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    Interspecific variation in evaporative water loss and temperature response, but not metabolic rate, among hibernating bats

    1.Lyman, C. P. & Chatfield, P. O. Physiology of hibernation in mammals. Physiol. Rev. 35, 403–425 (1955).CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Geiser, F. Hibernation. Curr. Biol. 23, R188–R193 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Humphries, M. M., Thomas, D. W. & Speakman, J. R. Climate-mediated energetic constraints on the distribution of hibernating mammals. Nature 418, 313–316 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Wilkinson, G. S. & Adams, D. M. Recurrent evolution of extreme longevity in bats. Biol. Lett. 15, 20180860 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Frick, W. F., Reynolds, D. S. & Kunz, T. H. Influence of climate and reproductive timing on demography of little brown myotis Myotis lucifugus. J. Anim. Ecol. 79, 128–136 (2010).PubMed 
    Article 

    Google Scholar 
    6.Willis, C. K. Trade-offs influencing the physiological ecology of hibernation in temperate-zone bats. Integr. Comp. Biol. 57, 1214–1224 (2017).PubMed 
    Article 

    Google Scholar 
    7.Lane, J. E. In Living in a Seasonal World 51–61 (Springer, 2012).8.Inouye, D. W., Barr, B., Armitage, K. B. & Inouye, B. D. Climate change is affecting altitudinal migrants and hibernating species. Proc. Natl. Acad. Sci. 97, 1630–1633 (2000).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Lane, J. E., Kruuk, L. E., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Feder, M. E. In New Directions in Ecological Physiology (eds M. E. Feder, A. F. Bennett, W. W. Burggren, & R. B Huey) 38–75 (Cambridge University Press, 1987).11.Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274. https://doi.org/10.1146/annurev.physiol.66.032102.115105 (2004).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    12.Boyles, J. G. et al. A global heterothermic continuum in mammals. Glob. Ecol. Biogeogr. 22, 1029–1039 (2013).Article 

    Google Scholar 
    13.Ruf, T. & Arnold, W. Effects of polyunsaturated fatty acids on hibernation and torpor: A review and hypothesis. Am. J. Physiol. Regul. Integr. Comp. Physiol. 294, R1044-1052. https://doi.org/10.1152/ajpregu.00688.2007 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Ruf, T. & Geiser, F. Daily torpor and hibernation in birds and mammals. Biol. Rev. Camb. Philos. Soc. https://doi.org/10.1111/brv.12137 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Heldmaier, G., Ortmann, S. & Elvert, R. Natural hypometabolism during hibernation and daily torpor in mammals. Respir. Physiol. Neurobiol. 141, 317–329 (2004).PubMed 
    Article 

    Google Scholar 
    16.van Breukelen, F. & Martin, S. L. The hibernation continuum: Physiological and molecular aspects of metabolic plasticity in mammals. Physiology 30, 273–281 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    17.Nowack, J., Levesque, D. L., Reher, S. & Dausmann, K. H. Variable climates lead to varying phenotypes: ‘Weird’mammalian torpor and lessons from non-Holarctic species. Front. Ecol. Evol. 8, 60 (2020).Article 

    Google Scholar 
    18.Stawski, C., Willis, C. & Geiser, F. The importance of temporal heterothermy in bats. J. Zool. 292, 86–100 (2014).Article 

    Google Scholar 
    19.Thomas, D. W., Dorais, M. & Bergeron, J.-M. Winter energy budgets and cost of arousals for hibernating little brown bats, Myotis lucifugus. J. Mammal. 71, 475–479 (1990).Article 

    Google Scholar 
    20.Kunz, T. H., Wrazen, J. A. & Burnett, C. D. Changes in body mass and fat reserves in pre-hibernating little brown bats (Myotis lucifugus). Ecoscience 5, 8–17 (1998).Article 

    Google Scholar 
    21.Thomas, D. W. & Cloutier, D. Evaporative water loss by hibernating little brown bats, Myotis lucifugus. Physiol. Zool. 65, 443–456 (1992).Article 

    Google Scholar 
    22.Kornfeld, S. F., Biggar, K. K. & Storey, K. B. Differential expression of mature microRNAs involved in muscle maintenance of hibernating little brown bats, Myotis lucifugus: A model of muscle atrophy resistance. Genom. Proteom. Bioinform. 10, 295–301 (2012).CAS 
    Article 

    Google Scholar 
    23.Eddy, S. F., Morin, P. Jr. & Storey, K. B. Differential expression of selected mitochondrial genes in hibernating little brown bats, Myotis lucifugus. J. Exp. Zool. A Comp. Exp. Biol. 305, 620–630 (2006).PubMed 
    Article 
    CAS 

    Google Scholar 
    24.Brigham, R., Ianuzzo, C., Hamilton, N. & Fenton, M. Histochemical and biochemical plasticity of muscle fibers in the little brown bat (Myotis lucifugus). J. Comp. Physiol. B. 160, 183–186 (1990).CAS 
    PubMed 
    Article 

    Google Scholar 
    25.McGuire, L. P., Mayberry, H. W. & Willis, C. K. R. White-nose syndrome increases torpid metabolic rate and evaporative water loss in hibernating bats. Am. J. Physiol. Regul. Integr. Comp. Physiol. 313, R680–R686 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    26.Jonasson, K. A. & Willis, C. K. Hibernation energetics of free-ranging little brown bats. J. Exp. Biol. 215, 2141–2149 (2012).PubMed 
    Article 

    Google Scholar 
    27.Klüg-Baerwald, B. J. & Brigham, R. M. Hung out to dry? Intraspecific variation in water loss in a hibernating bat. Oecologia 183, 977–985 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    28.Dunbar, M. B. & Brigham, R. M. Thermoregulatory variation among populations of bats along a latitudinal gradient. J. Comp. Physiol. B 180, 885–893 (2010).PubMed 
    Article 

    Google Scholar 
    29.Yacoe, M. E. Protein metabolism in the pectoralis muscle and liver of hibernating bats, Eptesicus fuscus. J. Comp. Physiol. 152, 137–144 (1983).ADS 
    CAS 
    Article 

    Google Scholar 
    30.Yacoe, M. E. Maintenance of the pectoralis muscle during hibernation in the big brown bat, Eptesicus fuscus. J. Comp. Physiol. 152, 97–104 (1983).Article 

    Google Scholar 
    31.Twente, J. W. & Twente, J. Biological alarm clock arouses hibernating big brown bats, Eptesicus fuscus. Can. J. Zool. 65, 1668–1674 (1987).Article 

    Google Scholar 
    32.Boratyński, J. S., Willis, C. K., Jefimow, M. & Wojciechowski, M. S. Huddling reduces evaporative water loss in torpid Natterer’s bats, Myotis nattereri. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 179, 125–132 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    33.Hope, P. R. & Jones, G. Warming up for dinner: Torpor and arousal in hibernating Natterer’s bats (Myotis nattereri) studied by radio telemetry. J. Comp. Physiol. B. 182, 569–578 (2012).PubMed 
    Article 

    Google Scholar 
    34.Park, K. J., Jones, G. & Ransome, R. D. Torpor, arousal and activity of hibernating greater horseshoe bats (Rhinolophus ferrumequinum). Funct. Ecol. 14, 580–588 (2000).Article 

    Google Scholar 
    35.Ben-Hamo, M., Muñoz-Garcia, A., Williams, J. B., Korine, C. & Pinshow, B. Waking to drink: Rates of evaporative water loss determine arousal frequency in hibernating bats. J. Exp. Biol. 216, 573–577 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Lausen, C. & Barclay, R. Winter bat activity in the Canadian prairies. Can. J. Zool. 84, 1079–1086 (2006).Article 

    Google Scholar 
    37.McGuire, L. P. et al. Similar physiology in hibernating bats across broad geographic ranges. J. Comp. Physiol. B. https://doi.org/10.1007/s00360-021-01400-x (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, New York, 2009).MATH 
    Book 

    Google Scholar 
    39.Hothorn, T. & Everitt, B. S. A handbook of statistical analyses using R (CRC Press, London, 2014).MATH 
    Book 

    Google Scholar 
    40.United States Fish and Wildlife Service. National white-nose syndrome decontamination protocol-Version 09-13-2018. http://www.whitenosesyndrome.org (2018).41.Canadian Cooperative Wildlife Health Centre. Guidelines for decontamination of equipment and clothing to prevent the spread of white-nose syndrome (the causal fungus: Pseudogymnoascus destructans) in Canada, http://www2.cwhc-rcsf.ca/wns_decontamination.php (2020).42.R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).43.McGuire, L. P., Guglielmo, C. G., Mackenzie, S. A. & Taylor, P. D. Migratory stopover in the long-distance migrant silver-haired bat, Lasionycteris noctivagans. J. Anim. Ecol. 81, 377–385 (2012).PubMed 
    Article 

    Google Scholar 
    44.Nagorsen, D. W. & Brigham, R. M. Bats of British Columbia. Vol. 1 (UBC Press, 1993).45.Villa, B. R. & Cockrum, E. L. Migration in the guano bat Tadarida brasiliensis mexicana (Saussure). J. Mammal. 43, 43–64 (1962).Article 

    Google Scholar 
    46.Kunkel, E. L. Ecology and energetics of partial migration and facultative hibernation of Mexican free-tailed bats MS thesis, Texas Tech University (2020).47.Sandel, J. K. et al. Use and selection of winter hibernacula by the eastern pipistrelle (Pipistrellus subflavus) in Texas. J. Mammal. 82, 173–178 (2001).Article 

    Google Scholar 
    48.Jones, C. & Pagels, J. Notes on a population of Pipistrellus subflavus in southern Louisiana. J. Mammal. 49, 134–139 (1968).Article 

    Google Scholar 
    49.McClure, M. M. et al. A hybrid corelative-mechanistic approach for modeling and mapping winter distributions of North American bat species. J. Biogeogr. 48, 2429–2444 (2021).Article 

    Google Scholar 
    50.McClure, M. M. et al. Linking surface and subterranean climate: Implications for the study of hibernating bats and other cave dwellers. Ecosphere 11, E03274 (2020).Article 

    Google Scholar 
    51.Perry, R. W. A review of factors affecting cave climates for hibernating bats in temperate North America. Environ. Rev. 21, 28–39. https://doi.org/10.1139/er-2012-0042 (2013).Article 

    Google Scholar 
    52.Hranac, C. R. et al. What is winter? Modelling spatial variation in bat host traits and hibernation and their implications for overwintering energetics. Ecol. Evol. 11, 11604–11614 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.McGuire, L., Muise, K. A., Shrivastav, A. & Willis, C. K. R. No evidence of hyperphagia during prehibernation in a northern population of little brown bats (Myotis lucifugus). Can. J. Zool. 94, 821–827 (2016).CAS 
    Article 

    Google Scholar 
    54.Czenze, Z. J., Jonasson, K. A. & Willis, C. K. Thrifty females, frisky males: Winter energetics of hibernating bats from a cold climate. Physiol. Biochem. Zool. 90, 502–511 (2017).PubMed 
    Article 

    Google Scholar 
    55.Kurta, A. The misuse of relative humidity in ecological studies of hibernating bats. Acta Chiropt. 16, 249–254 (2014).Article 

    Google Scholar 
    56.Weller, T. J. et al. A review of bat hibernacula across the western United States: Implications for white-nose syndrome surveillance and management. PLoS One 13, e0205647 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    57.Gearhart, C., Adams, A. M., Pinshow, B. & Korine, C. Evaporative water loss in Kuhl’s pipistrelles declines along an environmental gradient, from mesic to hyperarid. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 240, 110587 (2020).CAS 
    Article 

    Google Scholar 
    58.Thomas, D. W. & Geiser, F. Periodic arousals in hibernating mammals: Is evaporative water loss involved?. Funct. Ecol. 11, 585–591 (1997).Article 

    Google Scholar 
    59.Haase, C. G. et al. Incorporating evaporative water loss into bioenergetic models of hibernation to test for relative influence of host and pathogen traits on white-nose syndrome. PLoS One 14, e0222311 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Willis, C. K. Conservation physiology and conservation pathogens: White-nose syndrome and integrative biology for host–pathogen systems. Integr. Comp. Biol. 55, 631–641 (2015).PubMed 
    Article 

    Google Scholar 
    61.Frick, W. F. et al. Disease alters macroecological patterns of North American bats. Glob. Ecol. Biogeogr. 24, 741–749 (2015).Article 

    Google Scholar 
    62.Willis, C. K., Menzies, A. K., Boyles, J. G. & Wojciechowski, M. S. Evaporative water loss is a plausible explanation for mortality of bats from white-nose syndrome. Integr. Comp. Biol. 51, 364–373. https://doi.org/10.1093/icb/icr076 (2011).Article 
    PubMed 

    Google Scholar 
    63.Wilder, A. P., Frick, W. F., Langwig, K. E. & Kunz, T. H. Risk factors associated with mortality from white-nose syndrome among hibernating bat colonies. Biol. Lett. 7, 950–953 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol. Lett. 15, 1050–1057. https://doi.org/10.1111/j.1461-0248.2012.01829.x (2012).Article 
    PubMed 

    Google Scholar 
    65.Voigt, C. C. & Kingston, T. Bats in the Anthropocene: Conservation of Bats in a Changing World (Springer, New York, 2016).Book 

    Google Scholar 
    66.Kahle, D. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J. 5, 144–161 (2013).Article 

    Google Scholar  More

  • in

    Multidimensional natal isotopic niches reflect migratory patterns in birds

    1.Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).Article 

    Google Scholar 
    2.Chase, J. M. & Leibold, M. A. Ecological Niches. Linking Classical and Contemporary Approaches (University of Chicago Press, 2003).Book 

    Google Scholar 
    3.Holt, R. D. Bringing the Hutchinsonian niche into the 21st century: Ecological and evolutionary perspectives. Proc. Natl. Acad. Sci. 106, 19659–19665 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Heldbjerg, H. & Fox, T. Long-term population declines in Danish trans-Saharan migrant birds. Bird Study 55, 267–279 (2008).Article 

    Google Scholar 
    5.Evans, K. L., Newton, J., Mallord, J. W. & Markman, S. Stable isotope analysis provides new information on winter habitat use of declining avian migrants that is relevant to their conservation. PLoS ONE 7, e34542 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Vickery, J. A. et al. The decline of Afro-Palaearctic migrants and an assessment of potential causes. Ibis 156, 1–22 (2014).Article 

    Google Scholar 
    7.Heiss, M. The importance of Besh Barmag bottleneck (Azerbaijan) for Eurasian migrant birds. Acta Ornithol. 48, 151–164 (2013).Article 

    Google Scholar 
    8.Buechley, E. R. et al. Identifying critical migratory bottlenecks and high-use areas for an endangered migratory soaring bird across three continents. J. Avian Biol. 49, e01629 (2018).Article 

    Google Scholar 
    9.Cardenas-Ortiz, L., Bayly, N. J., Kardynal, K. J. & Hobson, K. A. Defining catchment origins of a geographical bottleneck: Implications of population mixing and phenological overlap for the conservation of Neotropical migratory birds. The Condor 122, 004 (2020).Article 

    Google Scholar 
    10.Yohannes, E., Hobson, K. A. & Pearson, D. J. Feather stable-isotope profiles reveal stopover habitat selection and site fidelity in nine migratory species moving through sub-Saharan Africa: Feather stable-isotope profiles reveal stopover habitat selection. J. Avian Biol. 38, 347–355 (2007).
    Google Scholar 
    11.Hobson, K. A. & Koehler, G. On the use of stable oxygen isotope (δ18O) measurements for tracking avian movements in North America. Ecol. Evol. 5, 799–806 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A. & Macleod, H. Determining trophic niche width: A novel approach using stable isotope analysis: Stable isotopes as measures of niche width. J. Anim. Ecol. 73, 1007–1012 (2004).Article 

    Google Scholar 
    13.Newsome, S. D., Martinez del Rio, C., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436 (2007).Article 

    Google Scholar 
    14.Hobson, K. Isotopic ornithology: A perspective. J. Ornithol. https://doi.org/10.1007/s10336-011-0653-x (2011).Article 

    Google Scholar 
    15.Hoenighaus, D. J., Winemiller, K. O. & Agostinho, A. A. Landscape-scale hydrologic characteristics differentiate patterns of carbon flow in large-river food webs. Ecosystems 10, 1019–1033 (2007).Article 

    Google Scholar 
    16.Hette-Tronquart, N. Isotopic niche is not equal to trophic niche. Ecol. Lett. 22, 1987–1989 (2019).PubMed 
    Article 

    Google Scholar 
    17.Inger, R. & Bearhop, S. Applications of stable isotope analyses to avian ecology: Avian stable isotope analysis. Ibis 150, 447–461 (2008).Article 

    Google Scholar 
    18.Bowen, G. J. Isoscapes: Spatial pattern in isotopic biogeochemistry. Annu. Rev. Earth Planet. Sci. 38, 161–187 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Hobson, K. A., Bowen, G. J., Wassenaar, L. I., Ferrand, Y. & Lormee, H. Using stable hydrogen and oxygen isotope measurements of feathers to infer geographical origins of migrating European birds. Oecologia 141, 477–488 (2004).ADS 
    PubMed 
    Article 

    Google Scholar 
    20.Magozzi, S., Vander Zanden, H. B., Wunder, M. B. & Bowen, G. J. Mechanistic model predicts tissue-environment relationships and trophic shifts in animal hydrogen and oxygen isotope ratios. Oecologia 191, 777–789 (2019).ADS 
    PubMed 
    Article 

    Google Scholar 
    21.Vander Zanden, H. B., Soto, D. X., Bowen, G. J. & Hobson, K. A. Expanding the isotopic toolbox: Applications of hydrogen and oxygen stable isotope ratios to food web studies. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2016 (2016).Article 

    Google Scholar 
    22.Pekarsky, S. et al. Enriching the isotopic toolbox for migratory connectivity analysis: A new approach for migratory species breeding in remote or unexplored areas. Divers. Distrib. 21, 416–427 (2015).Article 

    Google Scholar 
    23.Shipley, O. N. & Matich, P. Studying animal niches using bulk stable isotope ratios: An updated synthesis. Oecologia 193, 27–51 (2020).ADS 
    PubMed 
    Article 

    Google Scholar 
    24.Hobson, K. A. Tracing origins and migration of wildlife using stable isotopes: A review. Oecologia 120, 314–326 (1999).ADS 
    PubMed 
    Article 

    Google Scholar 
    25.Hobson, K. A. & Wassenaar, L. I. Tracking Animal Migration with Stable Isotopes (Academic Press, 2018).
    Google Scholar 
    26.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 16 (2002).
    Google Scholar 
    27.Abrantes, K. G., Barnett, A. & Bouillon, S. Stable isotope-based community metrics as a tool to identify patterns in food web structure in east African estuaries. Funct. Ecol. 28, 270–282 (2014).Article 

    Google Scholar 
    28.Wang, J., Chapman, D., Xu, J., Wang, Y. & Gu, B. Isotope niche dimension and trophic overlap between bigheaded carps and native filter-feeding fish in the lower Missouri River, USA. PLoS ONE 13, e0197584 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    29.Steenweg, R. J. et al. Stable isotopes can be used to infer the overwintering locations of prebreeding marine birds in the Canadian Arctic. Ecol. Evol. 7, 8742–8752 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Rader, J. A. et al. Isotopic niches support the resource breadth hypothesis. J. Anim. Ecol. 86, 405–413 (2017).PubMed 
    Article 

    Google Scholar 
    31.Ma, C., Shen, Y., Bearup, D., Fagan, W. F. & Liao, J. Spatial variation in branch size promotes metapopulation persistence in dendritic river networks. Freshw. Biol. 65, 426–434 (2020).Article 

    Google Scholar 
    32.Langin, K. M. et al. Hydrogen isotopic variation in migratory bird tissues of known origin: Implications for geographic assignment. Oecologia 152, 449–457 (2007).ADS 
    PubMed 
    Article 

    Google Scholar 
    33.Levey, D. J. & Stiles, F. G. Evolutionary precursors of long-distance migration: Resource availability and movement patterns in neotropical landbirds. Am. Nat. 140, 447–476 (1992).Article 

    Google Scholar 
    34.Cresswell, W. Migratory connectivity of Palaearctic-African migratory birds and their responses to environmental change: The serial residency hypothesis. Ibis 156, 493–510 (2014).Article 

    Google Scholar 
    35.Reif, J., Hořák, D., Krištín, A., Kopsová, L. & Devictor, V. Linking habitat specialization with species’ traits in European birds. Oikos 125, 405–413 (2016).Article 

    Google Scholar 
    36.Laube, I., Graham, C. H. & Böhning-Gaese, K. Niche availability in space and time: Migration in Sylvia warblers. J. Biogeogr. 42, 1896–1906 (2015).Article 

    Google Scholar 
    37.Ponti, R., Arcones, A., Ferrer, X. & Vieites, D. R. Seasonal climatic niches diverge in migratory birds. Ibis 162, 318–330 (2020).Article 

    Google Scholar 
    38.Dunn, E., Hobson, K., Wassenaar, L., Hussell, D. & Allen, M. Identification of summer origins of songbirds migrating through southern Canada in Autumn. Avian Conserv. Ecol. https://doi.org/10.5751/ACE-00048-010204 (2006).Article 

    Google Scholar 
    39.Briedis, M. et al. Breeding latitude leads to different temporal but not spatial organization of the annual cycle in a long-distance migrant. J. Avian Biol. 47, 743–748 (2016).Article 

    Google Scholar 
    40.Briedis, M. et al. Broad-scale patterns of the Afro-Palaearctic landbird migration. Glob. Ecol. Biogeogr. 29, 722–735 (2020).Article 

    Google Scholar 
    41.Cortesi, N., Gonzalez-Hidalgo, J. C., Brunetti, M. & Martin-Vide, J. Daily precipitation concentration across Europe 1971–2010. Nat. Hazards Earth Syst. Sci. 12, 2799–2810 (2012).ADS 
    Article 

    Google Scholar 
    42.Salewski, V., Bairlein, F. & Leisler, B. Niche partitioning of two Palearctic passerine migrants with Afrotropical residents in their West African winter quarters. Behav. Ecol. 14, 493–502 (2003).Article 

    Google Scholar 
    43.Jones, P., Salewski, V., Vickery, J. & Mapaure, I. Habitat use and densities of co-existing migrant Willow Warblers Phylloscopus trochilus and resident eremomelas Eremomela spp. in Zimbabwe. Bird Study 57, 44–55 (2010).Article 

    Google Scholar 
    44.Brändle, M., Prinzing, A., Pfeifer, R. & Brandl, R. Dietary niche breadth for Central European birds: Correlations with species-specific traits. Evol. Ecol. Res. 4(5), 643–657 (2002).
    Google Scholar 
    45.Hahn, S., Amrhein, V., Zehtindijev, P. & Liechti, F. Strong migratory connectivity and seasonally shifting isotopic niches in geographically separated populations of a long-distance migrating songbird. Oecologia 173, 1217–1225 (2013).ADS 
    PubMed 
    Article 

    Google Scholar 
    46.Finch, T., Butler, S. J., Franco, A. M. A. & Cresswell, W. Low migratory connectivity is common in long-distance migrant birds. J. Anim. Ecol. 86, 662–673 (2017).PubMed 
    Article 

    Google Scholar 
    47.Somveille, M., Manica, A. & Rodrigues, A. S. L. Where the wild birds go: Explaining the differences in migratory destinations across terrestrial bird species. Ecography 42, 225–236 (2019).Article 

    Google Scholar 
    48.Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. Do long-distance migratory birds track their niche through seasons? J. Biogeogr. 45, 1459–1468 (2018).Article 

    Google Scholar 
    49.Rubenstein, D. R. Linking breeding and wintering ranges of a migratory songbird using stable isotopes. Science 295, 1062–1065 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    50.Sanderson, F. J., Donald, P. F., Pain, D. J., Burfield, I. J. & van Bommel, F. P. J. Long-term population declines in Afro-Palearctic migrant birds. Biol. Conserv. 131, 93–105 (2006).Article 

    Google Scholar 
    51.Ockendon, N., Hewson, C. M., Johnston, A. & Atkinson, P. W. Declines in British-breeding populations of Afro-Palaearctic migrant birds are linked to bioclimatic wintering zone in Africa, possibly via constraints on arrival time advancement. Bird Study 59, 111–125 (2012).Article 

    Google Scholar 
    52.Keller, G. S. & Yahner, R. H. Declines of migratory songbirds: Evidence for Wintering-ground causes. Northeast. Nat. 13, 83–92 (2006).Article 

    Google Scholar 
    53.Morrison, C. A., Robinson, R. A., Clark, J. A., Risely, K. & Gill, J. A. Recent population declines in Afro-Palaearctic migratory birds: The influence of breeding and non-breeding seasons. Divers. Distrib. 19, 1051–1058 (2013).Article 

    Google Scholar 
    54.López-Calderón, C. et al. Environmental conditions during winter predict age- and sex-specific differences in reproductive success of a trans-Saharan migratory bird. Sci. Rep. 7, 18082 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    55.Møller, A. P. & Hobson, K. A. Heterogeneity in stable isotope profiles predicts coexistence of populations of barn swallows Hirundo rustica differing in morphology and reproductive performance. Proc. R. Soc. Lond. B Biol. Sci. 271, 1355–1362 (2004).Article 

    Google Scholar 
    56.Hobson, K., Møller, A. & Wilgenburg, S. L. V. A multi-isotope (δ13C, δ15N, δ2H) approach to connecting European breeding and African wintering populations of barn swallow (Hirundo rustica). Anim. Migr. https://doi.org/10.2478/ami-2012-0002 (2012).Article 

    Google Scholar 
    57.Newton, I. The Migration Ecology of Birds (Academic Press, 2007).
    Google Scholar 
    58.Thorup, K. et al. Resource tracking within and across continents in long-distance bird migrants. Sci. Adv. 3, e1601360 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Jenni, L. & Winkler, R. Moult and Ageing of European Passerines (Academic Press, 2011).
    Google Scholar 
    60.Pedrini, P., Rossi, F. & Rizzoli, F. Le Alpi italiane quale barriera ecologica nel corso della migrazione post-riproduttiva attraverso l’Europa. Risultati generali del della prima fase del Progetto Alpi (1997–2002). Biol. Conserv. Fauna 116, 1–336 (2008).
    Google Scholar 
    61.Bontempo, L. et al. Comparison of methods for stable isotope ratio (δ13C, δ15N, δ2H, δ18O) measurements of feathers. Methods Ecol. Evol. 5, 363–371 (2014).Article 

    Google Scholar 
    62.Wassenaar, L. I. & Hobson, K. A. Comparative equilibration and online technique for determination of non-exchangeable hydrogen of keratins for use in animal migration studies. Isotopes Environ. Health Stud. 39, 211–217 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    63.Wassenaar, L. I. & Hobson, K. A. Stable-hydrogen isotope heterogeneity in keratinous materials: Mass spectrometry and migratory wildlife tissue subsampling strategies. Rapid Commun. Mass Spectrom. 20, 2505–2510 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    64.Brand, W. A., Coplen, T. B., Vogl, J., Rosner, M. & Prohaska, T. Assessment of international reference materials for isotope-ratio analysis (IUPAC technical report). Pure Appl. Chem. 86, 425–467 (2014).CAS 
    Article 

    Google Scholar 
    65.Del Hoyo, J., Elliott, A., Sargatal, J. & Christie, D. Handbook of the Birds of the World (Lynx Edicions, 2013).
    Google Scholar 
    66.Møller, A. P., Rubolini, D. & Lehikoinen, E. Populations of migratory bird species that did not show a phenological response to climate change are declining. Proc. Natl. Acad. Sci. 105, 16195–16200 (2008).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian ellipses in R: Bayesian isotopic niche metrics. J. Anim. Ecol. 80, 595–602 (2011).PubMed 
    Article 

    Google Scholar  More

  • in

    A new ant-butterfly symbiosis in the forest canopy fills an evolutionary gap

    1.Kronauer, D. J. C. & Pierce, N. E. Myrmecophiles. Curr. Biol. 21, R208-209 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Parker, J. Myrmecophily in beetles (Coleoptera): evolutionary patterns and biological mechanisms. Myrmecol. News 22, 65–108 (2016).
    Google Scholar 
    3.Hölldobler, B. & Wilson, E. O. The Ants (Harvard University Press, 1990).Book 

    Google Scholar 
    4.Hughes, D. P., Pierce, N. E. & Boomsma, J. J. Social insect symbionts: evolution in homeostatic fortresses. Trends Ecol. Evol. 23, 672–677 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Lachaud, J.-P., Lenoir, A. & Witte, V. Ants and their parasites. Psyche 2012, Article ID 342157; https://doi.org/10.1155/2012/342157 (2012).6.Wheeler, W. M. Ants, their Structure, Development and Behavior. (Columbia University Press, 1910).
    Google Scholar 
    7.Buschinger, A. Social parasitism among ants: A review (Hymenoptera: Formicidae). Myrmecol. News 12, 219–235 (2009).
    Google Scholar 
    8.Nash, D. R. & Boomsma, J. J. Communication between hosts and social parasites. In Sociobiology of Communication: An Interdisciplinary Perspective (eds d’Ettorre, P. & Hughes, D. P.) 55–79 (Oxford University Press, 2008).Chapter 

    Google Scholar 
    9.Akino, T., Knapp, J. J., Thomas, J. A. & Elmes, G. W. Chemical mimicry and host specificity in the butterfly Maculinea rebeli, a social parasite of Myrmica ant colonies. Proc. R. Soc. Lond. B 266, 1419–1426 (1999).CAS 
    Article 

    Google Scholar 
    10.Barbero, F., Thomas, J. A., Bonelli, S., Balletto, E. & Schönrogge, K. Queen ants make distinctive sounds that are mimicked by a butterfly social parasite. Science 323, 782–785 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Kaminski, L. A., Volkmann, L., Callaghan, C. J., DeVries, P. J. & Vila, R. The first known riodinid ‘cuckoo’ butterfly reveals deep-time convergence and parallelism in ant social parasites. Zool. J. Linn. Soc. 192, 1–20. https://doi.org/10.1093/zoolinnean/zlaa150 (2021).Article 

    Google Scholar 
    12.Elmes, G. W., Barr, B., Thomas, J. A. & Clark, R. T. Extreme host specificity by Microdon mutabilis (Diptera, Syrphidae), a social parasite of ants. Proc. R. Soc. Lond. B 266, 447–453 (1999).Article 

    Google Scholar 
    13.Schönrogge, K. et al. Host propagation permits extreme local adaptation in a social parasite of ants. Ecol. Lett. 9, 1032–1040 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Bonelli, S. et al. Distribution, host specificity, and the potential for cryptic speciation in hoverfly Microdon myrmicae (Diptera: Syrphidae), a social parasite of Myrmica ants. Ecol. Entomol. 36, 135–143 (2011).Article 

    Google Scholar 
    15.Di Giulio, A. et al. The pied piper: A parasitic beetle’s melodies modulate ant behaviours. PLoS ONE 10, e0130541 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Van Oystaeyen, A. et al. Chemical strategies of the beetle Metoecus paradoxus, social parasite of the wasp Vespula vulgaris. J. Chem. Ecol. 41, 1137–1147 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    17.Yamamoto, S., Maruyama, M. & Parker, J. Evidence for social parasitism of early insect societies by Cretaceaous rove beetles. Nat. Commun. 7, 13658 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Hinton, H.E. Myrmecophilous Lycaenidae and other Lepidoptera—A summary. Proc. Trans. South Lond. Entomol. Nat. Hist. Soc. 1949–1950, 111–175 (1951).19.Pierce, N. E. Predatory and parasitic Lepidoptera: Carnivores living on plants. J. Lepid. Soc. 49, 412–453 (1995).
    Google Scholar 
    20.Dejean, A. et al. Ant-lepidopteran associations along African forest edges. Sci. Nat. 104, 7 (2017).Article 
    CAS 

    Google Scholar 
    21.Fiedler, K. Systematic, evolutionary, and ecological implications of myrmecophily within the Lycaenidae (Insecta: Lepidoptera: Papilionoidea). Bonn. Zool. Monogr. 31, 1–210 (1991).
    Google Scholar 
    22.Pierce, N. E. et al. The ecology and evolution of ant association in the Lycaenidae (Lepidoptera). Annu. Rev. Entomol. 47, 733–771 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.DeVries, P. J. Mutualism between Thisbe irenea butterflies and ants, and the role of ant ecology in the evolution of larval-ant associations. Biol. J. Linn. Soc. 43, 179–195 (1991).MathSciNet 
    Article 

    Google Scholar 
    24.DeVries, P. J. Evolutionary and ecological patterns in myrmecophilous riodinid butterflies. In Ant-Plant Interactions (eds Huxley, C. R. & Cutler, D. F.) 143–156 (Oxford University Press, 1991).
    Google Scholar 
    25.DeVries, P.J. Butterflies. Encyclopedia of Biodiversity 1, 559–573 (2001).26.Pierce, N. E. & Mead, P. S. Parasitoids as selective agents in the symbiosis between lycaenid butterfly larvae and ants. Science 211, 1185–1187 (1981).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Kaminski, L. A., Freitas, A. V. L. & Oliveira, P. S. Interaction between mutualisms: Ant-tended butterflies exploit enemy-free space provided by ant-treehopper associations. Am. Nat. 176, 322–334 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Balduf, W. V. The rise of entomophagy among Lepidoptera. Am. Nat. 72, 358–379 (1938).Article 

    Google Scholar 
    29.Cottrell, C. B. Aphytophagy in butterflies: Its relationship to myrmecophily. Zool. J. Linn. Soc. 79, 1–57 (1984).Article 

    Google Scholar 
    30.Fiedler, K. Lycaenid–ant interactions of the Maculinea type: Tracing their historical roots in a comparative framework. J. Insect Conserv. 2, 3–14 (1998).Article 

    Google Scholar 
    31.Kaliszewska, Z. A. et al. When caterpillars attack: Biogeography and life history evolution of the Miletinae (Lepidoptera: Lycaenidae). Evolution 69, 571–588 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Sala, M., Casacci, L. P., Balleto, E., Bonelli, S. & Barbero, F. Variation in butterfly larval acoustics as a strategy to infiltrate and exploit host ant colony resources. PLoS ONE 9, e94341 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    33.Schönrogge, K., Barbero, F., Casacci, L. P., Settele, J. & Thomas, J. A. Acoustic communication within ant societies and its mimicry by mutualistic and socially parasitic myrmecophiles. Anim. Behav. 134, 249–256 (2017).Article 

    Google Scholar 
    34.Casacci, L. P., Bonelli, S., Balleto, E. & Barbero, F. Multimodal signaling in myrmecophilous butterflies. Front. Ecol. Evol. 7, 454 (2019).Article 

    Google Scholar 
    35.Thomas, J. A., Elmes, G. W. & Wardlaw, J. C. Polymorphic growth in larvae of the butterfly Maculinea rebeli, a social parasite of Myrmica ant colonies. Proc. R. Soc. Lond. B 265, 1895–1901 (1998).Article 

    Google Scholar 
    36.Espeland, M. et al. Ancient Neotropical origin and recent recolonisation: Phylogeny, biogeography and diversification of the Riodinidae (Lepidoptera: Papilionoidea). Mol. Phylogenet. Evol. 93, 296–306 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Seraphim, N. et al. Molecular phylogeny and higher systematics of the metalmark butterflies (Lepidoptera: Riodinidae). Syst. Entomol. 43, 407–425 (2018).Article 

    Google Scholar 
    38.Seraphim, N. Riodinidae Species Checklist: a preliminary species checklist for the Riodinidae (2019). Available at: https://www2.ib.unicamp.br/labor/site/?page_id=805.39.DeVries P.J. The butterflies of Costa Rica and their natural history. Vol II: Riodinidae. Princeton University Press (1997).40.Campbell, D. L., Brower, A. V. Z. & Pierce, N. E. Molecular evolution of the wingless gene and its implications for the phylogenetic placement of the butterfly family Riodinidae (Lepidoptera: Papilionoidea). Mol. Biol. Evol. 17, 684–696 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Espeland, M. et al. A comprehensive and dated phylogenomic analysis of butterflies. Curr. Biol. 28, 770–778 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Mota, L. L., Kaminski, L. A. & Freitas, A. V. L. The tortoise caterpillar: carnivory and armoured larval morphology of the metalmark butterfly Pachythone xanthe (Lepidoptera: Riodinidae). J. Nat. Hist. 54, 309–319 (2020).Article 

    Google Scholar 
    43.Nielsen, G. J. & Kaminski, L. A. Immature stages of the Rubiaceae-feeding metalmark butterflies (Lepidoptera: Riodinidae), and a new function for the tentacle nectary organs. Zootaxa 4524, 1–32 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Kaminski, L. A., Carneiro, E., Dolibaina, D. R., Casagrande, M. M. & Mielke, O. H. H. Oviposition of Minstrellus grandis (Lepidoptera: Riodinidae) in a harmful ant-plant symbiosis. Acta Amazon. 50, 256–259 (2020).Article 

    Google Scholar 
    45.Kaminski, L. A. & Lima, L. D. Larval omnivory in the myrmecophilous butterfly Setabis lagus lagus (Riodinidae: Nymphidiini). J. Lepid. Soc. 73, 276–279 (2019).
    Google Scholar 
    46.Lamborn, W. A. On the relationship between certain West African insects, especially ants, Lycaenidae, and Homoptera. Trans. Ent. Soc. Lond. 1913, 436–498 (1914).
    Google Scholar 
    47.Eastwood, R. & Fraser, A. M. Associations between lycaenid butterflies and ants in Australia. Austral. Ecol. 24, 503–537 (1999).Article 

    Google Scholar 
    48.Bruch, C. Orugas mirmecofilas de Hameris epulus signatus – Stich. Rev. Soc. Entomol. Argent. 1, 2–9 (1926).
    Google Scholar 
    49.Bourquin, F. Notas sobre la metamorfosis de Hamearis susanae Orfila, 1953, con oruga mirmicófila (Lep. Riodin.). Rev. Soc. Entomol. Argent. 16, 83–87 (1953).50.Ross, G. N. Life-history studies on Mexican butterflies. IV. The ecology and ethology of Anatole rossi, a myrmecophilous metalmark (Lepidoptera: Riodinidae). Ann. Entomol. Soc. Am. 59, 985–1004 (1966).51.Kaminski, L. A. & Carvalho-Filho, F. S. Life history of Aricoris propitia (Lepidoptera: Riodinidae)—A myrmecophilous butterfly obligately associated with fire ants. Psyche 2012, Article ID 126876; https://doi.org/10.1155/2012/126876 (2012).52.Fiedler, K. The host genera of ant-parasitic Lycaenidae butterflies: a review. Psyche 2012, Article ID 153975; https://doi.org/10.1155/2012/153975 (2012).53.Rocha, F. H., Lachaud, J.-P. & Pérez-Lachaud, G. Myrmecophilous organisms associated with colonies of the ponerine ant Neoponera villosa (Hymenoptera: Formicidae) nesting in Aechmea bracteata bromeliads: a biodiversity hotspot. Myrmecol. News 30, 73–92 (2020).
    Google Scholar 
    54.Rocha, F. H., Lachaud, J.-P., Hénaut, Y., Pozo, C. & Pérez-Lachaud, G. Nest site selection during colony relocation in Yucatan Peninsula populations of the ponerine ant Neoponera villosa (Hymenoptera: Formicidae). Insects 11, 200; https://doi.org/10.3390/insects11030200 (2020).55.Mackay, W. P. & Mackay, E. E. The systematics and biology of the New World ants of the genus Pachycondyla (Hymenoptera: Formicidae) (The Edwin Mellen Press, 2010).
    Google Scholar 
    56.Wheeler, W. M. The ants of Texas, New Mexico and Arizona. Part I. Bull. Am. Mus. Nat. Hist. 24, 399–485 (1908).57.Lachaud, J.-P., Fresneau, D. & García-Pérez, J. Étude des stratégies d’approvisionnement chez 3 espèces de fourmis ponérines (Hymenoptera: Formicidae). Folia Entomol. Mex. 61, 159–177 (1984).
    Google Scholar 
    58.Hölldobler, B. Liquid food transmission and antennation signals in ponerine ants. Isr. J. Entomol. 19, 89–99 (1985).
    Google Scholar 
    59.Dejean, A. & Corbara, B. Predatory behavior of a Neotropical arboricolous ant: Pachycondyla villosa (Formicidae: Ponerinae). Sociobiology 17, 271–286 (1990).
    Google Scholar 
    60.Pérez-Bautista, M., Lachaud, J.-P. & Fresneau, D. L. división del trabajo en la hormiga primitiva Neoponera villosa (Hymenoptera : Formicidae). Folia Entomol. Mex. 65, 119–130 (1985).
    Google Scholar 
    61.Dejean, A., Olmsted, I. & Snelling, R. R. Tree-epiphyte-ant relationships in the low inundated forest of Sian Ka´an biosphere reserve, Quintana Roo, Mexico. Biotropica 27, 57–70 (1995).Article 

    Google Scholar 
    62.Fernandes, I. O., De Oliveira, M. L. & Delabie, J. H. C. Notes on the biology of Brazilian ant populations of the Pachycondyla foetida species complex (Formicidae: Ponerinae). Sociobiology 60, 380–386 (2013).Article 

    Google Scholar 
    63.Castaño-Meneses, G. et al. The ant community and their accompanying arthropods in cacao dry pods: An unexplored diverse habitat. Dugesiana 22, 29–35 (2015).
    Google Scholar 
    64.Dejean, A. Influence de l’environnement pré-imaginal et précoce dans le choix du site de nidification de Pachycondyla (= Neoponera) villosa (Fabr.) (Formicidae, Ponerinae). Behav. Process. 21, 107–125 (1990).65.Dejean, A. & Olmsted, I. Ecological studies on Aechmea bracteata (Swartz) (Bromeliaceae). J. Nat. Hist. 31, 1313–1334 (1997).Article 

    Google Scholar 
    66.Hénaut, Y. et al. A tank bromeliad favors spider presence in a Neotropical inundated forest. PLoS ONE 9, e114592 (2014).67.Pérez-Lachaud, G., Jervis, M. A., Reemer, M. & Lachaud, J.-P. An unusual, but not unexpected, evolutionary step taken by syrphid flies: the first record of true primary parasitoidism of ants by Microdontinae. Biol. J. Linn. Soc. 111, 462–472 (2014).Article 

    Google Scholar 
    68.Pérez-Lachaud, G. & Lachaud, J.-P. Hidden biodiversity in entomological collections: The overlooked co-occurrence of dipteran and hymenopteran ant parasitoids in stored biological material. PLoS ONE 12, e0184614 (2017).69.Benzing, D. H., Derr, J. A. & Titus, J. E. The water chemistry of microcosms associated with the bromeliad Aechmea bracteata. Am. Midl. Nat. 87, 60–70 (1972).CAS 
    Article 

    Google Scholar 
    70.Beutelspacher Baigts, C. R. Bromeliáceas Como Ecosistemas, con Especial Referencia a Aechmea bracteata (Swartz) Griseb. Plaza y Valdés, México (1999).71.Dézerald, O. et al. Environmental drivers of invertebrate population dynamics in Neotropical tank bromeliads. Freshw. Biol. 62, 229–242 (2017).Article 

    Google Scholar 
    72.Ivanova, N. V., DeWaard, J. R. & Hebert, P. D. N. An inexpensive, automation-friendly protocol for recovering high-quality DNA. Mol. Ecol. Notes 6, 998–1002 (2006).CAS 
    Article 

    Google Scholar 
    73.Hebert, P. D. N., Penton, E. H., Burns, J. M., Janzen, D. H. & Hallwachs, W. T. species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proc. Nat. Acad. Sci. USA 101, 14812–14817 (2004).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Montes-Ortiz, L. & Elías-Gutiérrez, M. Faunistic survey of the zooplankton community in an oligotrophic sinkhole, Cenote Azul (Quintana Roo, Mexico), using different sampling methods, and documented with DNA barcodes. J. Limnol. 77, 428–440 (2018).
    Google Scholar 
    75.Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    77.Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78.Guindon S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).79.Stehr, F. W. Order Lepidoptera. In: Stehr, F. W. (ed.) Immature insects. Vol. 1. Kendall-Hunt Publishing Company (1987).80.DeVries, P. J. The larval ant-organs of Thisbe irenea (Lepidoptera: Riodinidae) and their effects upon attending ants. Zool. J. Linn. Soc. 94, 379–393 (1988).Article 

    Google Scholar 
    81.Godman, F. D. & Salvin, O. Biologia Centrali-Americana. Insecta. Lepidoptera-Rhopalocera 1: 462, pl. 47, fig. 7–8. Published for the editors by R.H. Porter, London (1886).82.D’Abrera, B. Butterflies of the Neotropical Region. Part VI (Riodinidae). Hill House (1994).83.Lamas, G. Hesperioidea – Papilionoidea. In: Heppner, J. B. (ed.) Atlas of Neotropical Lepidoptera. Checklist: Part 4A. Association for Tropical Lepidoptera (2004).84.Hall, J. P. W. & Harvey, D. J. A phylogenetic analysis of the Neotropical riodinid butterfly genera Juditha, Lemonias, Thisbe and Uraneis, with a revision of Juditha (Lepidoptera: Riodinidae: Nymphidiini). Syst. Entomol. 26, 453–490 (2001).Article 

    Google Scholar 
    85.Zhang, J., Cong, Q., Shen, J., Opler, P. A. & Grishin, N. V. Genomic evidence suggests further changes of butterfly names. Taxon. Rep. Intern. Lepid. Surv. 8(7), 1–40 (2020).
    Google Scholar 
    86.Zhang, J., Cong, Q., Shen, J., Opler, P. A. & Grishin, N. V. Genomics-guided refinement of butterfly taxonomy. Taxon. Rep. Intern. Lepid. Surv. 9(3), 1–54 (2021).
    Google Scholar 
    87.Arellano-Covarrubias, A., Llorente-Bousquets, J. & Luis-Martínez, A. Distribución y fenología de la familia Riodinidae (Lepidoptera: Papilionoidea) en el bosque tropical subcaducifolio de Oaxaca, México. Rev. Biol. Trop. 66, 503–558 (2018).Article 

    Google Scholar 
    88.Pozo, C. et al. Seasonality and phenology of the butterflies (Lepidoptera: Papilionoidea and Hesperioidea) of Mexico’s Calakmul Region. Fla. Entomol. 91, 407–422 (2008).Article 

    Google Scholar 
    89.Erwin, T. L. Tropical forest canopies: the last biotic frontier. Bull. Entomol. Soc. Am. 29, 14–19 (1983).
    Google Scholar 
    90.Rico-Gray, V. & Oliveira, P. S. The Ecology and Evolution of Ant–Plant interactions (The University of Chicago Press, 2007).Book 

    Google Scholar 
    91.DeVries, P. J., Cabral, B. C. & Penz, C. M. The early stages of Apodemia paucipuncta (Riodinidae): myrmecophily, a new caterpillar ant-organ and consequences for classification. Milw. Public Mus. Contrib. Biol. Geol. 102, 1–13 (2004).
    Google Scholar 
    92.Kaminski, L. A., Mota, L. L., Freitas, A. V. L. & Moreira, G. R. P. Two ways to be a myrmecophilous butterfly: natural history and comparative immature-stage morphology of two species of Theope (Lepidoptera: Riodinidae). Biol. J. Linn. Soc. 108, 844–870 (2013).Article 

    Google Scholar 
    93.Kaminski, L. A., Mota, L. L. & Freitas, A. V. L. Larval cryptic coloration and mistletoe use in the metalmark butterfly Dachetola azora (Lepidoptera: Riodinidae). Entomol. Am. 120, 18–23 (2014).
    Google Scholar 
    94.Torres, P. J. & Pomerantz, A. F. Butterfly kleptoparasitism and first account of immature stages, myrmecophily, and bamboo host plant of the metalmark Adelotypa annulifera (Riodinidae). J. Lepid. Soc. 70, 130–138 (2016).
    Google Scholar 
    95.Gallard, J.-Y. Les Riodinidae de Guyane. Pensoft, Sofia (2017).96.Hall, J. P. W. A monograph of the Nymphidiina (Lepidoptera: Riodinidae: Nymphidiini): Phylogeny, taxonomy, biology, and biogeography (The Entomological Society of Washington, 2018).
    Google Scholar 
    97.Moraga Medina, R. 2014. Pachythone gigas (Riodinidae). Área de Conservación Guanacaste (2014). https://www.acguanacaste.ac.cr/paginas-de-especies/insectos/111-160riodinidae/581-i-pachythone-gigas-i-riodinidae98.Dupont, S. T., Zemeitat, D. S., Lohman, D. J. & Pierce, N. E. The setae of parasitic Liphyra brassolis butterfly larvae form a flexible armour for resisting attack by their ant hosts (Lycaenidae: Lepidoptera). Biol. J. Linn. Soc. 117, 607–619 (2016).Article 

    Google Scholar 
    99.DeVries, P. J., Chacon, I. A. & Murray, D. Toward a better understanding of host use and biodiversity in riodinid butterflies (Lepidoptera). J. Res. Lepid. 31, 103–126 (1992).
    Google Scholar 
    100.Davidson, D. W., Cook, S. C., Snelling, R. R. & Chua, T. H. Explaining the abundance of ants in lowland tropical rainforest canopies. Science 300, 969–972 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    101.Schmidt, C. A. & Shattuck, S. O. The higher classification of the ant subfamily Ponerinae (Hymenoptera: Formicidae), with a review of ponerine ecology and behavior. Zootaxa 3817, 1–242 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    102.Atsatt, P. R. Lycaenid butterflies and ants: Selection for enemy-free space. Am. Nat. 118, 638–654 (1981).Article 

    Google Scholar 
    103.Dáttilo, W. et al. Trait-mediated indirect interactions of ant shape on the attack of caterpillars and fruits. Biol. Lett. 12, 20160401 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    104.Orivel, J. & Dejean, A. Myrmecophily in Hesperiidae. The case of Vettius tertianus in ant gardens. C. R. Acad. Sci. Paris 323, 705–715 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    105.Meurville, M.-P. & LeBoeuf, A. C. Trophallaxis: The functions and evolution of social fluid exchange in ant colonies (Hymenoptera: Formicidae). Myrmecol. News 31, 1–30 (2021).
    Google Scholar 
    106.Hall, J. P. W. & Harvey, D. J. Basal subtribes of the Nymphidiini (Lepidoptera: Riodinidae): Phylogeny and myrmecophily. Cladistics 18, 539–569 (2002).Article 

    Google Scholar 
    107.Hall, J. P. W. Phylogenetic revision of the new Neotropical riodinid genus Minstrellus (Lepidoptera: Riodinidae). Ann. Entomol. Soc. Am. 100, 773–786 (2007).Article 

    Google Scholar 
    108.Ballmer, G. R. & Wright, D. M. Notes on the immature stages of Setabis sp., a myrmecophagous riodinid butterfly (Lepidoptera: Riodinidae). J. Res. Lepid. 47, 11–15 (2014).
    Google Scholar 
    109.Callaghan, C. J. Studies on Restinga butterflies: I. Life cycle and immature biology of Menander felsina (Riodinidae), a myrmecophilous metalmark. J. Lepid. Soc. 31, 173–182 (1977).
    Google Scholar 
    110.Hojo, M. K, Yamaguchi, S., Akino, T. & Yamaoka, R. Adoption of lycaenid Niphanda fusca (Lepidoptera: Lycaenidae) caterpillars by the host ant Camponotus japonicus (Hymenoptera: Formicidae). Entomol. Sci. 17, 59–65 (2014).111.Maschwitz, U., Nässig, W. A., Dumpert, K. & Fiedler, K. Larval carnivory and myrmecoxeny, and imaginal myrmecophily in miletine lycaenids (Lepidoptera, Lycaenidae) on the Malay Peninsula. Tyô to Ga 39, 167–181 (1988).
    Google Scholar  More

  • in

    The early maternal environment shapes the parental response to offspring UV ornamentation

    1.Trivers, R. Parental investment and sexual selection. In Sexual Selection and the Descent of Man (ed. Campbell, B.) 136–179 (Aldine, 1972).
    Google Scholar 
    2.Stearns, S. C. The Evolution of Life Histories (Oxford University Press, 1992).
    Google Scholar 
    3.Mock, D. W. & Parker, G. A. The Evolution of Sibling Rivalry (Oxford University Press, 1997).
    Google Scholar 
    4.Caro, S. M., Griffin, A. S., Hinde, C. A. & West, S. A. Unpredictable environments lead to the evolution of parental neglect in birds. Nat. Commun. https://doi.org/10.1038/ncomms10985 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Davis, J. N., Todd, P. M. & Bullock, S. Environment quality predicts parental provisioning decisions. Proc. R. Soc. Ser. B-Biol. 266(1430), 1791–1797 (1999).Article 

    Google Scholar 
    6.Haig, D. Brood reduction and optimal parental investment when offspring differ in quality. Am. Nat. 136, 550–556 (1990).Article 

    Google Scholar 
    7.O’Connor, R. J. Brood reduction in birds: Selection for fratricide, infanticide and suicide?. Anim. Behav. 26(Part 1), 79–96 (1978).Article 

    Google Scholar 
    8.Stenning, M. J. Hatching asynchrony, brood reduction and other rapidly reproducing hypotheses. Trends Ecol. Evol. https://doi.org/10.1016/0169-5347(96)10030-6 (1996).Article 
    PubMed 

    Google Scholar 
    9.Leonard, M. L., Horn, A. G. & Parks, E. The role of posturing and calling in the begging display of nestling birds. Behav. Ecol. Sociobiol. 54(2), 188–193 (2003).Article 

    Google Scholar 
    10.Kilner, R. M. The evolution of complex begging displays. In Wright J., Leonard M. L. (eds) The Evolution of Begging 87–106 (Springer, 2005).11.Thorogood, R., Ewen, J. G. & Kilner, R. M. Sense and sensitivity: Responsiveness to offspring signals varies with the parents’ potential to breed again. Proc. R. Soc. Ser. B-Biol. 278(1718), 2638–2645 (2011).Article 

    Google Scholar 
    12.Pirrello, S. et al. Skin and flange colour, but not ectoparasites, predict condition and survival in starling nestlings. Behav. Ecol. Sociobiol. https://doi.org/10.1007/s00265-017-2292-6 (2017).Article 

    Google Scholar 
    13.Maynard-Smith, J. & Harper, D. Animal signals. Oxford Series in Ecology and Evolution (Oxford University Press, 2003).
    Google Scholar 
    14.Laidre, M. E. & Johnstone, R. A. Animal signals. Curr. Biol. https://doi.org/10.1016/j.cub.2013.07.070 (2013).Article 
    PubMed 

    Google Scholar 
    15.Hamilton, W. D. & Zuk, M. Heritable true fitness and bright birds: A role for parasites?. Science 218(4570), 384–387 (1982).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Zahavi, A. The Handicap Principle: A Missing Piece of Darwin’s (Oxford University Press, 1997).
    Google Scholar 
    17.Morales, J. & Velando, A. Signals in family conflicts. Anim. Behav. 86(1), 11–16 (2013).Article 

    Google Scholar 
    18.Hinde, C. A., Johnstone, R. A. & Kilner, R. M. Parent-offspring conflict and coadaptation. Science 327(5971), 1373–1376 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    19.Grodzinski, U. & Johnstone, R. A. Parents and offspring in an evolutionary game: The effect of supply on demand when costs of care vary. Proc. R. Soc. Ser. B-Biol. 279(1726), 109–115 (2011).Article 

    Google Scholar 
    20.Kilner, R. & Johnstone, R. A. Begging the question: Are offspring solicitation behaviours signals of need?. Trends Ecol. Evol. https://doi.org/10.1016/S0169-5347(96)10061-6 (1997).Article 
    PubMed 

    Google Scholar 
    21.Roulin, A., Kölliker, M. & Richner, H. Barn owl (Tyto alba) siblings vocally negotiate resources. Proc. R. Soc. Ser. B-Biol. 267, 459–463 (2000).CAS 
    Article 

    Google Scholar 
    22.Godfray, H. C. Evolutionary theory of parent-offspring conflict. Nature 376, 133–138 (1995).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    23.De Ayala, R. M., Saino, N., Møller, A. P. & Anselmi, C. Mouth coloration of nestlings covaries with offspring quality and influences parental feeding behavior. Behav. Ecol. 18(3), 526–534 (2007).Article 

    Google Scholar 
    24.Godfray, H. C. J. Signalling of need by offspring to their parents. Nature 352, 328–330 (1991).ADS 
    Article 

    Google Scholar 
    25.Bize, P., Piault, R., Moureau, B. & Heeb, P. A UV signal of offspring condition mediates context-dependent parental favouritism. Proc. R. Soc. Ser. B-Biol. 273(1597), 2063–2068 (2006).Article 

    Google Scholar 
    26.Jourdie, V., Moureau, B., Bennett, A. T. D. & Heeb, P. Ultraviolet reflectance by the skin of nestlings. Nature 431(7006), 262 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Johnsen, A., Delhey, K., Andersson, S. & Kempenaers, B. Plumage colour in nestling blue tits: Sexual dichromatism, condition dependence and genetic effects. Proc. R. Soc. Ser. B-Biol. 270(1521), 1263–1270 (2003).Article 

    Google Scholar 
    28.Royle, N. J., Russell, A. F. & Wilson, A. J. The evolution of flexible parenting. Science 345, 776–781 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Morales, J. & Velando, A. Coloration of chicks modulates costly interactions among family members. Behav. Ecol. 29(4), 894–903 (2018).Article 

    Google Scholar 
    30.García-Campa, J., Müller, W., González-Braojos, S., García-Juárez, E. & Morales, J. J. Dietary carotenoid supplementation facilitates egg laying in a wild passerine. Ecol. Evol. 10(11), 4968–4978 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Roulin, A. The sibling negotiation hypothesis. In The Evolution of Begging: Competition, Cooperation and Communication (eds Wright, J. & Leonard, M. L.) 107–127 (Kluwer Academic Press, 2002).Chapter 

    Google Scholar 
    32.Limbourg, T., Mateman, A. C. & Lessells, C. M. Parental care and UV coloration in blue tits: Opposite correlations in males and females between provisioning rate and mate’s coloration. J. Avian Biol. 44(1), 017–026 (2013).Article 

    Google Scholar 
    33.Limbourg, T., Mateman, A. C. & Lessells, C. M. Opposite differential allocation by males and females of the same species. Bio. Lett. https://doi.org/10.1098/rsbl.2012.0835 (2013).Article 

    Google Scholar 
    34.García-Campa, J., Müller, W. S. & Morales, J. J. Experimental evidence that UV/yellow colouration functions as a signal of parental quality in the blue tit. Preprint at https://doi.org/10.1101/2020.09.14.293613 (2020)35.Jacot, A. & Kempenaers, B. Effects of nestling condition on UV plumage traits in blue tits: An experimental approach. Behav. Ecol. 18(1), 34–40 (2007).Article 

    Google Scholar 
    36.McGraw, K. J. (2006). Mechanisms of Carotenoid-based coloration. In G. E. Hill and K. J. McGraw (ed.) Bird Coloration Function and Evolution, Vol. II, 177–242 (Harvard University Press, 2006).37.Surai, P. F., Speake, B. K. & Sparks, N. H. C. Absorption, availability and levels in plasma and egg yolk in carotenoids in avian nutrition and embryonic development. J. Poult. Sci. 38, 1–27 (2001).CAS 
    Article 

    Google Scholar 
    38.Tschirren, B., Fitze, P. S. & Richner, H. Carotenoid-based nestling colouration and parental favouritism in the great tit. Oecologia 143, 477–482 (2005).ADS 
    PubMed 
    Article 

    Google Scholar 
    39.Biard, C., Surai, P. F. & Møller, A. P. An analysis of pre- and post-hatching maternal effects mediated by carotenoids in the blue tit. J. Evol. Biol. 20, 326–339 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    40.Galván, I., Amo, L. & Sanz, J. J. Ultraviolet-blue reflectance of some nestling plumage patches mediates parental favouritism in great tits Parus major. J. Avian Biol. 39(3), 277–282 (2008).Article 

    Google Scholar 
    41.Wiebe, K. L. & Slagsvold, T. Brood parasites may use gape size constraints to exploit provisioning rules of smaller hosts: An experimental test of mechanisms of food allocation. Behav. Ecol. 23, 391–396 (2012).Article 

    Google Scholar 
    42.Stalwick, J. A. & Wiebe, K. L. Prey size and nestling gape size affect allocation within broods of the Mountain Bluebird. J. Ornithol. 160(1), 145–154 (2019).Article 

    Google Scholar 
    43.Kölliker, M., Richner, H., Werner, I. & Heeb, P. Begging signals and biparental care: Nestling choice between parental feeding locations. Anim. Behav. 55(1), 215–222 (1998).Article 

    Google Scholar 
    44.Cantarero, A., López-Arrabé, J., Palma, A., Redondo, A. J. & Moreno, J. Males respond to female begging signals of need: A handicapping experiment in the pied flycatcher, Ficedula hypoleuca. Anim. Behav. 94, 167–173 (2014).Article 

    Google Scholar 
    45.Griffioen, M., Iserbyt, A. & Müller, W. Handicapping males does not affect their rate of parental provisioning, but impinges on their partners’ turn taking behavior. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00347 (2019).Article 

    Google Scholar 
    46.Santucci, D., Masterson, D. & Elwood, R. W. Effects of age, sex, and odours from conspecific adult males on ultrasonic vocalizations of infant CS1 mice. Behav. Process. 32, 285–295 (1994).CAS 
    Article 

    Google Scholar 
    47.Moreno, J., Carrascal, L. M. & Sanz, J. J. Parent-offspring interactions and feeding chases in the chinstrap penguin Pygoscelis antarctica. Bird Behav. 11(1), 31–34 (2011).Article 

    Google Scholar 
    48.Smiseth, P. T., Andrews, C., Brown, E. & Prentice, P. M. Chemical stimuli from parents trigger larval begging in burying beetles. Behav. Ecol. 21, 526–531 (2010).Article 

    Google Scholar 
    49.Velando, A., Kim, S. Y. & Noguera, J. C. Begging response of gull chicks to the red spot on the parental bill. Anim. Behav. 85(6), 1359–1366 (2013).Article 

    Google Scholar 
    50.Tinbergen, N. & Perdeck, A. C. On the stimulus situation releasing the begging response in the newly hatched herring gull chick (Larus argentatus argentatus Pont.). Behaviour 3, 1e39 (1950).
    Google Scholar 
    51.Bustamante, J., Cuervo, J. J. & Moreno, J. The function of feeding chases in the chinstrap penguin, Pygoscelis antarctica. Anim. Behav. 44(4), 753–759 (1992).Article 

    Google Scholar 
    52.Amininasab, S. M., Birker, M., Kingma, S. A., Hildenbrandt, H. & Komdeur, J. The effect of male incubation feeding on female nest attendance and reproductive performance in a socially monogamous bird. J. Ornithol. 158(3), 687–696 (2017).Article 

    Google Scholar 
    53.Bambini, G., Schlicht, E. & Kempenaers, B. Patterns of female nest attendance and male feeding throughout the incubation period in Blue Tits Cyanistes caeruleus. Ibis 161(1), 50–65 (2019).Article 

    Google Scholar 
    54.Iserbyt, A., Griffioen, M., Eens, M. & Müller, W. Enduring rules of care within pairs—How blue tit parents resume provisioning behaviour after experimental disturbance. Sci. Rep. 9, 1–9 (2019).CAS 
    Article 

    Google Scholar  More

  • in

    A global model to forecast coastal hardening and mitigate associated socioecological risks

    1.Dugan, J., Airoldi, L., Chapman, G. & Walker, S. in Treatise on Estuarine and Coastal Science Vol. 8 (eds Wolanski, E. & McLusky, D.) 17–41 (2011).2.Bugnot, A. B. et al. Current and projected global extent of marine built structures. Nat. Sustain. 4, 33–41 (2020).Article 

    Google Scholar 
    3.Connell, S. D. Floating pontoons create novel habitats for subtidal epibiota. J. Exp. Mar. Biol. Ecol. 247, 183–194 (2000).CAS 
    Article 

    Google Scholar 
    4.Glasby, T., Connell, S., Holloway, M. & Hewitt, C. Nonindigenous biota on artificial structures: could habitat creation facilitate biological invasions? Mar. Biol. 151, 887–895 (2007).Article 

    Google Scholar 
    5.Heery, E. C. et al. Identifying the consequences of ocean sprawl for sedimentary habitats. J. Exp. Mar. Biol. Ecol. 492, 31–48 (2017).Article 

    Google Scholar 
    6.Scherner, F. et al. Coastal urbanization leads to remarkable seaweed species loss and community shifts along the SW Atlantic. Mar. Pollut. Bull. 76, 106–115 (2013).CAS 
    Article 

    Google Scholar 
    7.Malerba, M. E., White, C. R. & Marshall, D. J. The outsized trophic footprint of marine urbanization. Front. Ecol. Environ. 17, 400–406 (2019).Article 

    Google Scholar 
    8.Dafforn, K. A., Glasby, T. M. & Johnston, E. L. Comparing the invasibility of experimental “reefs” with field observations of natural reefs and artificial structures. PLoS ONE 7, e38124 (2012).CAS 
    Article 

    Google Scholar 
    9.Airoldi, L., Turon, X., Perkol-Finkel, S. & Rius, M. Corridors for aliens but not for natives: effects of marine urban sprawl at a regional scale. Divers. Distrib. 21, 755–768 (2015).Article 

    Google Scholar 
    10.Hayes, K. R., Inglis, G. J. & Barry, S. C. The assessment and management of marine pest risks posed by shipping: the Australian and New Zealand experience. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00489 (2019).11.Floerl, O., Inglis, G., Dey, K. L. & Smith, A. The importance of transport hubs in stepping-stone invasions. J. Appl. Ecol. 46, 37–45 (2009).Article 

    Google Scholar 
    12.Kaluza, P., Kolzsch, A., Gastner, M. T. & Blasius, B. The complex network of global cargo ship movements. J. R. Soc. Interface 7, 1093–1103 (2010).Article 

    Google Scholar 
    13.Aguirre, D. et al. Loved to pieces: toward the sustainable management of the Waitematā Harbour and Hauraki Gulf. Reg. Stud. Mar. Sci. 8, 220–233 (2016).Article 

    Google Scholar 
    14.Molnar, J. L., Gamboa, R. L., Revenga, C. & Spalding, M. D. Assessing the global threat of invasive species to marine biodiversity. Front. Ecol. Environ. 6, 485–492 (2008).Article 

    Google Scholar 
    15.Seto, K. C., Güneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. USA 109, 16083–16088 (2012).CAS 
    Article 

    Google Scholar 
    16.Neumann, B., Vafeidis, A. T., Zimmermann, J. & Nicholls, R. J. Future coastal population growth and exposure to sea-level rise and coastal flooding—a global assessment. PLoS ONE 10, e0118571 (2015).Article 
    CAS 

    Google Scholar 
    17.Kulp, S. A. & Strauss, B. H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat. Commun. 10, 4844 (2019).CAS 
    Article 

    Google Scholar 
    18.Lombard, A. T. et al. Practical approaches and advances in spatial tools to achieve multi-objective marine spatial planning. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00166 (2019).19.Pelling, M. & Blackburn, S. Megacities and the Coast: Risk, Resilience and Transformation (Routledge, 2013).20.Sutton-Grier, A. E., Wowk, K. & Bamford, H. Future of our coasts: the potential for natural and hybrid infrastructure to enhance the resilience of our coastal communities, economies and ecosystems. Environ. Sci. Policy 51, 137–148 (2015).Article 

    Google Scholar 
    21.Keller, R., Drake, J., Drew, M. & Lodge, D. Linking environmental conditions and ship movements to estimate invasive species transport across the global shipping network. Divers. Distrib. 17, 93–102 (2011).Article 

    Google Scholar 
    22.How Can We Meet Increasing Demand for Ports in the Upper North Island? A Report for the Upper North Island Strategic Alliance (PricewaterhouseCoopers, 2012).23.Ernst & Young Port Future Study. A Report Prepared for Auckland Council (Auckland Council, 2016).24.NZIER Bigger Ships—Past, Present and Future Implications for New Zealand Supply Chains (New Zealand Economic Research Institute, 2017).25.Hino, M., Belanger, S. T., Field, C. B., Davies, A. R. & Mach, K. J. High-tide flooding disrupts local economic activity. Sci. Adv. 5, eaau2736 (2019).Article 

    Google Scholar 
    26.United Nations Review of Maritime Transport 109 (United Nations Conference on Trade and Development, 2019).27.Ferrario, F., Iveša, L., Jaklin, A., Perkol-Finkel, S. & Airoldi, L. The overlooked role of biotic factors in controlling the ecological performance of artificial marine habitats. J. Appl. Ecol. 53, 16–24 (2016).Article 

    Google Scholar 
    28.Firth, L. et al. Ocean sprawl: challenges and opportunities for biodiversity management in a changing world. Oceanogr. Mar. Biol. 54, 189–262 (2016).
    Google Scholar 
    29.Mayer-Pinto, M. et al. Functional and structural responses to marine urbanisation. Environ. Res. Lett. 13, 014009 (2018).Article 

    Google Scholar 
    30.Bannister, J., Sievers, M., Bush, F. & Bloecher, N. Biofouling in marine aquaculture: a review of recent research and developments. Biofouling 35, 631–648 (2019).CAS 
    Article 

    Google Scholar 
    31.Colautti, R. I., Bailey, S. A., van Overdijk, C. D. A., Amundsen, K. & MacIsaac, H. J. Characterised and projected costs of nonindigenous species in Canada. Biol. Invasions 8, 45–59 (2006).Article 

    Google Scholar 
    32.Mazur, K., Bath, A., Curtotti, R. & Summerson, R. An Assessment of the Non-market Value of Reducing the Risk of Marine Pest Incursions in Australia’s Waters (Australian Bureau of Agricultural and Resource Economics and Sciences, 2018).33.Hatami, R. et al. Improving New Zealand’s Marine Biosecurity Surveillance Programme Biosecurity New Zealand Technical Paper No. 2021/01 (Ministry for Primary Industries, 2021).34.Sardain, A., Sardain, E. & Leung, B. Global forecasts of shipping traffic and biological invasions to 2050. Nat. Sustain. 2, 274–282 (2019).Article 

    Google Scholar 
    35.Monios, J., Bergqvist, R. & Woxenius, J. Port-centric cities: the role of freight distribution in defining the port-city relationship. J. Transp. Geogr. 66, 53–64 (2018).Article 

    Google Scholar 
    36.The Ocean Economy in 2030 (Organisation for Economic Co-operation and Development, 2016).37.Halpern, B. S. et al. Recent pace of change in human impact on the world’s ocean. Sci. Rep. 9, 11609 (2019).Article 
    CAS 

    Google Scholar 
    38.Dafforn, K. A. et al. Marine urbanization: an ecological framework for designing multifunctional artificial structures. Front. Ecol. Environ. 13, 82–90 (2015).Article 

    Google Scholar 
    39.Diggon, S. et al. The marine plan partnership: Indigenous community-based marine spatial planning. Mar. Policy https://doi.org/10.1016/j.marpol.2019.04.014 (2019).40.Noble, M. M., Harasti, D., Pittock, J. & Doran, B. Understanding the spatial diversity of social uses, dynamics, and conflicts in marine spatial planning. J. Environ. Manag. 246, 929–940 (2019).Article 

    Google Scholar 
    41.Abhinav, K. A. et al. Offshore multi-purpose platforms for a blue growth: a technological, environmental and socio-economic review. Sci. Total Environ. 734, 138256 (2020).CAS 
    Article 

    Google Scholar 
    42.Jacob, C., Buffard, A., Pioch, S. & Thorin, S. Marine ecosystem restoration and biodiversity offset. Ecol. Eng. 120, 585–594 (2018).Article 

    Google Scholar 
    43.Hopkins, G. A. et al. Continuous bubble streams for controlling marine biofouling on static artificial structures. PeerJ 9, e11323 (2021).Article 

    Google Scholar 
    44.Vucko, M. J. et al. Cold spray metal embedment: an innovative antifouling technology. Biofouling 28, 239–248 (2012).CAS 
    Article 

    Google Scholar 
    45.Atalah, J., Newcombe, E. M., Hopkins, G. A. & Forrest, B. M. Potential biocontrol agents for biofouling on artificial structures. Biofouling 30, 999–1010 (2014).CAS 
    Article 

    Google Scholar 
    46.Airoldi, L. et al. Emerging solutions to return nature to the urban ocean. Ann. Rev. Mar. Sci. 13, 445–477 (2021).Article 

    Google Scholar 
    47.Keeley, N., Wood, S. A. & Pochon, X. Development and preliminary validation of a multi-trophic metabarcoding biotic index for monitoring benthic organic enrichment. Ecol. Indic. 85, 1044–1057 (2018).CAS 
    Article 

    Google Scholar 
    48.Zaiko, A., Pochon, X., Garcia-Vazquez, E., Olenin, S. & Wood, S. A. Advantages and limitations of environmental DNA/RNA tools for marine biosecurity: management and surveillance of non-indigenous species. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00322 (2018).49.Cristescu, M. E. Can environmental RNA revolutionize biodiversity science? Trends Ecol. Evol. 34, 694–697 (2019).Article 

    Google Scholar 
    50.Chakravarthy, K., Charters, F. & Cochrane, T. The impact of urbanisation on New Zealand freshwater quality. Policy Q. 15, 17–21 (2019).Article 

    Google Scholar 
    51.Gittman, R. K. et al. Engineering away our natural defenses: an analysis of shoreline hardening in the US. Front. Ecol. Environ. 13, 301–307 (2015).Article 

    Google Scholar 
    52.Hume, T. M., Snelder, T., Weatherhead, M. & Liefting, R. A controlling factor approach to estuary classification. Ocean Coast. Manag. 50, 905–929 (2007).Article 

    Google Scholar 
    53.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

    Google Scholar 
    54.Prasad, A. M., Iverson, L. R. & Liaw, A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9, 181–199 (2006).Article 

    Google Scholar 
    55.Olden, J. D., Lawler, J. J. & Poff, N. L. Machine learning methods without tears: a primer for ecologists. Q. Rev. Biol. 83, 171–193 (2008).Article 

    Google Scholar 
    56.Kursa, M. B. & Rudnicki, W. R. Feature selection with the boruta package. J. Stat. Softw. 36, 1–13 (2010).Article 

    Google Scholar 
    57.Zuur, A. F., Leno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    58.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
    Google Scholar 
    59.Kuhn, M. et al. caret: Classification and Regression Training (CRAN, 2019); https://CRAN.R-project.org/package=caret60.Ministry for the Environment & Stats NZ. New Zealand’s Environmental Reporting Series: Environment Aotearoa 2019 (Ministry for the Environment, 2019). More

  • in

    Next-generation ensemble projections reveal higher climate risks for marine ecosystems

    1.IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) (IPCC, 2019).2.Doney, S. C. et al. Climate change impacts on marine ecosystems. Ann. Rev. Mar. Sci. 4, 11–37 (2012).Article 

    Google Scholar 
    3.Bindoff, N. L. et al. in Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) Ch. 5 (IPCC, 2019).4.Griffith, G. P., Fulton, E. A., Gorton, R. & Richardson, A. J. Predicting interactions among fishing, ocean warming, and ocean acidification in a marine system with whole-ecosystem models. Conserv. Biol. 26, 1145–1152 (2012).Article 

    Google Scholar 
    5.Fu, C. et al. Risky business: the combined effects of fishing and changes in primary productivity on fish communities. Ecol. Modell. 368, 265–276 (2018).Article 

    Google Scholar 
    6.Tittensor, D. P. et al. Integrating climate adaptation and biodiversity conservation in the global ocean. Sci. Adv. https://doi.org/10.1126/sciadv.aay9969 (2019).7.IPBES: Summary for Policymakers. In Global Assessment Report on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) (IPBES Secretariat, 2019).8.Boyce, D. G., Lotze, H. K., Tittensor, D. P., Carozza, D. A. & Worm, B. Future ocean biomass losses may widen socioeconomic equity gaps. Nat. Commun. 11, 2235 (2020).CAS 
    Article 

    Google Scholar 
    9.Payne, M. R. et al. Uncertainties in projecting climate-change impacts in marine ecosystems. ICES J. Mar. Sci. 73, 1272–1282 (2016).Article 

    Google Scholar 
    10.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).Article 

    Google Scholar 
    11.Tittensor, D. P. et al. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0. Geosci. Model Dev. 11, 1421–1442 (2018).Article 

    Google Scholar 
    12.Lotze, H. K. et al. Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proc. Natl Acad. Sci. USA 116, 12907–12912 (2019).CAS 
    Article 

    Google Scholar 
    13.Bryndum-Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).Article 

    Google Scholar 
    14.Bryndum-Buchholz, A. et al. Differing marine animal biomass shifts under 21st century climate change between Canada’s three oceans. Facets 5, 105–122 (2020).Article 

    Google Scholar 
    15.Bryndum-Buchholz, A. et al. Climate-change impacts and fisheries management challenges in the North Atlantic Ocean. Mar. Ecol. Prog. Ser. 648, 1–17 (2020).Article 

    Google Scholar 
    16.Ruane, A. C. et al. The vulnerability, impacts, adaptation and climate services advisory board (VIACS AB v1.0) contribution to CMIP6. Geosci. Model Dev. 9, 3493–3515 (2016).Article 

    Google Scholar 
    17.Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).CAS 
    Article 

    Google Scholar 
    18.Séférian, R. et al. Tracking improvement in simulated marine biogeochemistry between CMIP5 and CMIP6. Curr. Clim. Change Rep. 6, 95–119 (2020).Article 

    Google Scholar 
    19.Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).Article 

    Google Scholar 
    20.Tebaldi, C. et al. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst. Dyn. 12, 253–293 (2021).Article 

    Google Scholar 
    21.Heneghan, R. F. et al. Disentangling diverse responses to climate change among global marine ecosystem models. Prog. Oceanogr. 198, 102659 (2021).Article 

    Google Scholar 
    22.Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).Article 

    Google Scholar 
    23.Kwiatkowski, L. et al. Emergent constraints on projections of declining primary production in the tropical oceans. Nat. Clim. Change 7, 355–358 (2017).CAS 
    Article 

    Google Scholar 
    24.Cabré, A., Marinov, I. & Leung, S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 Earth system models. Clim. Dyn. 45, 1253–1280 (2015).Article 

    Google Scholar 
    25.Laufkötter, C. et al. Drivers and uncertainties of future global marine primary production in marine ecosystem models. Biogeosciences 12, 6955–6984 (2015).Article 

    Google Scholar 
    26.Doney, S. C. Plankton in a warmer world. Nature 444, 695–696 (2006).CAS 
    Article 

    Google Scholar 
    27.Rykaczewski, R. R. & Dunne, J. P. Enhanced nutrient supply to the California Current Ecosystem with global warming and increased stratification in an Earth system model. Geophys. Res. Lett. 37, L21606 (2010).Article 

    Google Scholar 
    28.Laufkötter, C., John, J. G., Stock, C. A. & Dunne, J. P. Temperature and oxygen dependence of the remineralization of organic matter. Glob. Biogeochem. Cycles 31, 1038–1050 (2017).Article 
    CAS 

    Google Scholar 
    29.Coll, M. et al. Advancing global ecological modeling capabilities to simulate future trajectories of change in marine ecosystems. Front. Mar. Sci. 7, 741 (2020).Article 

    Google Scholar 
    30.Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).Article 

    Google Scholar 
    31.Frölicher, T. L., Rodgers, K. B., Stock, C. A. & Cheung, W. W. L. Sources of uncertainties in 21st century projections of potential ocean ecosystem stressors. Glob. Biogeochem. Cycles 30, 1224–1243 (2016).Article 
    CAS 

    Google Scholar 
    32.Gaines, S. D. et al. Improved fisheries management could offset many negative effects of climate change. Sci. Adv. 4, eaao1378 (2018).Article 

    Google Scholar 
    33.The State of World Fisheries and Aquaculture 2020 (FAO, 2020).34.Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
    Article 

    Google Scholar 
    35.Stuart-Smith, R. D., Edgar, G. J. & Bates, A. E. Thermal limits to the geographic distributions of shallow-water marine species. Nat. Ecol. Evol. 1, 1846–1852 (2017).Article 

    Google Scholar 
    36.Carozza, D. A., Bianchi, D. & Galbraith, E. D. Metabolic impacts of climate change on marine ecosystems: implications for fish communities and fisheries. Glob. Ecol. Biogeogr. 28, 158–169 (2019).Article 

    Google Scholar 
    37.du Pontavice, H., Gascuel, D., Reygondeau, G., Stock, C. & Cheung, W. W. L. Climate-induced decrease in biomass flow in marine food webs may severely affect predators and ecosystem production. Glob. Change Biol. 27, 2608–2622 (2021).Article 

    Google Scholar 
    38.Piroddi, C. et al. Effects of nutrient management scenarios on marine food webs: a pan-European assessment in support of the marine strategy framework directive. Front. Mar. Sci. 8, 179 (2021).Article 

    Google Scholar 
    39.Maury, O. An overview of APECOSM, a spatialized mass balanced ‘Apex Predators ECOSystem Model’ to study physiologically structured tuna population dynamics in their ecosystem. Prog. Oceanogr. 84, 113–117 (2010).Article 

    Google Scholar 
    40.Maury, O. & Poggiale, J. C. From individuals to populations to communities: a dynamic energy budget model of marine ecosystem size-spectrum including life history diversity. J. Theor. Biol. 324, 52–71 (2013).Article 

    Google Scholar 
    41.Carozza, D. A., Bianchi, D. & Galbraith, E. D. The ecological module of BOATS-1.0: a bioenergetically-constrained model of marine upper trophic levels suitable for studies of fisheries and ocean biogeochemistry. Geosci. Model Dev. 9, 1545–1565 (2016).Article 

    Google Scholar 
    42.Carozza, D. A. et al. Formulation, general features and global calibration of a bioenergetically-constrained fishery model. PLoS ONE 12, e0169763 (2017).Article 
    CAS 

    Google Scholar 
    43.Cheung, W. W. L. et al. Building confidence in projections of the responses of living marine resources to climate change. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsv250 (2016).Article 

    Google Scholar 
    44.Cheung, W. W. L., Dunne, J., Sarmiento, J. L. & Pauly, D. Integrating ecophysiology and plankton dynamics into projected maximum fisheries catch potential under climate change in the Northeast Atlantic. ICES J. Mar. Sci. 68, 1008–1018 (2011).Article 

    Google Scholar 
    45.Blanchard, J. L. et al. Potential consequences of climate change for primary production and fish production in large marine ecosystems. Phil. Trans. R. Soc. B 367, 2979–2989 (2012).Article 

    Google Scholar 
    46.Christensen, V. et al. The global ocean is an ecosystem: simulating marine life and fisheries. Glob. Ecol. Biogeogr. 24, 507–517 (2015).Article 

    Google Scholar 
    47.Gascuel, D., Guénette, S. & Pauly, D. The trophic-level-based ecosystem modelling approach: theoretical overview and practical uses. ICES J. Mar. Sci. 68, 1403–1416 (2011).Article 

    Google Scholar 
    48.Petrik, C. M., Stock, C. A., Andersen, K. H., van Denderen, P. D. & Watson, J. R. Bottom-up drivers of global patterns of demersal, forage, and pelagic fishes. Prog. Oceanogr. 176, 102124 (2019).Article 

    Google Scholar 
    49.Jennings, S. & Collingridge, K. Predicting consumer biomass, size-structure, production, catch potential, responses to fishing and associated uncertainties in the world’s marine ecosystems. PLoS ONE 10, e0133794 (2015).Article 
    CAS 

    Google Scholar 
    50.Heneghan, R. F. et al. A functional size-spectrum model of the global marine ecosystem that resolves zooplankton composition. Ecol. Modell. 435, 109265 (2020).CAS 
    Article 

    Google Scholar 
    51.Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).Article 

    Google Scholar 
    52.Dunne, J. P. et al. Carbon Earth system models. Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).Article 

    Google Scholar 
    53.Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth system model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).Article 

    Google Scholar 
    54.Dunne, J. P. et al. The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): overall coupled model description and simulation characteristics. J. Adv. Model. Earth Syst. 12, e2019MS002015 (2020).
    Google Scholar 
    55.Krasting, J. P. et al. NOAA-GFDL GFDL-ESM4 Model Output Prepared for MIP6 CMIP Historical Version 20190726 (Earth System Grid Federation, 2018); https://doi.org/10.22033/ESGF/CMIP6.859756.John, J. G. et al. NOAA-GFDL GFDL-ESM4 Model Output Prepared for CMIP6 ScenarioMIP ssp585 Version 20180701 (Earth System Grid Federation, 2018); https://doi.org/10.22033/ESGF/CMIP6.870657.Boucher, O. et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Model. Earth Syst. 12, e2019MS002010 (2020).Article 

    Google Scholar 
    58.Boucher, O. et al. IPSL IPSL-CM6A-LR Model Output Prepared for CMIP6 CMIP Version 20180727 (Earth System Grid Federation, 2018); https://doi.org/10.22033/ESGF/CMIP6.153459.Boucher, O. et al. IPSL IPSL-CM6A-LR Model Output Prepared for CMIP6 CMIP Historical Version 20180103 (Earth System Grid Federation, 2018); https://doi.org/10.22033/ESGF/CMIP6.5195 More

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    Climatic signatures in the different COVID-19 pandemic waves across both hemispheres

    Global statistical analysisOur first attempt to identify plausible effects of meteorological covariates on COVID-19 spread applied a comparative regression analysis. To this end, we focused on the exponential onset of the disease, as it is the epidemic phase that allows for a better comparison between countries or regions, without the confounding effect of intervention policies. We first determined, for each of the spatial units (either countries or NUTS (nomenclature of territorial units for statistics) 2 regions), the day in which 20 or more cumulative cases were officially reported. We then fitted the first-order polynomial function f(t) = x0 + rt for the next 20 days of log-transformed data, where t represents time (in days) and ({{x}_0}) is the value at initial condition t = 0. The r parameter can be understood as the exponential growth rate, and is then used to estimate the basic reproduction number (R0) using the estimated serial interval T for COVID-19 of 4.7 days53, such that R0 = 1 + rT (ref. 54). (We note that we are interested here in the relationship between the reproductive number and not in the actual inference of R0.) Once R0 was obtained for all our spatial units, we filtered our meteorological data to match the same fitting period (with a 10-day negative delay to account for an incubation and reporting lapse) for every spatial unit. To compute a single average of the meteorological variables per regional unit, we computed a weighted average on the basis of the population contribution of each grid cell to the total population of the region. We did so to have an aggregated value that would better represent the impact of these factors on the population transmission of COVID-19, as the same variation in weather in a high-density urban area is more likely to contribute to a change in population-level transmission than that of an unpopulated rural area. We then averaged the daily values of temperature and AH for each country and computed univariate linear models for each of these variables as predictors of R0. Given the somewhat arbitrary criteria to select the dates to estimate the R0 in each country, a sensitivity analysis was run to test the robustness of the regressions to changes in the related parameters. We tested 70 different combinations of two parameters: the total number of days used for the fit (18–27) and the threshold of cumulative COVID-19 cases used to select the initial day of the fit (15–45). We also calculated the weather averages by shifting the selected dates accordingly. Then, a linear model for each of the estimates was fitted for both T and AH. A summary of the distribution of parameter estimates (the regression slope coefficients and the R2 of the models) is shown in Extended Data Fig. 3.Bivariate time-series analysis with scale-dependent correlationsTo examine associations between cases and climate factors in more detail, SDC was performed on the daily time series of both COVID-19 incidence and a given meteorological variable. SDC is an optimal method for identifying dynamical couplings in short and noisy time series20,21. In general, Spearman correlations between incidence and a meteorological time series assess whether there is a monotonic relation between the variables. SDC analysis was specifically developed to study transitory associations that are local in time at a specified temporal scale corresponding to the size of the time intervals considered (s). The two-way implementation (TW-SDC) is a bivariate method that computes non-parametric Spearman rank correlations between two time series, for different pairs of time intervals along these series. Different window sizes (s) can be used to examine increasingly finer temporal resolution. The results are sensitive to the value of this window size, s, with expected significant and highest correlation values at the scale of the transient coupling between variables. Correlation values decrease in magnitude as window size increases, and averages are computed over too long a time interval. Values can also decrease and become non-significant for small windows when correlations are spurious. Here, the method was applied for windows of different length (from s = 75 to 14 days) and, despite a weekly cycle showing up in some cases for small s, results removing this cycle were robust. We therefore did not remove this cycle.The results are typically displayed in a figure with the following subplots: (1) the two time series, to the left and top of the matrix of correlation values, respectively; (2) the matrix or grid of correlation values itself in the center, with significant correlations colored in blue when positive and in red when negative, with rows and columns corresponding to the temporal localization of the moving window along the time series on the left and top, respectively; (3) a time series at the bottom, below this grid, with the highest significant correlations for a given time (vertically, and therefore for the variable that acts as the driver, here the meteorological time series). To read the results, one starts at the diagonal and moves vertically down from it to identify a given lag for which significant correlations are found (the closest to the main diagonal). In some of the SDC figures, the time intervals with high local correlations are highlighted with boxes. These intervals alternate with other ones (left blank) for which no significant correlation is found. All colored areas correspond to significance levels of at least P  fs/fr, where fs is the sampling rate and fr the minimum frequency. Another strategy is that M be large enough that the M-lagged vector incorporates the temporal scale of the time series that is of interest. The larger the M, the more detailed the resulting decomposition of the signal. In particular, the most detailed decomposition is achieved when the embedding dimension is approximately equal to half of the total signal length. A compromise must be reached, however, as a large M implies increased computation, and too large a value may produce mixing of components. SSA is especially well suited for separating components corresponding to different frequencies in nonlinear systems. Here, we applied it to remove the weekly cycle.MSDC analysisMSDC provides a scan of the SDC analyses over a range of different scales (here, S from 5 to 100 days at 5-day intervals), by selecting the maximum correlation values (positive or negative) closer to the diagonal. The goal is to consider the evolution of transient correlations at all scales pooled together in a single analysis. The MSDC plot displays time on the x axis and scale (S) on the y axis, and positive and negative correlations either jointly or separately. The rationale behind MSDC is that correlations at very small scales can occur by chance because of coincident similar patterns, but that as one moves up to larger scales (by increasing S), the correlation patterns that are spurious tend to vanish, whereas those reflecting mechanistic links increase in strength. This increase in correlation values should occur up to the real scale of interaction, decreasing afterwards. By ‘real’, we mean here the temporal scale covering the extent of the interaction between the driver and the response process (in this case, the response of disease transmission to a given climate factor). Thus, continuity of the same sign correlations together with transitions to larger values are indicative of causal effects, whereas the rapid vanishing of small-scale significant correlations signals spurious ones.Process-based modelDescriptionThe dynamical model is a discrete stochastic model that incorporates seven different compartments: S, E, I, C, Q, R and D. The model structure is illustrated in Fig. 4. The transition probabilities of the stochastic model are based on the corresponding rates of the transitions between classes in the deterministic (mean-field) model (specified in Fig. 4b). These probabilities are defined as follows. P(e) = (1.0 − exp(−β dt)) is the probability of infection exposure of the susceptible class, where β = (1/N)(βII + βQQ) is the infection rate (of the deterministic model). P(i) = (1.0 − exp(−γ dt)) is the probability that an new exposed individual becomes infectious, where γ denotes the incubation rate. P(r) = (1.0 − exp(−Λ dt)) is the recovery probability, where λ0(1 − exp(λ1t)) is the (deterministic) recovery rate. P(p) = (1.0 − exp(−α dt)) is the protection probability, where α = α0exp(α1t). P(d) = (1.0 − exp(−K dt)) is the mortality probability, with K = k0exp(k1t). P(re) = (1.0 − exp(−τ dt)) is the release probability from confinement, where τ = τ0exp(τ1t). Finally, P(q) = (1.0 − exp(−δ  dt)) is the detection probability, where δ is the quarantine rate (for example, at which infected individuals are isolated from the rest of the population).In the model, both infected non-detected and infected detected individuals can infect susceptible ones. In the model incorporating temperature in the transmission rate, the respective values of βI and βQ are calculated as follows:$${beta }_{I}(t)={beta }_{I},T_{mathrm{inv}}(t);quad {beta }_{Q}(t)={beta }_{Q},T_{mathrm{inv}}(t)$$where (T_{mathrm{inv}}=fleft(frac{1-T(t)}{bar{T}}right)), with (bar{T}) corresponding to the overall mean of the temperature time series and f(·) to a Savitzky–Golay filter, used to smooth the temperature series with a window size of 50 data points and a polynomial order of 3. When the infection rate is constant, we simply omit the temperature term. For further comparison, in a third model, β is specified with a sinusoidal function of period equal to 12 months and an estimated phase.The number of individuals transitioning from compartment i to j at time t are determined by means of binomial distributions P(Xi,P(y)), where Xi corresponds to one of the compartments S, E, I, Q, R, D, C, and P(y) to the respective transition probability defined above. Thus,

    e(t) = P(S(t), P(e)), new exposed individuals at time t

    p(t) = P(S(t), P(p)), protected individuals at time t

    i(t) = P(E(t), P(i)), new infected not detected individuals at time t

    q(t) = P(I(t), P(q)), new infected and detected individuals at time t

    r(t) = P(Q(t), P(r)), total recovered individuals at time t

    d(t) = P(Q(t), P(d)), total dead individuals at time t

    re(t) = P(C(t), P(re)), individuals released from confinement at time t

    Then, the final dynamics are given by the following equations:$$S(t)=S(t-{rm{d}}t)-e(t)-p(t)+re(t)$$$$E(t)=E(t-{rm{d}}t)+e(t)-i(t)$$$$I(t)=I(t-{rm{d}}t)+i(t)-q(t)$$$$Q(t)=Q(t-{rm{d}}t)+q(t)-r(t)-d(t)$$$$R(t)=R(t-{rm{d}}t)+r(t)$$$$D(t)=D(t-{rm{d}}t)+d(t)$$$$C(t)=C(t-{rm{d}}t)+p(t)-re(t)$$CalibrationThe model was implemented using Python and calibrated by means of the least squares algorithm of the scipy library. The error function minimized with this algorithm was obtained from the normalized residuals on the basis of total cases (Q + R + D) and deaths (D).To search parameter space, we ran 100 calibrations starting from different initial choices of parameter combinations. The tolerance for termination in the change of the cost function was set to 1 × 10−10. Tolerance for termination by the norm of the gradient was also set to 1 × 10−10, and the tolerance for termination by the change of the independent variables was set to 1 × 10−10. The solver was the lsmr method (which is suitable for problems with sparse and large Jacobian matrices) with a differential step of 1 × 10−5. With this configuration, each fitting run usually converged after ~500 iterations.ValidationTo compare the model including an effect of T in the transmission rate to those without it, we calculated the chi-square, Akaike information criterion (AIC) and Bayesian information criterion (BIC) indices for the residuals obtained from the optimization process. The resulting values are shown in Supplementary Table 1.Our choice of T to modulate the infection rate (β) instead of AH underlies the fact that the temporal dynamics of both factors roughly follow the same shape, with the advantage that T shows less oscillatory behavior than AH. This fact adds stability to the model when the inverse relationship is used in the calculation of β (Supplementary Information). This selection is further reinforced by the results from the SDC analyses, which yielded larger correlations for temperature, even when penalizing for the larger autocorrelation structure.Our choice to modulate β using T instead of AH follows from the fact that the temporal dynamics of both climate variables present roughly the same shape, with the advantage that T exhibits weaker oscillations. This less fluctuating pattern provides stability to the model fitting when the inverse relationship is used in the calculation of β (Supplementary Information). Additionally, the transient correlations obtained with SDC yielded higher values for T than for AH (even when accounting for concurrent levels of autoregression in the two variables). More

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    Climate impacts and adaptation in US dairy systems 1981–2018

    1.Dairy Production and Products: Milk and Milk Products (FAO, 2013); http://www.fao.org/dairy-production-products/production/dairy-animals/cattle/en/2.Background: Corn and Other Feedgrains (USDA ERS, 2018); https://www.ers.usda.gov/topics/animal-products/dairy/background/3.National Agricultural Statistics Service (US Department of Agriculture); https://www.nass.usda.gov/index.php4.Capper, J. L., Cady, R. A. & Bauman, D. E. The environmental impact of dairy production: 1944 compared with 2007. J. Anim. Sci. 87, 2160–2167 (2009).CAS 
    Article 

    Google Scholar 
    5.Niles, M. T. & Wiltshire, S. Tradeoffs in US dairy manure greenhouse gas emissions, productivity, climate, and manure management strategies. Environ. Res. Commun 1, 075003 (2019).Article 

    Google Scholar 
    6.Field, T. G. & Taylor, R. E. Scientific Farm Animal Production: An Introduction, Eleventh Edition (Pearson, 2018).7.Fuquay, J. W. Heat stress as it affects animal production. J. Anim. Sci. 52, 164–174 (1981).CAS 
    Article 

    Google Scholar 
    8.St-Pierre, N. R., Cobanov, B. & Schnitkey, G. Economic losses from heat stress by US livestock industries. J. Dairy Sci. 86, E52–E77 (2003).Article 

    Google Scholar 
    9.Kadzere, C. T., Murphy, M. R., Silanikove, N. & Maltz, E. Heat stress in lactating dairy cows: a review. Livest. Prod. Sci. 77, 59–91 (2002).Article 

    Google Scholar 
    10.Bouraoui, R., Lahmar, M., Majdoub, A., Djemali, M. & Belyea, R. The relationship of temperature–humidity index with milk production of dairy cows in a Mediterranean climate. Anim. Res. 51, 479–491 (2002).Article 

    Google Scholar 
    11.West, J. W. Effects of heat-stress on production in dairy cattle. J. Dairy Sci. 86, 2131–2144 (2003).CAS 
    Article 

    Google Scholar 
    12.Vitali, A. et al. Seasonal pattern of mortality and relationships between mortality and temperature–humidity index in dairy cows. J. Dairy Sci. 92, 3781–3790 (2009).CAS 
    Article 

    Google Scholar 
    13.Pragna, P. et al. Heat stress and dairy cow: impact on both milk yield and composition. Int. J. Dairy Sci. 12, 1–11 (2017).CAS 
    Article 

    Google Scholar 
    14.Hoffmann, I. Climate change and the characterization, breeding and conservation of animal genetic resources. Anim. Genet. 41, 32–46 (2010).Article 

    Google Scholar 
    15.Qi, L., Bravo-Ureta, B. E. & Cabrera, V. E. From cold to hot: a preliminary analysis of climatic effects on the productivity of Wisconsin dairy farms. AgEconSearch https://doi.org/10.22004/ag.econ.172411 (2014).16.Bohmanova, J., Misztal, I. & Cole, J. B. Temperature–humidity indices as indicators of milk production losses due to heat stress. J. Dairy Sci. 90, 1947–1956 (2007).CAS 
    Article 

    Google Scholar 
    17.Field, C. B. et al. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (IPCC, 2021); https://www.ipcc.ch/report/managing-the-risks-of-extreme-events-and-disasters-to-advance-climate-change-adaptation/18.Mueller, N. D. et al. Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Chang. 6, 317–322 (2016).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    19.Seneviratne, S. I., Donat, M. G., Mueller, B. & Alexander, L. V. No pause in the increase of hot temperature extremes. Nat. Clim. Chang. 4, 161–163 (2014).ADS 
    Article 

    Google Scholar 
    20.Dairy 2014: Dairy Cattle Management Practices in the United States, 2014 (USDA, APHIS, NAHMS, 2016); https://www.aphis.usda.gov/animal_health/nahms/dairy/downloads/dairy14/Dairy14_dr_PartI_1.pdf21.Mondaca, M. R. & Cook, N. B. Modeled construction and operating costs of different ventilation systems for lactating dairy cows. J. Dairy Sci. 102, 896–908 (2019).CAS 
    Article 

    Google Scholar 
    22.Ferreira, F. C., Gennari, R. S., Dahl, G. E. & De Vries, A. Economic feasibility of cooling dry cows across the United States. J. Dairy Sci. 99, 9931–9941 (2016).CAS 
    Article 

    Google Scholar 
    23.Hayhoe, K. et al. Emissions pathways, climate change, and impacts on California. Proc. Natl Acad. Sci. USA 101, 12422–12427 (2004).ADS 
    CAS 
    Article 

    Google Scholar 
    24.Klinedinst, P. L., Wilhite, D. A., Hahn, L. G. & Hubbard, K. G. The potential effects of climate change on summer seasonal dairy cattle milk production and reproduction. Clim. Chang. 23, 21–36 (1993).ADS 
    Article 

    Google Scholar 
    25.Mauger, G., Bauman, Y., Nennich, T. & Salathé, E. Impacts of climate change on milk production in the United States. Prof. Geogr. 67, 121–131 (2015).Article 

    Google Scholar 
    26.Key, N. & Sneeringer, S. Potential effects of climate change on the productivity of U.S. dairies. Am. J. Agric. Econ. 96, 1136–1156 (2014).Article 

    Google Scholar 
    27.Ortiz-Bobea, A., Knippenberg, E. & Chambers, R. G. Growing climatic sensitivity of U.S. agriculture linked to technological change and regional specialization. Sci. Adv. 4, eaat4343 (2018).ADS 
    Article 

    Google Scholar 
    28.Butler, E. E., Mueller, N. D. & Huybers, P. Peculiarly pleasant weather for US maize. Proc. Natl Acad. Sci. USA 115, 11935–11940 (2018).CAS 
    Article 

    Google Scholar 
    29.Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    30.Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl Acad. Sci. U. S. A. 115, 6644–6649 (2018).ADS 
    Article 

    Google Scholar 
    31.PRISM Climate Data (Oregon State Univ., 2019); http://www.prism.oregonstate.edu/32.Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. https://doi.org/10.1002/joc.1688 (2008).33.National Research Council. Nutrient Requirements of Dairy Cattle, Seventh Revised Edition (National Academies Press, 2001).34.Auldist, M. J., Walsh, B. J. & Thomson, N. A. Seasonal and lactational influences on bovine milk composition in New Zealand. J. Dairy Res. 65, 401–411 (1998).CAS 
    Article 

    Google Scholar 
    35.Lobell, D. B. Climate change adaptation in crop production: beware of illusions. Glob. Food Sec. 3, 72–76 (2014).Article 

    Google Scholar 
    36.Mukherjee, D., Bravo-Ureta, B. E. & De Vries, A. Dairy productivity and climatic conditions: econometric evidence from South-eastern United States. Aust. J. Agric. Resour. Econ. 57, 123–140 (2013).Article 

    Google Scholar 
    37.Milk Cost of Production Estimates: Cost-of-Production Estimates-2016 Base (USDA ERS, 2021); https://www.ers.usda.gov/data-products/milk-cost-of-production-estimates/milk-cost-of-production-estimates/#Milk38.Liang, X. Z. et al. Determining climate effects on US total agricultural productivity. Proc. Natl Acad. Sci. USA 114, E2285–E2292 (2017).CAS 
    Article 

    Google Scholar 
    39.Malikov, E., Miao, R. & Zhang, J. Distributional and temporal heterogeneity in the climate change effects on U.S. agriculture. J. Environ. Econ. Manage. 104, 102386 (2020).Article 

    Google Scholar 
    40.MacDonald, J. M., Law, J. & Mosheim, R. Consolidation in U.S. Dairy Farming Economic Research Report No. 274 (ERS, USDA, 2020); https://www.ers.usda.gov/publications/pub-details/?pubid=9890041.Hemme, T. & Otte, J. Pro-Poor Livestock Policy Initiative Status and Prospects for Smallholder Milk Production a Global Perspective (Food and Agriculture Organization of the United Nations, 2010).42.Osei-Amponsah, R. et al. Heat stress impacts on lactating cows grazing Australian summer pastures on an automatic robotic dairy. Animals 10, 869 (2020).Article 

    Google Scholar 
    43.Chang-Fung-Martel, J., Harrison, M. T., Rawnsley, R., Smith, A. P. & Meinke, H. The impact of extreme climatic events on pasture-based dairy systems: a review. Crop Pasture Sci 68, 1158 (2017).Article 

    Google Scholar 
    44.Livestock Hot Weather Stress. Operations Manual (NOAA, 1976); https://scirp.org/reference/referencespapers.aspx?referenceid=191321645.Pinheiro J., Bates D., Debroy S. S. D. Linear and nonlinear mixed effects models, R package nlme version 3.1-152 (2021).46.Conley, T. G. GMM estimation with cross sectional dependence. J. Econom. 92, 1–45 (1999).MathSciNet 
    Article 

    Google Scholar 
    47.Borchers, H. W. pracma: practical numerical math functions, version 2.2.9.1–393 (2019).48.Colin Cameron, A., Gelbach, J. B. & Miller, D. L. Robust inference with multiway clustering. J. Bus. Econ. Stat. 29, 238–249 (2011).MathSciNet 
    Article 

    Google Scholar 
    49.Zeileis, A., Köll, S. & Graham, N. Various versatile variances: an object-oriented implementation of clustered covariances in R. J. Stat. Softw. https://doi.org/10.18637/jss.v095.i01 (2020). More