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    Small lakes at risk from extensive solar-panel coverage

    Rafael Almeida and his colleagues estimate that floating solar panels on 5–10% of the area of large reservoirs could help the world to reach electricity decarbonization targets by 2050 (R. M. Almeida et al. Nature 606, 246–249; 2022). On small lakes in Europe and Asia, however, the existing coverage is significantly higher (averaging 50%, according to our unpublished data), with potentially greater ecological impact (G. Exley et al. Solar Energy 219, 24–33; 2021).
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    The authors declare no competing interests. More

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    No new evidence for an Atlantic eels spawning area outside the Sargasso Sea

    The Sargasso Sea was identified as the spawning area of the European eel (Anguilla anguilla) 100 years ago, and numerous subsequent surveys have verified that eel larvae just a week old are regularly recorded there. However, no adult eels or eel eggs have ever been found, leaving room for alternative hypotheses on the reproduction biology of this enigmatic species. Chang et al.1 theorize about an area along the Mid-Atlantic Ridge as a potential spawning ground. The main argument for this hypothesis was that the chemical signature found in eel otoliths would indicate that early stage larvae had been exposed to a volcanic environment, such as the one present along the Mid-Atlantic Ridge. Since this correlation was solely based on a mis-interpretation of cited literature data, no new, conclusive information to pinpoint the Mid-Atlantic Ridge as an additional or even alternative spawning area was presented by Chang et al.For more than 100 years, the life history of Atlantic eels remains a matter of scientific debate. In a recent paper by Chang and colleagues, published in Scientific Reports (Sci Rep 10, 15981 (2020)), it is hypothesized that the spawning areas of the European eel (Anguilla anguilla) and the American eel (A. rostrata) are located along the Mid-Atlantic Ridge at longitudes between 50° W and 40° W1. This area lies outside the Sargasso Sea, which has so far been widely assumed to be the spawning region of both species since the beginning of the twentieth century2. The Danish researcher Johannes Schmidt collected eel leptocephali 30 mm long or less, some as short as 9 mm, all south of 30° N and west of 50° W3,4. Since then, Schmidt’s assumption was supported by a number of investigations that found recently hatched European eel larvae ( More

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    Caught by a whisker

    The whiskers of seals are known to function as vibration receptors. Earlier experiments with blindfolded harbour seals in captivity have for example revealed that the animals can detect small water movements, and follow the hydrodynamic trails created by passing objects. But it is unclear if seals in the wild actively use this ability to find prey.
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    Prediction of the potential distribution of the predatory mite Neoseiulus californicus (McGregor) in China under current and future climate scenarios

    Moraes, G. J., Mcmurtry, J. A., Denmark, H. A. & Campos, C. B. A revised catalog of the mite family Phytoseiidae. Zootaxa 434, 1–494 (2004).Article 

    Google Scholar 
    Fraulo, A. B. & Liburd, O. E. Biological control of twospotted spider mite, Tetranychus urticae, with predatory mite, Neoseiulus californicus, in strawberries. Exp. Appl. Acarol. 43, 109–119 (2007).PubMed 
    Article 

    Google Scholar 
    Kuştutan, O. & Cakmak, I. Development, fecundity, and prey consumption of Neoseiulus californicus (McGregor) fed Tetranychus cinnabarinus Boisduval. Turk. J. Agric. For. 33, 19–28 (2009).
    Google Scholar 
    Kishimoto, H. et al. Occurrence of Neoseiulus californicus (Acari: Phytoseiidae) on citrus in Kyushu district, Japan. J. Acarol. Soc. Japan 16, 129–137 (2007).Article 

    Google Scholar 
    Albayrak, T., Yorulmaz, S., İnak, E., Toprak, U. & Van Leeuwen, T. Pirimicarb resistance and associated mechanisms in field-collected and selected populations of Neoseiulus californicus. Pestic. Biochem. Phys. 180, 104984 (2022).CAS 
    Article 

    Google Scholar 
    Abdellah, A., Abdelaziz, Z., Philipe, A., Serge, K. & Abdelhamid, E. M. Seasonal trend of Eutetranychus orientalis in Moroccan citrus orchards and its potential control by Neoseiulus californicus and Stethorus punctillum. Syst. Appl. Acarol. 26, 1458–1480 (2021).
    Google Scholar 
    Vidrih, M., Turnšek, A., Rak Cizej, M., Bohinc, T. & Trdan, S. Results of the single release efficacy of the predatory mite Neoseiulus californicus (McGregor) against the two-spotted spider mite (Tetranychus urticae Koch) on a hop plantation. Appl. Sci. 11, 118 (2021).CAS 
    Article 

    Google Scholar 
    Jiang, C. X., Chen, L., Huang, T. T., Mumtaz, M. & Li, Q. Neoseiulus californicus (Acari: Phytoseiidae) shows good predation potential when reared on an artificial diet supplemented with Tetranychus cinnabarinus. Syst. Appl. Acarol. 26, 2229–2246 (2021).
    Google Scholar 
    Katayama, H. et al. Density suppression of the citrus red mite Panonychus citri (Acari: Tetranychidae) due to the occurrence of Neoseiulus californicus (McGregor) (Acari: Phytoseiidae) on Satsuma mandarin. Appl. Entomol. Zool. 41, 679–684 (2006).Article 

    Google Scholar 
    Zhu, R., Guo, J. J., Yi, T. C., Xiao, R. & Jin, D. C. Preying potential of predatory mite Neoseiulus californicus to broad mite Polyphagotarsonemus latus. J. Plant Prot. 46, 465–471 (2019) ([In Chinese]).
    Google Scholar 
    Silva, D. E. et al. Impact of vineyard agrochemicals against Panonychus ulmi (Acari: Tetranychidae) and its natural enemy, Neoseiulus californicus (Acari: Phytoseiidae) in Brazil. Crop Prot. 123, 5–11 (2019).CAS 
    Article 

    Google Scholar 
    Sato, M. E., Da Silva, M. Z., De Souza Filho, M. F., Matioli, A. L. & Raga, A. Management of Tetranychus urticae (Acari: Tetranychidae) in strawberry fields with Neoseiulus californicus (Acari: Phytoseiidae) and acaricides. Exp. Appl. Acarol. 42, 107–120 (2007).PubMed 
    Article 

    Google Scholar 
    De Souza-Pimentel, G. C. et al. Biological control of Tetranychus urticae (Tetranychidae) on rosebushes using Neoseiulus californicus (Phytoseiidae) and agrochemical selectivity. Rev. Colombi. Entomol. 40, 80–84 (2014).
    Google Scholar 
    Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).Article 

    Google Scholar 
    Peterson, A. T. & Shaw, J. Lutzomyia vectors for cutaneous leishmaniasis in southern Brazil: ecological niche models, predicted geographic distribution, and climate change effects. Int. J. Parasitol. 33, 919–931 (2003).PubMed 
    Article 

    Google Scholar 
    Peterson, A. T. & Soberón, J. Species distribution modeling and ecological niche modeling: Getting the Concepts Right. Nat. Conserv. 10, 102–107 (2012).Article 

    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    Stockwell, D. & Peters, D. P. The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158 (1999).Article 

    Google Scholar 
    Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186, 251–270 (2005).Article 

    Google Scholar 
    Arslan, E. S. & Örücü, Ö. K. MaxEnt modelling of the potential distribution areas of cultural ecosystem services using social media data and GIS. Environ. Dev. Sustain. 23, 2655–2667 (2021).Article 

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

    Google Scholar 
    Soberon, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species distributions areas. Biodivers. Inf. 2, 1–10 (2005).
    Google Scholar 
    Ab Lah, N. Z., Yusop, Z., Hashim, M., Salim, J. M. & Numata, S. Predicting the habitat suitability of Melaleuca cajuputi based on the MaxEnt Species Distribution Model. Forests 12, 1449 (2021).Article 

    Google Scholar 
    Ali, H. et al. Expanding or shrinking? range shifts in wild ungulates under climate change in Pamir-Karakoram mountains, Pakistan. PLoS ONE 16, e0260031 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Boral, D. & Moktan, S. Predictive distribution modeling of Swertia bimaculata in Darjeeling-Sikkim Eastern Himalaya using MaxEnt: current and future scenarios. Ecol. Process. 10, 1–16 (2021).Article 

    Google Scholar 
    Kamyo, T. & Asanok, L. Modeling habitat suitability of Dipterocarpus alatus (Dipterocarpaceae) using MaxEnt along the Chao Phraya River in Central Thailand. Forest Sci. Technol. 16, 1–7 (2020).ADS 
    Article 

    Google Scholar 
    Barber, R. A., Ball, S. G., Morris, R. K. A. & Gilbert, F. Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Divers. Distrib. 28, 128–141 (2022).Article 

    Google Scholar 
    Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).Article 

    Google Scholar 
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

    Google Scholar 
    Comino, E., Fiorucci, A., Rosso, M., Terenziani, A. & Treves, A. Vegetation and Glacier Trends in the area of the Maritime Alps Natural Park (Italy): MaxEnt application to predict habitat development. Clim. 9, 54 (2021).Article 

    Google Scholar 
    Wang, R. L. et al. Prediction of the potential distribution of the predatory mite Neoseiulus californicus McGregor in China using MaxEnt. Glob. Ecol. Conserv. 29, e01733 (2021).Article 

    Google Scholar 
    Bertolino, S. et al. Spatially explicit models as tools for implementing effective management strategies for invasive alien mammals. Mamm. Rev. 50, 187–199 (2020).Article 

    Google Scholar 
    Raffini, F. et al. From nucleotides to satellite imagery: approaches to identify and manage the invasive Pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).CAS 
    Article 

    Google Scholar 
    Tang, J. T., Li, J. H., Lu, H., Lu, F. P. & Lu, B. Q. Potential distribution of an invasive pest, Euplatypus parallelus, in China as predicted by Maxent. Pest Manag. Sci. 75, 1630–1637 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chang, Y. et al. Predicting dynamics of the potential breeding habitat of Larus saundersi by MaxEnt model under changing land-use conditions in wetland nature reserve of Liaohe Estuary, China. Remote Sens. 14, 552 (2022).ADS 
    Article 

    Google Scholar 
    Freeman, B. G., Lee-Yaw, J. A., Sunday, J. M. & Hargreaves, A. L. Expanding, shifting and shrinking: The impact of global warming on species’ elevational distributions. Glob. Ecol. Biogeogr. 27, 1268–1276 (2018).Article 

    Google Scholar 
    Smeraldo, S. et al. Generalists yet different: distributional responses to climate change may vary in opportunistic bat species sharing similar ecological traits. Mamm. Rev. 51, 571–584 (2021).Article 

    Google Scholar 
    Pörtner, H. O. et al. Climate Change 2022: The Physical Science Basis. Working Group II contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 15. https://www.ipcc.ch/report/ar6/wg3/ (2022).Ahmed, S. E. et al. Scientists and software–surveying the species distribution modelling community. Divers. Distrib. 21, 258–267 (2015).Article 

    Google Scholar 
    Tognelli, M. F., Roig-Juñent, S. A., Marvaldi, A. E., Flores, G. E. & Lobo, J. M. An evaluation of methods for modelling distribution of Patagonian insects. Rev. Chil. Hist. Nat. 82, 347–360 (2009).Article 

    Google Scholar 
    Pangga, I., Salvacion, A., Hamor, N. & Yap, S. Maximum entropy (MaxEnt) modeling of the potential distribution of Aspidiotus rigidus Reyne (Hemiptera: Diaspididae) in the Philippines. Philipp. Agric. Sci. 104, 1–7 (2021).
    Google Scholar 
    Zhou, R. B. et al. Projecting the potential distribution of Glossina morsitans (Diptera: Glossinidae) under climate change using the MaxEnt model. Biol. 10, 1150 (2021).Article 

    Google Scholar 
    Soberon, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’s distribtional areas. Biodivers. Inform. 2, 1–10 (2005).Article 

    Google Scholar 
    Soberon, J. M. Niche and area of distribution modeling: a population ecology perspective. Ecography 33, 159–167 (2010).Article 

    Google Scholar 
    Soberon, J. M. & Nakamura, M. Niches and distributional areas: concepts, methods and assumptions. P. Natl. Acad. Sci. USA 106, 19644–19650 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhang, Y. X., Ji, J., Chen, X., Lin, J. Z. & Chen, B. L. The effect of temperature on reproduction and development duration of Neoseiulus (Amblyseius) californicus (Mcgregor). Fujian J. Agric. Sci. 27, 157–161 (2012) ([In Chinese]).
    Google Scholar 
    Neto, M. P., Reis, P. R., Zacarias, M. S. & Silva, R. A. Influence of rainfall on mite distribution in organic and conventional coffee systems. Coffee Sci. 5, 67–74 (2010).
    Google Scholar 
    Hu, Z., Gui, L. Y., Hua, D. K. & Luo, J. Effect of simulated rainfall on laboratory population dynamics of Tetranychus cinnabarinus. J. Environ. Entomol. 38, 936–941 (2016) ([In Chinese]).
    Google Scholar 
    Lawler, J. J. Climate change adaptation strategies for resource management and conservation planning. Ann. N. Y. Acad. Sci. 1162, 79–98 (2009).ADS 
    PubMed 
    Article 

    Google Scholar 
    www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained.Gotoh, T., Yamaguchi, K. & Mori, K. Effect of temperature on life history of the predatory mite Amblyseius (Neoseiulus) californicus (Acari: Phytoseiidae). Exp. Appl. Acarol. 32, 15–30 (2004).PubMed 
    Article 

    Google Scholar 
    Yuan, X. P., Wang, X. D., Wang, J. W. & Zhao, Y. Y. Effects of brief exposure to high temperature on Neoseiulus californicus. Ying Yong Sheng Tai Xue Bao 26, 853–858 (2015) ([In Chinese]).PubMed 

    Google Scholar 
    Zhang, G. H. et al. Intraspecific variations on thermal susceptibility in the predatory mite Neoseiulus barkeri Hughes (Acari: Phytoseiidae): responding to long-term heat acclimations and frequent heat hardenings. Biol. Control 121, 208–215 (2018).Article 

    Google Scholar 
    Phillips, S. J., Dudík, M. & Schapire, R. E.[Internet] Maxent software for modeling species niches and distributions (Version 3.4.1). url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 17 March 2022.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. url: https://www.R-project.org/ (2021).Seyedizadeh, S., Ghane-Jahromi, M., Sedaratian-Jahromi, A. & Faraji, F. Discovery of the predatory mite Neoseiulus californicus (Acari: Phytoseiidae) in some rose greenhouses in Iran and describing variation in spermathecal calyx shape. Pers. J. Acarol. 6, 67–70 (2017).
    Google Scholar 
    Fang, X. D., Nguyen, V. L., Ouyang, G. C. & Wu, W. N. Survey of phytoseiid mites (Acari: Mesostigmata, Phytoseiidae) in citrus orchards and a key for Amblyseiinae in Vietnam. Acarologia 60, 254–267 (2020).Article 

    Google Scholar 
    Greco, N. M., Tetzlaff, G. T. & Liljesthröm, G. G. Presence–absence sampling for Tetranychus urticae and its predator Neoseiulus californicus (Acari: Tetranychidae; Phytoseiidae) on strawberries. Int. J. Pest Manag. 50, 23–27 (2004).Article 

    Google Scholar 
    Beaulieu, F. & Beard, J. J. Acarine biocontrol agents Neoseiulus californicus sensu Athias-Henriot (1977) and N. barkeri Hughes (Mesostigmata: Phytoseiidae) redescribed, their synonymies assessed, and the identity of N. californicus (McGregor) clarified based on examination of types. Zootaxa 4500, 451–507 (2018).Kawashima, M. & Jung, C. Effects of sheltered ground habitats on the overwintering potential of the predacious mite Neoseiulus californicus (Acari: Phytoseiidae) in apple orchards on mainland Korea. Exp. Appl. Acarol. 55, 375–388 (2011).PubMed 
    Article 

    Google Scholar 
    Koller, M., Knapp, M. & Schausberger, P. Direct and indirect adverse effects of tomato on the predatory mite Neoseiulus californicus feeding on the spider mite Tetranychus evansi. Entomol. Exp. Appl. 125, 297–305 (2007).Article 

    Google Scholar 
    Ohno, S. et al. Geographic distribution of phytoseiid mite species (Acari: Phytoseiidae) on crops in Okinawa, a subtropical area of Japan. Entomol. Sci. 15, 115–120 (2012).Article 

    Google Scholar 
    Tixier, M. S., Otto, J., Kreiter, S., Dos Santos, V. & Beard, J. Is Neoseiulus wearnei the Neoseiulus californicus of Australia? Exp. Appl. Acarol. 62, 267–277 (2014).PubMed 
    Article 

    Google Scholar 
    Vacacela Ajila, H. E. et al. Supplementary food for Neoseiulus californicus boosts biological control of Tetranychus urticae on strawberry. Pest Manag. Sci. 75, 1986–1992 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Xu, X. N., Wang, B. M., Wang, E. D. & Zhang, Z. Q. Comments on the identity of Neoseiulus californicus sensu lato (Acari: Phytoseiidae) with a redescription of this species from southern China. Syst. Appl. Acarol. 18, 329–344 (2013).
    Google Scholar 
    Pringle, K. L. & Heunis, J. M. Biological control of phytophagous mites in apple orchards in the Elgin area of South Africa using the predatory mite, Neoseiulus californicus (McGregor) (Mesostigmata: Phytoseiidae): a benefit-cost analysis. Afr. Entomol. 14, 113–121 (2006).
    Google Scholar 
    Tai, Y. W. et al. R package ‘corrplot’: Visualization of a Correlation Matrix. url: https://github.com/taiyun/corrplot (2021).Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).Article 

    Google Scholar 
    Araujo, M. B., Pearson, R. G., Tuiller, W. & Erhard, M. Validation of species–climate impact models under climate change. Glob. Change Biol. 11, 1504–1513 (2005).ADS 
    Article 

    Google Scholar 
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

    Google Scholar  More

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    A feeding frenzy of 150 whales marks a species’ comeback

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    Evolutionary ecology of Miocene hominoid primates in Southeast Asia

    Spehar, S. N. et al. Orangutans venture out of the rainforest and into the anthropocene. Sci. Adv. 4, e1701422. https://doi.org/10.1126/sciadv.1701422 (2018).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Suganuma, Y. et al. Magnetostratigraphy of the Miocene Chiang Muan Formation, northern Thailand. Implications for revised chronology of the earliest Miocene hominoid in Southeast Asia. Palaeogeogr. Palaeoclimatol. Plaeoecol. 239, 75–86 (2006).
    Google Scholar 
    Coster, P. et al. A complete magnetic-polarity stratigraphy of the Miocene continental deposits of Mae Moh Basin, northern Thailand, and a reassessment of the age of hominoid-bearing localities in northern Thailand. Geol. Soc. Am. Bull. 122, 1180–1191 (2010).ADS 

    Google Scholar 
    Begun, D. R. The Miocene hominoid radiations. In A Companion to Paleoanthropology (ed. Begun, D. R.) 398–416 (Blackwell Publishing, 2013).
    Google Scholar 
    Pugh, K. D. Phylogenetic analysis of Middle-Late Miocene apes. J. Hum. Evol. 165, 1–33 (2022).
    Google Scholar 
    Chaimanee, Y. et al. Khoratpithecus piriyai, a Late Miocene Hominoid of Thailand. Am. J. Phys. Anthropol. 131, 311–323 (2006).PubMed 

    Google Scholar 
    Chavasseau, O. et al. Advances in the biochronology and biostratigraphy of the continental Neogene of Myanmar. In Fossil Mammals in Asia. Neogene Biostratigraphy and Chronology (eds Wang, X. et al.) 461–474 (Columbia University Press, 2013).
    Google Scholar 
    Patnaik, R. Indian Neogene Siwalik Mammalian biostratigraphy. An overview. In Fossil Mammals in Asia Neogene Biostratigraphy and Chronology (eds Wang, X. et al.) 423–444 (Columbia University Press, 2013).
    Google Scholar 
    Chaimanee, Y. et al. A middle Miocene hominoid from Thailand and orangutan origins. Nature 422, 61–65 (2003).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Chaimanee, Y. et al. A new orang-utan relative from the Late Miocene of Thailand. Nature 427, 439–441 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Chaimanee, Y., Lazzari, V., Chaivanich, K. & Jaeger, J.-J. First maxilla of a late Miocene hominid from Thailand and the evolution of pongine derived characters. J. Hum. Evol. 134, 102636. https://doi.org/10.1016/j.jhevol.2019.06.007 (2019).Article 
    PubMed 

    Google Scholar 
    Jaeger, J.-J. et al. First Hominoid from the Late Miocene of the Irrawaddy formation (Myanmar). PLoS ONE 6, 1–14 (2011).
    Google Scholar 
    Begun, D. R. European hominoids. In The Primate Fossil Record (ed. Hartwig, W. C.) 339–368 (Cambridge University Press, 2002).
    Google Scholar 
    Kelley, J. & Gao, F. Juvenile hominoid cranium from the late Miocene of southern China and hominoid diversity in Asia. Proc. Natl. Acad. Sci. U.S.A. 109, 6882–6885 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kettle, C. J., Maycock, C. R. & Burslem, D. New directions in dipterocarp biology and conservation: A synthesis. Biotropica 44, 658–660. https://doi.org/10.1111/j.1744-7429.2012.00912.x (2012).Article 

    Google Scholar 
    Cannon, C. H., Morley, R. J. & Bush, A. B. G. The current refugial rainforests of Sundaland are unrepresentative of their biogeographic past and highly vulnerable to disturbance. Proc. Natl. Acad. Sci. U.S.A. 106, 11188–11193. https://doi.org/10.1073/pnas.0809865106 (2009).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nelson, S. V. Isotopic reconstruction of habitat change surrounding the extinction of Sivapithecus, a Miocene hominoid, in the Siwalik Group of Pakistan. Palaeogeogr. Palaeoclimatol. Palaeoecol. 243, 204–222 (2007).
    Google Scholar 
    Bender, M. M. Variations in the 13C/12C ratios of plants in relation to the pathway of photosynthetic carbon dioxide fixation. Phytochemistry 10, 1239–1244 (1971).CAS 

    Google Scholar 
    Kohn, M. J. Carbon isotope compositions of terrestrial C3 plants as indicators of (paleo)ecology and (paleo)climate. Proc. Natl. Acad. Sci. 107, 19691–19695 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bonafini, M., Pellegrini, M., Ditchfield, P. & Pollard, A. M. Investigation of the ‘canopy effect’ in the isotope ecology of temperate woodlands. J. Archaeol. Sci. 40, 3926–3935. https://doi.org/10.1016/j.jas.2013.03.028 (2013).Article 

    Google Scholar 
    Krigbaum, J., Berger, M. H., Daegling, D. J. & McGraw, W. S. Stable isotope canopy effects for sympatric monkeys at Tai Forest, Cote d’Ivoire. Biol. Lett. 9, 20130466. https://doi.org/10.1098/rsbl.2013.0466 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dansgaard, W. Stable isotopes in precipitation. Tellus 16, 436–468 (1964).ADS 

    Google Scholar 
    Fannin, L. D. & McGraw, W. S. Does oxygen stable isotope composition in primates vary as a function of vertical stratification or folivorous behaviour?. Folia Primatol. Int. J. Primatol. 91, 219–227. https://doi.org/10.1159/000502417 (2020).Article 

    Google Scholar 
    Louys, J. & Roberts, P. Environmental drivers of megafauna and hominin extinction in Southeast Asia. Nature 586, 402–406. https://doi.org/10.1038/s41586-020-2810-y (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Zin-Maung-Maung-Thein, et al. Stable isotope analysis of the tooth enamel of Chaingzauk mammalian fauna (late Neogene, Myanmar) and its implication to paleoenvironment and paleogeography. Palaeogeogr. Palaeoclimatol. Palaeoecol. 300, 11–22. https://doi.org/10.1016/j.palaeo.2010.11.016 (2011).Article 

    Google Scholar 
    Patnaik, R., Cerling, T. E., Uno, K. T. & Fleagle, J. G. Diet and habitat of Siwalik primates Indopithecus, Sivaladapis and Theropithecus. Ann. Zool. Fenn. 51, 123–142. https://doi.org/10.5735/086.051.0214 (2014).Article 

    Google Scholar 
    Pushkina, D., Bocherens, H., Chaimanee, Y. & Jaeger, J.-J. Stable carbon isotope reconstructions of diet and paleoenvironment from the late Middle Pleistocene Snake Cave in Northeastern Thailand. Naturwissenschaften 97, 299–309 (2010).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Nelson, S. V. The paleoecology of early Pleistocene Gigantopithecus blacki inferred from isotopic analyses. Am. J. Phys. Anthropol. 155, 571–578. https://doi.org/10.1002/ajpa.22609 (2014).Article 
    PubMed 

    Google Scholar 
    Qu, Y. et al. Preservation assessments and carbon and oxygen isotopes analysis of tooth enamel of Gigantopithecus blacki and contemporary animals from Sanhe Cave, Chongzuo, South China during the Early Pleistocene. Quat. Int. 354, 52–58. https://doi.org/10.1016/j.quaint.2013.10.053 (2014).Article 

    Google Scholar 
    Bocherens, H. et al. Flexibility of diet and habitat in Pleistocene South Asian mammals. Implications for the fate of the giant fossil ape Gigantopithecus. Quat. Int. 434, 148–155 (2017).
    Google Scholar 
    Bacon, A.-M. et al. Nam Lot (MIS 5) and Duoi U’Oi (MIS 4) Southeast Asian sites revisited. Zooarchaeological and isotopic evidences. Palaeogeogr. Palaeoclimatol. Palaeoecol. 512, 132–144. https://doi.org/10.1016/j.palaeo.2018.03.034 (2018).Article 

    Google Scholar 
    Jiang, Q.-Y., Zhao, L., Guo, L. & Hu, Y.-W. First direct evidence of conservative foraging ecology of early Gigantopithecus blacki (~2 Ma) in Guangxi, southern China. Am. J. Phys. Anthropol. https://doi.org/10.1002/ajpa.24300 (2021).Article 
    PubMed 

    Google Scholar 
    Ma, J. et al. Isotopic evidence of foraging ecology of Asian elephant (Elephas maximus) in South China during the Late Pleistocene. Quat. Int. 443, 160–167. https://doi.org/10.1016/j.quaint.2016.09.043 (2017).Article 

    Google Scholar 
    Ma, J., Wang, Y., Jin, C., Hu, Y. & Bocherens, H. Ecological flexibility and differential survival of Pleistocene Stegodon orientalis and Elephas maximus in mainland southeast Asia revealed by stable isotope (C, O) analysis. Quat. Sci. Rev. 212, 33–44. https://doi.org/10.1016/j.quascirev.2019.03.021 (2019).ADS 
    Article 

    Google Scholar 
    Janssen, R. et al. Tooth enamel stable isotopes of Holocene and Pleistocene fossil fauna reveal glacial and interglacial paleoenvironments of hominins in Indonesia. Quat. Sci. Rev. 144, 145–154. https://doi.org/10.1016/j.quascirev.2016.02.028 (2016).ADS 
    Article 

    Google Scholar 
    Wang, W. et al. Sequence of mammalian fossils, including hominoid teeth, from the Bubing Basin caves, South China. J. Hum. Evol. 52, 370–379. https://doi.org/10.1016/j.jhevol.2006.10.003 (2007).Article 
    PubMed 

    Google Scholar 
    Suraprasit, K., Bocherens, H., Chaimanee, Y., Panha, S. & Jaeger, J.-J. Late Middle Pleistocene ecology and climate in Northeastern Thailand inferred from the stable isotope analysis of Khok Sung herbivore tooth enamel and the land mammal cenogram. Quat. Sci. Rev. 193, 24–42. https://doi.org/10.1016/j.quascirev.2018.06.004 (2018).ADS 
    Article 

    Google Scholar 
    Bocherens, H., Fizet, M. & Mariotti, A. Diet, physiology and ecology of fossil mammals as inferred from stable carbon and nitrogen biogeochemistry. Implications for Pleistocene bears. Palaeogeogr. Palaeoclimatol. Palaeoecol. 107, 213–225 (1994).
    Google Scholar 
    Koch, P. L., Tuross, N. & Fogel, M. L. The effects of sample treatment and diagenesis on the isotopic integrity of carbonate in biogenic hydroxylapatite. J. Archaeol. Sci. 24, 417–429 (1997).
    Google Scholar 
    Wright, L. E. & Schwarcz, H. P. Correspondence between stable carbon, oxygen and nitrogen isotopes in human tooth enamel and dentine. Infant diets at Kaminaljuyú. J. Archaeol. Sci. 26, 1159–1170 (1999).
    Google Scholar 
    Szpak, P., Metcalfe, J. Z. & Macdonald, R. A. Best practices for calibrating and reporting stable isotope measurments in archaeology. J. Archaeol. Sci. Rep. 13, 609–616 (2017).
    Google Scholar 
    Coplen, T. B. Guidelines and recommended terms for expression of stable-isotope-ratio and gas-ratio measurement results. Rapid Commun. Mass Spectrom. 25, 2538–2560 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bond, A. L. & Hobson, K. A. Reporting stable-isotope ratios in ecology. Recommended terminology, guidelines and best practices. Waterbirds 35, 324–331 (2012).
    Google Scholar 
    Craig, H. Carbon 13 in plants and the relationships between carbon 13 and carbon 14 variations in nature. J. Geol. 62, 115–149. https://doi.org/10.1086/626141 (1954).ADS 
    CAS 
    Article 

    Google Scholar 
    Cerling, T. E. & Harris, J. M. Carbon isotope fractionation between diet and bioapatite in ungulate mammals and implications for ecological and paleoecological studies. Oecologia 120, 347–363 (1999).ADS 
    PubMed 

    Google Scholar 
    Passey, B. H. et al. Carbon isotope fractionation between diet, breath CO2, and bioapatite in different mammals. J. Archaeol. Sci. 32, 1459–1470. https://doi.org/10.1016/j.jas.2005.03.015 (2005).Article 

    Google Scholar 
    Howland, M. R. et al. Expression of the dietary isotope signal in the compound-specific δ13C values of pig bone lipids and amino acids. Int. J. Osteoarchaeol. 13, 54–65. https://doi.org/10.1002/oa.658 (2003).Article 

    Google Scholar 
    Crowley, B. E. et al. Stable carbon and nitrogen isotope enrichment in primate tissues. Oecologia 164, 611–626. https://doi.org/10.1007/s00442-010-1701-6 (2010).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Keeling, C. D. The Suess effect: 13Carbon–14Carbon interrelations. Environ. Int. 2, 229–300. https://doi.org/10.1016/0160-4120(79)90005-9 (1979).CAS 
    Article 

    Google Scholar 
    Marino, B. D., McElroy, M. B., Salawitch, R. J. & Spaulding, W. G. Glacial-to-interglacial variations in the carbon isotopic composition of atmospheric CO2. Nature 357, 461–466. https://doi.org/10.1038/357461a0 (1992).ADS 
    CAS 
    Article 

    Google Scholar 
    Tipple, B. J., Meyers, S. R. & Pagani, M. Carbon isotope ratio of Cenozoic CO2 A comparative evaluation of available geochemical proxies. Paleoceanography https://doi.org/10.1029/2009PA001851 (2010).Article 

    Google Scholar 
    Zachos, J., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Cerling, T. E., Harris, J. M., Leakey, M. G., Passey, B. H. & Levin, N. E. Stable carbon and oxygen isotopes in East African Mammals. Modern and fossil. In Cenozoic Mammals of Africa (ed. Werdelin, L.) 941–952 (University of California Press, 2010).
    Google Scholar 
    Friedli, H., Lötscher, H., Oeschger, H., Siegenthaler, U. & Stauffer, B. Ice core record of the 13C/12C ratio of atmospheric CO2 in the past two centuries. Nature 324, 237–238. https://doi.org/10.1038/324237a0 (1986).ADS 
    CAS 
    Article 

    Google Scholar 
    Nelson, S. V. Paleoseasonality inferred from equid teeth and intra-tooth isotopic variability. Palaeogeogr. Palaeoclimatol. Palaeoecol. 222, 122–144 (2005).
    Google Scholar 
    Komsta, L. Processing data for outliers. R News 6, 10–13 (2006).
    Google Scholar 
    Hutchinson, G. E. Concluding remarks. In Cold spring Harbor Symposium on Quantitative Biology, edited by Q. Biology (1957).Hutchinson, G. E. An Introduction to Population Ecology (Yale University Press, 1978).MATH 

    Google Scholar 
    Baumann, C., Bocherens, H., Drucker, D. G. & Conard, N. J. Fox dietary ecology as a tracer of human impact on Pleistocene ecosystems. PLoS ONE 15, e0235692. https://doi.org/10.1371/journal.pone.0235692 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    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. J. Anim. Ecol. 80, 595–602. https://doi.org/10.1111/j.1365-2656.2011.01806.x (2011).Article 
    PubMed 

    Google Scholar 
    Nelson, S. V. & Hamilton, M. I. Evolution of the human dietary niche. Initial transitions. In Chimpanzees and Human Evolution (eds Muller, M. N. et al.) 286–310 (Harvard University Press, 2017).
    Google Scholar 
    Sun, F. et al. Paleoenvironment of the late Miocene Shuitangba hominoids from Yunnan, Southwest China: Insights from stable isotopes. Chem. Geol. 569, 120123. https://doi.org/10.1016/j.chemgeo.2021.120123 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Nelson, S. V. Chimpanzee fauna isotopes provide new interpretations of fossil ape and hominin ecologies. Proc. R. Soc. B: Biol. Sci. 280, 20132324. https://doi.org/10.1098/rspb.2013.2324 (2013).CAS 
    Article 

    Google Scholar 
    Merceron, G., Taylor, S., Scott, R., Chaimanee, Y. & Jaeger, J.-J. Dietary characterization of the hominoid Khoratpithecus (Miocene of Thailand). Evidence from dental topographic and microwear texture analyses. Naturwissenschaften 93, 329–333. https://doi.org/10.1007/s00114-006-0107-0 (2006).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Kay, R. F. The nut-crackers—A new theory of the adaptations of the ramapithecinae. Am. J. Phys. Anthropol. 55, 141–151 (1981).
    Google Scholar 
    Nelson, S. V. The Extinction of Sivapithecus. Faunal and Environmental Changes Surrounding the Disappearance of a Miocene Hominoid in the Siwaliks of Pakistan (Brill Academic Publishers, 2003).
    Google Scholar 
    Kanamori, T., Kuze, N., Bernard, H., Malim, T. P. & Kohshima, S. Feeding ecology of Bornean orangutans (Pongo pygmaeus morio) in Danum Valley, Sabah, Malaysia: A 3-year record including two mast fruitings. Am. J. Primatol. 72, 820–840. https://doi.org/10.1002/ajp.20848 (2010).Article 
    PubMed 

    Google Scholar 
    Vogel, E. R. et al. Nutritional ecology of wild Bornean orangutans (Pongo pygmaeus wurmbii) in a peat swamp habitat. Effects of age, sex, and season. Am. J. Primatol. 79, 1–20. https://doi.org/10.1002/ajp.22618 (2017).Article 
    PubMed 

    Google Scholar 
    Louys, J. et al. Sumatran orangutan diets in the Late Pleistocene as inferred from dental microwear texture analysis. Quat. Int. 603, 74–81. https://doi.org/10.1016/j.quaint.2020.08.040 (2021).Article 

    Google Scholar 
    Quade, J., Cerling, T. E. & Bowman, J. R. Development of Asian monsoon revealed by marked ecological shift during the latest Miocene in northern Pakistan. Nature 342, 163–166 (1989).ADS 

    Google Scholar 
    Hoorn, C., Ohja, T. & Quade, J. Palynological evidence for vegetation development and climatic change in the sub-Himalayan Zone (Neogene, Central Nepal). Palaeogeogr. Palaeoclimatol. Palaeoecol. 163, 133–161 (2000).
    Google Scholar 
    Morley, R. J. A review of the Cenozoic palaeoclimate history of Southeast Asia. In Biotic Evolution and Environmental Change in Southeast Asia (eds Gower, D. et al.) 79–114 (Cambridge University Press, 2012).
    Google Scholar 
    Morley, R. J. Assembly and division of the South and South-East Asian flora in relation to tectonics and climate change. J. Trop. Ecol. 34, 209–234. https://doi.org/10.1017/S0266467418000202 (2018).Article 

    Google Scholar 
    Sepulchre, P. et al. Mid-tertiary paleoenvironments in Thailand. Pollen evidence. Clim. Past 6, 461–473 (2010).
    Google Scholar 
    Sepulchre, P., Jolly, D., Ducrocq, S., Chaimanee, Y. & Jaeger, J.-J. Mid-tertiary palaeoenvironments in Thailand. Pollen evidence. Clim. Past Discuss. 5, 709–734 (2009).ADS 

    Google Scholar 
    Fleagle, J. G., Janson, C. H. & Reed, K. E. Primate Communities (Cambridge University Press, 1999).
    Google Scholar 
    Fleagle, J. G. Primate Adaptation and Evolution 3rd edn. (Elsevier, 2013).
    Google Scholar 
    Pilbeam, D. Gigantopithecus and the origins of Hominidae. Nature 225, 516–519. https://doi.org/10.1038/225516a0 (1970).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Jiang, Q.-Y., Zhao, L.-X. & Hu, Y.-W. Isotopic (C, O) variations of fossil enamel bioapatite caused by different preparation and measurement protocols: A case study of Gigantopithecus fauna. Vertebr. PalAsiat. 58, 159–168 (2020).
    Google Scholar 
    Hunt, K. D. Why are there apes? Evidence for the co-evolution of ape and monkey ecomorphology. J. Anat. 228, 630–685. https://doi.org/10.1111/joa.12454 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zihlman, A. L., Mcfarland, R. K. & Underwood, C. E. Functional anatomy and adaptation of male gorillas (Gorilla gorilla gorilla) with comparison to male orangutans (Pongo pygmaeus). Anat. Rec. Adv. Integr. Anat. Evol. Biol. 294, 1842–1855. https://doi.org/10.1002/ar.21449 (2011).Article 

    Google Scholar 
    Thorpe, S. K. & Crompton, R. H. Orangutan positional behavior and the nature of arboreal locomotion in Hominoidea. Am. J. Phys. Anthropol. 131, 384–401. https://doi.org/10.1002/ajpa.20422 (2006).Article 
    PubMed 

    Google Scholar 
    Barry, J. C. The history and chronology of Siwalik cercopithecids. J. Hum. Evol. 2, 47–58 (1987).
    Google Scholar 
    Jablonski, N. G., Whitfort, M. J., Roberts-Smith, N. & Qinqi, X. The influence of life history and diet on the distribution of catarrhine primates during the Pleistocene in eastern Asia. J. Hum. Evol. 39, 131–157 (2000).CAS 
    PubMed 

    Google Scholar 
    Takai, M., Saegusa, H., Thaung-Htike, & Zin-Maung-Maung-Thein,. Neogene mammalian fauna in Myanmar. Asian Paleoprimatol. 4, 143–172 (2006).
    Google Scholar 
    Houle, A., Chapman, C. A. & Vickery, W. L. Intratree vertical variation of fruit density and the nature of contest competition in frugivores. Behav. Ecol. Sociobiol. 64, 429–441. https://doi.org/10.1007/s00265-009-0859-6 (2010).Article 

    Google Scholar 
    Vuille, M., Werner, M., Bradley, R. S. & Keimig, F. Stable isotopes in precipitation in the Asian monsoon region. J. Geophys. Res. 110, D23108 (2005).ADS 

    Google Scholar  More

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    Cumulative cultural evolution and mechanisms for cultural selection in wild bird songs

    Study population and song recordingsAll animal procedures were carefully reviewed by the Williams College IACUC (WH-D), the Bowdoin College Research and Oversight Committee (2009–18), and the University of Guelph Animal Care Committee (08R601), and were carried out as specified by the Canadian Wildlife Service (banding permit 10789D).We studied Savannah sparrows (Passerculus sandwichensis) at the Bowdoin Scientific Station on Kent Island, New Brunswick, Canada (44.5818°N, 66.7547°W). Since 1988, individuals nesting within a 10 ha study area in the middle of the island (30–70 pairs each year; part of a larger population of 350–500 males breeding on Kent Island and two adjacent islands) have been colour-banded to facilitate visual identification, and complete demographic information is available for birds on the study site (though not for the entire population) for the years 1989–2004 and 2009–2013. Because of strong natal and breeding philopatry51, birds hatched on the study site itself represent 40–80% of adult breeders in that area, and because of the systematic banding program, ages are known. Each year adds a new generation to the population, with yearlings making up approximately half of the adult breeding males. The birds banded and recorded on the study site are estimated to make up 10–20% of the Savannah sparrow population on Kent Island and two nearby islands.Details of the recording methods used in this study (covering the years 1980, 1982, 1988-9, 1993-8, and 2003–13) can be found elsewhere36,49. Using digitally generated sound spectrograms (using SoundEdit Pro and Audacity), birds were scored as having either a) high note cluster=a final introductory segment interval including at least two different note types, or b) a click train=one or more introductory segment intervals including at least two clicks and no other note types, or c) both features36 (see Supplementary Fig. 1 for a full description of note types). Although a small proportion of birds (mean = 8.3%) did not include either feature in their songs (such birds either had no feature in the introductory segment intervals or one non-click note type in the final interval), we did not include this option in the model and omitted these birds from summaries of the data. We did not include data after the breeding year 2013 because of we began an experimental field tutoring study in the summer of 201364.ModellingWe used a dynamic, discrete time model which allowed us to focus our analysis to specific time points within the year that are related to song learning (the beginning and end of the breeding season). These were: (1) the return of older birds between breeding seasons, (2) the recruitment of young birds singing newly crystallized songs in the spring, and (3) reproduction, resulting in the addition of juveniles during the summer breeding season.Because survival data were not available for every year during the time span we studied, we captured the variation in survival rates observed in the field57 by using a binomial distribution centered on the average historical survival rate for each age class (addressing the possibility that cultural drift resulting from random differences in survival rates was responsible for the shift in song features). The model incorporates stochasticity to capture the variation in population dynamics and return rates by assigning parameter values for survival and return rates from empirically generated probability distributions.We did not include spatial distribution of song variants in the model; although spatial patterns can be important for the dynamics of language loss58, territories with birds singing click trains and high note clusters were intermixed and no spatial structure was apparent (Fig. 3).The model assumes that males choose which features to incorporate into the introductory sections of their songs during song development. Individuals fall into one of six mutually exclusive classes of male Savannah sparrows. The classes are defined by (1) the bird’s developmental stage in the song learning process: juvenile (J, the first year, when the song is plastic) or adult (A, after the first spring, when the song is crystallized), and (2) the variant or variants sung as part of the bird’s introduction (high note clusters, click trains, or both). Denoting note high note clusters with X and click trains with C, the adult classes are therefore AX, AC, and AXC, and the juvenile classes are JX, JC, and JXC. The sum of the individuals in these classes is the total male population.We used two times during each year – late spring and late summer – to correspond to stages in song development (Fig. 5). At a given time t, when breeding is underway in the late spring, the male population consists entirely of adults singing crystallized song, and therefore each juvenile class is empty. At the end of the summer, the population of males has been augmented by juveniles, which are initially assigned to the same variant class as their fathers. To capture these dynamics, we define an intermediate time step, denoted ti. Time t + 1 then corresponds to the following breeding season (late spring), when juvenile males hatched the previous year have completed song development, crystallized their songs, and joined the adult class.Fig. 5: Model of song development.We used two age classes (J = juvenile and A = adult) and three classes of introductions (C = click trains, X = high note clusters, and  XC = both). In the late spring of a given year (time = t), only adult males are present. In late summer, those adults have bred and both they and juvenile males are present; at this intermediate time (ti) each male is initially allocated the same introduction type as his father (solid lines). Then, as song development progresses and juvenile males can be influenced by other tutors, they may retain their initial introduction type or switch to either of the other two types (dashed lines) before they crystallize their songs late in the following spring (time = t+1), and join the breeding cohort, which also includes adult males from the previous year who returned to breed again.Full size imageIn the late summer the male population increases with the addition of juveniles hatched that year, some of which will return to join the singing population the following year; survivors will return to breed within a few hundred meters of where they hatched51. To fit the observed historical decline in the Kent Island population57, the total number of returning juveniles, r (including both those hatched on site and those immigrating from nearby populations at time), follows a Poisson distribution where m = 33.6 – .182x and x is the number of years since 1980 (this function results in a decline of 5 males per decade; the initial number on the study site used in the model, 70, was extrapolated from historical data). The size of each returning juvenile class at time ti then takes the form:$${{{{{{rm{JY}}}}}}}_{{{{{{{rm{t}}}}}}}^{{{{{{rm{i}}}}}}}} sim {{{{{rm{Poisson}}}}}}left(mright)frac{{{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}}{{{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{rm{t}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}}+{{{{{rm{AX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}}}$$
    (1)
    for each Y ∈ {X, C, XC}.After the following winter, the proportion of surviving adults at time t + 1 follows a binomial distribution where the mean survival rate s = 0.48 is derived from historical data. Therefore, each adult class takes the form:$${{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{rm{t}}}}}}+1} sim {{{{{rm{Binomial}}}}}}left({{{{{rm{AY}}}}}},{{{{{rm{s}}}}}}right)* {{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
    (2)
    At the beginning of the next breeding season, juveniles complete song learning64, choosing which variant to crystallize as part of the song, and enter an adult song class; thus all of the juvenile classes disappear at t + 1. Which adult class juveniles join depends on separate learning functions for each of the two variants, ϕX for the high note cluster and ϕC for the click train. The ϕ function takes values between 0 and 1 and gives the probability of crystallizing a song form during the transition from natal year to breeding, depending upon the frequency-dependent bias and selection parameters (see below). These functions define the proportion of features that appear in the next generation as compared to that of the previous generation. Therefore we have:$${{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{rm{t}}}}}}+1}={left({{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
    (3)
    $${{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}+1}={left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{left({{{upphi }}}_{{{{{{rm{C}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right){{{upphi }}}_{{{{{{rm{C}}}}}}}{{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
    (4)
    $${{{{{rm{A}}}}}}{{{{{{rm{XC}}}}}}}_{{{{{{rm{t}}}}}}+1}=2{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right){{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+2{{{upphi }}}_{{{{{{rm{C}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+({{{upphi }}}_{{{{{{rm{X}}}}}}}{{{upphi }}}_{{{{{{rm{C}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}})+{{{{{rm{A}}}}}}{{{{{{rm{XC}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
    (5)
    The sum of probabilities defining all of song crystallization outcomes for the songs of fathers with song type X is:$${left({{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}+{left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}+2{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)=1$$
    (6)
    Learning curvesTo define how young males’ song learning is influenced by the songs they hear, we used learning curves based on type III Holling response curves59 which provide a means to numerically capture functional responses. In our model, the type III curve models the response of juvenile to the song form of adults in the population based on two variables: (1) frequency-dependent bias that favors one form based on its prevalence within the adult population, and (2) selection that favors a particular form of the song.The learning curves, ϕx for the high note cluster and ϕc for the click train, are modified forms of the type III Holling response curve):$${{{upphi }}}_{{{{{{rm{x}}}}}}}=frac{{x}^{{{{{{rm{beta }}}}}}}/{{{{{rm{sigma }}}}}}}{{(1-x)}^{{{{{{rm{beta }}}}}}}+({x}^{{{{{{rm{beta }}}}}}}/{{{{{rm{sigma }}}}}})}$$
    (7)
    and$${{{upphi }}}_{{{{{{rm{c}}}}}}}=frac{{{{{{rm{sigma }}}}}},{c}^{{{{{{rm{beta }}}}}}}}{{(1-c)}^{{{{{{rm{beta }}}}}}}+{{{{{rm{sigma }}}}}}{{c}}^{{{{{{rm{beta }}}}}}}}$$
    (8)
    where x is the proportion of the high note cluster within the population, c is the proportion of the click train within the population, β is frequency-dependent bias (favoring learning the novel or retaining the common variant), and σ is selection on the novel variant (a preference for learning the variant that is not dependent on frequency of the variant and includes factors such as prestige bias, success bias, status, and content bias). Note that the two learning curves do not have identical equations, because selection is not frequency-dependent. In these equations, β  > 1 corresponds to conformist selection, and when β  1 correspond to selection for a novel variant and values of σ  More

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    Transatlantic spread of highly pathogenic avian influenza H5N1 by wild birds from Europe to North America in 2021

    Epidemiological description of exhibition farm outbreakThe index farm where highly pathogenic avian influenza (HPAI) H5N1 virus in captive birds occurred was an exhibition farm in St. John’s, Province of Newfoundland and Labrador, Canada. The farm housed 409 birds of different species, namely chickens, guineafowl, peafowl, emus, domestic ducks, domestic geese, and domestic turkeys. On 9th December 2021, the farm owner first noticed mortality. On 13th December, the farm owner reported the increased mortality to a local veterinarian. Autopsies on four chickens showed swollen heads and cutaneous haemorrhages. Oropharyngeal and cloacal swabs from these chickens tested positive for H5 avian influenza virus at the Atlantic Veterinary College, University of Prince Edward Island, and the Canadian Food Inspection Agency (CFIA) was notified. On 16th December, by which time 306 birds (mostly chickens, turkeys and guineafowl) had died, staff of the CFIA collected tissue samples from dead chickens, as well as oropharyngeal and cloacal swabs and sera from different species of captive birds still present (Table 1), after which all remaining captive birds were culled. All oropharyngeal and cloacal swabs tested positive for HPAI virus of the subtype H5N1 by real-time RT-PCR, and all sera tested positive for influenza nucleoprotein antibodies by ELISA. On 20th December, the CFIA confirmed the diagnosis of HPAI of the subtype H5N1.Table 1 List of samples for virological and serological analysis collected by CFIA on 17 December 2021 from different species of captive birds still present at the farm.Full size tableWild birds were frequently observed co-mingling with the captive population. Captive birds except emus were allowed to move freely in and out of the open pens in which they were housed. At an on-site pond, domestic ducks were reported to mingle with free-living mallards (scientific names of wild birds in Table 2), and a snowy egret had been observed around 2nd to 6th December. Other wild birds reported on the farm were common starlings, feral pigeons, and unspecified gulls.Table 2 Common and scientific species names of the birds mentioned in the text.Full size tableRetrospectively, HPAI H5N1 virus also was identified in tissues of a great black-backed gull found at a nearby pond in St. John’s. The gull had been found ill on 26th November 2021 and taken to a local wildlife rehabilitation centre, where it died the following day.Phylogenetic analysisPhylogenetic analyses were performed to compare the genome sequences of the Newfoundland viruses from the exhibition farm birds and a great black-backed gull found nearby to other influenza viruses in the database. Based on BLAST analysis all eight gene segments of the virus had a Eurasian origin, and the virus was identified as a clade 2.3.4.4b H5N1 virus. Based on maximum likelihood and time-resolved trees inferred by use of whole genome sequences, the Newfoundland viruses had a shared common ancestor with European viruses circulating in early 2021 (Figs. 1, 2). The dates for the most recent common ancestor (MRCA) of all gene segments ranged from December 2019 to April 2021 (Table 3). There was no evidence that the Newfoundland viruses were genetically closely related to other current or recent viruses circulating in Europe. In contrast to currently circulating European viruses, the sequences of the Newfoundland viruses had no evidence of reassortment with other avian influenza viruses after ancestral emergence (Fig. 3). The virus from the great black-backed gull was highly similar to those from the exhibition farm, except for a small number of nucleotide differences in the neuraminidase (N) gene segment.Figure 1Maximum likelihood phylogenetic tree of the H5 HA gene. Relationships among the European 2021 H5 2.3.4.4b HPAI strains (magenta) and the Newfoundland wild bird and outbreak strains (red) are shown. The tree is rooted by the outgroup and nodes placed in descending order. Clades are collapsed for clarity.Full size imageFigure 2Maximum likelihood phylogenetic tree of the H5 gene segments. Relationships among the European 2021 H5 2.3.4.4b HPAI strains (magenta) and the Newfoundland wild bird and outbreak strains (red) are shown. The tree is rooted by the outgroup and nodes placed in descending order; order: HA, NA, PA, PB1, PB2, NP, MP, NS.Full size imageTable 3 Dates for the most recent common ancestor (MRCA) of all gene segments.Full size tableFigure 3Phylogenetic incongruence analyses. Maximum likelihood trees for the H and N gene segments and internal gene segments from equivalent strains were connected across the trees. Tips and connecting lines are coloured according to the legend.Full size imageAnalysis of avian migration and potential routes for HPAI H5 virus to be carried across the Atlantic with migrating birdsThere are several pathways for HPAI H5N1 virus to be carried across the Atlantic with migrating birds, based on the multitude of migration routes of wild birds and their overlapping ranges at breeding, stop-over, and wintering sites. Ring-recovery data confirm the regular movements of wild birds from Europe to Iceland and other North Atlantic islands, and from there to North America, and also provide evidence for direct movements of for example seabirds and gulls (Supplementary Table 1). Ringed individuals with a European origin have been found on Newfoundland for 10 of the 24 species in Supplementary Table 1: barnacle goose (1 ringed individual), Eurasian wigeon (5), Eurasian teal (1), great skua (13), European herring gull (1), black-headed gull (1), black-legged kittiwake (102), purple sandpiper (1), Brunnich’s guillemot (15), and Atlantic puffin (50). Given that the most likely ancestor of the virus detected in Newfoundland was circulating in Northwest Europe between the beginning of the 2020/2021 outbreak in Europe in October 2020 and April 2021 (see above), likely routes include spring migration of bird species moving to Icelandic, Greenlandic or Canadian High Arctic breeding grounds, or migration directly across the Atlantic Ocean (Fig. 4).Figure 4Maps of transatlantic migration. Putative virus transmission pathways between Europe and Newfoundland via migratory waterfowl/shorebirds (a) and pelagic seabirds (b). Many Icelandic waterfowl and shorebirds (a) winter in Northwest Europe and return to Iceland to breed in spring (1), including whooper swans, greylag geese, pink-footed geese, Eurasian wigeons, Eurasian teals, northern pintails, common ringed plovers and purple sandpipers. Some bird populations use Iceland as a stopover site, and continue to breeding grounds in East Greenland (2; barnacle geese and pink-footed geese), the East Canadian Arctic (3; light-bellied brent geese, red knots, ruddy turnstones) and West Greenland (4; greater white-fronted geese). Migratory birds from Europe share these breeding areas with species that winter in North America, including Canada geese and snow geese from East Greenland and the East Canadian Arctic (5), and some Iceland-breeding species of duck, including small numbers of Eurasian wigeons, Eurasian teals, and tufted ducks (6). Several seabird species (b), such as gulls, skuas, fulmars and auks, have large breeding ranges in the Arctic. After the breeding season many species become fully pelagic and can roam large parts of the northern Atlantic. The mid-Atlantic ridge outside Newfoundland is an important non-breeding area for seabirds, and is frequented by auks from Iceland (7), Svalbard (8) and Norway (9), including large numbers of Atlantic puffins and Brünnich guillemots, and by black-legged kittiwakes and northern fulmars originating from Iceland, Norway and the United Kingdom (7–8, 10). There these birds are joined by seabirds from Canadian and Greenlandic waters (11). Direct migratory links to Newfoundland occurs through greater and lesser-black backed gulls as well as black-headed gulls from Iceland and Greenland (12, 13), and gulls also link the pelagic and the coastal zone around Newfoundland (14). Thickness of the lines highlights the relative approximate population sizes. Dashed lines show where small numbers of individuals, or vagrants, provide a potential pathway. For more details on species and population numbers see Table 2. This figure was prepared using the software R (version 4.0.5, https://www.r-project.org/) and the following packages: -ggplot2 (version 3.3.5, https://cran.r-project.org/web/packages/ggplot2/index.html), -sf (version 1.0.5, https://cran.r-project.org/web/packages/sf/index.html).Full size imageThe first possible route via Iceland seems to be the strongest link between Newfoundland and Europe14,15,16,17, because it is a meeting point of breeding bird populations which winter in Europe as well as along the East coast of North America. Numerous species, totaling almost two million individual birds, migrate annually from northwestern Europe to breeding grounds in Iceland and beyond. Several populations breed on Iceland, including swans (whooper swan) (Supplementary Table 1), geese (greylag goose, pink-footed goose), ducks (Eurasian wigeon, Eurasian teal, Northern pintail), gulls (great- and lesser black-backed gull, black-headed gull, black-legged kittiwake) and shorebirds (common ringed plover, purple sandpiper, Supplementary Table 1). In addition, several species (e.g. barnacle geese and pink-footed geese) migrating to breeding grounds further away (Greenland and/or Canadian High Arctic) make spring and autumn stopovers in Iceland18,19. This creates potential for the virus to have been spread northwards to Iceland (or further northward) in spring, where it could have circulated among breeding birds, or transmitted during autumn migration by species returning from the Arctic. Several Iceland-breeding species of ducks (Eurasian wigeon, Eurasian teal, tufted duck), gulls (lesser black-backed gull, black-legged kittiwake, black-headed gull) and alcids (Brunnich’s guillemot, Atlantic puffin) winter along the Atlantic coast of North America in variable numbers (Supplementary Table 1). If the virus was transmitted to one of these populations during their stay on Iceland, it could have been spread to Newfoundland during the subsequent autumn migration. Importantly, Iceland-breeding Eurasian wigeons or Eurasian teals could be responsible for both the journey to Iceland from European wintering grounds, as well as the journey from Iceland to Newfoundland, where these species are frequently encountered as vagrants (Supplementary Table 1)20,21.The second possible route is via species that migrate from northwestern Europe to the Canadian High Arctic and/or Northwest Greenland. These include shorebirds (e.g. ruddy turnstone, red knot) and some geese (light-bellied brent goose and greater white-fronted goose). If the virus circulated in these breeding populations and then moved to other coastal marine bird populations bordering Baffin Bay, which include huge numbers of colonial seabirds and marine waterfowl22,23, the virus could have followed a coastal or even pelagic route south with the large autumn migration of Arctic marine birds (various sea ducks, auks and larids)24,25 to emerge in Newfoundland. Alternatively, shorebirds and waterfowl may have played a role: several European-wintering populations have overlapping breeding grounds with populations wintering along the east coast of North America. Regarding geese, greater white-fronted geese share breeding grounds in western Greenland with Canada geese26,27, which migrate south along the Canadian Atlantic coast. Also, brent geese have overlapping breeding grounds with snow geese18. In addition, individual barnacle geese and pink-footed geese breeding in Greenland could also have travelled south to Newfoundland carrying the virus, as these birds are regular vagrants to the North American Atlantic coast28. While geese occur only in small numbers on Newfoundland, two barnacle geese and four pink-footed geese, probably originating from Greenland breeding grounds, were observed in the autumn of 2021. St. John’s is the first major population center on a coastal route south from Baffin Bay/Davis Strait and along the Labrador Shelf, so emergence in eastern Newfoundland is consistent with this route.Three wild bird species involved in the Iceland and/or Greenland/High Canadian Arctic routes deserve particular attention. Eurasian wigeon have been prominently involved in outbreaks in Eurasia, and are considered prime candidates for carrying HPAI virus over long distances29. Also, during the first stages of an outbreak they are one of the first species to be detected HPAI virus positive, often without clinical signs. Barnacle geese and greylag geese, which congregate in Iceland, were in the top three most abundant species detected H5-positive in Europe in late winter and early spring 20215. Given that both greylag and barnacle geese have populations breeding on Iceland/Greenland and wintering in Europe (particularly the UK), these two species are high on the list of probable vectors that transported the virus to Iceland/Greenland and finally to Newfoundland. The high involvement of infected geese in the HPAI dynamics, which was not seen before October 2020, together with the unusually high levels of HPAI H5 virus presence in wild birds in Northwest Europe in spring 2021, might also explain why HPAI H5 virus spread to Newfoundland this winter (2021/2022), and not in the previous winters (2020/2021, 2016/2017, 2014/2015, 2005/2006). It is, however, striking that no cases of HPAI H5 virus have been recorded on Iceland in 2021.A third possible, pelagic, route is directly across the Atlantic Ocean. Such a route could have started with coastal and pelagic seabirds in Northwest Europe, where the virus may have remained undetected for much of the summer of 2021. A subsequent migration of seabirds to Newfoundland waters in the autumn of 2021 could have brought the virus to North America. The large populations of black-legged kittiwakes and northern fulmars that breed in the U.K. have long been known to frequent Newfoundland waters30, and these movements have been corroborated by recent telemetry studies31. Further, recent telemetry information reveals that millions of pelagic seabirds breeding all across the Atlantic congregate over the Mid-Atlantic Ridge in the central North Atlantic at all times of year32, making a pelagic transmission route a possibility. From the pelagic wintering grounds off Newfoundland, a species that uses both pelagic and coastal habitats, possibly a gull, may have brought the virus to shore in St. John’s. Trans-Atlantic transmission via seabirds has been suggested for LPAI viruses, including detection of mosaic Eurasian-North American viruses in gulls and alcids12,33,34,35.For the time period and geographical frame considered, HPAI-H5-positive species included ducks (Eurasian wigeon, mallard, common eider), geese (barnacle, greylag, brent, pink-footed and greater white-fronted goose), swans (whooper), gulls (black-headed, herring, lesser black-backed, great black-backed), and shorebirds (red knot, ruddy turnstone) (Supplementary Table 2). Of these 15 species, ringed individuals with a European origin have been recorded on Newfoundland for barnacle goose (1 ringed individual), Eurasian wigeon (5), great skua (13), and black-headed gull (1) (Supplementary Table 1). Ringed individuals with a European origin have been found on Newfoundland for 5 species which were found to be HPAI-H5-positive between October 2020 and April 2021, such as Barnacle Goose (1), Eurasian Wigeon (5), Great Skua (13), Black-headed Gull (1). These species might be considered to be possible carriers of HPAI H5 virus from Europe in late winter 2020/2021 or early spring 2021 partly or all the way to Newfoundland. However, given the incompleteness of sampling and the possibility of wild birds carrying HPAI virus subclinically, the involvement of other wild bird species in transatlantic virus transport cannot be ruled out.Having reached the Avalon Peninsula of Newfoundland via one of above routes, the virus may have spread further within the abundant local populations of ducks and gulls wintering in the city of St. John’s. The peridomestic populations of some of these species may be candidates for incursion of the virus into the farm in St John’s. More