Stocker, B. D. et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nature Geoscience 12, 264‐+ (2019).ADS 
    Article 
    CAS 

    Google Scholar 
    Cheng, S. J. et al. Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems. Agr. Forest Meteorol. 201, 98–110 (2015).ADS 
    Article 

    Google Scholar 
    Zhang, M. et al. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China. Agr. Forest Meteorol. 151, 803–816 (2011).ADS 
    Article 

    Google Scholar 
    Oliphant, A. J. et al. The role of sky conditions on gross primary production in a mixed deciduous forest. Agr. Forest Meteorol. 151, 781–791 (2011).ADS 
    Article 

    Google Scholar 
    Urban, O. et al. Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation. Glob. Change Biol. 13, 157–168 (2007).ADS 
    Article 

    Google Scholar 
    Zhou, H. et al. Large contributions of diffuse radiation to global gross primary productivity during 1981–2015. Glob. Biogeochem. Cycle 35, e2021GB006957 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).ADS 
    Article 

    Google Scholar 
    Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. USA 111, E1327–E1333 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liu, L. & Cheng, Z. Detection of vegetation light-use efficiency based on solar-induced chlorophyll fluorescence separated from canopy radiance spectrum. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 3, 306–312 (2010).ADS 
    Article 

    Google Scholar 
    MacBean, N. et al. Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data (vol 8, 1973, 2018). Sci. Rep. 8, 10420 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Meroni, M. et al. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sens. Environ. 113, 2037–2051 (2009).ADS 
    Article 

    Google Scholar 
    Zheng, T. & Chen, J. M. Photochemical reflectance ratio for tracking light use efficiency for sunlit leaves in two forest types. ISPRS-J. Photogramm. Remote Sens. 123, 47–61 (2017).ADS 
    Article 

    Google Scholar 
    Damm, A. et al. Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP). Glob. Change Biol. 16, 171–186 (2010).ADS 
    Article 

    Google Scholar 
    Lee, J. E. et al. Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4. Glob. Change Biol. 21, 3469–3477 (2015).ADS 
    Article 

    Google Scholar 
    Pinto, F. et al. Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ. 39, 1500–1512 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Xie, X., Li, A., Jin, H., Yin, G. & Nan, X. Derivation of temporally continuous leaf maximum carboxylation rate (V-cmax) from the sunlit leaf gross photosynthesis productivity through combining BEPS model with light response curve at tower flux sites. Agr. Forest Meteorol. 259, 82–94 (2018).ADS 
    Article 

    Google Scholar 
    Chen, J. M., Liu, J., Leblanc, S. G., Lacaze, R. & Roujean, J. L. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sens. Environ. 84, 516–525 (2003).ADS 
    Article 

    Google Scholar 
    Chen, J. M. et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Glob. Biogeochem. Cycle 26, GB1019 (2012).ADS 
    Article 
    CAS 

    Google Scholar 
    Running, S. W., Thornton, P. E., Nemani, R. & Glassy, J. M. in Methods in Ecosystem Science. Ch.3 (Springer, New York, NY. Press, 2000).Wu, C., Munger, J. W., Niu, Z. & Kuang, D. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sens. Environ. 114, 2925–2939 (2010).ADS 
    Article 

    Google Scholar 
    Makela, A. et al. Developing an empirical model of stand GPP with the LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe. Glob. Change Biol. 14, 92–108 (2008).ADS 
    Article 

    Google Scholar 
    McCallum, I. et al. Satellite-based terrestrial production efficiency modeling. Carbon Balanc. Manag. 4, 8–8 (2009).Article 

    Google Scholar 
    Wang, H. et al. Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ 114, 2248–2258 (2010).ADS 
    Article 

    Google Scholar 
    Yu, R. An improved estimation of net primary productivity of grassland in the Qinghai-Tibet region using light use efficiency with vegetation photosynthesis model. Ecol. Model. 431, 109121 (2020).Article 

    Google Scholar 
    Yuan, W. et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agr. Forest Meteorol. 143, 189–207 (2007).ADS 
    Article 

    Google Scholar 
    Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

    Google Scholar 
    Zhang, Y. et al. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agr. Forest Meteorol. 223, 116–131 (2016).ADS 
    Article 

    Google Scholar 
    He, M. et al. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agr. Forest Meteorol. 173, 28–39 (2013).ADS 
    Article 

    Google Scholar 
    Zhou, Y. et al. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites. J. Geophys. Res.-Biogeosci. 121, 1045–1072 (2016).Article 

    Google Scholar 
    Friedlingstein, P. et al. Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks. J. Clim. 27, 511–526 (2014).ADS 
    Article 

    Google Scholar 
    Raich, J. W. et al. Potential net primary productivity in South-America – application of a global-model. Ecol. Appl. 1, 399–429 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    Li, J. et al. An algorithm differentiating sunlit and shaded leaves for improving canopy conductance and vapotranspiration estimates. J. Geophys. Res.-Biogeosci. 124, 807–824 (2019).ADS 
    Article 

    Google Scholar 
    Chen, J. M., Liu, J., Cihlar, J. & Goulden, M. L. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).CAS 
    Article 

    Google Scholar 
    Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 
    Article 

    Google Scholar 
    Korson, L., Drosthan, W. & Millero, F. J. Viscosity of water at various temperatures. J. Phys. Chem. 73, 34–39 (1969).CAS 
    Article 

    Google Scholar 
    Olofsson, P., Van Laake, P. E. & Eklundh, L. Estimation of absorbed PAR across Scandinavia from satellite measurements Part I: Incident PAR. Remote Sens. Environ. 110, 252–261 (2007).ADS 
    Article 

    Google Scholar 
    González, J. A. & Calbó, J. Modelled and measured ratio of PAR to global radiation under cloudless skies. Agr. Forest Meteorol. 110, 319–325 (2002).ADS 
    Article 

    Google Scholar 
    Zhang, X., Zhang, Y. & Zhoub, Y. Measuring and modelling photosynthetically active radiation in Tibet Plateau during April–October. Agr. Forest Meteorol. 102, 207–212 (2000).ADS 
    Article 

    Google Scholar 
    Yang, Y., Xiao, P., Feng, X. & Li, H. Accuracy assessment of seven global land cover datasets over China. ISPRS-J. Photogramm. Remote Sens. 125, 156–173 (2017).ADS 
    Article 

    Google Scholar 
    Liu, Y., Liu, R. & Chen, J. M. GLOBMAP global Leaf Area Index since 1981. Zenodo https://doi.org/10.5281/zenodo.4700264 (2019).Vermote, E. MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD09A1.006 (2015).Deng, F., Chen, J. M., Plummer, S., Chen, M. & Pisek, J. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans. Geosci. Remote Sens. 44, 2219–2229 (2006).ADS 
    Article 

    Google Scholar 
    Vermote, E. NOAA CDR Program. NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. LAI. NOAA National Centers for Environmental Information https://doi.org/10.7289/V5TT4P69 (2019).He, L., Chen, J. M., Pisek, J., Schaaf, C. & Strahler, A. Global clumping index map derived from the MODIS BRDF product. Remote Sens. Environ. 119, 118–130 (2012).ADS 
    Article 

    Google Scholar 
    Liu, R. G. & Liu, Y. Generation of new cloud masks from MODIS land surface reflectance products. Remote Sens. Environ. 133, 21–37 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Chen, J. M., Deng, F. & Chen, M. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Trans. Geosci. Remote Sens. 44, 2230–2238 (2006).ADS 
    Article 

    Google Scholar 
    Harris, I.C. CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data. Centre for Environmental Data Analysis http://catalogue.ceda.ac.uk/uuid/863a47a6d8414b6982e1396c69a9efe8 (2019).Li, X., Liang, H. & Cheng, W. Evaluation and comparison of light use efficiency models for their sensitivity to the diffuse PAR fraction and aerosol loading in China. Int. J. Appl. Earth Obs. Geoinf. 95, 102269 (2021).
    Google Scholar 
    Duan, Q. Y., Sorooshian, S. & Gupta, V. Effective and efficient global optimization for conceptual rain full-runoff models. Water Resour. Res. 28, 1015–1031 (1992).ADS 
    Article 

    Google Scholar 
    Gu, L. H. et al. Advantages of diffuse radiation for terrestrial ecosystem productivity. J. Geophys. Res.-Atmos. 107, 4050 (2002).ADS 

    Google Scholar 
    Bi, W. & Zhou, Y. A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020). Dryad https://doi.org/10.5061/dryad.dfn2z352k (2022).Ogutu, B. O. & Dash, J. Assessing the capacity of three production efficiency models in simulating gross carbon uptake across multiple biomes in conterminous USA. Agr. Forest Meteorol. 174, 158–169 (2013).ADS 
    Article 

    Google Scholar 
    Cai, W. et al. Large differences in terrestrial vegetation production derived from satellite-based light use efficiency models. Remote Sens. 6, 8945–8965 (2014).ADS 
    Article 

    Google Scholar 
    Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785–818 (2015).ADS 
    Article 

    Google Scholar 
    Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: A global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sens. 11, 2563 (2019).ADS 
    Article 

    Google Scholar 
    Alemohammad, S. H. et al. Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. Biogeosciences 14, 4101–4124 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).ADS 
    Article 

    Google Scholar 
    Wang, S., Zhang, Y., Ju, W., Qiu, B. & Zhang, Z. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci. Total Environ. 755, 142569 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Zheng, Y. et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data 12, 2725–2746 (2020).ADS 
    Article 

    Google Scholar 
    Running, S., Mu, Q. & Zhao, M. MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD17A2H.006 (2015).Ciais, P. et al. A three-dimensional synthesis study of delta O-18 in atmospheric CO2 .1. Surface fluxes. J. Geophys. Res.-Atmos. 102, 5857–5872 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhang, Y., Joiner, J., Gentine, P. & Zhou, S. Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Glob. Change Biol. 24, 2229–2230 (2018).ADS 
    Article 

    Google Scholar 
    Xie, X. et al. Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models. Sci. Total Environ. 690, 1120–1130 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Fang, H., Wei, S., Jiang, C. & Scipal, K. Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method. Remote Sens. Environ. 124, 610–621 (2012).ADS 
    Article 

    Google Scholar 
    Camacho, F., Cemicharo, J., Lacaze, R., Baret, F. & Weiss, M. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sens. Environ. 137, 310–329 (2013).ADS 
    Article 

    Google Scholar 
    Prince, S. D. & Goward, S. N. Global primary production: A remote sensing approach. J. Biogeogr. 22, 815–835 (1995).Article 

    Google Scholar 
    Verma, S. B. et al. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agr. Forest Meteorol. 131, 77–96 (2005).ADS 
    Article 

    Google Scholar 
    Yan, H. et al. Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants. Ecol. Model. 297, 42–59 (2015).CAS 
    Article 

    Google Scholar 
    Jiang, S. et al. Comparison of satellite-based models for estimating gross primary productivity in agroecosystems. Agr. Forest Meteorol. 297, 108253 (2021).ADS 
    Article 

    Google Scholar 
    Yang, X. et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhou, H. et al. Responses of gross primary productivity to diffuse radiation at global FLUXNET sites. Atmos. Environ. 244, 117905 (2021).CAS 
    Article 

    Google Scholar 
    Han, J. et al. Effects of diffuse photosynthetically active radiation on gross primary productivity in a subtropical coniferous plantation vary in different timescales. Ecol. Indic. 115, 106403 (2020).Article 

    Google Scholar 
    Grant, I. F., Prata, A. J. & Cechet, R. P. The impact of the diurnal variation of albedo on the remote sensing of the daily mean albedo of grassland. J. Appl. Meteorol. 39, 231–244 (2000).ADS 
    Article 

    Google Scholar 
    Singarayer, J. S., Ridgwell, A. & Irvine, P. Assessing the benefits of crop albedo bio-geoengineering. Environ. Res. Lett. 4, 045110 (2009).ADS 
    Article 

    Google Scholar 
    Tang, S. et al. LAI inversion algorithm based on directional reflectance kernels. J. Environ. Manage. 85, 638–648 (2007).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Impacts of larval host plant species on dispersal traits and free-flight energetics of adult butterflies

    Ehrlich, P. R. & Raven, P. H. Butterflies and plants: A study in coevolution. Evolution 18, 586 (1964).Article 

    Google Scholar 
    Raguso, R. A. et al. The raison d’être of chemical ecology. Ecology 96, 617–630 (2015).PubMed 
    Article 

    Google Scholar 
    Kariyat, R. R. & Portman, S. L. Plant–herbivore interactions: Thinking beyond larval growth and mortality. Am. J. Bot. 103, 789–791 (2016).PubMed 
    Article 

    Google Scholar 
    Raubenheimer, D. & Simpson, S. J. Nutritional ecology and foraging theory. Curr. Opin. Insect Sci. 27, 38–45 (2018).PubMed 
    Article 

    Google Scholar 
    Goehring, L. & Oberhauser, K. S. Effects of photoperiod, temperature, and host plant age on induction of reproductive diapause and development time in Danaus plexippus. Ecol. Entomol. 27, 674–685 (2002).Article 

    Google Scholar 
    Hahn, D. A. Larval nutrition affects lipid storage and growth, but not protein or carbohydrate storage in newly eclosed adults of the grasshopper Schistocerca americana. J. Insect Physiol. 51, 1210–1219 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Portman, S. L., Kariyat, R. R., Johnston, M. A., Stephenson, A. G. & Marden, J. H. Cascading effects of host plant inbreeding on the larval growth, muscle molecular composition, and flight capacity of an adult herbivorous insect. Funct. Ecol. 29, 328–337 (2015).Article 

    Google Scholar 
    Johnson, C. G. Physiological factors in insect migration by flight. Nature 198, 423–427 (1963).Article 

    Google Scholar 
    Harrison, R. G. Dispersal polymorphisms in insects. Annu. Rev. Ecol. Syst. 11, 95–118 (1980).Article 

    Google Scholar 
    Zera, A. J. & Denno, R. F. Physiology and ecology of dispersal polymorphism in insects. Annu. Rev. Entomol. 42, 207–231 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Marden, J. H. et al. Weight and nutrition affect pre-mRNA splicing of a muscle gene associated with performance, energetics and life history. J. Exp. Biol. 211, 3653–3660 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Raguso, R. A., Ojeda-Avila, T., Desai, S., Jurkiewicz, M. A. & Arthur Woods, H. The influence of larval diet on adult feeding behaviour in the tobacco hornworm moth, Manduca sexta. J. Insect Physiol. 53, 923–932 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cease, A. J. et al. Nutritional imbalance suppresses migratory phenotypes of the Mongolian locust (Oedaleus asiaticus). R. Soc. Open Sci. 4, https://doi.org/10.1098/rsos.161039 (2017).Reichstein, T., Von Euw, J., Parsons, J. A. & Rothschild, M. Heart poisons in the monarch butterfly. Science 161, 861–866 (1968).CAS 
    PubMed 
    Article 

    Google Scholar 
    Brower, L. P., Ryerson, W. N., Coppinger, L. L. & Glazier, S. C. Ecological chemistry and the palatability spectrum. Science 161, 1349–1351 (1968).CAS 
    PubMed 
    Article 

    Google Scholar 
    Young, A. M. An evolutionary-ecological model of the evolution of migratory behavior in the Monarch Butterfly, and its absence in the Queen Butterfly. Acta Biotheor. 31, 219–237 (1982).Article 

    Google Scholar 
    Agrawal, A. A. Monarchs and Milkweed: A Migrating Butterfly, a Poisonous Plant, and Their Remarkable Story of Coevolution. (Princeton University Press, 2017).Batalden, R. V. & Oberhauser, K. S. Potential changes in eastern north American monarch migration in response to an introduced Milkweed, Asclepias curassavica. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly 215–224 (2015).Tyler Flockhart, D. T. et al. Tracking multi-generational colonization of the breeding grounds by monarch butterflies in eastern North America. Proc. R. Soc. B Biol. Sci. 280, 20131087 (2013).Saunders, S. P., Ries, L., Oberhauser, K. S., Thogmartin, W. E. & Zipkin, E. F. Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus. Ecography. 41, 278–290 (2018).Article 

    Google Scholar 
    Pleasants, J. M. & Oberhauser, K. S. Milkweed loss in agricultural fields because of herbicide use: Effect on the monarch butterfly population. Insect Conserv. Divers. 6, 135–144 (2013).Article 

    Google Scholar 
    Borders, B. & Lee-Mäder, B. B. Project milkweed. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly. pp.190-196 (Cornell University press, 2015).Agrawal, A. A., Petschenka, G., Bingham, R. A., Weber, M. G. & Rasmann, S. Toxic cardenolides: Chemical ecology and coevolution of specialized plant-herbivore interactions. N. Phytologist 194, 28–45 (2012).CAS 
    Article 

    Google Scholar 
    Malcolm, S. B. Milkweeds, monarch butterflies and the ecological significance of cardenolides. Chemoecology 5–6, 101–117 (1994).Article 

    Google Scholar 
    Pocius, V. M., Debinski, D. M., Bidne, K. G., Hellmich, R. L. & Hunter, F. K. Performance of early Instar Monarch Butterflies (Danaus plexippus L.) on nine Milkweed species native to Iowa. J. Lepid. Soc. 71, 153–161 (2017).
    Google Scholar 
    Ali, J. G. & Agrawal, A. A. Specialist versus generalist insect herbivores and plant defense. Trends Plant Sci. 17, 293–302 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zalucki, M. P., Brower, L. P. & Alonso-M, A. Detrimental effects of latex and cardiac glycosides on survival and growth of first-instar monarch butterfly larvae Danaus plexippus feeding on the sandhill milkweed Asclepias humistrata. Ecol. Entomol. 26, 212–224 (2001).Article 

    Google Scholar 
    Agrawal, A. A., Hastings, A. P., Patrick, E. T. & Knight, A. C. Specificity of herbivore-induced hormonal signaling and defensive traits in five closely related milkweeds (Asclepias spp.). J. Chem. Ecol. 40, 717–729 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Agrawal, A. A., Ali, J. G., Rasmann, S. & Fishbein, M. Macroevolutionary trends in the defense of milkweeds against monarchs. Monarch. a Chang. World Biol. Conserv. Iconic Insect. Cornell University Press, Ithaca, NY. pp. 47–59 (2011).Pocius, V. M. et al. Milkweed matters: Monarch butterfly (Lepidoptera: Nymphalidae) survival and development on nine midwestern milkweed species. Environ. Entomol. 46, 1098–1105 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Petschenka, G. et al. Stepwise evolution of resistance to toxic cardenolides via genetic substitutions in the na+/k+-atpase of milkweed butterflies (lepidoptera: Danaini). Evolution (N. Y). 67, 2753–2761 (2013).CAS 

    Google Scholar 
    Agrawal, A. A. et al. Cardenolides, toxicity, and the costs of sequestration in the coevolutionary interaction between monarchs and milkweeds. Proc. Natl Acad. Sci. USA 118, e2024463118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Marden, J. H. Variability in the size, composition, and function of insect flight muscles. Annu. Rev. Physiol. 62, 157–178 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bicudo, J. E. P. W., Buttemer, W. A., Chappell, M. A., Pearson, J. T. & Bech, C. Ecological and Environmental Physiology of Birds. Ecological and Environmental Physiology of Birds 3 (Oxford University Press, 2010).Bailey, E. Biochemistry of Insect Flight. in Insect Biochemistry and Function. pp. 89–176 (Springer, 1975).Dudley, R. The biomechanics of insect flight: form, function, evolution. Annals of the Entomological Society of America 93 (Princeton University Press, 2000).Solensky, M. J. Overview of monarch migration. in The Monarch Butterfly: Biology and Conservation 79–83 (2004).Urquhart, F. A. & Urquhart, N. R. Monarch butterfly (Danaus plexippus L.) overwintering population in Mexico (Lep. Danaidae). Atalanta 7, 56–61 (1976).
    Google Scholar 
    Brower, L. P. Understanding and misunderstanding the migration of the monarch butterfly (Nymphalidae) in North America: 1857–1995. J. – Lepid. Soc. 49, 304–385 (1995).
    Google Scholar 
    Fisher, K. E., Adelman, J. S. & Bradbury, S. P. Employing Very High Frequency (VHF) radio telemetry to recreate monarch butterfly flight paths. Environ. Entomol. 49, 312–323 (2020).PubMed 
    Article 

    Google Scholar 
    Reppert, S. M. & de Roode, J. C. Demystifying monarch butterfly migration. Curr. Biol. 28, R1009–R1022 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhu, H., Gegear, R. J., Casselman, A., Kanginakudru, S. & Reppert, S. M. Defining behavioral and molecular differences between summer and migratory monarch butterflies. BMC Biol. 7, 1–14 (2009).Heinze, S. & Reppert, S. M. Anatomical basis of sun compass navigation I: The general layout of the monarch butterfly brain. J. Comp. Neurol. 520, 1599–1628 (2012).PubMed 
    Article 

    Google Scholar 
    Zhan, S. et al. The genetics of monarch butterfly migration and warning colouration. Nature 514, 317–321 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Soule, A. J., Decker, L. E. & Hunter, M. D. Effects of diet and temperature on monarch butterfly wing morphology and flight ability. J. Insect Conserv. 24, 961–975 (2020).Article 

    Google Scholar 
    Decker, L. E., Soule, A. J., de Roode, J. C. & Hunter, M. D. Phytochemical changes in milkweed induced by elevated CO2 alter wing morphology but not toxin sequestration in monarch butterflies. Funct. Ecol. 33, 411–421 (2019).Article 

    Google Scholar 
    Heinrich, B. Temperature regulation of the sphinx moth, Manduca sexta. I. Flight energetics and body temperature during free and tethered flight. J. Exp. Biol. 54, 141–152 (1971).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nicolson, S. W. & Louw, G. N. Simultaneous measurement of evaporative water loss, oxygen consumption, and thoracic temperature during flight in a carpenter bee. J. Exp. Zool. 222, 287–296 (1982).Article 

    Google Scholar 
    Rothe, U. & Nachtigall, W. Flight of the honey bee IV. J. Comp. Physiol. B 158, 711–718 (1989).Article 

    Google Scholar 
    Nachtigall, W., Hanauer-Thieser, U. & Mörz, M. Flight of the honey bee VII: Metabolic power versus flight speed relation. J. Comp. Physiol. B 165, 484–489 (1995).Article 

    Google Scholar 
    Niven, J. E. & Scharlemann, J. P. W. Do insect metabolic rates at rest and during flight scale with body mass? Biol. Lett. 1, 346–349 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zalucki, M. P., Parry, H. R. & Zalucki, J. M. Movement and egg laying in Monarchs: To move or not to move, that is the equation. Austral. Ecol. 41, 154–167 (2016).Article 

    Google Scholar 
    Marden, J. H. & Chai, Peng Aerial predation and butterfly design: How palatability, mimicry, and the need for evasive flight constrain mass allocation. Am. Nat. 138, 15–36 (1991).Article 

    Google Scholar 
    Levin, E., Lopez-Martinez, G., Fane, B. & Davidowitz, G. Hawkmoths use nectar sugar to reduce oxidative damage from flight. Science 355, 733–735 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Petschenka, G. & Agrawal, A. A. Milkweed butterfly resistance to plant toxins is linked to sequestration, not coping with a toxic diet. Proc. R. Soc. B Biol. Sci. 282, 20151865 (2015).Petschenka, G. & Agrawal, A. A. How herbivores coopt plant defenses: Natural selection, specialization, and sequestration. Curr. Opin. Insect Sci. 14, 17–24 (2016).PubMed 
    Article 

    Google Scholar 
    Tan, W. H., Tao, L., Hoang, K. M., Hunter, M. D. & de Roode, J. C. The effects of milkweed induced defense on parasite resistance in monarch butterflies, Danaus plexippus. J. Chem. Ecol. 44, 1040–1044 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Brower, L. P. & Glazier, S. C. Localization of heart poisons in the monarch butterfly. Science 188, 19–25 (1975).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zalucki, M. P. et al. It’s the first bites that count: Survival of first-instar monarchs on milkweeds. Austral. Ecol. 26, 547–555 (2001).Article 

    Google Scholar 
    Zalucki, M. P., Malcolm, S. B., Hanlon, C. C. & Paine, T. D. First-instar monarch larval growth and survival on milkweeds in Southern California: Effects of latex, leaf hairs and cardenolides. Chemoecology 22, 75–88 (2012).Article 

    Google Scholar 
    Ziegler, R. & Van Antwerpen, R. Lipid uptake by insect oocytes. Insect Biochem. Mol. Biol. 36, 264–272 (2006).Beenakkers, A. M. T., Van der Horst, D. J. & Van Marrewijk, W. J. A. Insect flight muscle metabolism. Insect Biochem. 14, 243–260 (1984).CAS 
    Article 

    Google Scholar 
    Beall, G. The fat content of a butterfly, Danaus Plexippus Linn., as affected by migration. Ecology 29, 80–94 (1948).Article 

    Google Scholar 
    James, D. G. Phenology of weight, moisture and energy reserves of Australian monarch butterflies, Danaus plexippus. Ecol. Entomol. 9, 421–428 (1984).Article 

    Google Scholar 
    Briegel, H. Metabolic relationship between female body size, reserves, and fecundity of Aedes aegypti. J. Insect Physiol. 36, 165–172 (1990).Article 

    Google Scholar 
    Hines, W. J. W. & Smith, M. J. H. Some aspects of intermediary metabolism in the desert locust (Schistocerca gregaria Forskål). J. Insect Physiol. 9, 463–468 (1963).CAS 
    Article 

    Google Scholar 
    Inagaki, S. & Yamashita, O. Metabolic shift from lipogenesis to glycogenesis in the last instar larval fat body of the silkworm, Bombyx mori. Insect Biochem. 16, 327–331 (1986).CAS 
    Article 

    Google Scholar 
    Venkatesh, K. & Morrison, P. E. Studies of weight changes and amount of food ingested by the stable fly, stomoxys calcitrans (Diptera: Muscidae). Can. Entomol. 112, 141–149 (1980).Article 

    Google Scholar 
    Arrese, E. L. & Soulages, J. L. Insect fat body: Energy, metabolism, and regulation. Annu. Rev. Entomol. 55, 207–225 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mevi-Schütz, J. & Erhardt, A. Larval nutrition affects female nectar amino acid preference in the map butterfly (Araschnia levana). Ecology 84, 2788–2794 (2003).Article 

    Google Scholar 
    Wassenaar, L. I. & Hobson, K. A. Natal origins of migratory monarch butterflies at wintering colonies in Mexico: New isotopic evidence. Proc. Natl Acad. Sci. USA 95, 15436–15439 (1998).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Majewska, A. A. & Altizer, S. Exposure to Non-Native Tropical Milkweed Promotes Reproductive Development in Migratory Monarch Butterflies. Insects 10, 253 (2019).Howard, E., Aschen, H. & Davis, A. K. Citizen science observations of monarch butterfly overwintering in the Southern United States. Psyche: A Journal of Entomology 2010, https://doi.org/10.1155/2010/689301 (2010).Satterfield, D. A., Maerz, J. C. & Altizer, S. Loss of migratory behaviour increases infection risk for a butterfly host. Proc. R. Soc. B Biol. Sci. 282, 20141734 (2015).Petschenka, G. et al. Relative selectivity of plant cardenolides for Na+/K+-ATPases from the monarch butterfly and non-resistant insects. Front. Plant Sci. 9, 1424 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jones, P. L., Petschenka, G., Flacht, L. & Agrawal, A. A. Cardenolide intake, sequestration, and excretion by the monarch butterfly along gradients of plant toxicity and larval ontogeny. J. Chem. Ecol. 45, 264–277 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tao, L., Hoang, K. M., Hunter, M. D. & de Roode, J. C. Fitness costs of animal medication: antiparasitic plant chemicals reduce fitness of monarch butterfly hosts. J. Anim. Ecol. 85, 1246–1254 (2016).PubMed 
    Article 

    Google Scholar 
    Lederhouse, R. C. The effect of female mating frequency on egg fertility in the black swallowtail, Papilio polyxenes asterius (Papilionidae). J. Lepid. Soc. 35, 266–277 (1981).
    Google Scholar 
    Jones, R. E., Hart, J. R. & Bull, G. D. Temperature, size and egg production in the Cabbage Butterfly, Pieris rapae L. Aust. J. Zool. 30, 159–168 (1982).Article 

    Google Scholar 
    Haukioja, E. & Neuvonen, S. The relationship between size and reproductive potential in male and female Epirrita autumnata (Lep., Geometridae). Ecol. Entomol. 10, 267–270 (1985).Article 

    Google Scholar 
    Altizer, S. M., Oberhauser, K. S. & Brower, L. P. Associations between host migration and the prevalence of a protozoan parasite in natural populations of adult monarch butterflies. Ecol. Entomol. 25, 125–139 (2000).Article 

    Google Scholar 
    Masters, A. R., Malcolm, S. B. & Brower, L. P. Monarch butterfly (Danaus plexippus) thermoregulatory behavior and adaptations for overwintering in Mexico. Ecology 69, 458–467 (1988).Article 

    Google Scholar 
    Kammer, A. E. Thoracic temperature, shivering, and flight in the monarch butterfly, Danaus plexippus (L.). Z. Vgl. Physiol. 68, 334–344 (1970).Article 

    Google Scholar 
    Pendar, H. & Socha, J. J. Estimation of instantaneous gas exchange in flow-through respirometry systems: A modern revision of bartholomew’s ztransform method. PLoS One 10, e0139508 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Lighton, J. R. B. Measuring Metabolic Rates: A Manual for Scientists. (Oxford University Press, 2008).Alonso-Mejía, A., Rendon-Salinas, E., Montesinos-Patiño, E. & Brower, L. P. Use of lipid reserves by monarch butterflies overwintering in Mexico: Implications for conservation. Ecol. Appl. 7, 934–947 (1997).Article 

    Google Scholar 
    Diaz, R., Overholt, W. A., Hahn, D. & Samayoa, A. C. Diapause induction in Gratiana boliviana (Coleoptera: Chrysomelidae), a biological control agent of tropical soda apple in Florida. Ann. Entomol. Soc. Am. 104, 1319–1326 (2011).Article 

    Google Scholar 
    Tschinkel, W. R. Sociometry and sociogenesis of colonies of the fire ant Solenopsis invicta during one annual cycle. Ecol. Monogr. 63, 425–457 (1993).Article 

    Google Scholar 
    Fink, L. S. & Brower, L. P. Birds can overcome the cardenolide defence of monarch butterflies in Mexico. Nature 291, 67–70 (1981).CAS 
    Article 

    Google Scholar 
    Ali, J. G. & Agrawal, A. A. Trade-offs and tritrophic consequences of host shifts in specialized root herbivores. Funct. Ecol. 31, 153–160 (2017).Article 

    Google Scholar 
    Woodson, R. E. The North American Species of Asclepias L. Ann. Mo. Bot. Gard. 41, 1 (1954).Article 

    Google Scholar 
    NRCS USDA. The PLANTS Database. National Plant Data Center. http://plants.usda.gov (2006).Agrawal, A. A., Salminen, J. P. & Fishbein, M. Phylogenetic trends in phenolic metabolism of milkweeds (Asclepias): Evidence for escalation. Evolution (N. Y). 63, 663–673 (2009).CAS 

    Google Scholar 
    Pocius, V. M. et al. Monarch butterflies show differential utilization of nine midwestern milkweed species. Front. Ecol. Evol. 6, 169 (2018).Pocius, V. M., Debinski, D. M., Pleasants, J. M., Bidne, K. G. & Hellmich, R. L. Monarch butterflies do not place all of their eggs in one basket: Oviposition on nine Midwestern milkweed species. Ecosphere 9, e02064 (2018).Article 

    Google Scholar 
    Ladner, D. T. & Altizer, S. Oviposition preference and larval performance of North American monarch butterflies on four Asclepias species. Entomol. Exp. Appl. 116, 9–20 (2005).Article 

    Google Scholar 
    Borders, B. A guide to the native milkweeds of Oregon. Xerces Soc. Invertebr. Conserv. www.xerces.org, 5, 12-23 (2012). More

  • in

    Population-specific association of Clock gene polymorphism with annual cycle timing in stonechats

    Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

    Google Scholar 
    Tauber, E. & Kyriacou, C. P. Review: Genomic approaches for studying biological clocks. Funct. Ecol. 22, 19–29 (2008).
    Google Scholar 
    White, E. R. & Hastings, A. Seasonality in ecology: Progress and prospects in theory. Ecol. Complex. 44, 100867 (2020).Article 

    Google Scholar 
    Ko, C. H. & Takahashi, J. S. Molecular components of the mammalian circadian clock. Hum. Mol. Genet. 15, R271–R277 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cassone, V. M. Avian circadian organization: A chorus of clocks. Front. Neuroendocrinol. 35, 76–88 (2014).PubMed 
    Article 

    Google Scholar 
    Kyriacou, C. P., Peixoto, A. A., Sandrelli, F., Costa, R. & Tauber, E. Clines in clock genes: Fine-tuning circadian rhythms to the environment. Trends Genet. 24, 124–132 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Partch, C. L., Green, C. B. & Takahashi, J. S. Molecular architecture of the mammalian circadian clock. Trends Cell Biol. 24, 90–99 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Helm, B. et al. Two sides of a coin: ecological and chronobiological perspectives of timing in the wild. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160246 (2017).Article 

    Google Scholar 
    Kalmbach, D. A. et al. Genetic basis of chronotype in humans: Insights from three landmark GWAS. Sleep https://doi.org/10.1093/sleep/zsw048 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Takahashi, J. S., Shimomura, K. & Kumar, V. Searching for genes underlying behavior: Lessons from circadian rhythms. Science 322, 909–912 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    Yoshimura, T. et al. Molecular analysis of avian circadian clock genes11Published on the World Wide Web on 23 May 2000. Mol. Brain Res. 78, 207–215 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gekakis, N. et al. Role of the CLOCK Protein in the Mammalian circadian mechanism. Science 280, 1564–1569 (1998).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    Saleem, Q., Anand, A., Jain, S. & Brahmachari, S. K. The polyglutamine motif is highly conserved at the Clock locus in various organisms and is not polymorphic in humans. Hum. Genet. 109, 136–142 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Darlington, T. K. et al. Closing the circadian loop: CLOCK-induced transcription of its own inhibitors per and tim. Science 280, 1599–1603 (1998).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    King, D. P. et al. Positional cloning of the mouse circadian clock gene. Cell 89, 641–653 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Follett, B. Rhythms and photoperiodism in birds. Biological rhythms and photoperiodism in plants (1998).Hazlerigg, D. G. & Wagner, G. C. Seasonal photoperiodism in vertebrates: from coincidence to amplitude. Trends Endocrinol. Metab. 17, 83–91 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gwinner, E. Circadian and circannual programmes in avian migration. J. Exp. Biol. 199, 39–48 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stirland, J. A., Mohammad, Y. N. & Loudon, A. S. I. A mutation of the circadian timing system (tau gene) in the seasonally breeding Syrian hamster alters the reproductive response to photoperiod change. Proc. R Soc. London Ser. B Biol. Sci. 263, 345–350 (1996).CAS 
    Article 
    ADS 

    Google Scholar 
    Bradshaw, W. E. & Holzapfel, C. M. Evolution of animal photoperiodism. Annu. Rev. Ecol. Evol. Syst. 38, 1–25 (2007).Article 

    Google Scholar 
    Graham, J. L., Cook, N. J., Needham, K. B., Hau, M. & Greives, T. J. Early to rise, early to breed: A role for daily rhythms in seasonal reproduction. Behav. Ecol. 28, 1266–1271 (2017).Article 

    Google Scholar 
    Rittenhouse, J. L., Robart, A. R. & Watts, H. E. Variation in chronotype is associated with migratory timing in a songbird. Biol. Lett. 15, 20190453 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    O’Malley, K. G., Ford, M. J. & Hard, J. J. Clock polymorphism in Pacific salmon: Evidence for variable selection along a latitudinal gradient. Proc. R. Soc. B Biol. Sci. 277, 3703–3714 (2010).Article 
    CAS 

    Google Scholar 
    O’Malley, K. G. & Banks, M. A. A latitudinal cline in the Chinook salmon (Oncorhynchus tshawytscha) Clock gene: Evidence for selection on PolyQ length variants. Proc. R. Soc. B Biol. Sci. 275, 2813–2821 (2008).Article 
    CAS 

    Google Scholar 
    Peterson, M. P. et al. Variation in candidate genes CLOCK and ADCYAP1 does not consistently predict differences in migratory behavior in the songbird genus Junco. F1000Research 2, 115 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Saino, N. et al. Polymorphism at the Clock gene predicts phenology of long-distance migration in birds. Mol. Ecol. 24, 1758–1773 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Saino, N. et al. Timing of molt of barn swallows is delayed in a rare Clock genotype. PeerJ 1, e17 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Johnsen, A. et al. Avian Clock gene polymorphism: Evidence for a latitudinal cline in allele frequencies. Mol. Ecol. 16, 4867–4880 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Liedvogel, M., Szulkin, M., Knowles, S. C. L., Wood, M. & Sheldon, B. C. Phenotypic correlates of Clock gene variation in a wild blue tit population: Evidence for a role in seasonal timing of reproduction. Mol. Ecol. 18, 2444–2456 (2009).PubMed 
    Article 

    Google Scholar 
    Caprioli, M. et al. Clock gene variation is associated with breeding phenology and maybe under directional selection in the migratory barn swallow. PLoS ONE 7, e35140 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    Dor, R. et al. Clock gene variation in Tachycineta swallows. Ecol. Evol. 2, 95–105 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dor, R. et al. Low variation in the polymorphic Clock gene poly-Q region despite population genetic structure across barn swallow (Hirundo rustica) populations. PLoS ONE 6, e28843 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    O’Brien, C. et al. Geography of the circadian gene clock and photoperiodic response in western North American populations of the three-spined stickleback Gasterosteus aculeatus. J. Fish Biol. 82, 827–839 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mueller, J. C., Pulido, F. & Kempenaers, B. Identification of a gene associated with avian migratory behaviour. Proc. R. Soc. B Biol. Sci. 278, 2848–2856 (2011).CAS 
    Article 

    Google Scholar 
    Liedvogel, M. & Sheldon, B. C. Low variability and absence of phenotypic correlates of Clock gene variation in a great tit Parus major population. J. Avian Biol. 41, 543–550 (2010).Article 

    Google Scholar 
    Lugo-Ramos, J. S., Delmore, K. E. & Liedvogel, M. Candidate genes for migration do not distinguish migratory and non-migratory birds. J. Comp. Physiol. A 203, 383–397 (2017).CAS 
    Article 

    Google Scholar 
    Majoy, S. B. & Heideman, P. D. Tau differences between short-day responsive and short-day nonresponsive white-footed mice (Peromyscus leucopus) do not affect reproductive photoresponsiveness. J. Biol. Rhythms 15, 501–513 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    O’Brien, C. et al. Geography of the circadian gene clock and photoperiodic response in western North American populations of the threespine stickleback Gasterosteus aculeatus. J. Fish Biol. 82, 827–839 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Contina, A., Bridge, E. S., Ross, J. D., Shipley, J. R. & Kelly, J. F. Examination of clock and Adcyap1 gene variation in a neotropical migratory passerine. PLoS ONE 13, e0190859 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Herzog, E. D. Neurons and networks in daily rhythms. Nat. Rev. Neurosci. 8, 790–802 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chahad-Ehlers, S. et al. Expanding the view of clock and cycle gene evolution in Diptera. Insect Mol. Biol. 26, 317–331 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Denlinger, D. L., Hahn, D. A., Merlin, C., Holzapfel, C. M. & Bradshaw, W. E. Keeping time without a spine: What can the insect clock teach us about seasonal adaptation?. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160257 (2017).Article 

    Google Scholar 
    van Noordwijk, A. J. et al. A framework for the study of genetic variation in migratory behaviour. J .Ornithol. 147, 221–233 (2006).Article 

    Google Scholar 
    Newton, I. The Migration Ecology of Birds (Academic Press, 2008).
    Google Scholar 
    Gohli, J., Lifjeld, J. T. & Albrecht, T. Migration distance is positively associated with sex-linked genetic diversity in passerine birds. Ethol. Ecol. Evol. 28, 42–52 (2016).Article 

    Google Scholar 
    Bazzi, G. et al. Clock gene polymorphism, migratory behaviour and geographic distribution: A comparative study of trans-Saharan migratory birds. Mol. Ecol. 25, 6077–6091 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Doren, B. M. V., Liedvogel, M. & Helm, B. Programmed and flexible: Long-term Zugunruhe data highlight the many axes of variation in avian migratory behaviour. J. Avian Biol. 48, 155–172 (2017).Article 

    Google Scholar 
    Helm, B., Gwinner, E. & Trost, L. Flexible seasonal timing and migratory behavior: Results from stonechat breeding programs. Ann. N. Y. Acad. Sci. 1046, 216–227 (2005).PubMed 
    Article 
    ADS 

    Google Scholar 
    Helm, B. & Gwinner, E. Migratory restlessness in an equatorial nonmigratory bird. PLoS Biol. 4, e110 (2006).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Helm, B. Geographically distinct reproductive schedules in a changing world: Costly implications in captive Stonechats. Integr Comp Biol 49, 563–579 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dhondt, A. A. Variations in the number of overwintering stonechats possibly caused by natural selection. Ringing Migr. 4, 155–158 (1983).Article 

    Google Scholar 
    Brown, C. R. & Brown, M. B. Weather-mediated natural selection on arrival time in cliff swallows (Petrochelidon pyrrhonota). Behav. Ecol. Sociobiol. 47, 339–345 (2000).Article 

    Google Scholar 
    GOUDET, J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. http://www2.unil.ch/popgen/softwares/fstat.htm (2001).Van Doren, B. M. et al. Correlated patterns of genetic diversity and differentiation across an avian family. Mol. Ecol. 26, 3982–3997 (2017).PubMed 
    Article 

    Google Scholar 
    Illera, J. C., Richardson, D. S., Helm, B., Atienza, J. C. & Emerson, B. C. Phylogenetic relationships, biogeography and speciation in the avian genus Saxicola. Mol. Phylogenet. Evol. 48, 1145–1154 (2008).PubMed 
    Article 

    Google Scholar 
    Illera, J. C. & Díaz, M. Reproduction in an endemic bird of a semiarid island: A food-mediated process. J. Avian Biol. 37, 447–456 (2006).Article 

    Google Scholar 
    Illera, J. C. & Díaz, M. Site fidelity in the Canary Islands stonechat Saxicola dacotiae in relation to spatial and temporal patterns of habitat suitability. Acta Oecol. 34, 1–8 (2008).Article 
    ADS 

    Google Scholar 
    Gwinner, E. & Dittami, J. Endogenous reproductive rhythms in a tropical bird. Science 249, 906–908 (1990).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    Dittami, J. & Gwinner, E. Annual cycles in the African stonechat Saxicola torquata axillaris and their relationship to environmental factors. J. Zool. 207, 357–370 (1985).Article 

    Google Scholar 
    Gwinner, E. Circannual rhythms in tropical and temperate-zone stonechats: A comparison of properties under constant conditions. Ökologie der Vögel 13, 5–14 (1991).
    Google Scholar 
    Gwinner, E. Circannual Rhythms: Endogenous Annual Clocks in the Organization of Seasonal Processes (Springer, 2012).
    Google Scholar 
    Helm, B., Fiedler, W. & Callion, J. Movements of European stonechats Saxicola torquata according to ringing recoveries. ARDEA-WAGENINGEN- 94, 33 (2006).
    Google Scholar 
    Opaev, A., Red’kin, Y., Kalinin, E. & Golovina, M. Species limits in Northern Eurasian taxa of the common stonechats, Saxicola torquatus complex (Aves: Passeriformes, Muscicapidae). Vertebr.ate Zool. 68, 199 (2018).
    Google Scholar 
    Gwinner, E. & Czeschlik, D. On the significance of spring migratory restlessness in caged birds. Oikos 30, 364–372 (1978).Article 

    Google Scholar 
    Krist, M., Munclinger, P., Briedis, M. & Adamík, P. The genetic regulation of avian migration timing: combining candidate genes and quantitative genetic approaches in a long-distance migrant. Oecologia https://doi.org/10.1007/s00442-021-04930-x (2021).Article 
    PubMed 

    Google Scholar 
    Berthold, P. & Pulido, F. Heritability of migratory activity in a natural bird population. Proc. R. Soc. London Ser. B Biol. Sci. 257, 311–315 (1994).Article 
    ADS 

    Google Scholar 
    Pulido, F. & Berthold, P. Current selection for lower migratory activity will drive the evolution of residency in a migratory bird population. Proc. Natl. Acad. Sci. 107, 7341–7346 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    Liedvogel, M. & Lundberg, M. The Genetics of Migration. In Animal Movement Across Scales (eds Hansson, L.-A. & Åkesson, S.) 219–231 (Oxford University Press, 2014). https://doi.org/10.1093/acprof:oso/9780199677184.003.0012.Chapter 

    Google Scholar 
    Åkesson, S. & Helm, B. Endogenous programs and flexibility in bird migration. Front. Ecol. Evol. 8, 78 (2020).Article 

    Google Scholar 
    Stevenson, T. J. & Kumar, V. Neural control of daily and seasonal timing of songbird migration. J. Comp. Physiol. A 203, 399–409 (2017).Article 

    Google Scholar 
    Verhagen, I. et al. Genetic and phenotypic responses to genomic selection for timing of breeding in a wild songbird. Funct. Ecol. 33, 1708–1721 (2019).Article 

    Google Scholar 
    Helm, B. & Gwinner, E. Timing of Postjuvenal molt in African (Saxicola Torquata Axillaris) and European (Saxicola Torquata Rubicola) stonechats: Effects of genetic and environmental factors. Auk 116, 589–603 (1999).Article 

    Google Scholar 
    Zink, R. M., Pavlova, A., Drovetski, S., Wink, M. & Rohwer, S. Taxonomic status and evolutionary history of the Saxicola torquata complex. Mol. Phylogenet. Evol. 52, 769–773 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Flinks, H. & Pfeifer, F. Brutzeit, Gelegegröße und Bruterfolg beim Schwarzkehlchen (Saxicola torquata). Charadrius 23, 128–140 (1987).
    Google Scholar 
    Urquhart, E. Stonechats (Christopher Helm, 2002).
    Google Scholar 
    Glutz von Blotzheim, U. Bauer Handbuch der Vögel Mitteleuropas KM: Bd. 11. Aula, Wiesbaden (1988).Yamaura, Y. et al. Tracking the Stejneger’s stonechat Saxicola stejnegeri along the East Asian-Australian Flyway from Japan via China to southeast Asia. J. Avian Biol. 48, 197–202 (2017).Article 

    Google Scholar 
    Gwinner, E., Neusser, V., Engl, D., Schmidl, D. & Bals, L. Haltung, Zucht und Eiaufzucht afrikanischer und europäischer Schwarzkehlchen Saxicola torquata. Gefiederte Welt 111, 118–120 (1987).
    Google Scholar 
    Flinks, H., Helm, B. & Rothery, P. Plasticity of moult and breeding schedules in migratory European Stonechats Saxicola rubicola. Ibis 150, 687–697 (2008).Article 

    Google Scholar 
    Humphrey, P. S. & Parkes, K. C. An approach to the study of molts and plumages. Auk 76, 1–31 (1959).Article 

    Google Scholar 
    Berthold, P. Bird Migration: A General Survey (Oxford University Press, 2001).
    Google Scholar 
    RStudio | Open source & professional software for data science teams. https://rstudio.com/.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2013).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. http://arxiv.org/abs/1406.5823 (2014).Lüdecke, D. & Lüdecke, M. D. Package ‘sjPlot’. (2015).del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & de Juana, E. Handbook of the Birds of the World Alive (Lynx Edicions, 2018).
    Google Scholar  More

  • in

    Validation of quantitative fatty acid signature analysis for estimating the diet composition of free-ranging killer whales

    Springer, A. M. et al. Sequential megafaunal collapse in the North Pacific Ocean: an ongoing legacy of industrial whaling?. Proc. Natl. Acad. Sci. 100, 12223–12228. https://doi.org/10.1073/pnas.1635156100 (2003).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Estes, J. A., Heithaus, M., McCauley, D. J., Rasher, D. B. & Worm, B. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Resour. 41, 83–116. https://doi.org/10.1146/annurev-environ-110615-085622 (2016).Article 

    Google Scholar 
    Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572. https://doi.org/10.1111/j.1748-7692.2009.00354.x (2010).CAS 
    Article 

    Google Scholar 
    Bowen, W. D. & Iverson, S. J. Methods of estimating marine mammal diets: a review of validation experiments and sources of bias and uncertainty. Mar. Mamm. Sci. 29, 719–754. https://doi.org/10.1111/j.1748-7692.2012.00604.x (2013).Article 

    Google Scholar 
    Krahn, M. M. et al. Use of chemical tracers in assessing the diet and foraging regions of eastern North Pacific killer whales. Mar. Environ. Res. 63, 91–114. https://doi.org/10.1016/j.marenvres.2006.07.002 (2007).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Remili, A. et al. Individual prey specialization drives PCBs in Icelandic killer whales. Environ. Sci. Technol. 55, 4923–4931. https://doi.org/10.1021/acs.est.0c08563 (2021).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Foote, A. D., Vester, H., Vikingsson, G. A. & Newton, J. Dietary variation within and between populations of northeast Atlantic killer whales, Orcinus orca, inferred from d13C and d15N analyses. Mar. Mamm. Sci. 28, E472–E485. https://doi.org/10.1111/j.1748-7692.2012.00563.x (2012).CAS 
    Article 

    Google Scholar 
    Remili, A. et al. Humpback whales (Megaptera novaeangliae) breeding off Mozambique and Ecuador show geographic variation of persistent organic pollutants and isotopic niches. Environ. Pollut. 267, 115575. https://doi.org/10.1016/j.envpol.2020.115575 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pinzone, M., Damseaux, F., Michel, L. N. & Das, K. Stable isotope ratios of carbon, nitrogen and sulphur and mercury concentrations as descriptors of trophic ecology and contamination sources of Mediterranean whales. Chemosphere 237, 124448. https://doi.org/10.1016/j.chemosphere.2019.124448 (2019).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Bourque, J. et al. Feeding habits of a new Arctic predator: insight from full-depth blubber fatty acid signatures of Greenland, Faroe Islands, Denmark, and managed-care killer whales Orcinus orca. Mar. Ecol. Prog. Ser. 603, 1–12. https://doi.org/10.3354/meps12723 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Krahn, M. M., Pitman, R. L., Burrows, D. G., Herman, D. P. & Pearce, R. W. Use of chemical tracers to assess diet and persistent organic pollutants in Antarctic Type C killer whales. Mar. Mamm. Sci. 24, 643–663. https://doi.org/10.1111/j.1748-7692.2008.00213.x (2008).CAS 
    Article 

    Google Scholar 
    Groß, J. et al. Interannual variability in the lipid and fatty acid profiles of east Australia-migrating humpback whales (Megaptera novaeangliae) across a 10-year timeline. Sci. Rep. 10, 18274. https://doi.org/10.1038/s41598-020-75370-5 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jory, C. et al. Individual and population dietary specialization decline in fin whales during a period of ecosystem shift. Sci. Rep. 11, 17181. https://doi.org/10.1038/s41598-021-96283-x (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Iverson, S. J., Field, C., Bowen, W. D. & Blanchard, W. Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol. Monogr. 74, 211–235. https://doi.org/10.1890/02-4105 (2004).Article 

    Google Scholar 
    McKinney, M. A. et al. Global change effects on the long-term feeding ecology and contaminant exposures of East Greenland polar bears. Glob. Change Biol. 19, 2360–2372. https://doi.org/10.1111/gcb.12241 (2013).ADS 
    Article 

    Google Scholar 
    Nordstrom, C. A., Wilson, L. J., Iverson, S. J. & Tollit, D. J. Evaluating quantitative fatty acid signature analysis (QFASA) using harbour seals Phoca vitulina richardsi in captive feeding studies. Mar. Ecol. Prog. Ser. 360, 245–263. https://doi.org/10.3354/meps07378 (2008).ADS 
    Article 

    Google Scholar 
    Bourque, J., Atwood, T. C., Divoky, G. J., Stewart, C. & McKinney, M. A. Fatty acid-based diet estimates suggest ringed seal remain the main prey of southern Beaufort Sea polar bears despite recent use of onshore food resources. Ecol. Evol. https://doi.org/10.1002/ece3.6043 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thiemann, G. W., Derocher, A. E. & Stirling, I. Polar bear Ursus maritimus conservation in Canada: an ecological basis for identifying designatable units. Oryx 42, 504–515. https://doi.org/10.1017/S0030605308001877 (2008).Article 

    Google Scholar 
    Choy, E. S. et al. A comparison of diet estimates of captive beluga whales using fatty acid mixing models with their true diets. J. Exp. Mar. Biol. Ecol. 516, 132–139. https://doi.org/10.1016/j.jembe.2019.05.005 (2019).ADS 
    Article 

    Google Scholar 
    Kirsch, P. E., Iverson, S. J. & Bowen, W. D. Effect of a low-fat diet on body composition and blubber fatty acids of captive Juvenile Harp Seals (Phoca groenlandica). Physiol. Biochem. Zool. 73, 45–59. https://doi.org/10.1086/316723 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    Koopman, H. N. Phylogenetic, ecological, and ontogenetic factors influencing the biochemical structure of the blubber of odontocetes. Mar. Biol. 151, 277–291. https://doi.org/10.1007/s00227-006-0489-8 (2007).Article 

    Google Scholar 
    Strandberg, U. et al. Stratification, composition, and function of marine mammal blubber: the ecology of fatty acids in marine mammals. Physiol. Biochem. Zool 81, 473–485. https://doi.org/10.1086/589108 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Choy, E. S. et al. Variation in the diet of beluga whales in response to changes in prey availability: insights on changes in the Beaufort Sea ecosystem. Mar. Ecol. Prog. Ser. 647, 195–210 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Koopman, H. N., Iverson, S. J. & Gaskin, D. E. Stratification and age-related differences in blubber fatty acids of the male harbour porpoise (Phocoena phocoena). J. Comp. Physiol. B. 165, 628–639. https://doi.org/10.1007/BF00301131 (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    Budge, S. M., Iverson, S. J. & Koopman, H. N. Studying trophic ecology in marine ecosystems using fatty acids: a primer on analysis and interpretation. Mar. Mamm. Sci. 22, 759–801. https://doi.org/10.1111/j.1748-7692.2006.00079.x (2006).Article 

    Google Scholar 
    Krahn, M. M. et al. Stratification of lipids, fatty acids and organochlorine contaminants in blubber of white whales and killer whales. J. Cetacean Res. Manag. 6, 175–189 (2004).
    Google Scholar 
    Loseto, L. L. et al. Summer diet of beluga whales inferred by fatty acid analysis of the eastern Beaufort Sea food web. J. Exp. Mar. Biol. Ecol. 374, 12–18. https://doi.org/10.1016/j.jembe.2009.03.015 (2009).CAS 
    Article 

    Google Scholar 
    Heide-Jørgensen, M.-P. Occurrence and hunting of killer whales in Greenland. Rit Fiskedeildar 11, 115–135 (1988).
    Google Scholar 
    Nøttestad, L. et al. Prey selection of offshore killer whales Orcinus orca in the Northeast Atlantic in late summer: spatial associations with mackerel. Mar. Ecol. Prog. Ser. 499, 275–283 (2014).ADS 
    Article 

    Google Scholar 
    Nikolioudakis, N. et al. Drivers of the summer-distribution of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2011 to 2017; a Bayesian hierarchical modelling approach. ICES J. Mar. Sci. 76, 530–548. https://doi.org/10.1093/icesjms/fsy085 (2019).Article 

    Google Scholar 
    Olafsdottir, A. H. et al. Geographical expansion of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2007 to 2016 was primarily driven by stock size and constrained by low temperatures. Deep Sea Res. Part II 159, 152–168. https://doi.org/10.1016/j.dsr2.2018.05.023 (2019).Article 

    Google Scholar 
    Jansen, T. et al. Ocean warming expands habitat of a rich natural resource and benefits a national economy. Ecol. Appl. 26, 2021–2032. https://doi.org/10.1002/eap.1384 (2016).Article 
    PubMed 

    Google Scholar 
    Ferguson, S. H., Higdon, J. W. & Westdal, K. H. Prey items and predation behavior of killer whales (Orcinus orca) in Nunavut, Canada based on Inuit hunter interviews. Aquat. Biosyst. 8, 3–3. https://doi.org/10.1186/2046-9063-8-3 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Laidre, K. L., Heide-Jørgensen, M. P. & Orr, J. R. Reactions of narwhals, Monodon monoceros, to killer whale, Orcinus orca, attacks in the eastern Canadian Arctic. Can. Field-Naturalist 120, 457–465 (2006).Article 

    Google Scholar 
    Willoughby, A. L., Ferguson, M. C., Stimmelmayr, R., Clarke, J. T. & Brower, A. A. Bowhead whale (Balaena mysticetus) and killer whale (Orcinus orca) co-occurrence in the U.S. Pacific Arctic, 2009–2018: evidence from bowhead whale carcasses. Polar Biol. 43, 1669–1679. https://doi.org/10.1007/s00300-020-02734-y (2020).Article 

    Google Scholar 
    Bloch, D. & Lockyer, C. Killer whales (Orcinus orca) in Faroese waters. Rit Fiskideildar 11, 55–64 (1988).
    Google Scholar 
    Pedro, S. et al. Blubber-depth distribution and bioaccumulation of PCBs and organochlorine pesticides in Arctic-invading killer whales. Sci. Total Environ. 601, 237–246. https://doi.org/10.1016/j.scitotenv.2017.05.193 (2017).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Samarra, F. I. P. et al. Prey of killer whales (Orcinus orca) in Iceland. PLoS ONE 13, 20. https://doi.org/10.1371/journal.pone.0207287 (2018).CAS 
    Article 

    Google Scholar 
    Jourdain, E. et al. Isotopic niche differs between seal and fish-eating killer whales (Orcinus orca) in northern Norway. Ecol. Evol. 10, 4115–4127. https://doi.org/10.1002/ece3.6182 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bromaghin, J. F., Budge, S. M., Thiemann, G. W. & Rode, K. D. Assessing the robustness of quantitative fatty acid signature analysis to assumption violations. Methods Ecol. Evol. 7, 51–59. https://doi.org/10.1111/2041-210X.12456 (2016).Article 

    Google Scholar 
    Jefferson, T. A., Stacey, P. J. & Baird, R. W. A review of Killer Whale interactions with other marine mammals: predation to co-existence. Mamm. Rev. 21, 151–180. https://doi.org/10.1111/j.1365-2907.1991.tb00291.x (1991).Article 

    Google Scholar 
    Bromaghin, J. F. QFASAR: quantitative fatty acid signature analysis with R. Methods Ecol. Evol. 8, 1158–1162. https://doi.org/10.1111/2041-210x.12740 (2017).Article 

    Google Scholar 
    Stewart, C., Iverson, S. & Field, C. Testing for a change in diet using fatty acid signatures. Environ. Ecol. Stat. 21, 775–792. https://doi.org/10.1007/s10651-014-0280-9 (2014).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    Zhang, J. et al. Review of estimating trophic relationships by quantitative fatty acid signature analysis. J. Marine Sci. Eng. 8, 1030 (2020).Article 

    Google Scholar 
    Budge, S. M., Penney, S. N., Lall, S. P. & Trudel, M. Estimating diets of Atlantic salmon (Salmo salar) using fatty acid signature analyses; validation with controlled feeding studies. Can. J. Fish. Aquat. Sci. 69, 1033–1046. https://doi.org/10.1139/f2012-039 (2012).CAS 
    Article 

    Google Scholar 
    Happel, A. et al. Evaluating quantitative fatty acid signature analysis (QFASA) in fish using controlled feeding experiments. Can. J. Fish. Aquat. Sci. 73, 1222–1229. https://doi.org/10.1139/cjfas-2015-0328 (2016).CAS 
    Article 

    Google Scholar 
    Bromaghin, J. F. Simulating realistic predator signatures in quantitative fatty acid signature analysis. Eco. Inform. 30, 68–71. https://doi.org/10.1016/j.ecoinf.2015.09.011 (2015).Article 

    Google Scholar 
    Bromaghin, J. F., Budge, S. M., Thiemann, G. W. & Rode, K. D. Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data. Ecol. Evol. 7, 6103–6113. https://doi.org/10.1002/ece3.3179 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Burns, J. M., Costa, D. P., Frost, K. & Harvey, J. T. Development of body oxygen stores in harbor seals: effects of age, mass, and body composition. Physiol. Biochem. Zool. 78, 1057–1068. https://doi.org/10.1086/432922 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    Noren, D. P. & Mocklin, J. A. Review of cetacean biopsy techniques: Factors contributing to successful sample collection and physiological and behavioral impacts. Mar. Mamm. Sci. 28, 154–199. https://doi.org/10.1111/j.1748-7692.2011.00469.x (2012).Article 

    Google Scholar  More