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    Silicon improves ion homeostasis and growth of liquorice under salt stress by reducing plant Na+ uptake

    Zhao, S. et al. Regulation of plant responses to salt stress. Int. J. Mol. Sci. 22, 4609 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Acosta-Motos, J. R. et al. Plant responses to salt stress: Adaptive mechanisms. Agronomy-Basel 7, 18 (2017).
    Google Scholar 
    Munns, R. & Tester, M. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 59, 651–681 (2008).CAS 
    PubMed 

    Google Scholar 
    Wu, H. H. Plant salt tolerance and Na+ sensing and transport. Crop J. 6, 215–225 (2018).
    Google Scholar 
    Ali, M. et al. Silicon mediated improvement in the growth and ion homeostasis by decreasing Na+ uptake in maize (Zea mays L.) cultivars exposed to salinity stress. Plant Physiol. Biochem. 158, 208–218 (2021).CAS 
    PubMed 

    Google Scholar 
    Javaid, T., Farooq, M. A., Akhtar, J., Saqib, Z. A. & Anwar-ul-Haq, M. Silicon nutrition improves growth of salt-stressed wheat by modulating flows and partitioning of Na+, Cl- and mineral ions. Plant Physiol. Biochem. 141, 291–299 (2019).CAS 
    PubMed 

    Google Scholar 
    Zelm, E. V., Zhang, Y. X. & Testerink, C. Salt tolerance mechanisms of plants. Annu. Rev. Plant Biol. 71, 403–433 (2020).PubMed 

    Google Scholar 
    Kumar, P. et al. Potassium: A key modulator for cell homeostasis. J. Biotechnol. 324, 198–210 (2020).CAS 
    PubMed 

    Google Scholar 
    Ahmad, P., Ahanger, M. A., Alam, P., Alyemeni, M. N. & Ashraf, M. Silicon (Si) supplementation alleviates NaCl toxicity in mung bean [Vigna radiata (L.) Wilczek] through the modifications of physio-biochemical attributes and key antioxidant enzymes. J. Plant Growth Regul. 38, 1–13 (2018).
    Google Scholar 
    Chiappero, J. et al. Antioxidant status of medicinal and aromatic plants under the influence of growth-promoting rhizobacteria and osmotic stress. Ind. Crops Prod. 167, 113541 (2021).CAS 

    Google Scholar 
    Conceicao, S. S. et al. Silicon modulates the activity of antioxidant enzymes and nitrogen compounds in sunflower plants under salt stress. Arch. Agron. Soil Sci. 65, 1237–1247 (2019).CAS 

    Google Scholar 
    Etesami, H. & Jeong, B. R. Silicon (Si): Review and future prospects on the action mechanisms in alleviating biotic and abiotic stresses in plants. Ecotoxicol. Environ. Saf. 147, 881–896 (2018).CAS 
    PubMed 

    Google Scholar 
    Epstein, E. Silicon. Annu. Rev. Plant Physiol. Plant Mol. Biol. 50, 641–664 (1999).CAS 
    PubMed 

    Google Scholar 
    Epstein, E. The anomaly of silicon in plant biology. Proc. Natl. Acad. Sci. U S A 91, 11–17 (1994).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jadhao, K. R., Bansal, A. & Rout, G. R. Silicon amendment induces synergistic plant defense mechanism against pink stem borer (Sesamia inferens Walker.) in finger millet (Eleusine coracana Gaertn.). Sci. Rep. 10, 15 (2020).
    Google Scholar 
    Li, Z. C. et al. Silicon enhancement of estimated plant biomass carbon accumulation under abiotic and biotic stresses. A meta-analysis. Agron. Sustain. Dev. 38, 19 (2018).CAS 

    Google Scholar 
    Yan, G. C. et al. Silicon improves rice salinity resistance by alleviating ionic toxicity and osmotic constraint in an organ-specific pattern. Front. Plant Sci. 11, 12 (2020).
    Google Scholar 
    Farouk, S., Elhindi, K. M. & Alotaibi, M. A. Silicon supplementation mitigates salinity stress on Ocimum basilicum L. via improving water balance, ion homeostasis, and antioxidant defense system. Ecotoxicol. Environ. Saf. 206, 11 (2020).
    Google Scholar 
    Yin, J. L. et al. Silicon enhances the salt tolerance of cucumber through increasing polyamine accumulation and decreasing oxidative damage. Ecotoxicol. Environ. Saf. 169, 8–17 (2019).CAS 
    PubMed 

    Google Scholar 
    Hurtado, A. C. et al. Different methods of silicon application attenuate salt stress in sorghum and sunflower by modifying the antioxidative defense mechanism. Ecotoxicol. Environ. Saf. 203, 11 (2020).
    Google Scholar 
    Gaur, S. et al. Fascinating impact of silicon and silicon transporters in plants: A review. Ecotoxicol. Environ. Saf. 202, 12 (2020).
    Google Scholar 
    Vandegeer, R. K. et al. Silicon deposition on guard cells increases stomatal sensitivity as mediated by K(+)efflux and consequently reduces stomatal conductance. Physiol. Plant 171, 358–370 (2021).CAS 
    PubMed 

    Google Scholar 
    Lina, et al. Silicon-mediated changes in polyamines participate in silicon-induced salt tolerance in Sorghum bicolor L.. Plant Cell Environ. 39, 245–258 (2016).
    Google Scholar 
    Hassanvand, F., Nejad, A. R. & Fanourakis, D. Morphological and physiological components mediating the silicon-induced enhancement of geranium essential oil yield under saline conditions. Ind. Crops Prod. 134, 19–25 (2019).CAS 

    Google Scholar 
    Altuntas, O., Dasgan, H. Y. & Akhoundnejad, Y. Silicon-induced salinity tolerance improves photosynthesis, leaf water status, membrane stability, and growth in pepper (Capsicum annuum L.). HortScience 53, 1820–1826 (2018).CAS 

    Google Scholar 
    Coskun, D. et al. The controversies of silicon’s role in plant biology. New Phytol. 221, 67–85 (2019).PubMed 

    Google Scholar 
    Jiang, M. Y. et al. An “essential herbal medicine”-licorice: A review of phytochemicals and its effects in combination preparations. J. Ethnopharmacol. 249, 14 (2020).
    Google Scholar 
    Zhang, X. Y. et al. Inhibition effect of glycyrrhiza polysaccharide (GCP) on tumor growth through regulation of the gut microbiota composition. J. Pharmacol. Sci. 137, 324–332 (2018).CAS 
    PubMed 

    Google Scholar 
    Baltina, L. A. et al. Glycyrrhetinic acid derivatives as Zika virus inhibitors: Synthesis and antiviral activity in vitro. Bioorg. Med. Chem. 41, 116204 (2021).CAS 
    PubMed 

    Google Scholar 
    Zhao, Z. Y. et al. Glycyrrhizic ccid nanoparticles as antiviral and anti-inflammatory agents for COVID-19 treatment. ACS Appl. Mater. Interfaces 13, 20995–21006 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lu, J. H., Lv, X., Wu, L. & Li, X. Y. Germination responses of three medicinal licorices to saline environments and their suitable ecological regions. Acta Pratacul. Sin. 22, 198–205 (2013).
    Google Scholar 
    Geng, G. Q. & Xie, X. R. Effect of drought and salt stress on the physiological and biochemical characteristics of Glycyrrhiza uralensis. Pratacult. Sci. 35, 113–120 (2018).
    Google Scholar 
    Cui, J. J., Zhang, X. H., Li, Y. T., Zhou, D. & Zhang, E. H. Effect of silicon addition on seedling morphological and physiological indicators of Glycyrrhiza uralensis under salt stress. Acta Pratacul. Sin. 24, 214–220 (2015).
    Google Scholar 
    Zhang, W. J. et al. Silicon alleviates salt and drought stress of Glycyrrhiza uralensis plants by improving photosynthesis and water status. Biol. Plant. 64, 302–313 (2020).CAS 

    Google Scholar 
    Zhang, W. J. et al. Silicon promotes growth and root yield of Glycyrrhiza uralensis under salt and drought stresses through enhancing osmotic adjustment and regulating antioxidant metabolism. Crop Prot. 107, 1–11 (2018).
    Google Scholar 
    Chen, D. Q. et al. Silicon moderated the K deficiency by improving the plant-water status in sorghum. Sci. Rep. 6, 14 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Cui, J. J., Zhang, E. H., Zhang, X. H. & Wang, Q. Silicon alleviates salinity stress in licorice (Glycyrrhiza uralensis) by regulating carbon and nitrogen metabolism. Sci. Rep. 11, 12 (2021).
    Google Scholar 
    Lichtenthaler, H. K. & Wellburn, A. R. Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Analysis 11, 591–592 (1983).CAS 

    Google Scholar 
    Yan, K., Wu, C. W., Zhang, L. H. & Chen, X. B. Contrasting photosynthesis and photoinhibition in tetraploid and its autodiploid honeysuckle (Lonicera japonica Thunb.) under salt stress. Front. Plant Sci. 6, 9 (2015).
    Google Scholar 
    Li, H. S. Principles and Techniques of Plant Physiological and Biochemical Experiments (Higher Education Press, 2000).
    Google Scholar 
    Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
    PubMed 

    Google Scholar 
    Lutts, S., Kinet, J. M. & Bouharmont, J. NaCl-induced senescence in leaves of rice (Oryza sativa L) cultivars differing in salinity resistance. Ann. Bot. 78, 389–398 (1996).CAS 

    Google Scholar 
    Havir, E. A. & Mchale, N. A. Biochemical and developmental characterization of multiple forms of catalase in tobacco leaves. Plant Physiol. 84, 450–455 (1987).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rizwan, M. et al. Mechanisms of silicon-mediated alleviation of drought and salt stress in plants: A review. Environ. Sci. Pollut. Res. 22, 15416–15431 (2015).CAS 

    Google Scholar 
    Al-Huqail, A. A., Alqarawi, A. A., Hashem, A., Malik, J. A. & Abd Allah, E. F. Silicon supplementation modulates antioxidant system and osmolyte accumulation to balance salt stress in Acacia gerrardii Benth. Saudi J. Biol. Sci. 26, 1856–1864 (2019).CAS 
    PubMed 

    Google Scholar 
    Hurtado, A. C. et al. Silicon application induces changes C:N: P stoichiometry and enhances stoichiometric homeostasis of sorghum and sunflower plants under salt stress. Saudi J. Biol. Sci. 27, 3711–3719 (2020).
    Google Scholar 
    Zhang, X. H., Zhang, W. J., Lang, D. Y., Cui, J. J. & Li, Y. T. Silicon improves salt tolerance of Glycyrrhiza uralensis Fisch by ameliorating osmotic and oxidative stresses and improving phytohormonal balance. Environ. Sci. Pollut. Res. 25, 25916–25932 (2018).CAS 

    Google Scholar 
    Liang, W. J., Ma, X. L., Wan, P. & Liu, L. Y. Plant salt-tolerance mechanism: A review. Biochem. Biophys. Res. Commun. 495, 286–291 (2018).CAS 
    PubMed 

    Google Scholar 
    Tester, M. & Davenport, R. Na+ tolerance and Na+ transport in higher plants. Ann. Bot. 91, 503–527 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khan, W. U. D. et al. Silicon nutrition mitigates salinity stress in maize by modulating ion accumulation, photosynthesis, and antioxidants. Photosynthetica 56, 1047–1057 (2018).CAS 

    Google Scholar 
    Zahoor, R. et al. Potassium fertilizer improves drought stress alleviation potential in cotton by enhancing photosynthesis and carbohydrate metabolism. Environ. Exp. Bot. 137, 73–83 (2017).CAS 

    Google Scholar 
    Hurtado, A. C. et al. Silicon alleviates sodium toxicity in sorghum and sunflower plants by enhancing ionic homeostasis in roots and shoots and increasing dry matter accumulation. SILICON 13, 475–486 (2021).CAS 

    Google Scholar 
    Yan, G. C. et al. Silicon alleviates salt stress-induced potassium deficiency by promoting potassium uptake and translocation in rice (Oryza sativa L.). J. Plant Physiol. 258, 7 (2021).
    Google Scholar 
    Dhiman, P. et al. Fascinating role of silicon to combat salinity stress in plants: An updated overview. Plant Physiol. Biochem. 162, 110–123 (2021).CAS 
    PubMed 

    Google Scholar 
    Bosnic, P., Bosnic, D., Jasnic, J. & Nikolic, M. Silicon mediates sodium transport and partitioning in maize under moderate salt stress. Environ. Exp. Bot. 155, 681–687 (2018).CAS 

    Google Scholar 
    Alamri, S. et al. Silicon-induced postponement of leaf senescence is accompanied by modulation of antioxidative defense and ion homeostasis in mustard (Brassica juncea) seedlings exposed to salinity and drought stress. Plant Physiol. Biochem. 157, 47–59 (2020).CAS 
    PubMed 

    Google Scholar 
    Ahmad, P. et al. Nitric oxide mitigates salt stress by regulating levels of osmolytes and antioxidant enzymes in chickpea. Front. Plant Sci. 7, 1–11 (2016).
    Google Scholar 
    Zhu, Y. X. et al. Silicon confers cucumber resistance to salinity stress through regulation of proline and cytokinins. Plant Physiol. Biochem. 156, 209–220 (2020).CAS 
    PubMed 

    Google Scholar  More

  • in

    First titanosaur dinosaur nesting site from the Late Cretaceous of Brazil

    Carballido, J. L. et al. A new giant titanosaur sheds light on body mass evolution among sauropod dinosaurs. Proc. R. Soc. B Biol. Sci. 284, 20171219 (2017).
    Google Scholar 
    Hechenleitner, E. M. et al. Two Late Cretaceous sauropods reveal titanosaurian dispersal across South America. Commun. Biol. 3, 622 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Otero, A., Carballido, J. L., Salgado, L., Canudo, I. & Garrido, A. C. Report of a giant titanosaur sauropod from the Upper Cretaceous of Neuquén Province, Argentina. Cretaceous Res. 122, 104754 (2021).
    Google Scholar 
    Sander, P. M. et al. Biology of the sauropod dinosaurs: The evolution of gigantism. Biol. Rev. 86, 117–155 (2011).PubMed 

    Google Scholar 
    Gorscak, E. & O’Connor, P. M. A new African titanosaurian sauropod dinosaur from the middle Cretaceous Galula Formation (Mtuka Member), Rukwa Rift Basin, southwestern Tanzania. PLoS One 14, e0211412 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Poropat, S. F. et al. Second specimen of the Late Cretaceous Australian sauropod dinosaur Diamantinasaurus matildae provides new anatomical information on the skull and neck of early titanosaurs. Zool. J. Linn. Soc. 192, 610–674 (2021).
    Google Scholar 
    González Riga, B. J. et al. An overview of the appendicular skeletal anatomy of South American titanosaurian sauropods, with definition of a newly recognized clade. An. Acad. Bras. Ciênc. 91, e20180374 (2019).
    Google Scholar 
    Gorscak, E. & O’connor, P. M. Time-calibrated models support congruency between Cretaceous continental rifting and titanosaurian evolutionary history. Biol. Lett. 12, 20151047 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Pérez Moreno, A., Carballido, J. L., Otero, A., Salgado, L. & Calvo, J. O. The axial skeleton of Rinconsaurus caudamirus (Sauropoda: Titanosauria) from the Late Cretaceous of Patagonia, Argentina. Ameghiniana 59, 1–46 (2022).
    Google Scholar 
    Chiappe, L. M., Jackson, F., Coria, R. A. & Dingus, L. Nesting titanosaurs from Auca Mahuevo and adjacent sites. In The Sauropods: Evolution and Paleobiology (eds Curry Rogers, K. A. & Wilson, J. A.) 285–302 (University of California Press, 2005).
    Google Scholar 
    Hechenleitner, E. M., Grellet-Tinner, G. & Fiorelli, L. E. What do giant titanosaur dinosaurs and modern Australasian megapodes have in common?. PeerJ 3, e1341 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Cojan, I., Renard, M. & Emmanuel, L. Palaeoenvironmental reconstruction of dinosaur nesting sites based on a geochemical approach to eggshells and associated palaeosols (Maastrichtian, Provence Basin, France). Palaeogeogr. Palaeoclimatol. Palaeoecol. 191, 111–138 (2003).
    Google Scholar 
    Grellet-Tinner, G., Codrea, V., Folie, A., Higa, A. & Smith, T. First evidence of reproductive adaptation to “island effect” of a dwarf Cretaceous Romanian titanosaur, with embryonic integument in ovo. PLoS One 7(3), e32051 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khosla, A. & Lucas, S. Late Cretaceous dinosaur eggs and eggshells of peninsular India. Top. Geobiol. 51, 1–295 (2020).
    Google Scholar 
    Vila, B., Galobart, A., Oms, O., Poza, B. & Bravo, A. M. Assessing the nesting strategies of Late Cretaceous titanosaurs: 3-D clutch geometry from a new megaloolithid egg site. Lethaia 43, 197–208 (2009).
    Google Scholar 
    Chiappe, L. M. et al. Sauropod dinosaur embryos from the Late Cretaceous of Patagonia. Nature 396, 258–261 (1998).ADS 
    CAS 

    Google Scholar 
    Salgado, L. et al. Upper Cretaceous dinosaur nesting sites of Río Negro (Salitral Ojo de Agua and Salinas de Trapalco-Salitral de Santa Rosa), Northern Patagonia, Argentina. Cretaceous Res. 28, 392–404 (2007).
    Google Scholar 
    Salgado, L., Magalhães Ribeiro, C. M., García, R. A. & Fernández, M. A. Late Cretaceous megaloolithid eggs from Salitral de Santa Rosa (Río Negro, Patagonia, Argentina): Inferences on the titanosaurian reproductive biology. Ameghiniana 46, 605–620 (2009).
    Google Scholar 
    Grellet-Tinner, G. & Fiorelli, L. E. A new Argentinean nesting site showing neosauropod dinosaur reproduction in a Cretaceous hydrothermal environment. Nat. Commun. 1, 32 (2010).ADS 
    PubMed 

    Google Scholar 
    Hechenleitner, E. M. et al. A new Upper Cretaceous titanosaur nesting site from La Rioja (NW Argentina), with implications for titanosaur nesting strategies. Palaeontology 59, 433–446 (2016).
    Google Scholar 
    Kundrát, M. et al. Specialized craniofacial anatomy of a titanosaurian embryo from Argentina. Curr. Biol. 30, 4263–4269 (2020).PubMed 

    Google Scholar 
    Chiappe, L. M., Salgado, L. & Coria, R. A. Embryonic skulls of titanosaur sauropod dinosaurs. Science 293, 2444–2446 (2001).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Grellet-Tinner, G., Chiappe, L. M., Norell, M. & Bottjer, D. Dinosaur eggs and nesting behaviors: A paleobiological investigation. Palaeogeogr. Palaeoclimatol. Palaeoecol. 232, 294–321 (2006).
    Google Scholar 
    Coria, R. A. & Chiappe, L. M. Embryonic skin from Late Cretaceous sauropods (Dinosauria) of Auca Mahuevo, Patagonia, Argentina. J. Paleontol. 81, 1528–1532 (2007).
    Google Scholar 
    Leuzinger, L. et al. Life and reproduction of titanosaurians: Isotopic hallmark of mid-palaeolatitude eggshells and its significance for body temperature, diet, and nesting. Chem. Geol. 583, 120452 (2021).ADS 
    CAS 

    Google Scholar 
    Faccio, G. Dinosaurian eggs from the upper Cretaceous of Uruguay. In Dinosaur Eggs and Babies (eds Carpenter, K. et al.) 47–53 (Cambridge University Press, 1994).
    Google Scholar 
    Vianey-Liaud, M., Hirsch, K., Sahni, A. & Sigé, B. Late Cretaceous Peruvian eggshells and their relationships with Laurasian and Eastern Gondwanian material. Geobios 30, 75–90 (1997).
    Google Scholar 
    Magalhães Ribeiro, C. M. Microstructural analysis of dinosaur eggshells from Bauru Basin (Late Cretaceous), Minas Gerais, Brasil. In First International Symposium on Dinosaur Eggs and Babies (eds Bravo, A. M. & Reyes, T.) 117–122 (Isona I Conca Dellà Catalonia, 2000).
    Google Scholar 
    Magalhães Ribeiro, C. M. Ovo e fragmentos de cascas de ovos de dinossauros, provenientes de região de Peirópolis, Uberaba, Minas Gerais. Arq. Mus. Nac. 60, 223–228 (2002).
    Google Scholar 
    Grellet-Tinner, G. & Zaher, H. Taxonomic identification of the Megaloolithidae egg and eggshells from Cretaceous Bauru Basin (Minas Gerais, Brazil): Comparison with the Auca Mahuevo (Argentina) titanosaurid eggs. Papéis Avulsos Zool. 47, 105–112 (2007).
    Google Scholar 
    Martinelli, A. G. & Teixeira, V. P. A. The Late Cretaceous vertebrate record from the Bauru Group in the Triângulo Mineiro, southeastern Brazil. Bol. Geol. Minero 126, 129–158 (2015).
    Google Scholar 
    Soares, M. V. T. et al. Sedimentology of a distributive fluvial system: The Serra da Galga Formation, a new lithostratigraphic unit (Upper Cretaceous, Bauru Basin, Brazil). Geol. J. 56, 951–975 (2021).
    Google Scholar 
    Soares, M. V. T. et al. Landscape and depositional controls on palaeosols of a distributive fluvial system (Upper Cretaceous, Brazil). Sedim. Geol. 409, 105774 (2020).
    Google Scholar 
    Martinelli, A. G. et al. Palaeoecological implications of an Upper Cretaceous tetrapod burrow (Bauru Basin; Peirópolis, Minas Gerais, Brazil). Palaeogeogr. Palaeoclimatol. Palaeoecol. 528, 147–159 (2019).
    Google Scholar 
    Grellet-Tinner, G., Chiappe, L. M. & Coria, R. A. Eggs of titanosaurid sauropods from the Upper Cretaceous of Auca Mahuevo (Argentina). Can. J. Earth Sci. 41, 949–960 (2004).ADS 

    Google Scholar 
    Wilson, J. A., Mohabey, D. M., Peters, S. E. & Head, J. J. Predation upon hatchling dinosaurs by a new snake from the Late Cretaceous of India. PLoS Biol. 8, e1000322 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Garcia, G. & Vianey-Liaud, M. Nouvelles données sur les coquilles d’œufs de dinosaures Megaloolithidae du Sud de la France: Systématique et variabilité intraspécifique. C. R. Acad. Sci. A. 332, 185–191 (2001).
    Google Scholar 
    Vianey-Liaud, M., Khosla, A. & Garcia, G. Relationships between European and Indian dinosaur eggs and eggshells of the oofamily Megaloolithidae. J. Vertebr. Paleontol. 23, 575–585 (2003).
    Google Scholar 
    Campos, D. A., Kellner, A. W. A., Bertini, R. J. & Santucci, R. M. On a titanosaurid (Dinosauria, Sauropoda) vertebral column from the Bauru Group, Late Cretaceous of Brazil. Arq. Mus. Nac. 63, 565–593 (2005).
    Google Scholar 
    Kellner, A. W. A., Campos, D. A. & Trotta, M. N. F. Description of a titanosaurid caudal series from the Bauru Group, Late Cretaceous of Brazil. Arq. Mus. Nac. 63, 529–564 (2005).
    Google Scholar 
    Machado, E. B., Avilla, L. S., Nava, W. R., Campos, D. A. & Kellner, A. W. A. A new titanosaur sauropod from the Late Cretaceous of Brazil. Zootaxa 3701, 301–321 (2013).PubMed 

    Google Scholar 
    Silva Junior, J. C. G. et al. Reassessment of Aeolosaurus maximus, a titanosaur dinosaur from the Late Cretaceous of Southeastern Brazil. Hist. Biol. 34, 402–411 (2022).
    Google Scholar 
    Bandeira, K. L. N. et al. A new giant Titanosauria (Dinosauria: Sauropoda) from the Late Cretaceous Bauru Group, Brazil. PLoS One 11, e0163373 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Pearce, J. M. Philopatry: A return to origins. Auk 124, 1085–1087 (2007).
    Google Scholar 
    Kokko, H. & López-Sepulcre, A. From individual dispersal to species ranges: Perspectives for a changing world. Science 313, 789–791 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Basilici, G., Hechenleitner, E. M., Fiorelli, L. E., Dal Bó, P. F. & Mountney, N. P. Preservation of titanosaur egg clutches in Upper Cretaceous cumulative palaeosols (Los Llanos Formation, La Rioja, Argentina). Palaeogeogr. Palaeoclimatol. Palaeoecol. 482, 83–102 (2017).
    Google Scholar 
    Mueller-Töwe, I. J., Sander, P. M., Schüller, H. & Thies, D. Hatching and infilling of dinosaur eggs as revealed by computed tomography. Palaeontogr. Abt. A 267, 119–168 (2002).
    Google Scholar 
    Paganelli, C. V. The physics of gas exchange across the avian eggshell. Am. Zool. 20, 329–338 (1980).
    Google Scholar 
    Grellet-Tinner, G., Fiorelli, L. E. & Salvador, R. B. Water vapor conductance of the Lower Cretaceous dinosaurian eggs from Sanagasta, La Rioja, Argentina: Paleobiological and paleoecological implications for South American faveoloolithid and megaloolithid eggs. Palaios 27, 35–47 (2012).ADS 

    Google Scholar 
    Grellet-Tinner, G. Phylogenetic interpretation of eggs and eggshells: Implications for phylogeny of Palaeognathae. Alcheringa 30, 141–182 (2006).
    Google Scholar 
    Mikhailov, K. E., Bray, E. S. & Hirsch, K. F. Parataxonomy of fossil egg remains (Veterovata): Principles and applications. J. Vertebr. Paleontol. 16, 763–769 (1996).
    Google Scholar 
    Nys, Y., Gautron, J., Garcia-Ruiz, J. M. & Hincke, M. T. Avian eggshell mineralization: Biochemical and functional characterization of matrix proteins. C. R. Palevol 3, 549–562 (2004).
    Google Scholar 
    Fernández, M. S., Passalacqua, K., Arias, J. I. & Arias, J. L. Partial biomimetic reconstitution of avian eggshell formation. J. Struct. Biol. 148, 1–10 (2004).PubMed 

    Google Scholar 
    Arias, J. L. & Fernández, M. S. Biomimetic processes through the study of mineralized shells. Mater. Charact. 50, 189–195 (2003).CAS 

    Google Scholar 
    Arias, J. L., Mann, K., Nys, Y., Garcia Ruiz, J. M. & Fernández, M. S. Eggshell growth and matrix macromolecules. In Handbook of Biomineralization (ed. Baeuerlein, E.) 309–328 (Wiley, 2007).
    Google Scholar 
    Chien, Y.-C., Hincke, M. T., Vali, H. & Mckee, M. D. Ultrastructural matrix–mineral relationships in avian eggshell, and effects of osteopontin on calcite growth in vitro. J. Struct. Biol. 163, 84–99 (2008).CAS 
    PubMed 

    Google Scholar 
    Hincke, M. T. et al. The eggshell: Structure, composition and mineralization. Front. Biosci. 17, 1266–1280 (2012).CAS 

    Google Scholar 
    Stapane, L. et al. Avian eggshell formation reveals a new paradigm for vertebrate mineralization via vesicular amorphous calcium carbonate. J. Biol. Chem. 295, 15853–15869 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Norell, M. A. et al. A theropod dinosaur embryo and the affinities of the Flaming Cliffs dinosaur eggs. Science 266, 779–782 (1994).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Araújo, R. et al. Filling the gaps of dinosaur eggshell phylogeny: Late Jurassic Theropod clutch with embryos from Portugal. Sci. Rep. 3, 1924 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xing, L. et al. An exquisitely preserved in-ovo theropod dinosaur embryo sheds light on avian-like prehatching postures. iScience 103516, 24 (2021).
    Google Scholar 
    Sato, T., Cheng, Y.-N., Wu, X.-C., Zelenitsky, D. K. & Hsiao, Y.-F. A pair of shelled eggs inside a female dinosaur. Science 308, 375 (2005).CAS 
    PubMed 

    Google Scholar 
    Norell, M. A., Clark, J. M. & Chiappe, L. M. A nesting dinosaur. Nature 378, 774–776 (1995).ADS 
    CAS 

    Google Scholar 
    Dong, Z. M. & Currie, P. J. On the discovery of an oviraptorid skeleton on a nest of eggs at Bayan Mandahu, Inner Mongolia, People’s Republic of China. Can. J. Earth Sci. 33, 631–636 (1996).ADS 

    Google Scholar  More

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    Water ecological security assessment and spatial autocorrelation analysis of prefectural regions involved in the Yellow River Basin

    Water ecological security evaluation results of Yellow River BasinIndex weight analysisThis study selects the index weights in 2009, 2014, and 2019 for comparative analysis. As shown in Table 3, in terms of space, in the pressure layer, indicator A6 (Water area) has the most prominent weight, and indicator A3 (Natural population growth rate) has the most negligible weight; in the state layer, indicator B6 (Proportion of wetland area to total area) has the most prominent weight, and B1 (COD emissions per 10,000 yuan GDP) has the most negligible weight; in the response layer, indicator C3 (Green area rate of built-up area) has the most prominent weight, and indicator C2 (Centralized treatment rate of urban domestic sewage) has the most negligible weight. In summary, water area, wetland area, and built-up green space are the key indicators affecting the water ecology of the Yellow River Basin, including natural factors and economic and social factors.Table 3 Water ecological security index weight.Full size tableIn terms of time, indicators A6 and B6 have equal weights in three years and have always been in an important position. The weight of indicator C1 (the rate of stable compliance of wastewater discharge by industrial enterprises) has fallen for three consecutive years, from 0.38 to 0.09. It shows that after years of environmental management in various cities, the rate of compliance with wastewater discharge standards of industrial enterprises has been continuously increasing. It plays a positive role in the construction of water ecological security. The weight of indicator C3 has increased significantly in three years, from 0.31 in 2009 to 0.90 in 2019, indicating that with the continuous development of urbanization, the built-up area has become larger and larger, which has a massive impact on water ecological security. Therefore, the green area in the built-up area is vital, which is the key to ensuring the urban ecological environment. It is also a critical factor in maintaining the water ecological security.Trend analysis of water ecological securityThis study is based on Eq. (4) to calculate the WESI of the nine provinces in the past ten years, as shown in Fig. 3. From the perspective of the changes in WESI from 2009 to 2019, the overall trend is slowly increasing. Compared with 2009, WESI increased by 5.96% in 2019, but the average annual growth rate was only 0.59%. The sharp rise stage was in 2009–2012, with an average annual growth rate of 1.84%. Since 2009, there has been no inferior V water in the main stream of the Yellow River, and the water quality has been improving year by year. During this period, the nine provinces implemented the Yellow River Basin Flood Control Plan under the guidance of The State Council. The plan calls for strengthening infrastructure construction in the Yellow River Basin and conducting work such as river improvement and soil and water conservation. Therefore, we will promote the restoration of water ecology in the river basin and improve the safety of water ecology. From 2012 to 2019, WESI showed a trend of ups and downs. This is because the provinces have gradually shifted their development focus to the economy after achieving significant results in restoring water ecology in the river basin. The rapid economic development has brought more significant pressure to environmental governance and hindered water ecological safety improvement.Figure 3Trend map of water ecological security index (WESI) of nine provinces.Full size imageCriterion layer quantitative resultsTo further study and appraise the water ecological security of the study area, this paper quantifies the criteria layers (i.e., pressure, state, response) on account of the SMI-P method. It selects 2009, 2014, and 2019 for comparative analysis. As shown in Fig. 4, the criterion layer has undergone specific changes over time. First of all, the distribution of pressure in 62 cities has not changed much in three years. The areas with more tremendous pressure on water ecological security are mainly concentrated in eastern cities, including Shuozhou, Taiyuan, Jinzhou, Luliang, Linfen, Jincheng, and Changzhi, Anyang, Hebi, Jiaozuo, Puyang, Liaocheng, and other cities. Areas with less pressure are mainly concentrated in western and eastern cities, including Guoluo Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture, Haibei Tibetan Autonomous Prefecture, Ordos, Bayannaoer, Yulin, and other cities. In 2009, the precipitation in spring and winter in Lanzhou is less, the degree of drought is serious, and the flood disaster is more severe in flood season, which brings tremendous pressure to the water ecological security. After 2015, Lanzhou continued to implement the Action Plan for Prevention and Control of Water Pollution and then the river chief system was implemented. In 2019, The Work Plan of Lanzhou Municipal Water Pollution Prevention and Control Action in 2019 was issued and implemented. All these measures and actions have laid a foundation for water ecological security. On the contrary, with the rapid development of urbanization and economy and society, the pressure of water ecological security in Jinan has increased.Figure 4Quantitative spatial distribution map of the 62 cities in the Yellow River Basin. Note This was created by ArcMap-GIS, version 10.5. https://www.esri.com/.Full size imageThe larger the value of the status layer, the better the aquatic ecological status. On the contrary, the worse the aquatic ecological security. The overall spatial distribution of the status layer has not changed significantly in the past three years, and the changes are mainly concentrated in some cities. For example, the water ecological security status of Wuhan and Ulan Chab has gradually deteriorated in three years. The reason is that the urban population is becoming denser and sewage discharge is increasing, but related management and measures have not been fully implemented. In Dongying, the water ecological security status improved in 2014 and 2019. According to the Environmental Status Bulletin, in 2014, Dongying deepened its drainage basin pollution control system, continuously strengthened the restraint mechanism to improve river water quality, and carried out a pilot wetland ecological restoration.In the three years of 2009, 2014, and 2019, the response layer has changed more significantly than the pressure and status layers. It can be seen that the degree of response scarcity has gradually shifted from western cities to eastern cities. The reason can be understood as that due to their superior natural conditions, western cities have relatively weak awareness of water ecological protection and governance, and their ability to respond to emergencies is insufficient. However, with the increasingly prominent ecological and environmental problems, the awareness of maintaining water ecological safety is increasing, and the protection and governance measures are constantly improving. For example, Guoluo Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture, and Haibei Tibetan Autonomous Prefecture. Eastern cities are densely populated, urbanization development is faster than western cities, and environmental problems occur more frequently. Therefore, the awareness of ecological and environmental protection is more substantial, the governance system is relatively complete, and responsiveness is relatively good. However, as time progresses, some cities have somewhat slackened their ecological environment governance, and therefore their responsiveness has also weakened. For example, Shuozhou, Jinzhou, Lvliang, Linfen, and other places.Final quantitative resultsIn order to show the water ecological security status of 62 cities more intuitively, this paper shows the water ecological status level in Table 2 through the GIS spatial distribution map (Fig. 5).Figure 5Distribution map of water ecological security status in 62 cities of the Yellow River Basin. Note This was created by ArcMap-GIS, version 10.5. https://www.esri.com/.Full size imageLooking at the overall situation in the past three years, the water ecological security status is relatively stable, with little overall change. The reasons mainly include natural geographical location and economic and social development. In terms of physical geography, the safer areas are concentrated in the upper reaches of the Yellow River Basin, all of which have the characteristics of large land and sparsely populated areas and relatively superior natural conditions. They provide good conditions and foundations for the construction of water ecological security. The moderate warning cities are primarily located in the Loess Plateau and the North China Plain, where water resources are scarce, and the dense population, posing a threat to water ecological security. In terms of economic and social development, relatively safe areas are located in remote areas with inconvenient transportation. The region is dominated by agriculture and animal husbandry, with relatively backward economic development and a low level of urbanization. In addition, the threat to water ecological security is relatively tiny. Residents in the moderate warning area have a significant living demand, and the over-exploitation and utilization of natural resources have led to the destruction of the ecological environment. Therefore, it poses a more significant threat to water ecological security.Combining Fig. 5 and Table A.2 of appendix, it can be seen that in 2009, there were 8 safer cities, 22 with early warning level, and 32 with moderate warning. Relatively safe cities are concentrated in the southwest and north of the Yellow River Basin; cities with moderate warning level are distributed in the central and eastern areas. In 2014, the number of safer cities increased to 10, and the number of cities with moderate warning level decreased to 30. The means that water ecological security has received more and more attention, and cities have consciously strengthened the protection and governance of water ecology to maintain water ecological security. In 2019, there are 11 relatively safe cities, 21 cities with warning level, and 30 cities with moderate warning level. The overall situation has not changed much, and some cities have changed significantly. For example, Erdos had increased from an early warning status in 2009 to a safer status in 2014, and its safety index has risen from 0.57 to 0.65. Wuzhong has been upgraded from the warning level in 2009 (0.39) to the relatively safe in 2014 (0.44), and the safety index (0.47) in 2019 has also increased. Binzhou had improved from its early warning status (0.60) in 2009 to a relatively safe level (0.64) in 2014, and its safety index (0.66) has also increased in 2019, but the increase is not significant. On the contrary, Jinan has deteriorated from the early warning level in 2009 and 2014 to the moderate warning level in 2019, indicating that the water ecological security of Jinan has been seriously threatened in the process of rapid development.Spatial autocorrelation analysis of 62 cities in the Yellow River BasinGlobal spatial autocorrelation analysisThis paper selects 2009, 2014 and 2019, and analyzes the global spatial autocorrelation based on GeoDa. Combining Table 4 and Fig. 6, the Moran index for these three years was 0.298, 0.359, and 0.334 respectively, which were all in the [0,1] interval, indicating the water ecological security of 62 cities in the past three years showed significant spatial autocorrelation. Moreover, there is a positive spatial correlation, and the spatial autocorrelation is strong. The four quadrants of the scatter chart are high-high (i.e., first quadrant) aggregation area, low–high (i.e., second quadrant) aggregation area, low-low (i.e., third quadrant) aggregation area, and high-low (i.e., fourth quadrant) aggregation area. After testing, z-value  > 1.96, p-value  More

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    Tracing the invasion of a leaf-mining moth in the Palearctic through DNA barcoding of historical herbaria

    Detection of archival Phyllonorycter mines in historical herbariaOnly 1.5% (225 out of 15,009) of herbarium specimens of Tilia spp. examined from the Palearctic contained Ph. issikii leaf mines. These 225 herbarium specimens occurred in 185 geographical locations across the Palearctic, with the westernmost point in Germany (Hessen; the herbarium specimen dated by 2004) to the most eastern locations in Japan (on the island of Hokkaido; 1885–1974) (Fig. 1).Figure 1The localities where herbarium specimens of Tilia spp. carrying Phyllonorycter mines were collected in the Palearctic in the last 253 years. The dotted line divides Ph. issikii range to native (below the line) and invaded (above the line). The map was generated using ArcGIS 9.3 (Release 9.3. New York St., Redlands, CA. Environmental Systems Research Institute, http://www.esri.com/software/arcgis/eval-help/arcgis-93).Full size imageMost specimens with leaf mines (90%; 203/225) originated from Eastern Palearctic, in particular from the Russian Far East (RFE) (67.5%, 137/203) (Fig. 2a). In some cases, leaves were severely attacked, carrying up to 12 mines per leaf (as documented in the Russian Far East in 1930s–1960s). On the other hand, we found only 22 herbarium specimens with mines (10%; 22/225) from the putative invaded region in Western Palearctic, with the majority of herbarium specimens with mines (7% 15/225) from European Russia (Fig. 2b).Figure 2The presence of Phyllonorycter issikii mines in the herbarium specimens collected in the putative native (a) and invaded (b) ranges over the past 253 years (1764–2016). The number of herbarium specimens with and without mines and the percentage of the specimens with mines in each region or country from all herbarium specimens examined in a region or country (in brackets) are given next to each graph. The total number of herbarium specimens, including those with and without mines, is given for Eastern (a) and Western Palearctic (b) separately and altogether (a + b).Full size imageThe average number of leaf mines per herbarium specimen found in native (5.68 ± 0.77) and invaded regions (6.09 ± 1.70) was not significantly different (Mann–Whitney U-test: U = 20,145; Z = 0.43; p = 0.43). However, the infestation rate by Ph. issikii, i.e. percentage of leaves with mines per herbarium specimen was statistically higher in the West than in the East: 35% ± 8.19 versus 23% ± 1.94 (Mann–Whitney U-test: U = 1339; Z = 2.30; p = 0.02).Leaf mines from the East were significantly older than those from the West (Mann–Whitney U-test: U = 81; Z =  − 4.4; p  400 bp) were obtained for 71 archival specimens that were between 7 and 162 years old (Fig. 4, the points in dashed frame) (Table S4). Nine of these 71 specimens were over one century old (106–162-year-old): eight originated from the Palearctic and one from the Nearctic (Fig. 4, the points in gray cloud).In the Palearctic, the oldest successfully DNA barcoded Ph. issikii specimen (obtained sequence length 408 bp) was a 162-year-old larva dissected from the leaf mine on Tilia amurensis from the RFE (village Busse, Amur Oblast, the year 1859), sequence ID LMINH119-19 (Fig. 5, Table S5). In the Nearctic, the oldest sequenced specimen (obtained sequence length 658 bp) was 127-year-old larva of Ph. tiliacella on T. americana from USA, Pennsylvania (Fig. 5, Table S5).Figure 5A maximum likelihood tree of 81 COI sequences of Phyllonorycter spp. Overall, 71 archival sequenced specimens were dissected from herbaria collected in the Palearctic and the Nearctic in 1859–2014 and ten specimens (highlighted in blue) originated from the modern range20. The tree was generated with the K2P nucleotide substitution model and bootstrap method (2500 iterations), p  More

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    Effects of COVID-19 lockdowns on shorebird assemblages in an urban South African sandy beach ecosystem

    Graded lockdowns imposed by the South African government to manage the COVID-19 pandemic27,28,29 has afforded us a unique opportunity to quantify shorebird responses to increasing human density in Muizenberg Beach over 8 months in 2020, including a 2-month period of virtual human exclusion. In spite of our study being limited to one beach over 2 years, we were able to take advantage of data collected prior to- (2019) and during the 2020 COVID lockdowns, to better understand a pervasive feature of sandy beach ecosystems (human recreation) that is predicted to intensify in future10.Findings for the 2019–2020 component of our study generally conformed to hypotheses posed. Firstly, shorebird abundance was inversely associated with human abundance and was positively related to lockdown level in 2020. Secondly, shorebird abundance was generally greatest during lockdown levels 5 and 4, when humans were effectively absent from the beach. To contextualise, shorebird abundance was roughly six times greater at the start of lockdown level 5 (2020) than the equivalent period in 2019. Thirdly, lowest shorebird abundance occurred during lockdown level 1 when human abundance was greatest in 2020. Collectively, these findings indicate a strong inverse association between shorebird- and human abundance on Muizenberg Beach and align with results of other studies36,37,38,39. Cumulatively, our findings, allied with prior research highlight the potential for human recreational activity, particularly at high intensities, to impact shorebird utilisation of sandy beach ecosystems, which may in turn affect ecological functions they provide that contribute to ecosystem multifunctionality.The inverse relationship that we recorded between human- and shorebird abundance likely manifests through the diverse ways in which recreational activity impacts fundamental processes and ecosystem components, which in turn link ecologically to shorebirds10,36,37,38,39,40. Muizenberg Beach is popular for surfing, bait-harvesting and general recreational activities, and it is these activities that likely drive the human-shorebird relationship that we report, particularly in 2020. When carried out under high human densities, such activities can lead to a reduction in space available, rendering the ecosystem less suitable as a substrate for birds36. Noise pollution and the presence of dogs may further depress habitat suitability41. Repeated trampling of sediment can negatively impact macrofaunal populations, which together with altered sedimentary biogeochemistry (e.g. increased anoxia), can reduce trophic resource availability to shorebirds, with benthic bait-collecting compounding these effects42,43. At the start of our data collection in 2020, we were unable to identify shorebird species due to lockdown levels 5 and 4 prohibiting human presence on the beach27,28,29. It is probable though that shorebird assemblages during lockdown levels 5 and 4 were not the same as those we identified between lockdown level 3 to 1 (mainly gulls; Table 3). This is based on research showing that increasing environmental disturbances can induce switches in biotic assemblages to those that can tolerate human activities44. Thus, the shorebird assemblages we identified during lockdown levels 3 to 1 is potentially the end-result of the mechanisms highlighted above (space reduction, noise, reduced resource availability) acting on shorebird assemblages in the absence of humans (lockdown levels 5 and 4) following humans being permitted onto the beach.At an inter-annual level, our data revealed idiosyncratic patterns that raise interesting questions about human-shorebird relationships. In 2019, in the absence of any lockdowns, shorebird abundance rose over the winter period (May–August). Winter peaks in abundance have previously been recorded in the literature45,46,47, including for kelp gulls (Larus dominicanus), which were the dominant shorebird in Muizenberg Beach. Specifically, winter abundance peaks for this species have been recorded in sandy beaches in the Eastern Cape, the Swartkops Estuary and Algoa Bay in South Africa (southeast coast)45,46,47. However, the absence of a winter abundance peak in 2020 raises the possibility that the 2019 winter-peak was not seasonal but an opportunistic response to decreased human abundance (see Fig. 4A). In South Africa, coastal ecosystems generally experience greatest human numbers in summer, due to warmer conditions and long end-of-year-vacation periods, based on our observations and experiences.The second inter-annual trend worth noting in our findings is that shorebird abundance was greater in 2019 than 2020, despite lockdowns being implemented in 2020. This counterintuitive finding is likely due to lockdowns that excluded people from the beach in 2020 (levels 5 to 3) being too short in duration to facilitate increases in bird numbers in 2020 beyond the 2019 level. This is supported by our data showing that humans were excluded from the beach for a total of 2 months (April and May 2020; levels 5-4) out of the 8-month period during which photographs were analysed. It would have been expected at the onset of the study that humans would be excluded from the beach during lockdown level 329, which would have resulted in an additional two and a half months of human exclusion and potentially a higher mean shorebird abundance for 2020. However, it is clear from our data that humans were present on the beach during level 3. On closer inspection, it is evident that human numbers increased even prior to the end of lockdown level 4. In fact, human abundance was greater under lockdown level 3 in 2020 than in the same period in 2019. Such high numbers of humans on the beach despite prohibitions are likely due to a lack of compliance, confusion around regulations and/or ‘covid fatigue’, which describes the propensity of humans to grow tired of COVID-19 regulations48. An additional consideration is that human numbers on the beach increased dramatically during lockdown levels 2 and 1, being almost twice the level recorded in 2019 in the same period. The lower 2020 bird count that we recorded is thus likely a product of the short duration of human exclusions in 2020 (lockdown levels 4 and 5) and the magnitude and rate of increase in human numbers thereafter (levels 3-1). Separately, our findings additionally suggest that surrogates (lockdown levels in our case) are unreliable estimators of human presence or abundance and align with findings elsewhere24.The last noteworthy inter-annual trend in our data was the difference in strength of human-shorebird relationships. While the inverse relationship between human and shorebird numbers was evident in both years, it was only during 2020, when humans were excluded from Muizenberg Beach, that the extent of this relationship was revealed. Specifically, in 2020, human exclusion at the start of lockdown level 5 was accompanied by a six-fold increase in shorebird abundance relative to 2019 at the same period. Additional support for the difference in strength of the human-shorebird relationship is the (1) significant interaction recorded between human numbers and year in explaining shorebird abundance and (2) the almost twofold stronger negative relationship (based on regression slopes) between shorebird and human abundance in 2020 vs 2019. These findings suggest that were it not for the COVID lockdowns in 2020, the extent of increasing human numbers on shorebirds may have been masked. However, it must be borne in mind that inter-annual variation may have played some role in the difference in trends recorded for 2019 versus 2020, though we cannot quantify this, given that we only have data for 2 years. Nevertheless, we suggest that when making conservation/management recommendations, decision-makers need to be cognisant of the potential for human effects on sandy beach ecosystems to be underestimated in studies based on variation in human density, in which human exclusion at appropriate spatial and temporal scales is absent24. Concerns have been expressed in the past about the failure of studies to consistently detect large-scale changes in sandy beach ecosystems, including those induced by recreational activities19. We suggest that such deficiencies may relate in part to the scarcity of true human exclusions in disturbance studies at meaningful scales in space and time.Findings from the in situ component of our study suggested that shorebird assemblages were negligibly affected by the transition from lockdown level 3 to 1, but that spatial differences among zones were more prominent. The lack of cases in which lockdown levels interacted statistically with zones (Tables 2, 4) further reinforces our conclusion regarding lockdown effects. Shorebird assemblage structure did vary between lockdown levels 3 and 2, due mainly to increasing contributions of Chroicocephalus hartlaubii (Hartlaub’s Gull) from level 3 to 2 and the opposite for L. dominicanus. Contrary to our hypothesis, differences in assemblage (Shannon–Wiener diversity was the exception) and species metrics were not detected among lockdown levels. This was likely due to the gradient in human abundance being weak among lockdown levels 3 to 1, relative to levels 5 and 4, with there being no virtual exclusion of humans under level 3 lockdown, as would have been expected given government regulations29. It is also possible that under lockdown levels 3, 2 and 1, the shorebird assemblage was simplified and comprised species tolerant of human activities44. The increase in Shannon–Wiener diversity value from lockdown level 3 to 2 was counter expectation, but likely reflects increased evenness during lockdown level 2, brought on by the declining dominance of L. dominicanus and a greater contribution of C. hartlaubii.Taken in its entirety, our findings provide valuable perspectives on human-shorebird interactions in sandy beaches. Based on our 2020 data spanning lockdowns of decreasing severity, our findings suggest that shorebirds are likely to benefit from human-free periods. This benefit is in reality likely to extend across multiple-trophic levels and is unlikely to be shorebird-specific, based on prior research reporting positive organism metrics at lower trophic levels in low human and/or human-free conditions in beach ecosystems20. Broadly, our findings attest to the value of using current and future lockdowns associated with managing the global COVID-19 pandemic to provide data on responses of birds and other organism groups to human-free spaces and times25,26,49. These human-free conditions can additionally provide invaluable data on sensitivities of ecosystem components and processes to increasing human density25,26,49. Data collected during lockdowns can provide better approximations of baseline conditions in sandy beach ecosystems, thereby providing a more meaningful basis for (1) evaluating future ecosystem change in response to human and global change stressors and (2) developing ecosystem restoration programs. This would be central to preventing long-term ecosystem degradation through the shifting base-line syndrome, where successive generations of decision makers/scientists judge the magnitude of change experienced by ecosystem components against increasingly deteriorating conditions over generational time-scales50. We also advocate for data emanating from COVID lockdown studies to be used in public education initiatives, so that beach users are made aware of the ways in which recreational activities can influence beach ecosystems. Such initiatives can improve involvement of public stakeholders in management of sandy beach ecosystems, which has been shown to provide cost-effective and sound decision-making, while increasing support for conservation initiatives51,52,53.Lastly, our findings have shed light on the sensitivity of shorebirds to increasing human numbers, mainly for recreational purposes. By moving beyond binary contrasts of human presence/absence, our work has also shown the magnitude of increasing human numbers on shorebirds, by virtue of the 34.18% increase in human abundance in our study corresponding with a 79.63% decline in bird numbers during the transition from lockdown level 4 to 3 in 2020. This finding is highly relevant considering that our work was based on an urban ecosystem—such systems are thought to have avian communities that are more disturbance tolerant relative to rural or suburban ecosystems54. Broadly, our work emphasises the need for environmental managers and city planners to be cognisant of the sensitivity of shorebirds to human recreational activities, even in urban settings, and to develop appropriate management plans in conjunction with scientists and stakeholders51,52,53. It should be noted that bird responses that we recorded in 2020 are unlikely to be driven solely by changing human numbers in Muizenberg Beach. Processes influencing bird assemblages in beaches surrounding our focal study area, including changes in human numbers and behaviour, may also have been influential determinants of trends recorded. We lack the data to comment meaningfully on this, but is an area worth exploring in future studies.Concluding perspectivesThe global COVID-19 anthropause has been described as the greatest large-scale experiment in modern history. This period has afforded scientists a unique opportunity to refine understanding of the consequences of human activities on Earth’s natural environments25,26,49. This is particularly relevant for human-dominated ecosystems such as sandy beaches, which are arguably the most utilised of Earth’s ecosystems for recreational purposes. In the absence of the COVID-19 anthropause, it is doubtful whether human exclusions could be carried out at scales that would allow meaningful detection of responses to human recreational disturbance. Our findings broadly attest to the points raised thus far, illustrating not only the potential for conventional approaches to underestimate human effects in sandy beaches, but also the sensitivity of shorebirds to human recreation and the magnitude of human influence. We hope that our findings stimulate further research on human recreational effects on sandy beach ecosystems, particularly with a view towards quantifying disturbance sensitivities and response thresholds of fundamental processes that drive multifunctionality in these heavily utilised, yet highly significant coastal ecosystems. We suggest that this is an imperative, given the exponential human population growth expected in the future, particularly along the coast, and the increasing demand predicted on sandy beach ecosystems from recreation, tourism and commercial sectors10,18. At its broadest level, our work dovetails with prior calls for scientists to capitalise on current and future COVID lockdowns to refine our understanding of human-nature interactions25, so that ecosystems and socio-ecological services provided can be sustainably utilised in the future. More

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    Electromagnetic sensing and infiltration measurements to evaluate turfgrass salinity and reclamation

    Corwin, D. L. & Lesch, S. M. Apparent soil electrical conductivity measurements in agriculture. Comput. Electron. Agric. 46, 11–43 (2005).
    Google Scholar 
    Akramkhanov, A., Lamers, J. P. A. & Martius, C. Conversion factors to estimate soil salinity based on electrical conductivity for soils in Khorezm region, Uzbekistan. In Sustainable Management of Saline Waters and Salt-Affected Soils for Agriculture (ed. Qadir, S. et al.) 19–25 (Syria, 2009).Boettinger, J. L., Doolittle, J. A., West, N. E., Bork, E. W. & Schupp, E. W. Nondestructive assessment of rangeland soil depth to petrocalcic horizon using electromagnetic induction. Arid. Land Res. Manag. 11, 372–390 (1997).
    Google Scholar 
    Herrero, J., Ba, A. A. & Aragues, R. Soil salinity and its distribution determined by soil sampling and electromagnetic techniques. Soil Use Manag. 19, 119–126 (2003).
    Google Scholar 
    Corwin, D. L. Past, present, and future trends in soil electrical conductivity measurements using geophysical methods. In Handbook of Agricultural Geophysics (eds. Allred, B. J., Daniels, J. J. & Ehsani, M. R.) 17–44 (Boca Raton, 2008).Triantafillou, J., Lesch, S. M., La Lau, K. & Buchanan, S. M. Field level digital mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model. Aust. J. Soil Res. 47, 651–663 (2009).
    Google Scholar 
    Lardo, E., Arous, A., Palese, A. M., Nuzzo, V. & Celano, G. Electromagnetic induction: A support tool for the evaluation of soil CO2 emissions and soil organic carbon content in olive orchards under semi-arid conditions. Geoderma 264, 188–194 (2016).ADS 
    CAS 

    Google Scholar 
    Yao, R. J. et al. Geostatistical monitoring of soil salinity for precision management using proximally sensed electromagnetic induction (EMI) method. Environ. Earth Sci. 75(20), 1362. https://doi.org/10.1007/s12665-016-6179-z (2016).CAS 

    Google Scholar 
    Corwin, D. L. & Lesch, S. M. Protocols and guidelines for field-scale measurement of soil salinity distribution with ECa-directed soil sampling. J. Environ. Eng. Geophys. 18(1), 1–25 (2013).
    Google Scholar 
    Heil, K. & Schmidhalter, U. The application of EM38: Determination of soil parameters, selection of soil sampling points and use in agriculture and archaeology. Sensors. 17, 2540 (2017).ADS 
    PubMed Central 

    Google Scholar 
    Rhoades, J. D., Corwin, D. L. & Lesch, S. M. Geospatial measurements of soil electrical conductivity to assess soil salinity and diffuse salt loading from irrigation. In Assessment of Non-point Source Pollution in the Vadose Zone (eds. Corwin, D. L., Loague, K. & Ellsworth, T. R.) 197–215 (Geophysical Monogram, 1999).Sadler, E. J., Camp, C. R. & Evans, R. G. New and future technology. In Irrigation of Agricultural Crops (eds. Steward, B. A. & Nelson, D. R.) 609–626 (Agronomy Monograph, 2007).Carrow, R. N., Krum, J. M., Flitcroft, I. & Cline, V. Precision turfgrass management: Challenges and field application for mapping turfgrass soil and stress. Precis. Agric. 11, 115–134 (2010).
    Google Scholar 
    Devitt, D. A., Lockett, M. & Bird, B. M. Spatial and temporal distribution of salts on fairways and greens irrigated with reuse water. Agronomy 99, 692–700 (2007).
    Google Scholar 
    Corwin D.L., Lesch S.M. & Lobell D.B. Laboratory and field measurements. In Agricultural Salinity Assessment and Management (eds. Wallender, W. W. & Tanji, K. K.) (2012).Lesch, S. M., Rhoades, J. D., Corwin, D. L., Robinson, D. A. & Suárez, D. L. ESAP-RSSD version 2.30R. User manual and tutorial guide. Res. Report 148 in USDA-ARS. George E. Brown, Jr., Salinity Laboratory, Riverside, California. (2002).Lesch, S. M., Rhoades, J. D., Corwin, D. L., Robinson, D. A. & Suárez, D. L. ESAP-SaltMapper version 2.30R. User manual and tutorial guide. Res. Report 149 USDA-ARS. George E. Brown, Jr., Salinity Laboratory, Riverside, California. (2002).Lesch, S. M., Rhoades, J. D. & Corwin, D. L. ESAP-95 Version 2.01R: User manual and tutorial guide. Res. Rep. 146. USDA-ARS. George E. Brown, Jr., Salinity Laboratory, Riverside, California. (2000).Lesch, S. M., Strauss, D. J. & Rhoades, J. D. Spatial prediction of soil salinity using electromagnetic induction techniques: 1. Statistical prediction models: A comparison of multiple linear regression and cokriging. Water Resour. Res. 31, 373–386 (1995).ADS 

    Google Scholar 
    Amezketa, E. Soil salinity assessment using directed soil sampling from a geophysical survey with electromagnetic technology: A case study. Span. J. Agric. Res. 5(1), 91–101 (2007).
    Google Scholar 
    Grieve C. M., Grattan, S. R. & Mass, E. V. Plant salt tolerance. In Agricultural Salinity Assessment and Management (eds. Walender W. W. & Tanji K.K.) (ASCE, 2012).Shahba, M. Interaction effects of salinity and mowing on performance and physiology of bermudagrass cultivars. Crop Sci. 50, 2620–2631 (2010).
    Google Scholar 
    Marcum, K. B. & Pessarakli, M. Salinity tolerance and salt gland excretion efficiency of bermudagrass turf cultivars. Crop Sci. 46, 2571–2574 (2006).
    Google Scholar 
    Xiang, M., Moss, J. Q., Martin, D. L., Su, K. & Dunn, B. L. Evaluating the salinity tolerance of clonal-type bermudagrass cultivars and an experimental selection. Hortic. Sci. 51(1), 185–191 (2017).
    Google Scholar 
    Ganjegunte, G. K. et al. Soil salinity of an urban park after long term irrigation with saline ground water. Agronomy 109, 3011–3018 (2017).CAS 

    Google Scholar 
    Keren, R. & Miyamoto, S. Reclamation of saline, sodic and boron affected soils. In Agricultural Salinity Assessment and Management (eds. Walender W. W. & Tanji K. K.) (ASCE, 2012).Thomas, G. W. & Phillips, R. E. Consequences of water-movement in macropores. J. Environ. Qual. 8, 149–152 (1979).
    Google Scholar 
    White, R. E. The influence of macropores on the transport of dissolved and suspended matter through soil. In Advances in Soil Science (ed. Stewart, B. A.) 95–120 (Springer, 1985).Workman, S. & Skaggs, R. PREFLO: A water management model capable of simulating preferential flow. Trans. ASAE. 33, 1939–1948 (1990).
    Google Scholar 
    Huang, B., Duncan, R. R. & Carrow, R. N. Drought-resistance mechanisms of seven warm-season turfgrasses under surface soil drying: II. Root aspects. Crop Sci. 7(6), 863–1869 (1997).
    Google Scholar 
    Liu, X. H. & Huang, B. R. Cytokinin effects on creeping bentgrass response to heat stress: II. Leaf senescence and antioxidant metabolism. Crop Sci. 42, 466–472 (2002).CAS 

    Google Scholar  More

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    Improving biodiversity protection through artificial intelligence

    A biodiversity simulation frameworkWe have developed a simulation framework modelling biodiversity loss to optimize and validate conservation policies (in this context, decisions about data gathering and area protection across a landscape) using an RL algorithm. We implemented a spatially explicit individual-based simulation to assess future biodiversity changes based on natural processes of mortality, replacement and dispersal. Our framework also incorporates anthropogenic processes such as habitat modifications, selective removal of a species, rapid climate change and existing conservation efforts. The simulation can include thousands of species and millions of individuals and track population sizes and species distributions and how they are affected by anthropogenic activity and climate change (for a detailed description of the model and its parameters see Supplementary Methods and Supplementary Table 1).In our model, anthropogenic disturbance has the effect of altering the natural mortality rates on a species-specific level, which depends on the sensitivity of the species. It also affects the total number of individuals (the carrying capacity) of any species that can inhabit a spatial unit. Because sensitivity to disturbance differs among species, the relative abundance of species in each cell changes after adding disturbance and upon reaching the new equilibrium. The effect of climate change is modelled as locally affecting the mortality of individuals based on species-specific climatic tolerances. As a result, more tolerant or warmer-adapted species will tend to replace sensitive species in a warming environment, thus inducing range shifts, contraction or expansion across species depending on their climatic tolerance and dispersal ability.We use time-forward simulations of biodiversity in time and space, with increasing anthropogenic disturbance through time, to optimize conservation policies and assess their performance. Along with a representation of the natural and anthropogenic evolution of the system, our framework includes an agent (that is, the policy maker) taking two types of actions: (1) monitoring, which provides information about the current state of biodiversity of the system, and (2) protecting, which uses that information to select areas for protection from anthropogenic disturbance. The monitoring policy defines the level of detail and temporal resolution of biodiversity surveys. At a minimal level, these include species lists for each cell, whereas more detailed surveys provide counts of population size for each species. The protection policy is informed by the results of monitoring and selects protected areas in which further anthropogenic disturbance is maintained at an arbitrarily low value (Fig. 1). Because the total number of areas that can be protected is limited by a finite budget, we use an RL algorithm42 to optimize how to perform the protecting actions based on the information provided by monitoring, such that it minimizes species loss or other criteria depending on the policy.We provide a full description of the simulation system in the Supplementary Methods. In the sections below we present the optimization algorithm, describe the experiments carried out to validate our framework and demonstrate its use with an empirical dataset.Conservation planning within a reinforcement learning frameworkIn our model we use RL to optimize a conservation policy under a predefined policy objective (for example, to minimize the loss of biodiversity or maximize the extent of protected area). The CAPTAIN framework includes a space of actions, namely monitoring and protecting, that are optimized to maximize a reward R. The reward defines the optimality criterion of the simulation and can be quantified as the cumulative value of species that do not go extinct throughout the timeframe evaluated in the simulation. If the value is set equal across all species, the RL algorithm will minimize overall species extinctions. However, different definitions of value can be used to minimize loss based on evolutionary distinctiveness of species (for example, minimizing phylogenetic diversity loss), or their ecosystem or economic value. Alternatively, the reward can be set equal to the amount of protected area, in which case the RL algorithm maximizes the number of cells protected from disturbance, regardless of which species occur there. The amount of area that can be protected through the protecting action is determined by a budget Bt and by the cost of protection ({C}_{t}^{c}), which can vary across cells c and through time t.The granularity of monitoring and protecting actions is based on spatial units that may include one or more cells and which we define as the protection units. In our system, protection units are adjacent, non-overlapping areas of equal size (Fig. 1) that can be protected at a cost that cumulates the costs of all cells included in the unit.The monitoring action collects information within each protection unit about the state of the system St, which includes species abundances and geographic distribution:$${S}_{t}={{{{H}}}_{{{t}}},{{{D}}}_{{{t}}},{{{F}}}_{{{t}}},{{{T}}}_{{{t}}},{{{C}}}_{{{t}}},{{{P}}}_{{{t}}},{B}_{t}}$$
    (1)
    where Ht is the matrix with the number of individuals across species and cells, Dt and Ft are matrices describing anthropogenic disturbance on the system, Tt is a matrix quantifying climate, Ct is the cost matrix, Pt is the current protection matrix and Bt is the available budget (for more details see Supplementary Methods and Supplementary Table 1). We define as feature extraction the result of a function X(St), which returns for each protection unit a set of features summarizing the state of the system in the unit. The number and selection of features (Supplementary Methods and Supplementary Table 2) depends on the monitoring policy πX, which is decided a priori in the simulation. A predefined monitoring policy also determines the temporal frequency of this action throughout the simulation, for example, only at the first time step or repeated at each time step. The features extracted for each unit represent the input upon which a protecting action can take place, if the budget allows for it, following a protection policy πY. These features (listed in Supplementary Table 2) include the number of species that are not already protected in other units, the number of rare species and the cost of the unit relative to the remaining budget. Different subsets of these features are used depending on the monitoring policy and on the optimality criterion of the protection policy πY.We do not assume species-specific sensitivities to disturbance (parameters ds, fs in Supplementary Table 1 and Supplementary Methods) to be known features, because a precise estimation of these parameters in an empirical case would require targeted experiments, which we consider unfeasible across a large number of species. Instead, species-specific sensitivities can be learned from the system through the observation of changes in the relative abundances of species (x3 in Supplementary Table 2). The features tested across different policies are specified in the subsection Experiments below and in the Supplementary Methods.The protecting action selects a protection unit and resets the disturbance in the included cells to an arbitrarily low level. A protected unit is also immune from future anthropogenic disturbance increases, but protection does not prevent climate change in the unit. The model can include a buffer area along the perimeter of a protected unit, in which the level of protection is lower than in the centre, to mimic the generally negative edge effects in protected areas (for example, higher vulnerability to extreme weather). Although protecting a disturbed area theoretically allows it to return to its initial biodiversity levels, population growth and species composition of the protected area will still be controlled by the death–replacement–dispersal processes described above, as well as by the state of neighbouring areas. Thus, protecting an area that has already undergone biodiversity loss may not result in the restoration of its original biodiversity levels.The protecting action has a cost determined by the cumulative cost of all cells in the selected protection unit. The cost of protection can be set equal across all cells and constant through time. Alternatively, it can be defined as a function of the current level of anthropogenic disturbance in the cell. The cost of each protecting action is taken from a predetermined finite budget and a unit can be protected only if the remaining budget allows it.Policy definition and optimization algorithmWe frame the optimization problem as a stochastic control problem where the state of the system St evolves through time as described in the section above (see also Supplementary Methods), but it is also influenced by a set of discrete actions determined by the protection policy πY. The protection policy is a probabilistic policy: for a given set of policy parameters and an input state, the policy outputs an array of probabilities associated with all possible protecting actions. While optimizing the model, we extract actions according to the probabilities produced by the policy to make sure that we explore the space of actions. When we run experiments with a fixed policy instead, we choose the action with highest probability. The input state is transformed by the feature extraction function X(St) defined by the monitoring policy, and the features are mapped to a probability through a neural network with the architecture described below.In our simulations, we fix monitoring policy πX, thus predefining the frequency of monitoring (for example, at each time step or only at the first time step) and the amount of information produced by X(St), and we optimize πY, which determines how to best use the available budget to maximize the reward. Each action A has a cost, defined by the function Cost(A, St), which here we set to zero for the monitoring action (X) across all monitoring policies. The cost of the protecting action (Y) is instead set to the cumulative cost of all cells in the selected protection unit. In the simulations presented here, unless otherwise specified, the protection policy can only add one protected unit at each time step, if the budget allows, that is if Cost(Y, St)  More

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    Divergence in life-history traits among three adjoining populations of the sea snake Emydocephalus annulatus (Hydrophiinae, Elapidae)

    Andersson, M. Sexual selection (Princeton University Press, 2019).
    Google Scholar 
    Davy, A. J. & Smith, H. Life-history variation and environment. In Plant Population Ecology (eds Davy, A. J., Hutchings, M. J. & Watkinson, A. R.) 1–22. 28th Symposium of the British Ecological Society, Sussex, 1987 (Blackwell, 1988).Wilson, K. L., De Gisi, J., Cahill, C. L., Barker, O. E. & Post, J. R. Life-history variation along environmental and harvest clines of a northern freshwater fish: plasticity and adaptation. J. Anim. Ecol. 88, 717–733 (2019).PubMed 

    Google Scholar 
    Laiolo, P. & Obeso, J. R. Life-history responses to the altitudinal gradient. In High Mountain Conservation in a Changing World (eds Catalan, J. et al.) 253–283 (Springer, 2017).
    Google Scholar 
    Schwarz, R. & Meiri, S. The fast-slow life-history continuum in insular lizards: a comparison between species with invariant and variable clutch sizes. J. Biogeogr. 44, 2808–2815 (2017).
    Google Scholar 
    Holm, S. et al. Size-related life-history traits in geometrid moths: a comparison of a temperate and a tropical community. Ecol. Entomol. 44, 711–716 (2019).
    Google Scholar 
    Ferguson, G. W. & Fox, S. F. Annual variation of survival advantage of large juvenile side-blotched lizards, Uta stansburiana: Its causes and evolutionary significance. Evolution 38, 342–349 (1984).PubMed 

    Google Scholar 
    Madsen, T., Ujvari, B., Shine, R. & Olsson, M. Rain, rats and pythons: Climate-driven population dynamics of predators and prey in tropical Australia. Austral Ecol. 31, 30–37 (2006).
    Google Scholar 
    Brown, G. P. & Shine, R. Rain, prey and predators: climatically driven shifts in frog abundance modify reproductive allometry in a tropical snake. Oecologia 154, 361–368 (2007).ADS 
    PubMed 

    Google Scholar 
    James, C. & Shine, R. Life-history strategies of Australian lizards: a comparison between the tropics and the temperate zone. Oecologia 75, 307–316 (1988).ADS 
    PubMed 

    Google Scholar 
    Mesquita, D. O. et al. Life-history patterns of lizards of the world. Am. Nat. 187, 689–705 (2016).PubMed 

    Google Scholar 
    Meiri, S. et al. The global diversity and distribution of lizard clutch sizes. Glob. Ecol. Biogeogr. 29, 1515–1530 (2020).
    Google Scholar 
    Andrews, R. M. Growth rate in island and mainland anoline lizards. Copeia 1976, 477–482 (1976).
    Google Scholar 
    Jessop, T. S. et al. Maximum body size among insular Komodo dragon populations covaries with large prey density. Oikos 112, 422–429 (2006).
    Google Scholar 
    Van Buskirk, J. & Crowder, L. B. Life-history variation in marine turtles. Copeia 1994, 66–81 (1994).
    Google Scholar 
    Broderick, A. C., Godley, B. J. & Hays, G. C. Trophic status drives interannual variability in nesting numbers of marine turtles. Proc. R. Soc. B 268, 1481–1487 (2001).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Broderick, A. C., Glen, F., Godley, B. J. & Hays, G. C. Variation in reproductive output of marine turtles. J. Exp. Mar. Biol. Ecol. 288, 95–109 (2003).
    Google Scholar 
    Pike, D. A. Climate influences the global distribution of sea turtle nesting. Glob. Ecol. Biogeogr. 22, 555–566 (2013).
    Google Scholar 
    Ujvari, B., Shine, R., Luiselli, L. & Madsen, T. Climate-induced reaction norms for life-history traits in pythons. Ecology 92, 1858–1864 (2011).PubMed 

    Google Scholar 
    Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).
    Google Scholar 
    Shine, R., Brown, G. P. & Goiran, C. Population dynamics of the sea snake Emydocephalus annulatus (Elapidae, Hydrophiinae). Sci. Rep. 11, 20701 (2021). ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 

    Google Scholar 
    Ineich, I. & Laboute, P. Les Serpents Marins de Nouvelle-Calédonie (IRD éditions, 2002).Udyawer, V., Goiran, C. & Shine, R. Peaceful coexistence between people and deadly wildlife: Why are recreational users of the ocean so rarely bitten by sea snakes? People Nat. 3, 335–346 (2021).
    Google Scholar 
    Goiran, C., Brown, G. P. & Shine, R. The behaviour of sea snakes (Emydocephalus annulatus) shifts with the tides. Sci. Rep. 10, 1–8 (2020).
    Google Scholar 
    Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Shine, R., Goiran, C., Shine, T., Fauvel, T. & Brischoux, F. Phenotypic divergence between seasnake (Emydocephalus annulatus) populations from adjacent bays of the New Caledonian lagoon. Biol. J. Linn. Soc. 107, 824–832 (2012).
    Google Scholar 
    Avolio, C., Shine, R. & Pile, A. J. The adaptive significance of sexually dimorphic scale rugosity in sea snakes. Am. Nat. 167, 728–738 (2006).PubMed 

    Google Scholar 
    White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
    Google Scholar 
    Russell, B. C., Anderson, G. R. V. & Talbot, F. H. Seasonality and recruitment of coral reef fishes. Mar. Freshw. Res. 28, 521–528 (1977).
    Google Scholar 
    Shine, R., Bonnet, X., Elphick, M. J. & Barrott, E. G. A novel foraging mode in snakes: browsing by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae). Funct. Ecol. 18, 16–24 (2004).
    Google Scholar 
    Calow, P. Adaptive aspects of energy allocation. In Fish Energetics (eds Tytler, P. & Calow, P.) 13–31 (Springer, 1985).
    Google Scholar 
    Lenormand, T. Gene flow and the limits to natural selection. Trends Ecol. Evol. 17, 183–189 (2002).
    Google Scholar 
    Bronikowski, A. M. Experimental evidence for the adaptive evolution of growth rate in the garter snake Thamnophis elegans. Evolution 54, 1760–1767 (2000).CAS 
    PubMed 

    Google Scholar 
    Cook, T. R., Bonnet, X., Fauvel, T., Shine, R. & Brischoux, F. Foraging behaviour and energy budgets of sea snakes from New Caledonia: Insights from implanted data-loggers. J. Zool. 298, 82–93 (2016).
    Google Scholar 
    Bonnet, X., Brischoux, F., Briand, M. & Shine, R. Plasticity matches phenotype to local conditions despite genetic homogeneity across 13 snake populations. Proc. R. Soc. B 288, 20202916 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Lukoschek, V., Waycott, M. & Marsh, H. Phylogeography of the olive sea snake, Aipysurus laevis (Hydrophiinae) indicates Pleistocene range expansion around northern Australia but low contemporary gene flow. Mol. Ecol. 16, 3406–3422 (2007).CAS 
    PubMed 

    Google Scholar 
    Nitschke, C. R., Hourston, M., Udyawer, V. & Sanders, K. L. Rates of population differentiation and speciation are decoupled in sea snakes. Biol. Lett. 14, 20180563 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Heatwole, H., Grech, A., Monahan, J. F., King, S. & Marsh, H. Thermal biology of sea snakes and sea kraits. Integr. Comp. Biol. 52, 257–273 (2012).PubMed 

    Google Scholar 
    Brischoux, F., Rolland, V., Bonnet, X., Caillaud, M. & Shine, R. Effects of oceanic salinity on body condition in sea snakes. Integr. Comp. Biol. 52, 235–244 (2012).PubMed 

    Google Scholar 
    Bonnet, X. et al. Spatial variation in age structure among populations of a colonial marine snake: the influence of ectothermy. J. Anim. Ecol. 84, 925–933 (2015).PubMed 

    Google Scholar 
    Heatwole, H. Sea Snakes 2nd edn. (Krieger Publishing, 1999).
    Google Scholar 
    Blouin-Demers, G. & Weatherhead, P. J. Thermal ecology of black rat snakes (Elaphe obsoleta) in a thermally challenging environment. Ecology 82, 3025–3043 (2001).
    Google Scholar 
    Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).
    Google Scholar 
    Lowe, J. R. et al. Regional versus latitudinal variation in the life-history traits and demographic rates of a reef fish, Centropyge bispinosa, in the Coral Sea and Great Barrier Reef Marine Parks, Australia. J. Fish Biol. 99, 1602–1612. https://doi.org/10.1111/jfb.14865 (2021).PubMed 

    Google Scholar 
    Gust, N., Choat, J. & Ackerman, J. Demographic plasticity in tropical reef fishes. Mar. Biol. 140, 1039–1051 (2002).
    Google Scholar 
    Kingsford, M. J., Welch, D. & O’Callaghan, M. Latitudinal and cross-shelf patterns of size, age, growth, and mortality of a tropical damselfish Acanthochromis polyacanthus on the Great Barrier Reef. Diversity 11, 67 (2019).
    Google Scholar  More