Ecology

1.Warren, J. & Mackenzie, S. Why are all colour combinations not equally represented as flower-colour polymorphisms?. New Phytol. 151, 237–241 (2001).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Armbruster, S., Fenster, C. & Dudash, M. Pollination ‘principles’ revisited: specialization, pollination syndromes, and the evolution of flowers. Scandanavian Assoc. Pollinat. Ecol. 39, 179–200 (2000).
Google Scholar 
3.Hargreaves, A. L., Harder, L. D. & Johnson, S. D. Consumptive emasculation: the ecological and evolutionary consequences of pollen theft. Biol. Rev. 84, 259–276 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Hansen, D. M., van der Niet, T. & Johnson, S. D. Floral signposts: testing the significance of visual ‘nectar guides’ for pollinator behaviour and plant fitness. Proc. R. Soc. B Biol. Sci. 279, 634–639 (2012).Article 

Google Scholar 
5.Rosas-Guerrero, V. et al. A quantitative review of pollination syndromes: do floral traits predict effective pollinators?. Ecol. Lett. 17, 388–400 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Narbona, E., Wang, H., Ortiz, P. L., Arista, M. & Imbert, E. Flower colour polymorphism in the Mediterranean Basin: occurrence, maintenance and implications for speciation. Plant Biol. 20, 8–20 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
7.Altshuler, D. L. Flower color, hummingbird pollination, and habitat irradiance in four neotropical forests1. Biotropica 35, 344 (2003).Article 

Google Scholar 
8.Riordan, C. E., Ault, J. G., Langreth, S. G. & Keithly, J. S. Cryptosporidium parvum Cpn60 targets a relict organelle. Curr. Genet. 44, 138–147 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Rodríguez-Gironés, M. A. & Santamaría, L. Why are so many bird flowers red?. PLoS Biol. 2, 1515–1519 (2004).Article 
CAS 

Google Scholar 
10.Whibley, A. C. et al. Evolutionary paths underlying flower color variation in Antirrhinum. Science (80-.) 313, 963–966 (2006).CAS 
Article 
ADS 

Google Scholar 
11.Papiorek, S. et al. Bees, birds and yellow flowers: pollinator-dependent convergent evolution of UV patterns. Plant Biol. 18, 46–55 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Wilson, P., Castellanos, M., Wolfe, A. D. & Thomson, J. D. Shifts between bee and bird pollination in penstemons. Plant-Pollinat. Interact. Spec. Gen. 3, 47–68 (2006).13.Wilson, P., Castellanos, M. C., Hogue, J. N., Thomson, J. D. & Armbruster, W. S. A multivariate search for pollination syndromes among penstemons. Oikos 104, 345–361 (2004).Article 

Google Scholar 
14.Sutherland, S. D. & Vickery, R. K. Jr. On the relative importance of floral color, shape, and nectar rewards in attracting pollinators to Mimulus. Gt. Basin Nat. 53, 107–117 (1993).
Google Scholar 
15.Wester, P. & Lunau, K. Plant-Pollinator Communication. Advances in Botanical Research Vol. 82 (Elsevier, 2017).
Google Scholar 
16.de Camargo, M. G. G. et al. How flower colour signals allure bees and hummingbirds: a community-level test of the bee avoidance hypothesis. New Phytol. 222, 1112–1122 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
17.van der Kooi, C. J., Dyer, A. G., Kevan, P. G. & Lunau, K. Functional significance of the optical properties of flowers for visual signalling. Ann. Bot. https://doi.org/10.1093/aob/mcy119 (2018).Article 
PubMed Central 

Google Scholar 
18.Castellanos, M. C., Wilson, P. & Thomson, J. D. ‘ Anti-bee ’ and ‘ pro-bird ’ changes during the evolution of hummingbird pollination in Penstemon flowers. J. Evol. Biol. 17, 876–885 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.del Carmen Salas-Arcos, L., Lara, C., Castillo-Guevara, C., Cuautle, M. & Ornelas, J. F. “Pro-bird” floral traits discourage bumblebee visits to Penstemon gentianoides (Plantaginaceae), a mixed-pollinated herb. Sci. Nat. 106, 1–11 (2019).Article 
CAS 

Google Scholar 
20.Armbruster, W. S. Evolution of floral morphology and function: an integrative approach to adaptation, constraint, and compromise in Dalechampia (Euphorbiaceae). Flor. Biol. https://doi.org/10.1007/978-1-4613-1165-2_9 (1996).Article 

Google Scholar 
21.Chittka, L. & Schürkens, S. Successful invasion of a floral market. Nature 411, 653 (2001).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
22.Ellis, T. J. & Field, D. L. Repeated gains in yellow and anthocyanin pigmentation in flower colour transitions in the Antirrhineae. Ann. Bot. 117, 1133–1140 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Tanaka, Y., Sasaki, N. & Ohmiya, A. Biosynthesis of plant pigments: anthocyanins, betalains and carotenoids. Plant J. 54, 733–749 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Lázaro, A., Lundgren, R. & Totland, Ø. Pollen limitation, species’ floral traits and pollinator visitation: different relationships in contrasting communities. Oikos 124, 174–186 (2015).Article 

Google Scholar 
25.Jones, K. N. & Reithel, J. S. Pollinator-mediated selection on a flower color polymorphism in experimental populations of Anthirrhinum (Scrophulariaceae). Am. J. Bot. 88, 447–454 (2001).Article 

Google Scholar 
26.Teixido, A. L., Barrio, M. & Valladares, F. Size matters: understanding the conflict faced by large flowers in mediterranean environments. Bot. Rev. 82, 204–228 (2016).Article 

Google Scholar 
27.Ortiz, P. L., Fernández-Díaz, P., Pareja, D., Escudero, M. & Arista, M. Do visual traits honestly signal floral rewards at community level?. Funct. Ecol. 35, 369–383 (2021).Article 

Google Scholar 
28.Fenster, C. B., Cheely, G., Dudash, M. R. & Reynolds, R. J. Nectar reward and advertisement in hummingbird. Am. J. Bot. 93, 1800 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Simpson, B. B., Neff, J. L. & Simpson, B. B. Floral rewards: alternatives to pollen and nectar. Ann. Mo. Bot. Gard. 68, 301–322 (2015).Article 

Google Scholar 
30.Canto, A., Herrera, C. M., García, I. M., Pérez, R. & Vaz, M. Intraplant variation in nectar traits in Helleborus foetidus (Ranunculaceae) as related to floral phase, environmental conditions and pollinator exposure. Flora Morphol. Distrib. Funct. Ecol. Plants 206, 668–675 (2011).
Google Scholar 
31.Parachnowitsch, A. L., Manson, J. S. & Sletvold, N. Evolutionary ecology of nectar. Ann. Bot. https://doi.org/10.1093/aob/mcy132 (2018).Article 
PubMed Central 

Google Scholar 
32.Gómez, J. M. et al. Association between floral traits and rewards in Erysimum mediohispanicum (Brassicaceae). Ann. Bot. 101, 1413–1420 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Worley, A. C. & Barrett, S. C. H. Evolution of floral display in Eichhornia paniculata (Pontederiaceae): genetic correlations between flower size and number. J. Evol. Biol. 14, 469–481 (2001).Article 

Google Scholar 
34.Lunau, K. The ecology and evolution of visual pollen signals. Plant Syst. Evol. 222, 89–111 (2000).Article 

Google Scholar 
35.Nicholls, E. & Hempel de Ibarra, N. Assessment of pollen rewards by foraging bees. Funct. Ecol. 31, 76–87 (2017).Article 

Google Scholar 
36.Tang, L.-L. & Huang, S.-Q. Evidence for reductions in floral attractants with increased selfing rates in two heterandrous species. New Phytol. https://doi.org/10.1111/j.1469-8137.2007.02115.x (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Faegri, K. & Van Der Pijl, L. Principles of Pollination Ecology (Elsevier, 2013).
Google Scholar 
38.Dellinger, A. S. Pollination syndromes in the 21st century: where do we stand and where may we go?. New Phytol. https://doi.org/10.1111/nph.16793 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
39.Kostyun, J. L., Gibson, M. J. S., King, C. M. & Moyle, L. C. A simple genetic architecture and low constraint allow rapid floral evolution in a diverse and recently radiating plant genus. New Phytol. 223, 1009–1022 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Roguz, K. et al. Diversity of nectar amino acids in the Fritillaria (Liliaceae) genus: ecological and evolutionary implications. Sci. Rep. 9, 1–12 (2019).CAS 
Article 
ADS 

Google Scholar 
41.Roguz, K. et al. Functional diversity of nectary structure and nectar composition in the genus Fritillaria (liliaceae). Front. Plant Sci. 9, 1–21 (2018).Article 
ADS 

Google Scholar 
42.Zych, M. & Stpiczyńska, M. Neither protogynous nor obligatory out-crossed: Pollination biology and breeding system of the European Red List Fritillaria meleagris L. (Liliaceae). Plant Biol. 14, 285–294 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Day, P. D. et al. Evolutionary relationships in the medicinally important genus Fritillaria L. (Liliaceae). Mol. Phylogenet. Evol. 80, 11–19 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Hayashi, K. Molecular systematics of Lilium and allied genera (Liliaceae): phylogenetic relationships among Lilium and related genera based on the rbcL and matK gene sequence data. Plant Species Biol. 15, 73–93 (2000).Article 

Google Scholar 
45.Stpiczyńska, M., Nepi, M. & Zych, M. Nectaries and male-biased nectar production in protandrous flowers of a perennial umbellifer Angelica sylvestris L. (Apiaceae). Plant Syst. Evol. https://doi.org/10.1007/s00606-014-1152-3 (2014).Article 

Google Scholar 
46.Hill, L. A taxonomic history of Japanese endemic Fritillaria (Liliaceae). Kew Bull. 66, 227–240 (2018).Article 

Google Scholar 
47.Kiani, M. et al. Iran supports a great share of biodiversity and floristic endemism for Fritillaria spp. (Liliaceae): a review. Plant Divers. 39, 245–262 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Shaw, A. J. Phylogeny of the Sphgnpsida based on chloroplast and nuclear DNA sequences. Bryologist 103, 277–306 (2000).CAS 
Article 

Google Scholar 
49.Rønsted, N., Law, S., Thornton, H., Fay, M. F. & Chase, M. W. Molecular phylogenetic evidence for the monophyly of Fritillaria and Lilium (Liliaceae; Liliales) and the infrageneric classification of Fritillaria. Mol. Phylogenet. Evol. 35, 509–527 (2005).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
50.Tekşen, M. & Aytaç, Z. The revision of the genus Fritillaria L. (Liliaceae) in the Mediterranean region (Turkey). Turk. J. Bot. 35, 447–478 (2011).
Google Scholar 
51.Roguz, K., Hill, L., Roguz, A. & Zych, M. Evolution of bird and insect flower traits in Fritillaria L. (Liliaceae). Front. Plant Sci. 12, 656783 (2020).Article 

Google Scholar 
52.Zaharof, E. Variation and taxonomy of Fritillaria graeca (Liliaceae) in Greece. Plant Syst. Evol. 154, 41–61 (1986).Article 

Google Scholar 
53.Búrquez, A. & Burquez, A. Blue tits, Parus caeruleus, as pollinators of the crown imperial, Fritillaria imperialis, in Britain. Oikos 55, 335 (1989).Article 

Google Scholar 
54.Peters, W. S., Pirl, M., Gottsberger, G. & Peters, D. Pollination of the crown imperial Fritillaria imperialis by great tits Parus major. J. Ornithol. 136, 207–212 (1995).Article 

Google Scholar 
55.Pendegrass, K. & Robinson, A. A recovery plan for Fritillaria gentneri (Gentner’s fritillary). Agric. U.S.F.a.W. Serv. (2005).56.Zox, H. Ecology of black lily (Fritillaria camschatcensis): a Washington State sensitive species. Douglasia (2008).57.Cronk, Q. & Ojeda, I. Bird-pollinated flowers in an evolutionary and molecular context. J. Exp. Bot. 59, 715–727 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Lunau, K. & Verhoeven, C. Wie Bienen Blumen sehen: Falschfarbenaufnahmen von Blüten. Biol. Unserer Zeit 47, 120–127 (2017).Article 

Google Scholar 
59.Kranas, H., Spalik, K. & Banasiak, Ł. MatPhylobi, 0.1 (University of Warsaw, 2018).
Google Scholar 
60.Kuraku, S., Zmasek, C. M., Nishimura, O. & Katoh, K. Leaves facilitates on-demand exploration of metazoan gene family trees on MAFFT sequence alignment server with enhanced interactivity. Nucleic Acids Res. https://doi.org/10.1093/nar/gkt389 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
61.Maddison, W. P. & Maddison, D. R. Mesquite: a modular system for evolutionary analysis. Evolution 62, 1103–1118 (2018).
Google Scholar 
62.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinform. Appl. 30, 1312–1313 (2014).CAS 
Article 

Google Scholar 
63.Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. https://doi.org/10.1093/ve/vey016 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Kim, J. S. & Kim, J. H. Updated molecular phylogenetic analysis, dating and biogeographical history of the lily family (Liliaceae: Liliales). Bot. J. Linn. Soc. 187, 579–593 (2018).Article 

Google Scholar 
65.Cockerell, T. D. A. Two new plants from the tertiary rocks of the west. Torrey Bot. Soc. 14, 135–137 (1914).
Google Scholar 
66.Ettingshausen, C. B. III. ‘ Report on Phyto-Palaeontologieal Investigations of Fossil Flora of Alum Bay.’ By Dr. (1AD).67.Conran, J. G., Carpenter, R. J. & Jordan, G. J. Early Eocene Ripogonum (Liliales: Ripogonaceae) leaf macrofossils from southern Australia. Aust. Syst. Bot. 22, 219–228 (2009).Article 

Google Scholar 
68.Lanfear, R., Calcott, B., Ho, S. Y. W. & Guindon, S. PartitionFinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. https://doi.org/10.1093/molbev/mss020 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
69.Paradis, E. & Schliep, K. Phylogenetics ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics https://doi.org/10.1093/bioinformatics/bty633 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
70.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
71.Garland, T., Dickerman, A. W., Janis, C. M. & Jones, J. A. Phylogenetic Analysis of Covariance by Computer Simulation. vol. 42, 1993. https://academic.oup.com/sysbio/article/42/3/265/1629506 (Accessed 09 March 2021).72.Orme, C. D. L. The caper package: comparative analyses in phylogenetics and evolution in R, 1–36, 2012. See http://caper.r-forge.r-project.org/. (Accessed 09 March 2021).73.TEAM, R. C. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
Google Scholar 
74.Dyer, A. G. et al. Parallel evolution of angiosperm colour signals: common evolutionary pressures linked to hymenopteran vision. Proc. R. Soc. B Biol. Sci. 279, 3606–3615 (2012).Article 

Google Scholar 
75.Ollerton, J. Reconciling ecological processes with phylogenetic patterns: the apparent paradox of plant-pollinator systems. J. Ecol. 84, 767–769 (1996).Article 

Google Scholar 
76.Wessinger, C. A. & Rausher, M. D. Predictability and irreversibility of genetic changes associated with flower color evolution in Penstemon barbatus. Evolution (N. Y.) 68, 1058–1070 (2014).CAS 

Google Scholar 
77.Wittmann, D., Radtke, R., Cure, J. R. & Schifino-Wittmann, M. T. Coevolved reproductive strategies in the oligolectic bee Callonychium petuniae (Apoidea, Andrenidae) and three purple flowered Petunia species (Solanaceae) in southern Brazil. J. Zool. Syst. Evol. Res. 28, 157–165 (1990).Article 

Google Scholar 
78.Chittka, L. & Waser, N. M. Why red flowers are not invisible to bees. Isr. J. Plant Sci. 45, 169–183 (1997).Article 

Google Scholar 
79.Kołodziejska-Degórska, I. & Zych, M. Bees substitute birds in pollination of ornitogamous climber Campsis radicans (L.) seem. in Poland. Acta Soc. Bot. Pol. 75, 79–85 (2006).Article 

Google Scholar 
80.Mayr, G. New specimens of the early oligocene old world hummingbird Eurotrochilus inexpectatus. J. Ornithol. 148, 105–111 (2007).Article 

Google Scholar 
81.Mayr, G. Old world fossil record of modern-type hummingbirds. Science (80-.) 304, 861–864 (2004).CAS 
Article 
ADS 

Google Scholar 
82.Schiestl, F. P. & Johnson, S. D. Pollinator-mediated evolution of floral signals. Trends Ecol. Evol. 28, 307–315 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Daumer, K. Blumenfarben, wie sie die Bienen sehen. Z. Vgl. Physiol. 41, 49–110 (1958).
Google Scholar 
84.Kevan, P. G. Floral colours in the high Arctic with reference to insect flower relations and pollination. Can. J. Bot. 50, 2289–2316 (1972).Article 

Google Scholar 
85.Chittka, L., Shmida, A., Troje, N. & Menzel, R. Ultraviolet as a component of flower reflections, and the colour perception of hymenoptera. Vis. Res. 34, 1489–1508 (1994).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Lunau, K. Stamens and mimic stamens as components of floral colour patterns. Bot. Jahrbücher für Syst. Pflanzengeschichte und Pflanzengeographie 127, 13–41 (2006).Article 

Google Scholar 
87.Koski, M. H. & Ashman, T. L. Dissecting pollinator responses to a ubiquitous ultraviolet floral pattern in the wild. Funct. Ecol. 28, 868–877 (2014).Article 

Google Scholar 
88.Menzel, R. & Shmida, A. The ecology of flower colours and the natural colour vision of insect pollinators: the Israeli flora as a study case. Biol. Rev. 68, 81–120 (1993).Article 

Google Scholar 
89.van der Kooi, C. J. & Stavenga, D. G. Vividly coloured poppy flowers due to dense pigmentation and strong scattering in thin petals. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 205, 363–372 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
90.Kevan, P., Giurfa, M. & Chittka, L. Why are there so many and so few white flowers?. Trends Plant Sci. 1, 280–284 (1996).Article 

Google Scholar 
91.Kapustjansky, A., Chittka, L. & Spaethe, J. Bees use three-dimensional information to improve target detection. Naturwissenschaften 97, 229–233 (2010).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
92.Chittka, L. & Raine, N. E. Recognition of flowers by pollinators. Curr. Opin. Plant Biol. 9, 428–435 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Hansen, D. M., Olesen, J. M., Mione, T., Johnson, S. D. & Müller, C. B. Coloured nectar: Distribution, ecology, and evolution of an enigmatic floral trait. Biol. Rev. 82, 83–111 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
94.Raguso, R. A. Start making scents: the challenge of integrating chemistry into pollination ecology. Entomol. Exp. Appl. 128, 196–207 (2008).CAS 
Article 

Google Scholar 
95.Sapir, Y., Shmida, A. & Ne’eman, G. Morning floral heat as a reward to the pollinators of the Oncocyclus irises. Oecologia 147, 53–59 (2006).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
96.Bazzaz, F. A. & Carslon, R. W. Photosynthetic contribution of flowers and seeds to reproductive effort of an annual colonizer. New Phytol. 82, 223–232 (1979).Article 

Google Scholar  More

1.Sæther, B.-E. Environmental stochasticity and population dynamics of large herbivores: A search for mechanisms. Trends Ecol. Evol. 12, 143–149 (1997).PubMed 
Article 

Google Scholar 
2.Gaillard, J.-M., Festa-Bianchet, M. & Yoccoz, N. G. Population dynamics of large herbivores: Variable recruitment with constant adult survival. Trends Ecol. Evol. 13, 58–63 (1998).CAS 
PubMed 
Article 

Google Scholar 
3.Coulson, T. et al. Estimating individual contributions to population growth: Evolutionary fitness in ecological time. Proc. R. Soc. B 273, 547–555 (2006).CAS 
PubMed 
Article 

Google Scholar 
4.Pelletier, F., Clutton-Brock, T., Pemberton, J., Tuljapurkar, S. & Coulson, T. The evolutionary demography of ecological change: Linking trait variation and population growth. Science 315, 1571–1574 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Pettorelli, N., Coulson, T., Durant, S. M. & Gaillard, J.-M. Predation, individual variability and vertebrate population dynamics. Oecologia 167, 305 (2011).ADS 
PubMed 
Article 

Google Scholar 
6.Forchhammer, M. C., Clutton-Brock, T. H., Lindström, J. & Albon, S. D. Climate and population density induce long-term cohort variation in a northern ungulate. J. Anim. Ecol. 70, 721–729 (2001).Article 

Google Scholar 
7.Owen-Smith, N., Mason, D. R. & Ogutu, J. O. Correlates of survival rates for 10 African ungulate populations: Density, rainfall and predation. J. Anim. Ecol. 74, 774–788 (2005).Article 

Google Scholar 
8.Gaillard, J.-M., Festa-Bianchet, M., Yoccoz, N., Loison, A. & Toigo, C. Temporal variation in fitness components and population dynamics of large herbivores. Annu. Rev. Ecol. Syst. 31, 367–393 (2000).Article 

Google Scholar 
9.Griffin, K. A. et al. Neonatal mortality of elk driven by climate, predator phenology and predator community composition. J. Anim. Ecol. 80, 1246–1257 (2011).PubMed 
Article 

Google Scholar 
10.Kilgo, J. C., Vukovich, M., Scott Ray, H., Shaw, C. E. & Ruth, C. Coyote removal, understory cover, and survival of white-tailed deer neonates. J. Wildl. Manag. 78, 1261–1271 (2014).Article 

Google Scholar 
11.Coltman, D. W., Bowen, W. D. & Wright, J. M. Birth weight and neonatal survival of harbour seal pups are positively correlated with genetic variation measured by microsatellites. Proc. R. Soc. B 265, 803–809 (1998).CAS 
PubMed 
Article 

Google Scholar 
12.Kolbe, J. & Janzen, F. The influence of propagule size and maternal nest-site selection on survival and behaviour of neonate turtles. Funct. Ecol. 15, 772–781 (2001).Article 

Google Scholar 
13.Kissner, K. J. & Weatherhead, P. J. Phenotypic effects on survival of neonatal northern watersnakes Nerodia sipedon. J. Anim. Ecol. 74, 259–265 (2005).Article 

Google Scholar 
14.Carstensen, M., Delgiudice, G. D., Sampson, B. A. & Kuehn, D. W. Survival, birth characteristics, and cause-specific mortality of white-tailed deer neonates. J. Wildl. Manag. 73, 175–183 (2009).Article 

Google Scholar 
15.Guttery, M. R. et al. Effects of landscape-scale environmental variation on greater sage-grouse chick survival. PLoS One 8, e65582 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Duquette, J. F., Belant, J. L., Svoboda, N. J., Beyer, D. E. Jr. & Lederle, P. E. Effects of maternal nutrition, resource use and multi-predator risk on neonatal white-tailed deer survival. PLoS One 9, 1–10 (2014).Article 

Google Scholar 
17.Pimentel, D. In Managing Vertebrate Invasive Species: Proceedings of an International Symposium. (eds. Pitt, W.C. et al.) 2–8 (USDA/APHIS/WS, 2007).18.Pitt, W. C., Beasley, J. & Witmer, G. W. Ecology and Management of Terrestrial Vertebrate Invasive Species in the United States. 7–31 (CRC Press, 2018).19.Strickland, B. K., Smith, M. D., Smith, A. L. Wild pig damage to resources. In Invasive Wild Pigs in North America: Ecology, Impacts, and Management (eds. Vercauteren, K. C. et al.) 143–174 (CRC Press, 2020).20.Lowe, S., Browne, M., Boudjelas, S. & De Poorter, M. In 100 of the World’s Worst Invasive Alien Species: A Selection from the Global Invasive Species Database. Vol. 12 (Invasive Species Specialist Group, Species Survival Commission, World Conservation Union (IUCN), 2000).21.Keiter, D. A., Mayer, J. J. & Beasley, J. C. What is in a “common” name? A call for consistent terminology for nonnative Sus scrofa. Wild. Soc. Bull. 40, 384–387 (2016).Article 

Google Scholar 
22.Smyser, T. J. et al. Mixed ancestry from wild and domestic lineages contributes to the rapid expansion of invasive feral swine. Mol. Ecol. 29, 1103–1119 (2020).PubMed 
Article 

Google Scholar 
23.Bevins, S. N., Pedersen, K., Lutman, M. W., Gidlewski, T. & Deliberto, T. J. Consequences associated with the recent range expansion of nonnative feral swine. BioSci. 64, 291–299 (2014).Article 

Google Scholar 
24.Mohr, D., Cohnstaedt, L. W. & Topp, W. Wild boar and red deer affect soil nutrients and soil biota in steep oak stands of the Eifel. Soil Biol. Biochem. 37, 693–700 (2005).CAS 
Article 

Google Scholar 
25.Barrios-García, M. N. & Ballari, S. A. Impact of wild boar (Sus scrofa) in its introduced and native range: A review. Biol. Invasions 14, 2283–2300 (2012).Article 

Google Scholar 
26.Beasley, J. C., Ditchkoff, S. S., Mayer, J. J., Smith, M. D. & Vercauteren, K. C. Research priorities for managing invasive wild pigs in North America. J. Wildl. Manag. 82, 674–681 (2018).Article 

Google Scholar 
27.Ditchkoff, S. S. & Bodenchuk, M. J. Management of wild pigs. In Invasive Wild Pigs in North America: Ecology, Impacts, and Management (eds. Vercauteren, K. C. et al.) 175–198 (CRC Press, 2020).28.Bieber, C. & Ruf, T. Population dynamics in wild boar Sus scrofa: Ecology, elasticity of growth rate and implications for the management of pulsed resource consumers. J. Appl. Ecol. 42, 1203–1213 (2005).Article 

Google Scholar 
29.Hanson, L. B. et al. Effect of experimental manipulation on survival and recruitment of feral pigs. Wildl. Res. 36, 185–191 (2009).Article 

Google Scholar 
30.Keiter, D. A. et al. Effects of scale of movement, detection probability, and true population density on common methods of estimating population density. Sci. Rep. 7, 1–12 (2017).CAS 
Article 

Google Scholar 
31.Keiter, D. A., Kilgo, J. C., Vukovich, M. A., Cunningham, F. L. & Beasley, J. C. Development of known-fate survival monitoring techniques for juvenile wild pigs (Sus scrofa). Wildl. Res. 44, 165–173 (2017).Article 

Google Scholar 
32.Snow, N. P., Miller, R. S., Beasely, J. C. & Pepin, K. M. Wild pig population dynamics. In Invasive Wild Pigs in North America: Ecology, Impacts, and Management (eds. Vercauteren, K. C. et al.) 57–82 (CRC Press, 2020).33.Alonso-Spilsbury, M., Ramirez-Necoechea, R., Gonzalez-Lozano, M., Mota-Rojas, D. & Trujillo-Ortega, M. Piglet survival in early lactation: A review. J. Anim. Vet. Adv. 1, 76–86 (2007).
Google Scholar 
34.Baubet, E., Servanty, S. & Brandt, S. Tagging piglets at the farrowing nest in the wild: Some preliminary guidelines. Acta Sylvatica Lig. Hung. 5, 159–166 (2009).
Google Scholar 
35.Kerr, J. & Cameron, N. Reproductive performance of pigs selected for components of efficient lean growth. Anim. Sci. 60, 281–290 (1995).Article 

Google Scholar 
36.Van der Lende, T., KnoI, E. & Leenhouwers, J. Prenatal development asa predisposing factor for perinatal lossesin pigs. Reproduction 58, 247–261 (2001).PubMed 
PubMed Central 

Google Scholar 
37.Mount, L. The heat loss from new-born pigs to the floor. Res. Vet. Sci. 8, 175–186 (1967).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Herpin, P., Damon, M. & Le Dividich, J. Development of thermoregulation and neonatal survival in pigs. Livest. Prod. Sci. 78, 25–45 (2002).Article 

Google Scholar 
39.Gaillard, J.-M., Pontier, D., Brandt, S., Jullien, J.-M. & Allaine, D. Sex differentiation in postnatal growth rate: A test in a wild boar population. Oecologia 90, 167–171 (1992).ADS 
Article 

Google Scholar 
40.Trivers, R. L. & Willard, D. E. Natural selection of parental ability to vary the sex ratio of offspring. Science 179, 90–92 (1973).ADS 
CAS 
PubMed 
Article 

Google Scholar 
41.Clutton-Brock, T. H., Albon, S. D. & Guinness, F. E. Parental investment and sex differences in juvenile mortality in birds and mammals. Nature 313, 131–133 (1985).ADS 
Article 

Google Scholar 
42.Theil, P. K., Nielsen, M. O., Sørensen, M. T. & Lauridsen, C. Lactation, milk and suckling. In Nutritional Physiology of Pigs: with emphasis on Danish production conditions (eds. Knudsen et al.) 1–49 (University of Copenhagen, 2012).43.Theil, P. K., Lauridsen, C. & Quesnel, H. Neonatal piglet survival: Impact of sow nutrition around parturition on fetal glycogen deposition and production and composition of colostrum and transient milk. Animal 8, 1021–1030 (2014).CAS 
PubMed 
Article 

Google Scholar 
44.Mayer, J. & Brisbin Jr, I. L. Wild pigs of the Savannah River Site. Report No. SRNL-RP-2011-00295, 114 (Savannah River National Laboratory, 2012).45.Withey, J. C., Bloxton, T. D. & Marzluff, J. M. Effects of tagging and location error in wildlife telemetry studies. In Radio Tracking and Animal Populations. 43–75 (Academic Press, 2001).46.Webster, S. C. & Beasley, J. C. Influence of lure choice and survey duration on scent stations for carnivore surveys. Wildl. Soc. Bull. 43, 661–668 (2019).Article 

Google Scholar 
47.Matschke, G. H. Aging European wild hogs by dentition. J. Wildl. Manag. 31, 109–113 (1967).Article 

Google Scholar 
48.Mayer, J. J., Martin, F. D. & Brisbin, I. L. Characteristics of wild pig farrowing nests and beds in the upper Coastal Plain of South Carolina. Appl. Anim. Behav. Sci. 78, 1–17 (2002).Article 

Google Scholar 
49.Kilgo, J. C., Ray, H. S., Vukovich, M., Goode, M. J. & Ruth, C. Predation by coyotes on white-tailed deer neonates in South Carolina. J. Wildl. Manag. 76, 1420–1430 (2012).Article 

Google Scholar 
50.Mayer, J. J. & Brisbin, I. J., Jr. Wild Pigs: Biology, Damage, Control Techniques and Management. Report No. SRNL-RP-2009-00869, 77–104 (Savannah River National Laboratory, 2009).51.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using {lme4}. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
52.R: A language and environment for statistical computing. v. 3.5.3 (R Foundation for Statistical Computing, Vienna, Austria, 2020).53.Weinbeck, S. W., Viner, B. J., Rivera-Giboyeaux A. M. Meteorological Monitoring Program at the Savannah River Site. Report No. SRNL-TR-2020-00197 (Savannah River National Laboratory, 2020).54.Plummer, M. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing. 1–10 (Vienna, Austria).55.Denwood, M. J. runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. J. Stat. Softw. 71, 1–25 (2016).Article 

Google Scholar 
56.Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).MATH 

Google Scholar 
57.Pollock, K. H., Winterstein, S. R., Bunck, C. M. & Curtis, P. D. Survival analysis in telemetry studies: The staggered entry design. J. Wildl. Manag. 53, 7–15 (1989).Article 

Google Scholar 
58.Harrell, F. Regression Modeling Strategies (ed. Harrell, F.) 60–64 (Springer, 2001).59.McCoy, D. E. et al. A comparative study of litter size and sex composition in a large dataset of callitrichine monkeys. Am. J. Primatol. 81, e23038. https://doi.org/10.1002/ajp.23038 (2019).60.Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).61.Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. In Model Selection and Multimodel Inference, 2nd edn. 75–117 (Springer, 2002).62.Taylor, R. B., Hellgren, E. C., Gabor, T. M. & Ilse, L. M. Reproduction of feral pigs in southern Texas. J. Mammal. 79, 1325–1331 (1998).Article 

Google Scholar 
63.Mittwoch, U. Blastocysts prepare for the race to be male. Hum. Reprod. 8, 1550–1555 (1993).CAS 
PubMed 
Article 

Google Scholar 
64.Stanton, H. & Carroll, J. Potential mechanisms responsible for prenatal and perinatal mortality or low viability of swine. J. Anim. Sci. 38, 1037–1044 (1974).CAS 
PubMed 
Article 

Google Scholar 
65.Hartsock, T. G. & Graves, H. Neonatal behavior and nutrition-related mortality in domestic swine. J. Anim. Sci. 42, 235–241 (1976).CAS 
PubMed 
Article 

Google Scholar 
66.Spicer, E. et al. Causes of preweaning mortality on a large intensive piggery. Aus. Vet. J. 63, 71–75 (1986).CAS 
Article 

Google Scholar 
67.Hendrix, W. F., Kelley, K. W., Gaskins, C. T. & Hinrichs, D. J. Porcine neonatal survival and serum gamma globulins. J. Anim. Sci. 47, 1281–1286 (1978).CAS 
PubMed 
Article 

Google Scholar 
68.De Roth, L. & Downie, H. Evaluation of viability of neonatal swine. Can. Vet. J. 17, 275–279 (1976).PubMed 
PubMed Central 

Google Scholar 
69.Williams, G. The question of adaptive sex ratio in outcrossed vertebrates. Proc. R. Soc. Lond. B 205, 567–580 (1979).ADS 
CAS 
PubMed 
Article 

Google Scholar 
70.Servanty, S., Gaillard, J.-M., Allainé, D., Brandt, S. & Baubet, E. Litter size and fetal sex ratio adjustment in a highly polytocous species: The wild boar. Behav. Ecol. 18, 427–432 (2007).Article 

Google Scholar 
71.Fernández-Llario, P., Carranza, J. & Mateos-Quesada, P. Sex allocation in a polygynous mammal with large litters: The wild boar. Anim. Behav. 58, 1079–1084 (1999).PubMed 
Article 

Google Scholar 
72.Focardi, S., Gaillard, J.-M., Ronchi, F. & Rossi, S. Survival of wild boars in a variable environment: unexpected life-history variation in an unusual ungulate. J. Mammal. 89, 1113–1123 (2008).Article 

Google Scholar 
73.Gamelon, M. et al. Do age-specific survival patterns of wild boar fit current evolutionary theories of senescence?. Evolution 68, 3636–3643 (2014).PubMed 
Article 

Google Scholar 
74.Saïd, S., Tolon, V., Brandt, S. & Baubet, E. Sex effect on habitat selection in response to hunting disturbance: The study of wild boar. Eur. J. Wildl. Res. 58, 107–115 (2012).Article 

Google Scholar 
75.Caro, T. The adaptive significance of coloration in mammals. BioSci. 55, 125–136 (2005).Article 

Google Scholar 
76.Tewes, M. E., Mock, J. M. & Young, J. H. Bobcat predation on quail, birds, and mesomammals. In Proc. Nat. Quail Symp. 65–70. (2002).77.Jones, M. P., Pierce, K. E. Jr. & Ward, D. Avian vision: a review of form and function with special consideration to birds of prey. J. Ex. Pet Med. 16, 69–87 (2007).Article 

Google Scholar 
78.Walsberg, G. E. Coat color and solar heat gain in animals. BioSci. 33, 88–91 (1983).Article 

Google Scholar 
79.Lack, D. The Natural Regulation of Animal Numbers. (ed. Lack, D.) 343 (Oxford University Press, 1954).80.Stearns, S. C. Life-history tactics: A review of the ideas. Q. Rev. Biol. 51, 3–47 (1976).CAS 
PubMed 
Article 

Google Scholar 
81.Gamelon, M. et al. The relationship between phenotypic variation among offspring and mother body mass in wild boar: Evidence of coin-flipping?. J. Anim. Ecol. 82, 937–945 (2013).PubMed 
Article 

Google Scholar 
82.Mitchell, G. & Stevens, C. Primiparous and multiparous monkey mothers in a mildly stressful social situation: First three months. Dev. Psychobiol. J. Int. Soc. Dev. Psychobiol. 1, 280–286 (1968).Article 

Google Scholar 
83.Okai, D., Aherne, F. & Hardin, R. Effects of sow nutrition in late gestation on the body composition and survival of the neonatal pig. Can. J. Anim. Sci. 57, 439–448 (1977).CAS 
Article 

Google Scholar  More

1.Somero, G. N. Thermal physiology and vertical zonation of intertidal animals: Optima, limits, and costs of living. Integr. Comp. Biol. 42(4), 780–789 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Hochachka, P. W. & Somero, G. N. Biochemical Adaptation: Mechanism and Process in Physiological Evolution (Oxford University Press, 2002).
Google Scholar 
3.Helmuth, B. et al. Living on the Edge of Two Changing Worlds: Forecasting the Responses of Rocky Intertidal Ecosystems to Climate Change Vol. 37 (ECU Publications, 2006).
Google Scholar 
4.Harley, C. D. et al. The impacts of climate change in coastal marine systems. Ecol. Lett. 9(2), 228–241 (2006).ADS 
MathSciNet 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Woodward, A. Climate change: Disruption, risk and opportunity. Glob. Transit. 1, 44–49 (2019).Article 

Google Scholar 
6.Hoffmann, K. H. 6—Metabolic and enzyme adaptation to temperature and pressure. In The Mollusca (ed. Hochachka, P. W.) 219–255 (Academic Press, 1983).
Google Scholar 
7.Pörtner, H. O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315(5808), 95 (2007).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
8.Pörtner, H.-O., Bock, C. & Mark, F. C. Oxygen- and capacity-limited thermal tolerance: Bridging ecology and physiology. J. Exp. Biol. 220(15), 2685–2696 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Pörtner, H. Climate change and temperature-dependent biogeography: Oxygen limitation of thermal tolerance in animals. Naturwissenschaften 88(4), 137–146 (2001).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Verberk, W. C. et al. Does oxygen limit thermal tolerance in arthropods? A critical review of current evidence. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 192, 64–78 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Jutfelt, F. et al. Oxygen- and capacity-limited thermal tolerance: Blurring ecology and physiology. J. Exp. Biol. 221(1), jeb169615 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Ern, R. et al. Some like it hot: Thermal tolerance and oxygen supply capacity in two eurythermal crustaceans. Sci. Rep. 5, 10743 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Mitchell, P. et al. Regulation of Metabolic Processes in Mitochondria (Elsevier, 1966).
Google Scholar 
14.Hüttemann, M. et al. Regulation of oxidative phosphorylation, the mitochondrial membrane potential, and their role in human disease. J. Bioenerg. Biomembr. 40(5), 445 (2008).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
15.Iftikar, F. I. & Hickey, A. J. Do mitochondria limit hot fish hearts? Understanding the role of mitochondrial function with heat stress in Notolabrus celidotus. PLoS One 8(5), e64120 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Schulte, P. M. The effects of temperature on aerobic metabolism: Towards a mechanistic understanding of the responses of ectotherms to a changing environment. J. Exp. Biol. 218(Pt 12), 1856–1866 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Power, A. et al. Uncoupling of oxidative phosphorylation and ATP synthase reversal within the hyperthermic heart. Physiol. Rep. 2(9), e12138 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Lemieux, H., Blier, P. U. & Gnaiger, E. Remodeling pathway control of mitochondrial respiratory capacity by temperature in mouse heart: Electron flow through the Q-junction in permeabilized fibers. Sci. Rep. 7(1), 2840 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Christen, F. et al. Thermal tolerance and thermal sensitivity of heart mitochondria: Mitochondrial integrity and ROS production. Free Radic. Biol. Med. 116, 11–18 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Kiyatkin, E. A. Brain hyperthermia as physiological and pathological phenomena. Brain Res. Brain Res. Rev. 50(1), 27–56 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Kiyatkin, E. A. Brain temperature homeostasis: Physiological fluctuations and pathological shifts. Front. Biosci. (Landmark Ed) 15, 73–92 (2010).CAS 
Article 

Google Scholar 
22.Wang, H. et al. Brain temperature and its fundamental properties: A review for clinical neuroscientists. Front. Neurosci.-switz 8, 307–307 (2014).
Google Scholar 
23.Pellerin, L. & Magistretti, P. J. How to balance the brain energy budget while spending glucose differently. J. Physiol. 546(Pt 2), 325–325 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Zhao, Y. & Boulant, J. A. Temperature effects on neuronal membrane potentials and inward currents in rat hypothalamic tissue slices. J. Physiol. 564(Pt 1), 245–257 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Obel, L. F. et al. Brain glycogen-new perspectives on its metabolic function and regulation at the subcellular level. Front. Neuroenergetics 4, 3 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.White, M. G. et al. Mitochondrial dysfunction induced by heat stress in cultured rat CNS neurons. J. Neurophysiol. 108(8), 2203–2214 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Walter, E. J. & Carraretto, M. The neurological and cognitive consequences of hyperthermia. Crit. Care (London, England) 20(1), 199–199 (2016).Article 

Google Scholar 
28.Vornanen, M. & Paajanen, V. Seasonal changes in glycogen content and Na+-K+-ATPase activity in the brain of crucian carp. Am. J. Physiol. Regul. Integr. Comp. Physiol. 291(5), R1482–R1489 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Hochachka, P. W. et al. Unifying theory of hypoxia tolerance: Molecular/metabolic defense and rescue mechanisms for surviving oxygen lack. Proc. Natl. Acad. Sci. U. S. A. 93(18), 9493–9498 (1996).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Chung, D. J., Bryant, H. J. & Schulte, P. M. Thermal acclimation and subspecies-specific effects on heart and brain mitochondrial performance in a eurythermal teleost (Fundulus heteroclitus). J. Exp. Biol. 220(8), 1459–1471 (2017).PubMed 
PubMed Central 

Google Scholar 
31.Brahim, A., Mustapha, N. & Marshall, D. J. Non-reversible and reversible heat tolerance plasticity in tropical intertidal animals: Responding to habitat temperature heterogeneity. Front. Physiol. 9, 1909–1909 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Pagel, M. Inferring evolutionary processes from phylogenies. Zool. Scr. 26(4), 331–348 (1997).Article 

Google Scholar 
33.Hilton, Z., Clements, K. D. & Hickey, A. J. Temperature sensitivity of cardiac mitochondria in intertidal and subtidal triplefin fishes. J. Comp. Physiol. B 180(7), 979–990 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.McArley, T. J., Hickey, A. J. R. & Herbert, N. A. Hyperoxia increases maximum oxygen consumption and aerobic scope of intertidal fish facing acutely high temperatures. J. Exp. Biol. 221(22), 189993 (2018).Article 

Google Scholar 
35.Gout, E. et al. Interplay of Mg2+, ADP, and ATP in the cytosol and mitochondria: Unravelling the role of Mg2+ in cell respiration. Proc. Natl. Acad. Sci. U. S. A. 111(43), E4560–E4567 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Pham, T. et al. Mitochondrial inefficiencies and anoxic ATP hydrolysis capacities in diabetic rat heart. Am. J. Physiol. Cell Physiol. 307(6), C499-507 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Masson, S. W. C. et al. Mitochondrial glycerol 3-phosphate facilitates bumblebee pre-flight thermogenesis. Sci. Rep. 7(1), 13107 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Chinopoulos, C. et al. A novel kinetic assay of mitochondrial ATP-ADP exchange rate mediated by the ANT. Biophys. J. 96(6), 2490–2504 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Devaux, J. B. L. et al. Acidosis maintains the function of brain mitochondria in hypoxia-tolerant triplefin fish: A strategy to survive acute hypoxic exposure? Front. Physiol. 9, 1941 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Goo, S. et al. Multiscale measurement of cardiac energetics. Clin. Exp. Pharmacol. Physiol. 40(9), 671–681 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Lagerspetz, K. Y. Temperature effects on different organization levels in animals. Symp. Soc. Exp. Biol. 41, 429–449 (1987).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Rosenthal, J. J. & Bezanilla, F. A comparison of propagated action potentials from tropical and temperate squid axons: Different durations and conduction velocities correlate with ionic conductance levels. J. Exp. Biol. 205(Pt 12), 1819–1830 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Robertson, R. M. Thermal stress and neural function: Adaptive mechanisms in insect model systems. J. Therm. Biol. 29(7), 351–358 (2004).CAS 
Article 

Google Scholar 
44.Miller, N. A. & Stillman, J. H. Neural thermal performance in porcelain crabs, Genus Petrolisthes. Physiol. Biochem. Zool. 85(1), 29–39 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Gladwell, R. T., Bowler, K. & Duncan, C. J. Heat death in the crayfish Austropotamobius pallipes—Ion movements and their effects on excitable tissues during heat death. J. Therm. Biol. 1(2), 79–94 (1976).CAS 
Article 

Google Scholar 
46.Chen, I. & Lui, F. Neuroanatomy, Neuron Action Potential (StatPearls Publishing, 2019).
Google Scholar 
47.Milligan, L. P. & McBride, B. W. Energy costs of ion pumping by animal tissues. J. Nutr. 115(10), 1374–1382 (1985).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Buzatu, S. The temperature-induced changes in membrane potential. Riv. Biol. 102(2), 199–217 (2009).PubMed 
PubMed Central 

Google Scholar 
49.Krans, J. L., Rivlin, P. K. & Hoy, R. R. Demonstrating the temperature sensitivity of synaptic transmission in a Drosophila mutant. J. Undergrad. Neurosci. Educ. 4(1), A27–A33 (2005).PubMed 
PubMed Central 

Google Scholar 
50.Khan, J. R. et al. Thermal plasticity of skeletal muscle mitochondrial activity and whole animal respiration in a common intertidal triplefin fish, Forsterygion lapillum (Family: Tripterygiidae). J. Comp. Physiol. B 184(8), 991–1001 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.McArley, T. et al. Intertidal triplefin fishes have a lower critical oxygen tension (Pcrit), higher maximal aerobic capacity, and higher tissue glycogen stores than their subtidal counterparts. J. Comp. Physiol. B. 189, 399–411 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Pfleger, J., He, M. & Abdellatif, M. Mitochondrial complex II is a source of the reserve respiratory capacity that is regulated by metabolic sensors and promotes cell survival. Cell Death Dis. 6(7), e1835–e1835 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Brand, M. D. The efficiency and plasticity of mitochondrial energy transduction. Biochem. Soc. Trans. 33(Pt 5), 897–904 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Brown, J. H. et al. Toward a metabolic theory of ecology. Ecology 85(7), 1771–1789 (2004).ADS 
Article 

Google Scholar 
55.Salin, K. et al. Variation in the link between oxygen consumption and ATP production, and its relevance for animal performance. Proc. Biol. Sci. 2015(282), 20151028–20151028 (1812).
Google Scholar 
56.Findly, R. C., Gillies, R. J. & Shulman, R. G. In vivo phosphorus-31 nuclear magnetic resonance reveals lowered ATP during heat shock of Tetrahymena. Science 219(4589), 1223 (1983).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Sharma, H. S. Neurobiology of Hyperthermia (Elsevier, 2011).
Google Scholar 
58.Salin, K. et al. Simultaneous measurement of mitochondrial respiration and ATP production in tissue homogenates and calculation of effective P/O ratios. Physiol. Rep. 4(20), e13007 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Hinkle, P. C. P/O ratios of mitochondrial oxidative phosphorylation. Biochim. Biophys. Acta BBA Bioenerg. 1706(1), 1–11 (2005).CAS 

Google Scholar  More

1.Lindström, J. Early development and fitness in birds and mammals. Trends Ecol. Evol. 14, 343–348 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Cam, E. & Aubry, L. Early development, recruitment and life history trajectory in long-lived birds. J. Ornithol. 152, 187–201 (2011).Article 

Google Scholar 
3.Cam, E., Monnat, J. Y. & Hines, J. E. Long-term fitness consequences of early conditions in the kittiwake. J. Anim. Ecol. 72, 411–424 (2003).Article 

Google Scholar 
4.Tilgar, V., Mänd, R., Kilgas, P. & Mägi, M. Long-term consequences of early ontogeny in free-living Great Tits Parus major. J. Ornithol. 151, 61–68 (2010).Article 

Google Scholar 
5.Stamps, J. A. The silver spoon effect and habitat selection by natal dispersers. Ecol. Lett. 9, 1179–1185 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Briga, M., Koetsier, E., Boonekamp, J. J., Jimeno, B. & Verhulst, S. Food availability affects adult survival trajectories depending on early developmental conditions. Proc. R. Soc. B Biol. Sci. 284, 20162287 (2017).Article 

Google Scholar 
7.Cooper, E. B. & Kruuk, L. E. Ageing with a silver-spoon: A meta-analysis of the effect of developmental environment on senescence. Evol. Lett. 2, 460–471 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Song, Z. et al. Silver spoon effects of hatching order in an asynchronous hatching bird. Behav. Ecol. Sociobiol. 30, 509–517 (2019).Article 

Google Scholar 
9.Descamps, S., Boutin, S., Berteaux, D., McAdam, A. G. & Gaillard, J. M. Cohort effects in red squirrels: The influence of density, food abundance and temperature on future survival and reproductive success. J. Anim. Ecol. 77, 305–314 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Van De Pol, M., Bruinzeel, L. W., Heg, D., Van Der Jeugd, H. P. & Verhulst, S. A silver spoon for a golden future: Long-term effects of natal origin on fitness prospects of oystercatchers (Haematopus ostralegus). J. Anim. Ecol. 75, 616–626 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Murgatroyd, M. et al. Sex-specific patterns of reproductive senescence in a long-lived reintroduced raptor. J. Anim. Ecol. 87, 1587–1599 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Dmitriew, C. & Rowe, L. Effects of early resource limitation and compensatory growth on lifetime fitness in the ladybird beetle (Harmonia axyridis). J. Evol. Biol. 20, 1298–1310 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Hopwood, P. E., Moore, A. J. & Royle, N. J. Effects of resource variation during early life and adult social environment on contest outcomes in burying beetles: A context-dependent silver spoon strategy?. Proc. R. Soc. B Biol. Sci. 281, 20133102 (2014).Article 

Google Scholar 
14.Royle, N. J., Lindström, J. & Metcalfe, N. B. A poor start in life negatively affects dominance status in adulthood independent of body size in green swordtails Xiphophorus helleri. Proc. R. Soc. B Biol. Sci. 272, 1917–1922 (2005).Article 

Google Scholar 
15.Mugabo, M., Marquis, O., Perret, S. & Le Galliard, J. F. Immediate and delayed life history effects caused by food deprivation early in life in a short-lived lizard. J. Evol. Biol. 23, 1886–1898 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Vitikainen, E. I., Thompson, F. J., Marshall, H. H. & Cant, M. A. Live long and prosper: Durable benefits of early-life care in banded mongooses. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180114 (2019).Article 

Google Scholar 
17.Sumasgutner, P., Tate, G. J., Koeslag, A. & Amar, A. Family morph matters: Factors determining survival and recruitment in a long-lived polymorphic raptor. J. Anim. Ecol. 85, 1043–1055 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Emaresi, G. et al. Melanin-specific life-history strategies. Am. Nat. 183, 269–280 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Grunst, M. L. et al. Actuarial senescence in a dimorphic bird: Different rates of ageing in morphs with discrete reproductive strategies. Proc. R. Soc. B Biol. Sci. 285, 20182053 (2018).Article 

Google Scholar 
20.Nebel, C., Sumasgutner, P., McPherson, S. C., Tate, G. J. & Amar, A. Contrasting parental color-morphs increase regularity of prey deliveries in an African raptor. Behav. Ecol. 31, 1142–1149 (2020).Article 

Google Scholar 
21.Morosinotto, C. et al. Fledging mass is color morph specific and affects local recruitment in a wild bird. Am. Nat. 196, 609–619 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Chakarov, N., Boerner, M. & Krüger, O. Fitness in common buzzards at the cross-point of opposite melanin–parasite interactions. Funct. Ecol. 22, 1062–1069 (2008).Article 

Google Scholar 
23.Roulin, A. Proximate basis of the covariation between a melanin-based female ornament and offspring quality. Oecologia 140, 668–675 (2004).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Rödel, H. G., Von Holst, D. & Kraus, C. Family legacies: short-and long-term fitness consequences of early-life conditions in female European rabbits. J. Anim. Ecol. 78, 789–797 (2009).PubMed 
Article 
PubMed Central 

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

Google Scholar 
26.Cockburn, A. Prevalence of different modes of parental care in birds. Proc. R. Soc. B Biol. Sci. 273, 1375–1383 (2006).Article 

Google Scholar 
27.Norris, K. & Evans, M. R. Ecological immunology: Life history trade-offs and immune defense in birds. Behav. Ecol. Sociobiol. 11, 19–26 (2000).Article 

Google Scholar 
28.van der Most, P. J., de Jong, B., Parmentier, H. K. & Verhulst, S. Trade-off between growth and immune function: A meta-analysis of selection experiments. Funct. Ecol. 25, 74–80 (2011).Article 

Google Scholar 
29.Aastrup, C. & Hegemann, A. Jackdaw nestlings rapidly increase innate immune function during the nestling phase but no evidence for a trade-off with growth. Dev. Comparat. Immunol. 2, 103967 (2020).
Google Scholar 
30.Ratikainen, I. I. & Kokko, H. Differential allocation and compensation: Who deserves the silver spoon?. Behav. Ecol. Sociobiol. 21, 195–200 (2010).Article 

Google Scholar 
31.Limbourg, T., Mateman, A. C. & Lessells, C. M. Opposite differential allocation by males and females of the same species. Biol. Let. 9, 20120835 (2013).Article 

Google Scholar 
32.Järvistö, P. E., Calhim, S., Schuett, W., Velmala, W. & Laaksonen, T. Foster, but not genetic, father plumage coloration has a temperature-dependent effect on offspring quality. Behav. Ecol. Sociobiol. 69, 335–346 (2015).Article 

Google Scholar 
33.Pryke, S. R. & Griffith, S. C. Socially mediated trade-offs between aggression and parental effort in competing color morphs. Am. Nat. 174, 455–464 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Amar, A., Koeslag, A. & Curtis, O. Plumage polymorphism in a newly colonized black sparrowhawk population: Classification, temporal stability and inheritance patterns. J. Zool. 289, 60–67 (2013).Article 

Google Scholar 
35.Tate, G., Sumasgutner, P., Koeslag, A. & Amar, A. Pair complementarity influences reproductive output in the polymorphic black sparrowhawk Accipiter melanoleucus. J. Avian Biol. 48, 387–398 (2017).Article 

Google Scholar 
36.Tinbergen, J. M. & Boerlijst, M. C. Nestling weight and survival in individual great tits (Parus major). J. Anim. Ecol. 59, 1113–1127 (1990).Article 

Google Scholar 
37.Cleasby, I. R., Nakagawa, S., Gillespie, D. O. S. & Burke, T. The influence of sex and body size on nestling survival and recruitment in the house sparrow. Biol. J. Lin. Soc. 101, 680–688 (2010).Article 

Google Scholar 
38.Christe, P., Møller, A. P. & de Lope, F. Immunocompetence and nestling survival in the house martin: The tasty chick hypothesis. Oikos 83, 175–179 (1998).CAS 
Article 

Google Scholar 
39.Ringsby, T. H., Sæther, B.-E. & Solberg, E. J. Factors affecting juvenile survival in house sparrow Passer domesticus. J. Avian Biol. 29, 241–247 (1998).Article 

Google Scholar 
40.Losdat, S. et al. Nestling erythrocyte resistance to oxidative stress predicts fledging success but not local recruitment in a wild bird. Biol. Lett. 9, 20120888 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Vermeulen, A., Müller, W. & Eens, M. J. Vitally important–does early innate immunity predict recruitment and adult innate immunity?. Ecol. Evol. 6, 1799–1808 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Vennum, C. R. et al. Early life conditions and immune defense in nestling Swainson’s Hawks. Physiol. Biochem. Zool. 92, 419–429 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Bowers, E. K. et al. Neonatal body condition, immune responsiveness, and hematocrit predict longevity in a wild bird population. Ecology 95, 3027–3034 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Calder, P. C. & Sonnenfeld, G. in Nutrition, Immunity, and Infection 1–18 (CRC Press, 2017).Book 

Google Scholar 
45.Wilcoxen, T. E., Boughton, R. K. & Schoech, S. J. Selection on innate immunity and body condition in Florida scrub-jays throughout an epidemic. Biol. Let. 6, 552–554 (2010).Article 

Google Scholar 
46.Hegemann, A., Marra, P. P. & Tieleman, B. I. Causes and consequences of partial migration in a passerine bird. Am. Nat. 186, 531–546 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Hegemann, A., Matson, K. D., Flinks, H. & Tieleman, B. I. Offspring pay sooner, parents pay later: Experimental manipulation of body mass reveals trade-offs between immune function, reproduction and survival. Front. Zool. 10, 77 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Apanius, V. Ontogeny of Immune Function (Oxford University Press, 1998).
Google Scholar 
49.Klasing, K. C. & Leshchinksy, T. V. Functions, Costs, and Benefits of the Immune System During Development and Growth Ostrich, 69, 2817–2835 (1999).50.Bonneaud, C. et al. Assessing the cost of mounting an immune response. Am. Nat. 161, 367–379 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Costantini, D. & Moller, A. P. Does immune response cause oxidative stress in birds? A meta-analysis. Comparat. Biochem. Physiol. Part A 153, 339–344 (2009).Article 
CAS 

Google Scholar 
52.Hanssen, S. A., Hasselquist, D., Folstad, I. & Erikstad, K. E. Costs of immunity: Immune responsiveness reduces survival in a vertebrate. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 925–930 (2004).Article 

Google Scholar 
53.Hanssen, S. A. Costs of an immune challenge and terminal investment in a long-lived bird. Ecology 87, 2440–2446 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Matson, K. D., Ricklefs, R. E. & Klasing, K. C. A hemolysis–hemagglutination assay for characterizing constitutive innate humoral immunity in wild and domestic birds. Dev. Comp. Immunol. 29, 275–286 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Müller-Eberhard, H. J. Molecular organization and function of the complement system. Annu. Rev. Biochem. 57, 321–347 (1988).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Dobryszycka, W. Biological functions of haptoglobin-new pieces to an old puzzle. Eur. J. Clin. Chem. Clin. Biochem. 35, 647–654 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Matson, K. D., Horrocks, N. P. C., Versteegh, M. A. & Tieleman, B. I. Baseline haptoglobin concentrations are repeatable and predictive of certain aspects of a subsequent experimentally-induced inflammatory response. Comparat. Biochem. Physiol. Part A Mol. Integr. Physiol. 162, 7–15 (2012).CAS 
Article 

Google Scholar 
58.Cray, C., Zaias, J. & Altman, N. H. Acute phase response in animals: A review. Comp. Med. 59, 517–526 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Hegemann, A., Matson, K. D., Both, C. & Tieleman, B. I. Immune function in a free-living bird varies over the annual cycle, but seasonal patterns differ between years. Oecologia 170, 605–618 (2012).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Alexander, C. & Rietschel, E. T. Invited review: Bacterial lipopolysaccharides and innate immunity. J. Endotoxin Res. 7, 167–202 (2016).
Google Scholar 
61.Hegemann, A., Matson, K. D., Versteegh, M. A., Villegas, A. & Tieleman, B. I. Immune response to an endotoxin challenge involves multiple immune parameters and is consistent among the annual-cycle stages of a free-living temperate zone bird. J. Exp. Biol. 216, 2573–2580 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Vermeulen, A., Eens, M., Zaid, E. & Müller, W. Baseline innate immunity does not affect the response to an immune challenge in female great tits (Parus major). Behav. Ecol. Sociobiol. 70, 585–592 (2016).Article 

Google Scholar 
63.Vinterstare, J., Hegemann, A., Nilsson, P. A., Hulthén, K. & Brönmark, C. Defence versus defence: Are crucian carp trading off immune function against predator-induced morphology?. J. Anim. Ecol. 88, 1510–1521 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
64.Lei, B., Amar, A., Koeslag, A., Gous, T. A. & Tate, G. J. Differential haemoparasite intensity between black sparrowhawk (Accipiter melanoleucus) morphs suggests an adaptive function for polymorphism. PLoS ONE 8, e81607 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Suri, J., Sumasgutner, P., Hellard, É., Koeslag, A. & Amar, A. Stability in prey abundance may buffer Black Sparrowhawks Accipiter melanoleucus from health impacts of urbanization. Ibis 159, 38–54 (2017).Article 

Google Scholar 
66.Råberg, L., Grahn, M., Hasselquist, D. & Svensson, E. On the adaptive significance of stress-induced immunosuppression. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 1637–1641 (1998).Article 

Google Scholar 
67.Sadd, B. M. & Siva-Jothy, M. T. Self-harm caused by an insect’s innate immunity. Proc. R. Soc. Lond. Ser. B Biol. Sci. 273, 2571–2574 (2006).
Google Scholar 
68.Gyan, B. et al. Elevated levels of nitric oxide and low levels of haptoglobin are associated with severe malarial anaemia in African children. Acta Trop. 83, 133–140 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Alonso-Alvarez, C. & Tella, J. L. Effects of experimental food restriction and body-mass changes on the avian T-cell-mediated immune response. Can. J. Zool. 79, 101–105 (2001).Article 

Google Scholar 
70.Merino, S. et al. Phytohaemagglutinin injection assay and physiological stress in nestling house martins. Anim. Behav. 58, 219–222 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Ochsenbein, A. F. & Zinkernagel, R. M. Natural antibodies and complement link innate and acquired immunity. Immunol. Today 21, 624–630 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Boes, M. Role of natural and immune IgM antibodies in immune responses. Mol. Immunol. 37, 1141–1149 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Grönwall, C., Vas, J. & Silverman, G. J. Protective roles of natural IgM antibodies. Front. Immunol. 3, 66 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
74.Martin, L. B., Weil, Z. M. & Nelson, R. J. Seasonal changes in vertebrate immune activity: Mediation by physiological trade-offs. Philos. Trans. R. Soc. B Biol. Sci. 363, 321–339 (2008).Article 

Google Scholar 
75.Klasing, K. C. The costs of immunity. Acta Zool. Sin. 50, 961–969 (2004).CAS 

Google Scholar 
76.Van Noordwijk, A. J. & de Jong, G. Acquisition and allocation of resources: Their influence on variation in life history tactics. Am. Nat. 128, 137–142 (1986).Article 

Google Scholar 
77.Glazier, D. S. Trade-offs between reproductive and somatic (storage) investments in animals: A comparative test of the Van Noordwijk and De Jong model. Evol. Ecol. 13, 539–555 (1999).Article 

Google Scholar 
78.Newton, I., McGrady, M. J. & Oli, M. K. A review of survival estimates for raptors and owls. Ibis 158, 227–248 (2016).Article 

Google Scholar 
79.Kennedy, P. L. & Ward, J. M. Effects of experimental food supplementation on movements of juvenile northern goshawks (Accipiter gentilis atricapillus). Oecologia 134, 284–291 (2003).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Terraube, J., Vasko, V. & Korpimäki, E. Mechanisms and reproductive consequences of breeding dispersal in a specialist predator under temporally varying food conditions. Oikos 124, 762–771 (2015).Article 

Google Scholar 
81.Delgado, M. D. M., Penteriani, V. & Nams, V. O. How fledglings explore surroundings from fledging to dispersal. A case study with Eagle Owls Bubo bubo. Ardea 97, 7–15 (2009).Article 

Google Scholar 
82.Rosenfield, R. N. et al. Body mass of female Cooper’s Hawks is unrelated to longevity and breeding dispersal: Implications for the study of breeding dispersal. J. Raptor Res. 50, 305–312 (2016).Article 

Google Scholar 
83.Klein, S. L. Hormonal and immunological mechanisms mediating sex differences in parasite infection. Parasite Immunol. 26, 247–264 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
84.Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Zuk, M. Reproductive strategies and disease susceptibility: An evolutionary viewpoint. Parasitol. Today 6, 231–233 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Zuk, M. & McKean, K. A. Sex differences in parasite infections: patterns and processes. Int. J. Parasitol. 26, 1009–1024 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Alexander, J. & Stimson, W. H. Sex hormones and the course of parasitic infection. Parasitol. Today 4, 189–193 (1988).Article 

Google Scholar 
88.Roulin, A. et al. Which chick is tasty to parasites? The importance of host immunology vs. parasite life history. J. Anim. Ecol. 72, 75–81 (2003).Article 

Google Scholar 
89.Hockey, P. A. R., Dean, W. R. J., Ryan, P. G., Maree, S. & Brickman, B. M. Roberts’ Birds of Southern Africa 7th edn. (John Voelcker Bird Book Fund, 2005).
Google Scholar 
90.Christie, D. A. & Ferguson-Lees, J. Raptors of the World (Christopher Helm Publishers, 2010).
Google Scholar 
91.Martin, R. O. et al. Phenological shifts assist colonisation of a novel environment in a range-expanding raptor. Oikos 123, 1457–1468 (2014).Article 

Google Scholar 
92.Rose, S., Sumasgutner, P., Koeslag, A. & Amar, A. Does seasonal decline in breeding performance differ for an African raptor across an urbanization gradient?. Front. Ecol. Evol. 5, 47 (2017).Article 

Google Scholar 
93.Horrocks, N. P. et al. Immune indexes of larks from desert and temperate regions show weak associations with life history but stronger links to environmental variation in microbial abundance. Physiol. Biochem. Zool. 85, 504–515 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
94.Horrocks, N. P. et al. Environmental proxies of antigen exposure explain variation in immune investment better than indices of pace of life. Oecologia 177, 281–290 (2015).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
95.Sergio, F., Blas, J., Forero, M. G., Donázar, J. A. & Hiraldo, F. Sequential settlement and site dependence in a migratory raptor. Behav. Ecol. Sociobiol. 18, 811–821 (2007).Article 

Google Scholar 
96.Rose, S., Thomson, R. L., Oschadleus, H.-D. & Lee, A. T. Summarising biometrics from the SAFRING database for southern African birds. Ostrich 2, 1–5 (2019).
Google Scholar 
97.Paijmans, D. M., Rose, S., Oschadleus, H.-D. & Thomson, R. L. SAFRING ringing report for 2017. Biodivers. Observ. 10, 1–11 (2019).
Google Scholar 
98.Katzenberger, J., Tate, G., Koeslag, A. & Amar, A. Black Sparrowhawk brooding behaviour in relation to chick age and weather variation in the recently colonised Cape Peninsula, South Africa. J. Ornithol. 156, 903–913 (2015).Article 

Google Scholar 
99.Buehler, D. M. et al. Constitutive immune function responds more slowly to handling stress than corticosterone in a shorebird. Physiol. Biochem. Zool. 81, 673–681 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
100.Zylberberg, M. Common measures of immune function vary with time of day and sampling protocol in five passerine species. J Exp Biol 218, 757–766 (2015).PubMed 
PubMed Central 

Google Scholar 
101.van de Crommenacker, J. et al. Effects of immune supplementation and immune challenge on oxidative status and physiology in a model bird: Implications for ecologists. J. Exp. Biol. 213, 3527–3535 (2010).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
102.French, S. S. & Neuman-Lee, L. A. Improved ex vivo method for microbiocidal activity across vertebrate species. Biol. Open 1, 482–487 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
103.Eikenaar, C. & Hegemann, A. Migratory common blackbirds have lower innate immune function during autumn migration than resident conspecifics. Biol. Let. 12, 20160078 (2016).Article 
CAS 

Google Scholar 
104.Hegemann, A., Pardal, S. & Matson, K. D. Indices of immune function used by ecologists are mostly unaffected by repeated freeze-thaw cycles and methodological deviations. Front. Zool. 14, 43 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
105.R Core Team. R: A language and environment for statistical computing. Vienna, Austria (R Foundation for Statistical Computing, 2019).106.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 2 (2017).Article 

Google Scholar 
107.McCurdy, D. G., Shutler, D., Mullie, A. & Forbes, M. R. Sex-biased parasitism of avian hosts: relations to blood parasite taxon and mating system. Oikos 82, 303–312 (1998).CAS 
Article 

Google Scholar 
108.Parejo, D., Silva, N. & Avilés, J. M. Within-brood size differences affect innate and acquired immunity in roller Coracias garrulus nestlings. J. Avian Biol. 38, 717–725 (2007).Article 

Google Scholar 
109.Kanikowska, D., Hyun, K. J., Tokura, H., Azama, T. & Nishimura, S. Circadian rhythm of acute phase proteins under the influence of bright/dim light during the daytime. Chronobiol. Int. 22, 137–143 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
110.Laake, J. L. RMark: an R interface for analysis of capture-recapture data with MARK. AFSC Processed Rep 2013-01, Seattle, WA (Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv., 2013).111.White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).Article 

Google Scholar 
112.Burnham, K. P. Design and Analysis Methods for Fish Survival Experiments Based on Release-Recapture (American Fisheries Society, 1987).
Google Scholar 
113.Coquet, R., Lebreton, J.-D., Gimenez, O. & Reboulet, A.-M. U-CARE: Utilities for performing goodness of fit tests and manipulating CApture-REcapture data. Ecography 32, 1071–1074 (2009).Article 

Google Scholar 
114.Sauer, J. R. & Byron, K. W. Generalized procedures for testing hypotheses about survival or recovery raes. J. Wildl. Manag. 53, 137–142 (1989).Article 

Google Scholar 
115.Nebel, C., Amar, A., Hegemann, A., Isaksson, C. & Sumasgutner, P. Parental morph combination does not influence innate immune function in nestlings of a colour-polymorphic African raptor: Data, Zivahub, https://doi.org/10.25375/uct.12780803 (2021). More

1.Duarte, C. M., Losada, I. J., Hendriks, I. E., Mazarrasa, I. & Marbà, N. The role of coastal plant communities for climate change mitigation and adaptation. Nat. Clim. Change 3, 961–968 (2013).ADS 
CAS 
Article 

Google Scholar 
2.Cullen-Unsworth, L. C. et al. Seagrass meadows globally as a coupled social–ecological system: Implications for human wellbeing. Mar. Pollut. Bull. 83, 387–397 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Nellemann, C. et al. Blue Carbon: a rapid response assessment. United Nations Environment Programme, GRID-Arendal (2009).4.Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

Google Scholar 
5.Fourqurean, J. W. et al. Seagrass ecosystems as a globally significant carbon stock. Nat. Geosci. 5, 505–509 (2012).ADS 
CAS 
Article 

Google Scholar 
6.Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. PNAS 106, 12377–12381 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.de los Santos, C. B. et al. Recent trend reversal for declining European seagrass meadows. Nature Commun. 10, 1–8 (2019).8.Lefcheck, J. S. et al. Long-term nutrient reductions lead to the unprecedented recovery of a temperate coastal region. PNAS 115, 3658–3662 (2018).ADS 
PubMed 
Article 
CAS 

Google Scholar 
9.Pendleton, L. et al. Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS ONE 7, e43542 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 1–13 (2019).Article 
CAS 

Google Scholar 
11.Jayathilake, D. R. M. & Costello, M. J. A modelled global distribution of the seagrass biome. Biol. Conserv. 226, 120–126 (2018).Article 

Google Scholar 
12.McKenzie, L. et al. The global distribution of seagrass meadows. Environ. Res. Lett. 15, 074041 (2020).ADS 
Article 

Google Scholar 
13.Den Hartog, C., & Kuo, J. Taxonomy and biogeography of seagrasses. In Seagrasses: biology, ecology and conservation (pp. 1–23). Springer, Dordrecht (2007).14.Duarte, C. M. & Chiscano, C. L. Seagrass biomass and production: a reassessment. Aquat. Bot. 65, 159–174 (1999).Article 

Google Scholar 
15.Serrano, O., Lavery, P. S., Rozaimi, M. & Mateo, M. A. Influence of water depth on the carbon sequestration capacity of seagrasses. Global Biogeochemic. Cy. 28, 950–961 (2014).ADS 
CAS 
Article 

Google Scholar 
16.Gullström, M. et al. Blue carbon storage in tropical seagrass meadows relates to carbonate stock dynamics, plant–sediment processes, and landscape context: insights from the western Indian Ocean. Ecosystems 21, 551–566 (2018).Article 
CAS 

Google Scholar 
17.Lavery, P. S., Mateo, M. Á., Serrano, O. & Rozaimi, M. Variability in the carbon storage of seagrass habitats and its implications for global estimates of blue carbon ecosystem service. PLoS ONE 8, e73748 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Campbell, J. E., Lacey, E. A., Decker, R. A., Crooks, S. & Fourqurean, J. W. Carbon storage in seagrass beds of Abu Dhabi United Arab Emirates. Estuaries Coast 38, 242–251 (2015).CAS 
Article 

Google Scholar 
19.Miyajima, T. et al. Geographic variability in organic carbon stock and accumulation rate in sediments of East and Southeast Asian seagrass meadows. Global Biogeochemic. Cy. 29, 397–415 (2015).ADS 
CAS 
Article 

Google Scholar 
20.Röhr, M. E. et al. Blue carbon storage capacity of temperate eelgrass (Zostera marina) meadows. Global Biogeochemic. Cy. 32, 1457–1475 (2018).ADS 
Article 
CAS 

Google Scholar 
21.Serrano, O. et al. Australian vegetated coastal ecosystems as global hotspots for climate change mitigation. Nat. Commun. 10, 1–10 (2019).CAS 
Article 

Google Scholar 
22.Prentice, C. et al. A synthesis of blue carbon stocks, sources and accumulation rates in eelgrass (Zostera marina) meadows in the Northeast Pacific. Global Biogeochemic. Cy. 34, e2019GB006345 (2020).23.Herrera-Silveira, J. A. et al. Blue carbon of Mexico, carbon stocks and fluxes: a systematic review. PeerJ 8, e8790 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
24.Carruthers, T. J. B., Barnes, P. A., Jacome, G. & Fourqurean, J. W. Lagoon scale processes in a coastally influenced Caribbean system: implications for the seagrass Thalassia testudinum. Caribb. J. Sci. 41, 441–455 (2005).
Google Scholar 
25.Tamis, J. E., & Foekema, E. M. A review of blue carbon in the Netherlands. IMARES Report C151/15. 13 p. (2016).26.Thorhaug, A. L. et al. Gulf of Mexico estuarine blue carbon stock, extent and flux: Mangroves, marshes, and seagrasses: A North American hotspot. Sci. Total Environ. 653, 1253–1261 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
27.Novak, A. B. et al. Factors influencing carbon stocks and accumulation rates in eelgrass meadows across New England, USA. Estuaries Coast. 43, 2076–2091 (2020).CAS 
PubMed 
Article 

Google Scholar 
28.Howard, J. L., Creed, J. C., Aguia, M. V. & Fourqurean, J. W. CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon”. Limnol. Oceanogr. 63, 160–172 (2018).ADS 
CAS 
Article 

Google Scholar 
29.Nóbrega, G. N., Romero, D. J., Otero, X. L., & Ferreira, T. O. Pedological studies of subaqueous soils as a contribution to the protection of seagrass meadows in Brazil. Rev Bras Cienc Solo 42 (2018).30.Gómez-Lopez, D., C. et al. Informe técnico Final Proyecto de Actualización cartográfica del atlas de pastos marinos de Colombia: Sectores Guajira, Punta San Bernardo y Chocó: Extensión y estado actual. PRY-BEM-005–13 FONADE-INVEMAR. Santa Marta. 136 pp. (2014).31.Díaz, M., Barrios Suárez, L. M., & Gómez López, D. I. Las praderas de pastos marinos en Colombia: Estructura y distribución de un ecosistema estratégico, Instituto de Investigaciones Marinas y Costeras-INVEMAR (2003).32.Green, E. P., Short, F. T., & Frederick, T. World atlas of seagrasses. Univ of California Press (2003).33.Phang, V. X., Chou, L. M. & Friess, D. A. Ecosystem carbon stocks across a tropical intertidal habitat mosaic of mangrove forest, seagrass meadow, mudflat and sandbar. Earth Surf Process Landf 40, 1387–1400 (2015).ADS 
CAS 
Article 

Google Scholar 
34.Alongi, D. M. et al. Indonesia’s blue carbon: a globally significant and vulnerable sink for seagrass and mangrove carbon. Wetl. Ecol. Manag. 24, 3–13 (2016).Article 

Google Scholar 
35.Thorhaug, A., Poulos, H. M., López-Portillo, J., Ku, T. C. & Berlyn, G. P. Seagrass blue carbon dynamics in the Gulf of Mexico: Stocks, losses from anthropogenic disturbance, and gains through seagrass restoration. Sci. Total Environ. 605, 626–636 (2017).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
36.Oreska, M. P., McGlathery, K. J. & Porter, J. H. Seagrass blue carbon spatial patterns at the meadow-scale. PLoS ONE 12, e0176630 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Serrano, O. et al. Can mud (silt and clay) concentration be used to predict soil organic carbon content within seagrass ecosystems?. Biogeosciences 17, 4915–4926 (2016).ADS 
Article 
CAS 

Google Scholar 
38.Chen, G. et al. Mangroves as a major source of soil carbon storage in adjacent seagrass meadows. Sci. Rep. 7, 42406 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Cusack, M. et al. Organic carbon sequestration and storage in vegetated coastal habitats along the western coast of the Arabian Gulf. Environ. Res. Lett. 13, 074007 (2018).ADS 
Article 
CAS 

Google Scholar 
40.Lafratta, A. et al. Challenges to select suitable habitats and demonstrate ‘additionality’ in Blue Carbon projects: A seagrass case study. Ocean Coast. Manag. 197, 105295 (2020).Article 

Google Scholar 
41.Gómez-López, D.I. et al. Reporte del estado de los arrecifes coralinos y pastos marinos en Colombia (2016–2017). Serie de publicaciones Generales del Invemar # 101, Santa Marta. 100 pp. (2018).42.Alvarez-León, R., Aguilera-Quiñonez, J., Andrade-Maya, C. C. & Novak, P. Caracterización general de la zona de surgencia en la Guajira Colombiana. Rev. Acad. Colomb. Cienc. 19, 679–694 (1995).
Google Scholar 
43.Wilson, S. S., Furman, B. T., Hall, M. O. & Fourqurean, J. W. Assessment of Hurricane Irma impacts on South Florida seagrass communities using long-term monitoring programs. Estuaries Coast. 43, 1119–1132 (2020).CAS 
Article 

Google Scholar 
44.Hiraishi, T. et al. 2013 supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: Wetlands. IPCC, Switzerland (2014).45.Diaz-M, J. M. & Gómez-López, D. I. Historic changes in the abundance and distribution of seagrass beds in the Cartagena bay and neighbouring areas (Colombia). Bol. Invest. Mar. Cost. 32, 57–74 (2003).
Google Scholar 
46.Ricart, A. M. et al. High variability of Blue Carbon storage in seagrass meadows at the estuary scale. Sci. Rep. 10, 1–12 (2020).ADS 
Article 
CAS 

Google Scholar 
47.Macreadie, P. I., Allen, K., Kelaher, B. P., Ralph, P. J. & Skilbeck, C. G. Paleoreconstruction of estuarine sediments reveal human-induced weakening of coastal carbon sinks. Glob. Change Biol. 18, 891–901 (2012).ADS 
Article 

Google Scholar 
48.Geister, J., & Díaz-Merlano J. M. Reef environments and geology of an oceanic archipelago: San Andrés, Old Providence and Santa Catalina (Caribbean Sea, colombia) with Field Guide. Ingeominas. 104 pp (2007).49.Rodríguez-Ramírez, A. et al. Recent dynamics and condition of coral reefs in the Colombian Caribbean. Rev. Biol. Trop. 58, 107–131 (2010).PubMed 
Article 

Google Scholar 
50.Serrano, O., Lavery, P. S., Lopez-Merino, L., Ballesteros, E. & Mateo, M. A. Location and associated carbon storage of erosional escarpments of seagrass Posidonia mats. Front. Mar. Sci. 3, 42 (2016).Article 

Google Scholar 
51.Freile, D., & Hillis, L. Carbonate productivity by Halimeda incrassata land in a proximal lagoon, Pico Feo, San Blas, Panama. In Proceedings 8th International Coral Reef Symposium 1, 767–772 (1997).52.van Tussenbroek, B. I. & van Dijk, J. K. Spatial and temporal variability in biomass and production of Psammophytic Halimeda incrassata (Bryopsidales, Chlorophyta) in a Caribbean reef lagoon. J. Phycol. 43, 69–77 (2007).Article 
CAS 

Google Scholar 
53.Macreadie, P. I., Serrano, O., Maher, D. T., Duarte, C. M. & Beardall, J. Addressing calcium carbonate cycling in blue carbon accounting. Limnol. Oceanogr. Lett. 2, 195–201 (2017).Article 

Google Scholar 
54.CDIAC, 2020. Carbon Dioxide Information Analysis Center55.Saderne, V. et al. Role of carbonate burial in Blue Carbon budgets. Nat. Commun. 10, 1–9 (2019).CAS 
Article 

Google Scholar 
56.Challener, R. C., Robbins, L. L. & McClintock, J. B. Variability of the carbonate chemistry in a shallow, seagrass-dominated ecosystem: implications for ocean acidification experiments. Mar. Freshw. Res. 67, 163–172 (2016).CAS 
Article 

Google Scholar 
57.Gattuso, J. P. et al. Ocean solutions to address climate change and its effects on marine ecosystems. Front. Mar. Sci. 5, 337 (2018).Article 

Google Scholar 
58.Conservation International (2020). Accesses in November 2020. https://www.conservation.org/stories/critical-investment-in-blue-carbon59.Coralina-Invemar. Gómez-López, D. I., C. Segura-Quintero, P. C. Sierra-Correa y J. Garay-Tinoco (Eds). Atlas de la Reserva de Biósfera Seaflower. Archipiélago de San Andrés, Providencia y Santa Catalina. Instituto de Investigaciones Marinas y Costeras “José Benito Vives De Andréis” -INVEMAR- y Corporación para el Desarrollo Sostenible del Archipiélago de San Andrés, Providencia y Santa Catalina -CORALINA-. Serie de Publicaciones Especiales de INVEMAR # 28. Santa Marta, Colombia 180 p. (2012).60.Glew, J. R., Smol, J. P., & Last, W. M. Sediment Core Collection and Extrusion. In: Tracking Environmental Change UsingLake Sediments. Kluwer Academic Publishers, 73–105 (2005).61.Heiri, O., Lotter, A. F. & Lemcke, G. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J. Paleolimnol. 25, 101–110 (2001).ADS 
Article 

Google Scholar 
62.Sanchez-Cabeza, J. A., Masqué, P. & Ani-Ragolta, I. 210Pb and 210Po analysis in sediments and soils by microwave acid digestion. J. Radioanal. Nucl. Chem. 227, 19–22 (1998).CAS 
Article 

Google Scholar 
63.Krishnaswamy, S., Lal, D., Martin, J. M. & Meybeck, M. Geochronology of lake sediments. Earth Planet. Sci. Lett. 11, 407–414 (1971).ADS 
CAS 
Article 

Google Scholar 
64.Stuiver, M. & Polach, H. A. Discussion reporting of 14 C data. Radiocarbon 19, 355–363 (1977).Article 

Google Scholar 
65.Blaauw, M. & Christen, J. A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Anal. 6, 457–474 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
66.Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).CAS 
Article 

Google Scholar 
67.Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399 (2013).MathSciNet 

Google Scholar 
68.Parnell, A. C. Package “simmr”: A Stable Isotope Mixing Model. https://doi.org/10.1371/journal.pone.0009672 (2019). More

1.Simões, R. A., Reis, L. G., Bento, J. M., Solter, L. F. & Delalibera, I. Jr. Biological and behavioral parameters of the parasitoid Cotesia flavipes (Hymenoptera: Braconidae) are altered by the pathogen Nosema sp. (Microsporidia: Nosematidae). Biol. Control 63, 164–171 (2012).Article 

Google Scholar 
2.Frago, E., Dicke, M. & Godfray, H. C. J. Insect symbionts as hidden players in insect–plant interactions. Trends Ecol. Evol. 27, 705–711 (2012).Article 

Google Scholar 
3.Himler, A. G. et al. Rapid spread of a bacterial symbiont in an invasive whitefly is driven by the fitness benefits and female bias. Science 332, 254–256 (2011).ADS 
CAS 
Article 

Google Scholar 
4.Lu, M., Wingfield, M. J., Gillette, N. & Sun, J. H. Do novel genotypes drive the success of an invasive bark beetle–fungus complex? Implications for potential reinvasion. Ecology 92, 2013–2019 (2011).Article 

Google Scholar 
5.Vilcinskas, A., Stoecker, K., Schmidtberg, H., Röhrich, C. R. & Vogel, H. Invasive harlequin ladybird carries biological weapons against native competitors. Science 340, 862–863 (2013).ADS 
CAS 
Article 

Google Scholar 
6.Zhao, L. et al. A native fungal symbiont facilitates the prevalence and development of an invasive pathogen–native vector symbiosis. Ecology 94, 2817–2826 (2013).Article 

Google Scholar 
7.Solter, L. F., Becnel, J. J. & Vávra, J. Research methods for entomopathogenic microsporidia and other protists. Manual of Techniques in Invertebrate Pathology 329–371 (2012).8.Maddox, J. V. Protozoan diseases. Epizootiol. Insect Dis. 1, 417–452 (1987).
Google Scholar 
9.Latchininsky, A. V. & VanDyke, K. A. Grasshopper and locust control with poisoned baits: a renaissance of the old strategy?. Outlooks Pest Manag. 17, 105–111 (2006).Article 

Google Scholar 
10.Sweeney, A. W. & Becnel, J. J. Potential of microsporidia for the control of mosquitoes. Parasitol. Today. 7, 217–220 (1991).CAS 
Article 

Google Scholar 
11.Capella-Gutierrez, S., Marcet-Houben, M. & Gabaldon, T. Phylogenomics supports microsporidia as the earliest diverging clade of sequenced fungi. BMC Biol. 10, 47–52 (2012).Article 

Google Scholar 
12.Tokarev, Y. S. et al. A formal redefinition of the genera Nosema and Vairimorpha (Microsporidia: Nosematidae) and reassignment of species based on molecular phylogenetics. J. Invertebr. Pathol. 169, 107279 (2020).CAS 
Article 

Google Scholar 
13.Tooke, F. G. C. The Eucalyptus Snout beetle, Gonipterus scutellatus Gyll. A study of its ecology and control by biological means Union of South Africa, Department of Agriculture. Entomol. Mem. 3, 1–184 (1955).
Google Scholar 
14.Mapondera, T. S., Burgess, T., Matsuki, M. & Oberprieler, R. G. Identification and molecular phylogenetics of the cryptic species of the Gonipterus scutellatus complex (Coleoptera: Curculionidae: Gonipterini). Aust. J. Entomol. 51, 175–188 (2012).Article 

Google Scholar 
15.Valente, C. et al. Economic outcome of classical biological control: a case study on the Eucalyptus snout beetle, Gonipterus platensis, and the parasitoid Anaphes nitens. Ecol Econ. 149, 40–47 (2018).Article 

Google Scholar 
16.Ansari, M. J., Al-Ghamdi, A., Nuru, A., Khan, K. A. & Alattal, Y. Geographical distribution and molecular detection of Nosema ceranae from indigenous honeybees of Saudi Arabia. Saudi J. Biol. Sci 24, 983–991 (2017).Article 

Google Scholar 
17.Ovcharenko, M., Świątek, P., Ironside, J. & Skalski, T. Orthosomella lipae sp. n. (Microsporidia) a parasite of the weevil, Liophloeus lentus Germar, 1824 (Coleoptera: Curculionidae). J. Invertebr. Pathol. 112, 33–40 (2013).Article 

Google Scholar 
18.Weiser, J. A new microsporidian from the bark beetle Pityokteines curvidens Germar (Coleoptera, Scolytidae) in Czechoslovakia. J. Invertebr. Pathol. 3, 324–329 (1961).
Google Scholar 
19.Malone, L. A. A new pathogen, Microsporidium itiiti n. sp. (Microsporida), from the Argentine Stem Weevil, Listronotus bonariensis (Coleoptera, Curculionidae). J. Protozool. 32, 535–541 (1985).Article 

Google Scholar 
20.Purrini, K. & Weiser, J. Ultrastructural study of the microsporidian Chytridiopsis typographi (Chytridiopsida: Microspora) infecting the bark beetle, Ips typographus (Scolytidae: Coleoptera), with new data on spore dimorphism. J. Invertebr. Pathol. 45, 66–74 (1985).Article 

Google Scholar 
21.Yaman, M., Radek, R., Aslan, I. & Erturk, O. Characteristic features of Nosema phyllotretae Weiser 1961, a microsporidian parasite of Phyllotreta atra (Coleoptera: Chrysomelidae) in Turkey. Zool. Stud. Taipei. 44, 368 (2005).
Google Scholar 
22.Zhu, F. et al. A new isolate of Nosema sp. (Microsporidia, Nosematidae) from Phyllobrotica armata Baly (Coleoptera, Chrysomelidae) from China. Jour J. Invertebr. Pathol. 106, 339–342 (2011).CAS 
Article 

Google Scholar 
23.Andreadis, T. G., Takaoka, H., Otsuka, Y. & Vossbrinck, C. R. Morphological and molecular characterization of a microsporidian parasite, Takaokaspora nipponicus n. gen. n. sp. from the invasive rock pool mosquito, Ochlerotatus japonicus japonicus. J. Invertebr. Pathol. 114, 161–172 (2013).CAS 
Article 

Google Scholar 
24.Sapir, A. et al. Microsporidia-nematode associations in methane seeps reveal basal fungal parasitism in the deep sea. Front. Microbiol. 5, 43–52 (2014).Article 

Google Scholar 
25.Solter, L. F., Maddox, J. V. & McManus, M. L. Host specificity of microsporidia (Protista: Microspora) from European populations of Lymantria dispar (Lepidoptera: Lymantriidae) to indigenous North American Lepidoptera. J. Invertebr. Pathol. 69, 135–150 (1997).CAS 
Article 

Google Scholar 
26.Knell, J. D., Allen, G. E. & Hazard, E. I. Light and electron microscope study of Thelohania solenopsae n. sp. (Microsporida: Protozoa) in the red imported fire ant Solenopsis invict. J. Invertebr. Pathol. 29, 192–200 (1977).CAS 
Article 

Google Scholar 
27.Henry, J. E., & Oma, E. A. Pest control by Nosema locustae, a pathogen of grasshoppers and crickets. Microbial Control of Pests and Plant Diseases 1970–1980 (1981).28.Vávra, J. & Maddox, J. V. Methods in microsporidiology. In Biology of the Microsporidia 281–319 (Springer, Boston, 1976).
Google Scholar 
29.Simões, R. A., Feliciano, J. R., Solter, L. F. & Delalibera, I. Jr. Impacts of Nosema sp. (Microsporidia: Nosematidae) on the sugarcane borer, Diatraea saccharalis (Lepidoptera: Crambidae). J. Invertebr. Pathol. 129, 7–12 (2015).Article 

Google Scholar 
30.Inglis, G. D., Lawrence, A. M. & Davis, F. M. Impact of a novel species of Nosema on the southwestern corn borer (Lepidoptera: Crambidae). J. Econ. Entomol. 96, 12–20 (2003).CAS 
Article 

Google Scholar 
31.Zheng, H. Q. et al. Spore loads may not be used alone as a direct indicator of the severity of Nosema ceranae infection in honey bees Apis mellifera (Hymenoptera: Apidae). J. Econ. Entomol. 107, 2037–2044 (2014).Article 

Google Scholar 
32.Goettel, M. S., Inglis, G. D. & Lacey, L. A. Manual of Techniques in Invertebrate Pathology (Academic Press, 2012).
Google Scholar 
33.Canning, E. U., Curry, A., Cheney, S., Lafranchi-Tristem, N. J., Haque, M. A. Vairimorpha imperfecta n. sp., a microsporidian exhibiting an abortive octosporous sporogony in Plutella xylostella L. (Lepidoptera: Yponomeutidae). Parasitology 119, 273–286 (1999).34.Tsai, S. J., Lo, C. F., Soichi, Y. & Wang, C. H. The characterization of microsporidian isolates (Nosematidae: Nosema) from five important lepidopteran pests in Taiwan. J. Invertebr. Pathol. 83, 51–59 (2003).CAS 
Article 

Google Scholar 
35.Cai, S. F., Lu, X. M., Qiu, H. H., Li, M. Q. & Feng, Z. Z. Phagocytic uptake of Nosema bombycis (Microsporidia) spores by insect cell lines. J. Integr. Agric. 11, 1321–1326 (2012).Article 

Google Scholar 
36.Dong, S., Shen, Z., Xu, L. & Zhu, F. Sequence and phylogenetic analysis of SSU rRNA gene of five microsporidia. Curr. Microbiol. 60, 30 (2010).CAS 
Article 

Google Scholar 
37.Becnel, J. J. & Andreadis, T. G. Microsporidia in insects. The microsporidia and microsporidiosis 447-501 (1999).38.Knell, R. J. & Webberley, K. M. Sexually transmitted diseases of insects: Distribution, evolution, ecology and host behaviour. Biol. Rev. 79, 557–581 (2004). (PERMANECE)39.Bell, H. A., Down, R. E., Kirkbride‐Smith, A. E. & Edwards, J. P. Effect of microsporidian infection in Lacanobia oleracea (Lep., Noctuidae) on prey selection and consumption by the spined soldier bug Podisus maculiventris (Het., Pentatomidae). J. Appl. Entomol. 128(8), 548–553 (2004).40.Dakhel, W. H., Latchininsky, A. V. & Jaronski, S. T. Efficacy of two entomopathogenic fungi, Metarhizium brunneum, strain F52 alone and combined with Paranosema locustae against the migratory grasshopper, Melanoplus sanguinipes, under laboratory and greenhouse conditions. Insects 10(4), 94–102 (2019).Article 

Google Scholar 
41.Guo, Y., An, Z. & Shi, W. Control of grasshoppers by combined application of Paranosema locustae and an insect growth regulator (IGR) (cascade) in rangelands in China. J. Econ. Entomol. 105(6), 1915–1920 (2012).Article 

Google Scholar 
42.Lockwood, J. A., Bomar, C. R. & Ewen, A. B. The history of biological control with Nosema locustae: Lessons for locust management. Int. J. Trop. Insect Sci. 19(4), 333–350 (1999).Article 

Google Scholar 
43.Larem, A., Fritsch, E., Undorf-Spahn, K., Kleespies, E. G. & Jehle, J. A. Interaction of Phthorimaea operculella granulovirus with a Nosema sp. microsporidium in larvae of Phthorimaea operculella. J. Invertebr. Pathol. 160, 76–86 (2019).Article 

Google Scholar 
44.Tokarev, Y. S., Grizanova, E. V., Ignatieva, A. N. & Dubovskiy, I. M. Greater wax moth Galleria mellonella (Lepidoptera: Pyralidae) as a resistant model host for Nosema pyrausta (Microsporidia: Nosematidae). J. Invertebr. Pathol. 157, 1–3 (2018).Article 

Google Scholar 
45.Coombs, N. J., Gough, A. C. & Primrose, J. N. Optimisation of DNA and RNA extraction from archival formalin-fixed tissue. Nucleic Acids Res. 27, e12-I (1999).46.Huang, W. F., Tsai, S. J., Lo, C. F., Soichi, Y. & Wang, C. H. The novel organization and complete sequence of the ribosomal RNA gene of Nosema bombycis. Fungal Genet. Biol 41, 473–481 (2004).CAS 
Article 

Google Scholar 
47.Karnovsky, M. J. A formaldehyde glutaraldehyde fixative of high osmolality for use in electron microscopy. J. Cell. Biol. 27, 1A-149A (1965).Article 

Google Scholar  More

In this multi-scale assessment of temporal variation in bird-window collisions, our predictions related to the diel timing of collisions were only partly supported. We predicted more casualties would occur during morning than other times of day, which should have resulted in our detection of the greatest number of fatal collisions on midday surveys and more non-fatal collisions during morning surveys than midday and evening surveys. However, greatest numbers of both fatal and non-fatal collisions were observed on morning surveys, indicating that more collisions occurred during overnight and early morning periods than mid-to-late morning and afternoon. As predicted, this diel pattern was consistent across seasons. Our predictions about monthly and seasonal patterns were also only partly supported. Unexpectedly, total collision mortality was highest in the spring migration month of May, and avian residency status interacted with season such that roughly equivalent high numbers of resident and migrant collisions occurred in spring, more resident than migrant collisions occurred in summer (and overall from Apr to Oct), and more migrant than resident collisions occurred in fall. Further, unlike many past studies, collision mortality of migrants was roughly equivalent in spring and fall migration.Diel collision patternsWe observed more fatal and non-fatal collisions in the morning than in midday and evening surveys combined, even though we included fewer morning surveys in diel analyses. These differences are likely conservative in that an even greater proportion of fatal collisions than we observed likely occurred overnight and in the early morning, but went undetected because of bias associated with observer detection and scavenger removal of carcasses. Concurrent work in our study area20 found that relatively inexperienced volunteers detected a slightly lower proportion of carcasses (0.69) than experienced surveyors (0.76). Because roughly 10% of morning surveys were conducted by less-experienced volunteers and all midday or evening surveys were conducted by full-time technicians or the authors, we likely missed more carcasses on morning surveys. Additionally, most scavenging events (68%) were at night20, so bird carcasses from overnight collisions were the least likely to persist until the subsequent survey. Thus, underestimation of fatal collisions in the preceding interval was almost certainly greater for morning surveys than for midday and evening surveys.A prevailing hypothesis for why daytime bird-window collisions occur is that birds making local (e.g., foraging) movements fail to perceive a barrier when flying toward objects either on the other side of glass or reflected on a glassy surface8,15. Under this hypothesis, daytime collisions for both residents and migrating birds at stopover locations would be expected to occur most frequently when birds are most active, which is typically near dawn regardless of season. Our finding of the greatest number of collision fatalities on morning surveys circumstantially supports the above hypothesis and expectation, as do past descriptive studies of diel variation in bird-window collisions26,27,38,39. However, our study design did not allow differentiation between nocturnal and early morning collisions, and a nontrivial proportion of carcasses detected on morning surveys likely represented collisions from the preceding overnight period. Nighttime collisions may occur at any structural component not easily detectable at night (i.e., they are not limited to glass surfaces), and can be exacerbated by artificial light emission that attracts and disorients migrating birds36,37,44,45. Nonetheless, the observation of more non-fatal collisions (including directly witnessed collisions) during morning than midday or evening surveys does strongly suggest that morning carcass counts included many collisions that occurred near or shortly after dawn.A potential limitation of our study regarding time-of-day analyses is the longer interval between evening and morning surveys than between other survey periods. If collisions occurred uniformly or randomly in time, we would expect to find more bird carcasses during morning surveys due to the longer preceding time interval. However, as described above, the early morning peak observed for non-fatal collisions (Fig. 1), which are less persistent than carcasses and therefore do not accumulate over time, suggests that collisions do not have a uniform or random temporal distribution and that a real peak in collision frequency occurs sometime shortly prior to when we conducted morning surveys. Another possible bias is that we conducted surveys during fixed time periods rather than adjusting them to seasonally varying sunrise and sunset times. This limitation would have most strongly affected our summer results, potentially inflating summer morning counts as a result of collisions for both early morning and late evening (the two periods when birds are most active) being grouped into the same survey period. However, this limitation was unlikely to greatly influence our conclusions about diel collision patterns because: (1) these patterns were fairly consistent across seasons, suggesting a relatively small influence of varying sunrise and sunset times, and (2) sunrise and sunset times vary by only a few hours over the course of the entire year whereas the broad time periods for which we compared collisions (overnight, morning, afternoon) consist of longer lengths of time. Further research is needed to identify the exact timing of collisions, including during overnight periods, and this could be accomplished with collision surveys conducted more frequently during the day and night or remote detection methods, such as video cameras, motion-triggered still cameras, microphones that record sounds of impact, and glass-mounted pressure sensors that detect vibrations from collision impact.We predicted diel collision patterns would not vary seasonally because the pattern of birds being most active near sunrise and sunset is relatively consistent across seasons. Hager and Cosentino46 provide excellent guidelines for conducting bird-window collision surveys, but their recommendation to conduct surveys in mid-to-late afternoon is based on summer monitoring that found mortality to peak between late morning and early afternoon in Illinois, USA29. We suspect differences in diel patterns between that study and ours relate to geographic variation and/or seasonal sampling coverage, as our larger sample of surveys included spring and fall migration in addition to summer. Although many collision-prone species migrate nocturnally, the diel collision peak for migrants could still occur in early morning because nocturnally-migrating birds often set-down into stopover habitats during early morning47,48 and may be most susceptible to collisions at this time. There could be subtle seasonal variation in diel collision patterns that we failed to detect; however, the majority of collisions across seasons appear to occur near or before dawn (see also26,27,38,39). In combination with the previous study showing that scavenging peaks overnight20, we suggest that conducting daily collision surveys in the morning could result in the least biased mortality estimates, especially in urbanized areas where humans (e.g., cleaning crews) may remove carcasses in the early morning. As noted by Hager and Cosentino46, further research is needed to identify how the optimal survey time is influenced by factors such as geography, the bird community, animal scavengers, and removal of bird carcasses by humans.Monthly and seasonal collision patternsWe expected more collisions in fall than other seasons because bird populations in North America are larger after summer breeding and include higher proportions of young birds that have less experience with flight, migration, and human structures. Also, numerous studies have found the greatest window collision mortality in fall, a pattern driven largely by migrant birds 12,18,23,27,31,40,49. Contrary to expectation, both raw counts and bias-adjusted estimates of collision fatalities were highest in the spring migration month of May and higher overall in spring than fall. This pattern resulted from a high number of both migrant and resident bird collisions. In fact, we found that roughly equal numbers of resident and migrant collisions occurred in spring. When considering migrating individuals only, we found roughly similar numbers of collisions in spring and fall migration, a finding that also is unexpected in light of past studies. Notably, two other studies that found that a large proportion of total collisions consisted of resident birds30,50 also documented a seasonal pattern of total collisions that was less skewed toward fall. An explanation for the relatively large amount of total spring mortality, and for our finding that migrant mortality was roughly similar in spring and fall, is that some long-distance migrants follow elliptical migration paths where migration routes in fall are farther east than in spring51,52, such that in central North America, numbers of some species are greatest during spring migration. This explanation is supported by our observation of some elliptical migrants colliding during spring but not fall (e.g., Swainson’s Thrush [Catharus ustulatus]). Our study adds further nuance to the understanding of seasonal variation in bird collisions and exemplifies the need to study bird-window collisions in a wider array of geographic contexts to allow region-relevant management recommendations.Our predictions regarding avian residency status were only partly supported; more migrants than non-migrants were indeed killed during fall migration. In spring, however, roughly equal numbers of each group collided, and far more residents than migrants collided in summer (and overall from Apr–Oct). This result does not account for varying abundance (overall and seasonally) of each species group, so it does not necessarily imply that resident species are more vulnerable to, or at greater risk of, window collisions relative to their abundance or period of residency in our study area. However, the finding of a high proportion of non-migrant casualties was still unexpected given that previous similar studies have almost universally reported higher mortality among migrants15,25,26,39,49,53,54, although most sampled during migratory periods only. Even with our individual-based residency designations, we may have slightly underestimated migrant mortality because all individuals of some migratory species were classified as unknown due to overlapping resident and migratory periods. However, even if all unknown individuals were migrating, there were too few birds in this category (22 of 341 [7%] total carcasses) to change our conclusions regarding the migrant-resident comparisons. Anecdotally, many spring and summer collision fatalities were recently fledged juveniles (n = 26 [25%] in May; n = 17 [30%] in June), clearly indicating that some collision victims were indeed not migrating, and therefore, that the high number of resident collisions is not an artifact of our classification system. Moreover, we did not observe collisions of any migrant individuals during June, even though a few species migrate through our study area in small numbers during this period (e.g., shorebirds [order Charadriiformes] and some tyrant flycatchers [family Tyrannidae])55. It is possible, however, that some resident individuals were undergoing post-breeding dispersal at the time of collision, as evidenced by a small late-June peak of Tufted Titmouse (Baeolophus bicolor) and Carolina Chickadee (Poecile carolinensis) collision victims with brood patches (TJO unpublished data).Although our sampling captured the peak months of spring and fall migration in our study region, we undoubtedly missed some early-spring migrants before April and late-fall migrants after October. However, greater than 10 years of near-daily collision surveys at one of the most collision-prone buildings in our study (TJO unpublished data) suggests this number of missed collisions was relatively small. Specifically, total collisions at this building (including residents and migrants) dropped from an average of  > 8 birds in October to  More

Global Health Program, Smithsonian Conservation Biology Institute, Washington DC, USAJames M. Hassell & Dawn ZimmermanDepartment of Epidemiology of Microbial Disease, Yale School of Public Health, New Haven, CT, USAJames M. Hassell & Dawn ZimmermanCentre for Biodiversity & Environment Research (CBER), Department of Genetics, Evolution and Environment, University College London, London, UKTim Newbold & Lydia H. V. FranklinosDepartment of Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, USAAndrew P. DobsonSanta Fe Institute, Santa Fe, NM, USAAndrew P. DobsonWalter Reed Biosystematics Unit (WRBU), Smithsonian Institution Museum Support Center, Suitland, MD, USAYvonne-Marie LintonDepartment of Entomology, Smithsonian National Museum of Natural History, Washington DC, USAYvonne-Marie LintonWalter Reed Army Institute of Research (WRAIR), Silver Spring, MD, USAYvonne-Marie LintonMarine Disease Ecology Laboratory, Smithsonian Environmental Research Center, Edgewater, MD, USAKatrina M. Pagenkopp Lohan More