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    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

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    Different patterns of human activities in nature during Covid-19 pandemic and African swine fever outbreak confirm direct impact on wildlife disruption

    1.DeStefano, S. & DeGraaf, R. M. Exploring the ecology of suburban wildlife. Front. Ecol. Environ. 1, 95 (2003).Article 

    Google Scholar 
    2.Treves, A., Wallace, R. B., Naughton-Treves, L. & Morales, A. Co-managing human–wildlife conflicts: a review. Hum. Dimens. Wildl. 11, 383–396 (2006).Article 

    Google Scholar 
    3.Oberosler, V., Groff, C., Iemma, A., Pedrini, P. & Rovero, F. The influence of human disturbance on occupancy and activity patterns of mammals in the Italian Alps from systematic camera trapping. Mamm. Biol. 87, 50–61 (2017).Article 

    Google Scholar 
    4.Tyler, N. J. C. Short-term behavioural responses of Svalbard reindeer Rangifer tarandus platyrhynchus to direct provocation by a snowmobile. Biol. Conserv. 56, 179–194 (1991).Article 

    Google Scholar 
    5.Tolvanen, A. & Kangas, K. Tourism, biodiversity and protected areas—review from northern Fennoscandia. J. Environ. Manage. 169, 58–66 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Ballantyne, M. & Pickering, C. M. Tourism and recreation: a common threat to IUCN red-listed vascular plants in Europe. Biodivers. Conserv. 22, 3027–3044 (2013).Article 

    Google Scholar 
    7.Pickering, C. M., Hill, W., Newsome, D. & Leung, Y. F. Comparing hiking, mountain biking and horse riding impacts on vegetation and soils in Australia and the United States of America. J. Environ. Manage. 91, 551–562 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Coppes, J., Ehrlacher, J., Thiel, D., Suchant, R. & Braunisch, V. Outdoor recreation causes effective habitat reduction in capercaillie Tetrao urogallus: a major threat for geographically restricted populations. J. Avian Biol. 48, 1583–1594 (2017).Article 

    Google Scholar 
    9.Siikamäki, P., Kangas, K., Paasivaara, A. & Schroderus, S. Biodiversity attracts visitors to national parks. Biodivers. Conserv. 24, 2521–2534 (2015).Article 

    Google Scholar 
    10.Gerstenberg, T., Baumeister, C. F., Schraml, U. & Plieninger, T. Hot routes in urban forests: the impact of multiple landscape features on recreational use intensity. Landsc. Urban Plan. 203, 103888 (2020).Article 

    Google Scholar 
    11.Fischer, L. K. & Kowarik, I. Dogwalkers’ views of urban biodiversity across five European cities. Sustain. 12, 1–11 (2020).
    Google Scholar 
    12.Lundgren, J. O. Polar tourism: tourism in the Arctic and Antarctic regions. in The tourism space penetration processes in northern Canada and Scandinavia: a comparison 43–61 (1995).13.Balmford, A. et al. Walk on the wild side: estimating the global magnitude of visits to protected areas. PLoS Biol. 13, 1–6 (2015).Article 
    CAS 

    Google Scholar 
    14.George, S. L. & Crooks, K. R. Recreation and large mammal activity in an urban nature reserve. Biol. Conserv. 133, 107–117 (2006).Article 

    Google Scholar 
    15.Zhong, L., Zhang, X., Deng, J. & Pierskalla, C. Recreation ecology research in China’s protected areas: progress and prospect. Ecosyst. Heal. Sustain. 6 (2020).16.Mancini, F., Leyshon, B., Manson, F., Coghill, G. M. & Lusseau, D. Monitoring tourists’ specialisation and implementing adaptive governance is necessary to avoid failure of the wildlife tourism commons. Tour. Manag. 81, 104160 (2020).Article 

    Google Scholar 
    17.Abate, M., Christidis, P. & Purwanto, A. J. Government support to airlines in the aftermath of the COVID-19 pandemic. J. Air Transp. Manag. 89, 101931 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Castanho, R. A. et al. The impact of SARS-CoV-2 outbreak on the accommodation selection of Azorean tourists. A study based on the assessment of the Azores population’s attitudes. Sustainability 12, 9990 (2020).CAS 
    Article 

    Google Scholar 
    19.Neupane, D. How conservation will be impacted in the COVID-19 pandemic. Wildlife Biol. 2020, 19–21 (2020).Article 

    Google Scholar 
    20.Herrero, C. & Villar, A. A synthetic indicator on the impact of COVID-19 on the community’s health. PLoS ONE 15, 1–14 (2020).
    Google Scholar 
    21.World Health Organization (WHO). Coronavirus Disease (COVID-19) Situation Reports Updates 27 September 2020. World Health Organization Technical Report Series (2020).22.da Silva, F. C. T. & Neto, M. L. R. Psychological effects caused by the COVID-19 pandemic in health professionals: a systematic review with meta-analysis. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 104, 110 (2021).Article 
    CAS 

    Google Scholar 
    23.Sohrabi, C. et al. World health organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 76, 71–76 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Hellewell, J. et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob. Heal. 8, e488–e496 (2020).Article 

    Google Scholar 
    25.Steidtmann, D., McBride, S. & Mishkind, M. C. Experiences of mental health clinicians and staff in rapidly converting to full-time telemental health and work from home during the COVID-19 pandemic. Telemed. e-Health 27(7), 785–791 (2021).Article 

    Google Scholar 
    26.Chiu, W. A., Fischer, R. & Ndeffo-Mbah, M. L. State-level needs for social distancing and contact tracing to contain COVID-19 in the United States. Nat. Hum. Behav. 4, 1080–1090 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Rutz, C. et al. COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife. Nat. Ecol. Evol. 4, 1156–1159 (2020).PubMed 
    Article 

    Google Scholar 
    28.Zellmer, A. J. et al. What can we learn from wildlife sightings during the COVID-19 global shutdown?. Ecosphere 11, e03215 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Ghahremanloo, M., Lops, Y., Choi, Y. & Mousavinezhad, S. Impact of the COVID-19 outbreak on air pollution levels in East Asia. Sci. Total Environ. 754, 142226 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    30.Rosenbloom, D. & Markard, J. A COVID-19 recovery for climate. Science 368, 447–447 (2020).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Lokhandwala, S. & Gautam, P. Indirect impact of COVID-19 on environment: a brief study in Indian context. Environ. Res. 188, 109807 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Manenti, R. et al. The good, the bad and the ugly of COVID-19 lockdown effects on wildlife conservation: insights from the first European locked down country. Biol. Conserv. 249, 108728 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Corlett, R. T. et al. Impacts of the coronavirus pandemic on biodiversity conservation. Biol. Conserv. 246, 8–11 (2020).Article 

    Google Scholar 
    34.Bates, A. E., Primack, R. B., Moraga, P. & Duarte, C. M. COVID-19 pandemic and associated lockdown as a “Global Human Confinement Experiment” to investigate biodiversity conservation. Biol. Conserv. 248, 108665 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Arias, M., Jurado, C., Gallardo, C., Fernández-Pinero, J. & Sánchez-Vizcaíno, J. M. Gaps in African swine fever: analysis and priorities. Transbound. Emerg. Dis. 65, 235–247 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Galindo, I. & Alonso, C. African swine fever virus: a review. Viruses 9, 103 (2017).PubMed Central 
    Article 
    CAS 
    PubMed 

    Google Scholar 
    37.Taylor, R. A. et al. Predicting spread and effective control measures for African swine fever—should we blame the boars?. Transbound Emerg. Dis. https://doi.org/10.1111/tbed.13690 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Mason-D’Croz, D. et al. Modelling the global economic consequences of a major African swine fever outbreak in China. Nat. Food. 1, 221–228 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Podgórski, T. & Śmietanka, K. Do wild boar movements drive the spread of African Swine Fever?. Transbound. Emerg. Dis. 65, 1588–1596 (2018).PubMed 
    Article 

    Google Scholar 
    40.Petit, K. et al. Assessment of the impact of forestry and leisure activities on wild boar spatial disturbance with a potential application to ASF risk of spread. Transbound. Emerg. Dis. 67, 1164–1176 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Watanabe, S. & Wahlqvist, M. L. Covid-19 and dietary socioecology: Risk minimisation. Asia Pac. J. Clin. Nutr. 29, 207–219 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Geng, D., Innes, J., Wu, W. & Wang, G. Impacts of COVID-19 pandemic on urban park visitation: a global analysis. J. For. Res. https://doi.org/10.1007/s11676-020-01249-w (2020).Article 

    Google Scholar 
    43.Godbersen, H., Hofmann, L. A. & Ruiz-Fernández, S. How people evaluate anti-corona measures for their social spheres: attitude, subjective norm, and perceived behavioral control. Front. Psychol. 11, 1–20 (2020).Article 

    Google Scholar 
    44.Cukor, J. et al. Wild boar deathbed choice in relation to ASF : Are there any differences between positive and negative carcasses? Prev. Vet. 177, 1–7 (2020).
    Google Scholar 
    45.McGinlay, J. et al. The impact of COVID-19 on the management of European protected areas and policy implications. Forests 11, 1–15 (2020).Article 

    Google Scholar 
    46.Derks, J., Giessen, L. & Winkel, G. COVID-19-induced visitor boom reveals the importance of forests as critical infrastructure. For. Policy Econ. 118, 102253 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Venter, Z. S., Barton, D. N., Gundersen, V., Figari, H., Nowell, M. Urban nature in a time of crisis: Recreational use of green space increases during the COVID-19 outbreak in Oslo, Norway. Environ. Res. Lett. 15, 1–11 (2020).Article 
    CAS 

    Google Scholar 
    48.Jůza, R., Jarský, V., Riedl, M., Zahradník, D. & Šišák, L. Possibilities for harmonisation between recreation services and their production within the forest sector—a case study of municipal forest enterprise hradec Králové (CZ). Forests 12, 13 (2020).Article 

    Google Scholar 
    49.Dellicour, S. et al. Unravelling the dispersal dynamics and ecological drivers of the African swine fever outbreak in Belgium. J. Appl. Ecol. 57, 1619–1629 (2020).Article 

    Google Scholar 
    50.Carnol, M. et al. Ecosystem services of mixed species forest stands and monocultures: comparing practitioners and scientists perceptions with formal scientific knowledge. Forestry 87, 639–653 (2014).Article 

    Google Scholar 
    51.Dušek, D., Kacálek, D., Novák, J. & Slodičák, M. Public perception of recreation needs—a questionnaire study from Ostrava urban forests (Czech Republic). Zpravy Lesn. Vyzk Rep. For. Res. 62, 174–181 (2017).
    Google Scholar 
    52.Meo, I. D., Paletto, A. & Cantiani, M. G. The attractiveness of forests: Preferences and perceptions in a mountain community in Italy. Ann. For. Res. 58, 145–156 (2015).
    Google Scholar 
    53.Sadecký, D., Pejcha, J. & Šišák, L. Analysis of the public opinion on forest and forest management in the žďárské vrchy protected landscape area, czech republic [Analýza názorů veřejnosti na les a lesní hospodářství v chráněné krajinné oblasti žďárské vrchy]. Zpravy Lesn. Vyzk. 59, 11–17 (2014).
    Google Scholar 
    54.Ciuti, S. et al. Effects of Humans on Behaviour of Wildlife Exceed Those of Natural Predators in a Landscape of Fear. PLoS ONE 7, 1–16 (2012).Article 
    CAS 

    Google Scholar 
    55.Palacios, M. G., D’Amico, V. L. & Bertellotti, M. Ecotourism effects on health and immunity of Magellanic penguins at two reproductive colonies with disparate touristic regimes and population trends. Conserv. Physiol. 6, 1–13 (2018).Article 
    CAS 

    Google Scholar 
    56.Schuttler, S. G. et al. Deer on the lookout: how hunting, hiking and coyotes affect white-tailed deer vigilance. J. Zool. 301, 320–327 (2017).Article 

    Google Scholar 
    57.Preisser, E. L., Bolnick, D. I. & Benard, M. F. Scared to death? The effects of intimidation and consumption in predator-prey interactions. Ecology 86, 501–509 (2005).Article 

    Google Scholar 
    58.Creel, S., Winnie, J., Maxwell, B., Hamlin, K. & Creel, M. Elk alter habitat selection as an antipredator response to wolves. Ecology 86, 3387–3397 (2005).Article 

    Google Scholar 
    59.French, S. S., Denardo, D. F., Greives, T. J., Strand, C. R. & Demas, G. E. Human disturbance alters endocrine and immune responses in the Galapagos marine iguana (Amblyrhynchus cristatus). Horm. Behav. 58, 792–799 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Beehner, J. C. & Bergman, T. J. The next step for stress research in primates: to identify relationships between glucocorticoid secretion and fitness. Horm. Behav. 91, 68–83 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    61.Dhabhar, F. S. Effects of stress on immune function: the good, the bad, and the beautiful. Immunol. Res. 58, 193–210 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    62.Almasi, B., Béziers, P., Roulin, A. & Jenni, L. Agricultural land use and human presence around breeding sites increase stress-hormone levels and decrease body mass in barn owl nestlings. Oecologia 179, 89–101 (2015).ADS 
    PubMed 
    Article 

    Google Scholar 
    63.Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 21, 55–89 (2000).CAS 
    PubMed 

    Google Scholar 
    64.Szwagrzyk, J. et al. Effects of species and environmental factors on browsing frequency of young trees in mountain forests affected by natural disturbances. For. Ecol. Manage. 474, 1–13 (2020).Article 

    Google Scholar 
    65.Möst, L., Hothorn, T., Müller, J. & Heurich, M. Creating a landscape of management: unintended effects on the variation of browsing pressure in a national park. For. Ecol. Manage. 338, 46–56 (2015).Article 

    Google Scholar 
    66.Cukor, J. et al. Effects of bark stripping on timber production and structure of Norway Spruce forests in relation to climatic factors. Forests 10, 13–17 (2019).Article 

    Google Scholar 
    67.Vacek, Z. et al. Bark stripping, the crucial factor affecting stem rot development and timber production of Norway spruce forests in Central Europe. For. Ecol. Manage. 474, 118360 (2020).Article 

    Google Scholar 
    68.Barrueto, M., Ford, A. T. & Clevenger, A. P. Anthropogenic effects on activity patterns of wildlife at crossing structures. Ecosphere 5, 1–19 (2014).Article 

    Google Scholar 
    69.Ignatavičius, G. et al. Temporal patterns of ungulate-vehicle collisions in a sparsely populated country. Eur. J. Wildl. Res. 66, 1–9 (2020).Article 

    Google Scholar 
    70.Price, M. V., Strombom, E. H. & Blumstein, D. T. Human activity affects the perception of risk by mule deer. Curr. Zool. 60, 693–699 (2014).Article 

    Google Scholar 
    71.Romero, L. M., Dickens, M. J. & Cyr, N. E. The reactive scope model—a new model integrating homeostasis, allostasis, and stress. Horm. Behav. 55, 375–389 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Cukor, J., Havránek, F., Rohla, J. & Bukovjan, K. Estimation of red deer density in the west part of the Ore Mts (Czech Republic). Zpravy Lesn. Vyzk. Rep. For. Res. 62, 288–295 (2017).
    Google Scholar 
    73.Carpio, A. J., Apollonio, M. & Acevedo, P. Wild ungulate overabundance in Europe: contexts, causes, monitoring and management recommendations. Mamm. Rev. 51, 95–108 (2021).Article 

    Google Scholar 
    74.Iacolina, L., Corlatti, L., Buzan, E., Safner, T. & Šprem, N. Hybridisation in European ungulates: an overview of the current status, causes, and consequences. Mamm. Rev. 49, 45–59 (2019).Article 

    Google Scholar 
    75.Kangas, K., Luoto, M., Ihantola, A., Tomppo, E. & Siikamäki, P. Recreation-induced changes in boreal bird communities in protected areas. Ecol. Appl. 20, 1775–1786 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Tost, D., Strauß, E., Jung, K. & Siebert, U. Impact of tourism on habitat use of black grouse (Tetrao tetrix) in an isolated population in northern Germany. PLoS ONE 15, e0238660 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    77.Köppen, W. Das Geographische System der Klimate, Handbuch der Klimatologie (Gebrüder Borntraeger, 1936).
    Google Scholar 
    78.Rob, F. et al. Compliance, safety concerns and anxiety in patients treated with biologics for psoriasis during the COVID-19 pandemic national lockdown: a multicenter study in the Czech Republic. J. Eur. Acad. Dermatol. Venereol. 76, jdv.16771 (2020).
    Google Scholar 
    79.Government of the Czech Republic. Measures adopted by the Czech Government against the coronavirus. (2021). Available at: https://www.vlada.cz/en/media-centrum/aktualne/measures-adopted-by-the-czech-government-against-coronavirus-180545/. (Accessed: 5th February 2021).80.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (2016). More

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    Fish can use hydrostatic pressure to determine their absolute depth

    We have demonstrated that Mexican tetra fish can locate their depth with high fidelity by using hydrostatic pressure alone. Crucially, the fish can use hydrostatic pressure not only as a gradient, giving information about upward and downward movement but also as a distance-based cue that can allow precise localisation of their vertical position. This newly identified sensory capability indicates how fish can achieve the complex task of navigating through three-dimensional environments.The basis of navigation in all animals, hinges on the individual knowing the spatial relationship between their current location and an intended destination. Although all animals inhabit a three-dimensional world, many, including humans, are constrained to travelling over surfaces with three degrees of freedom: two translational and one rotational14. The addition of the vertical dimension enlarges the size of the navigable space from a two-dimensional plane to a three-dimensional volume2, leading to a multiplicative increase in the complexity of a navigational task14,15,16. Reliable information on vertical position would therefore be a significant benefit for three-dimensional navigation.Although it is likely that in the wild fish rely on multiple cues to navigate, a sense of pressure would be particularly useful when other cues are unavailable or unreliable, for example, in turbid waters where visual landmarks are absent or obscured, and in turbulent waters where olfactory plumes cannot provide fine-scale information. The stability and ubiquity of hydrostatic pressure in aquatic environments allow fish access to a reliable navigational cue and could explain why two separate experiments, each testing a different species, found that fish perceived vertical information as the more reliable cue when horizontal and vertical information conflicted.The physiological mechanism underlying depth perception in fish is yet to be identified, although the swim-bladder has been implicated. In this putative mechanism, absolute depth is estimated during fast, steady vertical displacements by combining a measurement of vertical speed with a measurement of the fractional rate of change of swim-bladder volume. If this is the mechanism that these and other bony fish are using to sense their depth, there are likely to be important ecological and welfare consequences for fish that suffer barotrauma from angling or transit through hydroelectric power facilities, where the damage caused from exposure to rapid changes in barometric pressure may cause swim bladder ruptures17. Therefore, governments need to be aware of key migratory paths that fish use to move between feeding and breeding sites to enable them to protect important species. Similarly, fish that contract parasitic infections of the swim bladder are likely to find their vertical navigation is severely compromised. While there are currently no studies on the pressure sensing in fish with parasitic infections of the swim bladder, previous research has reported that infected Koi carp (Cyprinus carpio) are less able to achieve and sustain neutral buoyancy and demonstrate abnormal swimming behaviour18. Similarly, silver eels (Anguilla Anguilla) infected with a swim bladder nematode experienced a loss of buoyancy resulting in them expending more energy while swimming, impeding their migration19.While the swim bladder appears to be a good candidate organ for sensing hydrostatic pressure in bony fish, many cartilaginous or deep-sea species do not possess a gas phase, despite still being able to navigate vertically. Previous research has suggested that instead of relying on fractional changes in swim bladder volume, these species may rely on the sensory afferents of their lateral line system; with evidence that swimming crabs (Callinectes ornatus), mud crabs (Panopeus herbstii) and dogfish (Scyliorhinus canicula) sense pressure changes via the bending of hair cells oriented to sense either vertical or horizontal displacements20. The ability of fish to use hydrostatic pressure to accurately locate a point in the vertical dimension may be important for fisheries management. Known points of interest, for example, food sites, refugia, heavily predated areas, present in the vertical column could be learned and remembered by fish, with them either returning to or avoiding these areas as necessary. Further field studies on individual fish and shoals of fish using hydrostatic pressure in this context are needed to identify how this cue is used both in the wild and in farmed fisheries.Our findings reveal novel sensory information that A. mexicanus, and possibly other fish species, use to gain detailed navigational information over short distances in the vertical dimension. Extrapolating from this, we argue that it is likely that fish could use pressure to navigate over larger distances as the pressure magnitudes will increase as the vertical distance increases. Together, this study reveals a new sensory capacity that has great adaptive value in the fish’s volumetric world. More

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    Climate change benefits negated by extreme heat

    1.Mueller, N. D. et al. Nat. Food https://doi.org/10.1038/s43016-021-00372-z (2021).2.IPCC Climate Change 2021: The Physical Science Basis Summary for Policymakers (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in the press).3.Harrison, M. T., Tardieu, F., Dong, Z., Messina, C. D. & Hammer, G. L. Glob. Change Biol. 20, 867–878 (2014).ADS 
    Article 

    Google Scholar 
    4.Chang-Fung-Martel, J., Harrison, M. T., Rawnsley, R., Smith, A. P. & Meinke, H. Crop Pasture Sci. 68, 1158–1169 (2017).Article 

    Google Scholar 
    5.Climate Change and the Global Dairy Cattle Sector: The Role of the Dairy Sector in a Low-Carbon Future (FAO and GDP, 2018).6.World Dairy Map 2020: Shifting Gears in Global Dairy Trade (Rabobank, 2020); https://research.rabobank.com/far/en/sectors/dairy/world-dairy-map-2020.html7.Harrison, M. T., Cullen, B. R. & Armstrong, D. Agric. Syst. 155, 19–32 (2017).Article 

    Google Scholar 
    8.Harrison, M. T. et al. Anim. Prod. Sci. 56, 370–384 (2016).CAS 
    Article 

    Google Scholar 
    9.Harrison, M. T. et al. Glob. Change Biol. https://doi.org/10.1111/gcb.15816 (2021).10.Chang-Fung-Martel, J. et al. Int. J. Biometeorol. https://doi.org/10.1007/s00484-021-02167-0 (2021).11.U.S. Climate Extremes Index (CEI) (NOAA National Centers for Environmental Information, accessed 19 September 2021); https://www.ncdc.noaa.gov/extremes/cei/graph/us/01-12/2 More

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    Exploring the potential effect of COVID-19 on an endangered great ape

    Study site and demographic dataThe study was carried out in Volcanoes National Park, the Rwandan part of the Virunga massif, which is further shared with Uganda and the Democratic Republic of the Congo. We focused on habituated mountain gorilla groups monitored by the Dian Fossey Gorilla Fund’s Karisoke Research Center, often referred to as the Karisoke subpopulation. Since 1967, groups in this subpopulation have been followed on a near daily basis. Through the mid-2000s, the Karisoke groups generally numbered three but over the last decade, group fission events and new group formations resulted in an average of ten groups in the region (see42,43). During daily observations, detailed demographic data are recorded, such as group composition, birthdate and death date, group transfers (for further details see Strier et al.50). The data used for this study covers demographic data from 1967 to 2018 and includes 396 recognized individuals.Epidemiological dataWe obtained published data on four variables that control the disease dynamics of COVID-19 in humans, namely (a) the basic reproductive number (R0)34,35, (b) the infection fatality rate (IFR) based on estimates from China and Italy24,25,36,37, (c) the probability of developing immunity and (d) the duration of immunity37,38,39,41.Stochastic projection modelWe used the stochastic projection model proposed by Colchero et al.51, that models population dynamics for both sexes on fully age-dependent demographic rates. The model incorporates the yearly variance–covariance between demographic rates, while it accounts for infanticide as a function of the number of silverbacks (mature males > 12 years old) in the population51. Because of this relationship between infanticide and number of silverbacks, this source of mortality changes in time and cannot be assumed to be part of the infant mortality rate. To explore the extinction probability for the Karisoke subpopulation as a function of different diseases, we gathered information from the model on the proportion of individuals that died for each disease and the frequency of outbreaks (i.e., how often outbreaks occurred).Demographic-epidemiological projection model for COVID-19We constructed a predictive population model that combines the species’ baseline demographic rates with a model based on the susceptible-infected-recovered-susceptible (SIRS) framework. As the baseline demographic rates, we used the age-specific mortality and fecundity estimated by Colchero et al.51 for mountain gorillas (Karisoke subpopulation). We defined four epidemiological stages, namely (a) susceptible, (b) infected, (c) immune and (d) dead, each of which we further divided into a fully age-specific structure (Fig. 1). Based on recent research on COVID-19 on humans, we assumed that the dynamics of the model allowed for the recovered individuals to be divided into either susceptible or immune37,38,39,41. Furthermore, we incorporated the potential age-specific infection fatality rate (IFR) based on current estimates from medical and epidemiological research24,25,36,37, adjusted to the lifespan of the gorillas by means of the logistic function$$qleft(xright)=frac{{q}_{M}}{1+{text{exp}}left[-0.2left(x-25right)right]},$$
    (1)
    where qM is the maximum infected mortality probability. Similarly, we modeled the probability of developing immunity as a function of the strength of the disease, which, based on recent research, we measured as mirroring Eq. (1) as$$mleft(xright)=frac{{M}_{I}}{1+{text{exp}}left[-0.2left(x-25right)right]},$$
    (2)
    where MI is the maximum immunity probability (Fig. 2B).To explore the potential impact of COVID-19 on the growth rate of the Karisoke mountain gorilla subpopulation, we varied four of the critical epidemiological variables, namely (a) the basic reproductive number, R0, from 0.5 to 6 (which helps to simulate factors such as increased group density, which may increase the likelihood of transmission), (b) the maximum infected mortality probability, qM = (0.3, 0.6) (Fig. 2A), (c) the immunity duration, TI to 1, 3, 6, and 12 months, and (d) the maximum immunity probability, MI, from 0.2 to 0.8 (Fig. 2B). As time units we used year fractions in half months (i.e., t1 − t0 = 0.5/12), which allowed us to simplify the model, based on current information on the average time of serial interval and incubation period in humans21. This implementation assumes that susceptible individuals could become infected at the beginning of the time interval, while infected individuals in time interval t would either recover (immune or susceptible) or die in t + 1.The deterministic structure of the model implies that the number of individuals in each sex, age and epidemiological stage was given by the possible contribution from the other stages 1/2 month before. This is, the number of susceptible individuals of age x at time t is given by the difference equation$$begin{aligned} n_{s,x,t} & = p_{x – 1} left{ {n_{s,x – 1,t – 1} + n_{i,x – 1,t – 1} left[ {1 – qleft( {x – 1} right)} right]left[ {1 – mleft( {x – 1} right)} right]} right} \ & quad + n_{{m,x – T_{i} ,t – T_{i} }} prodlimits_{{j = x – T_{i} :j > 0}}^{x – 1} {p_{j} – n_{i,x,t} } , \ end{aligned}$$where the ns,x,t is the number of susceptible individuals of age x at time t, and subscripts i and m refer to infected and immune individuals, respectively. For simplicity of notation, we do not include a subscript for sex, although the model does distinguish between sexes. The probability px is the age-specific survival probability. Functions q(x) and m(x) are as in Eqs. (1) and (2). Similarly, the number of immune individuals at time t and age x are$${n}_{m,x,t}={n}_{i,x-1,t-1}left[1-qleft(x-1right)right]mleft(xright)+sum_{{j:0le jle {T}_{i}wedge x-j >0}}{p}_{x-j}{n}_{i,x-j,t-j}.$$We incorporated this mechanistic structure into a stochastic model, where all contributions from time t to t + 1 were drawn from binomial or Poisson distributions. For instance, the total new number of infected individuals, Ni,t, was obtained as a random draw from a Poisson distribution with expected value$$Eleft[{N}_{i,t}right]={text{min}}left[{{R}_{0}N}_{i,t-1},{N}_{t}right],$$where Nt is the total number of individuals in the study subpopulation. We then distributed randomly these individuals into different available ages and sex corresponding to the term ni,x,t, in the susceptible equation above. The number of newborns, Bx,t, at each age for which there were available females at time t was drawn from a binomial distribution with expected value$$Eleft[{B}_{x,t}right]=left({n}_{s,x,t}+{n}_{m,x,t}right){f}_{x}$$where fx is the age-specific average female fecundity rate and ns,x,t and nm,x,t refers to the number of susceptible and immune females, respectively, of age x at time t. The sex of each newborn was then determined by means of a Bernoulli draw with probability given by the proportion of males in the population. Thus, if the draw produced 1 for that individual, it became a male, and if 0 a female.For each scenario, we ran stochastic simulations for 2000 iterations for 10 years and recorded the average number of individuals at each age–sex and epidemiological state at every month. We then ran long-term stochastic simulations for four scenarios with R0 = 3 and maximum immunity probability MI = 0.2. For these, we recorded also the number of subpopulations that went extinct at each month. More

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    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