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    Spatio-temporal inhabitation of settlements by Hystrix cristata L., 1758

    Emlen, S. T. & Oring, L. W. Ecology, sexual selection, and evolution of mating systems. Science 197(4300), 215–223 (1977).ADS 
    CAS 
    Article 

    Google Scholar 
    Lagos, V. O., Bozinovic, F. & Contreras, L. C. Microhabitat use by a small diurnal rodent (Octodon degus) in a semiarid environment: Thermoregulatory constraints or predation risk? J. Mammal. 76(3), 900–905 (1995).Article 

    Google Scholar 
    Lagos, V. O., Contreras, L. C., Meserve, P. L., Gutiérrez, J. R. & Jaksic, F. M. Effects of predation risk on space use by small mammals: A field experiment with a neotropical rodent. Oikos 74, 259–264 (1995).Article 

    Google Scholar 
    Schradin, C. & Pillay, N. Female striped mice (Rhabdomys pumilio) change their home ranges in response to seasonal variation in food availability. Behav. Ecol. 17(3), 452–458. https://doi.org/10.1093/beheco/arj047 (2006).Article 

    Google Scholar 
    Hayes, L. D., Chesh, A. S. & Ebensperger, L. A. Ecological predictors of range areas and use of burrow systems in the diurnal rodent, Octodon degus. Ethology 113, 155–165. https://doi.org/10.1111/j.1439-0310.2006.01305.x (2007).Article 

    Google Scholar 
    Brivio, F. et al. Forecasting the response to global warming in a heat-sensitive species. Sc. Rep. 9, 3048. https://doi.org/10.1038/s41598-019-39450-5 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Santamaría, A. E., Olea, P. P., Vinuela, J. & Garcia, J. T. Spatial and seasonal variation in occupation and abundance of common vole burrows in highly disturbed agricultural ecosystems. Eur. J. Wildl. Res. 65, 52. https://doi.org/10.1007/s10344-019-1286-2 (2019).Article 

    Google Scholar 
    Kinlaw, A. A review of burrowing by semi-fossorial vertebrates in arid environments. J. Arid Environ. 41, 127–145 (1999).ADS 
    Article 

    Google Scholar 
    Daly, M., Beherends, P. R. & Wilson, M. I. Activity patterns of kangaroo rats—Granivores in a desert habitat. In Activity Patterns in Small Mammals: An Ecological Approach (eds Halle, S. & Stenseth, N. C.) 145–158 (Springer, 2000).Chapter 

    Google Scholar 
    Mackin-Rogalska, R., Adamczewska-Andrzejewska, K. & Nabaglo, L. Common vole numbers in relation to the utilization of burrow system. Acta Theriol. 31(2), 17–44 (1986).Article 

    Google Scholar 
    Powell, R. A. & Fried, J. J. Helping by juvenile pine voles (Microtus pinetorum), growth and survival of younger siblings, and the evolution of pine vole sociality. Behav. Ecol. 3, 325–333 (1992).Article 

    Google Scholar 
    Randall, J. A., Rogovin, K., Parker, P. G. & Eimes, J. A. Flexible social structure of a desert rodent, Rhombomys opimus: Philopatry, kinship, and ecological constraints. Behav. Ecol. 16, 961–973 (2005).Article 

    Google Scholar 
    Ebensperger, L. A. et al. Burrow limitations and group living in the communally rearing rodent, Octodon degus. J. Mammal. 92(1), 21–30 (2011).Article 

    Google Scholar 
    Santini, L. The habits and influence on the environment of the old world porcupine Hystrix cristata L. in the northernmost part of its range. In Proc. 9th Vertebrate Pest Conference, Vol. 34, 149–153 (1980).Felicioli, A., Grazzini, A. & Santini, L. The mounting and copulation behaviour of the crested porcupine Hystrix cristata. Ital. J. Zool. 64, 155–161 (1997).Article 

    Google Scholar 
    Felicioli, A., Grazzini, A. & Santini, L. The mounting behaviour of a pair of crested porcupine H. cristata L.. Mammalia 61(1), 123–126 (1997).
    Google Scholar 
    Felicioli, A. Analisi spazio-temporale dell’attività motoria in Hystrix cristata L. Dissertation, University of Pisa (1991).Felicioli, A. & Santini, L. Burrow entrance-hole orientation and first emergence time in the crested porcupine Hystrix cristata L.: Space-time dependence on sunset. Pol. Ecol. Stud. 20(3–4), 317–321 (1994).
    Google Scholar 
    Mori, E., Nourisson, D. H., Lovari, S., Romeo, G. & Sforzi, A. Self-defence may not be enough: Moonlight avoidance in a large, spiny rodent. J. Zool. 294, 31–40 (2014).Article 

    Google Scholar 
    Corsini, M. T., Lovari, S. & Sonnino, S. Temporal activity patterns of crested porcupine Hystrix cristata. J. Zool. Lond. 236, 43–54 (1995).Article 

    Google Scholar 
    Coppola, F., Vecchio, G. & Felicioli, A. Diurnal motor activity and “sunbathing” behaviour in crested porcupine (Hystrix cristata L., 1758). Sci. Rep. 9, 14283 (2019).ADS 
    Article 

    Google Scholar 
    Pigozzi, G. Crested porcupines (Hystrix cristata) within badger setts (Meles meles) in the Maremma Natural Park, Italy. Saugetierk. Mitt. 33, 261–263 (1986).
    Google Scholar 
    Coppola, F. & Felicioli, A. Reproductive behaviour in free-ranging crested-porcupine Hystrix cristata L., 1758. Sci. Rep. 11, 20142. https://doi.org/10.1038/s41598-021-99819-3 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Monetti, L., Massolo, A., Sforzi, A. & Lovari, S. Site selection and fidelity by crested porcupines for denning. Ethol. Ecol. Evol. 17, 149–159 (2005).Article 

    Google Scholar 
    Coppola, F., Dari, C., Vecchio, G., Scarselli, D. & Felicioli, A. Co-habitation of settlements among Crested Porcupines (Hystrix cristata), Red Foxes (Vulpes vulpes) and European Badgers (Meles meles). Curr. Sci. 119(5), 817–822 (2020).Article 

    Google Scholar 
    De Villiers, M. S., Van Aarde, R. J. & Dott, H. M. Habitat utilization by the Cape porcupine Hystrix africaeaustralis in a savanna ecosystem. J. Zool. Lond. 232, 539–549 (1994).Article 

    Google Scholar 
    Corbet, N. U. & de Aarde, R. J. Social organization and space use in the Cape porcupine in a Southern African savanna. Afr. J. Ecol. 34, 1–14 (1996).Article 

    Google Scholar 
    Massolo, A., Dani, F. R. & Bella, N. Sexual and individual cues in the peri-anal gland secretum of crested porcupines (Hystrix cristata). Mamm. Biol. 74, 488–496 (2009).Article 

    Google Scholar 
    Mori, E. & Lovari, S. Sexual size monomorphism in the crested porcupine (Hystrix cristata). Mamm. Biol. 79, 157–160 (2014).Article 

    Google Scholar 
    Mori, E. et al. Patterns of spatial overlap in a monogamous large rodent, the crested porcupine. Behav. Process. 107, 112–118 (2014).Article 

    Google Scholar 
    Mukherjee, A., Pilakandy, R., Kumara, H. N., Manchi, S. S. & Bhupathy, S. Burrow characteristics and its importance in occupancy of burrow dwelling vertebrates in Semiarid area of Keoladeo National Park, Rajasthan, India. J. Arid Environ. 141, 7–15 (2017).ADS 
    Article 

    Google Scholar 
    Mukherjee, A., Pal, A., Velankar, A. D., Kumara, H. N. & Bhupathy, S. Stay awhile in my burrow! Interspecific associations of vertebrates to Indian crested porcupine burrows. Ethol. Ecol. Evol. 3(4), 313–328 (2019).Article 

    Google Scholar 
    Fernandez, N. & Palomares, F. The selection of breeding dens by the endangered Iberian lynx (Lynx pardinus): Implications for its conservation. Biol. Conserv. 94, 51–61 (2000).Article 

    Google Scholar 
    Ross, S., Kamnitzer, R., Munkhtsog, B. & Harris, S. Den-site selection is critical for Pallas’s cats (Otocolobus manul). Can. J. Zool. 88(9), 905–913. https://doi.org/10.1139/Z10-056 (2010).Article 

    Google Scholar 
    Libal, N. S., Belant, J. L., Leopold, B. D., Wang, G. & Owen, A. Despotism and risk of infanticide influence grizzly bear den-site selection. PLoS ONE 6(9), e24133. https://doi.org/10.1371/journal.pone.0024133 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Elbroch, L. M., Lendrum, P. E. & Quigley, H. Cougar den site selection in the Southern Yellowstone ecosystem. Mamm. Res. 60, 89–96. https://doi.org/10.1007/s13364-015-0212-6 (2015).Article 

    Google Scholar 
    Solomon, N. G., Christiansen, A. M., Kirk Lin, Y. & Hayes, L. D. Factors affecting nest location of prairie voles (Microtus ochrogaster). J. Mammal. 86(3), 555–560 (2005).Article 

    Google Scholar 
    Pereoglou, F. et al. Refuge site selection by the eastern chestnut mouse in recently burnt heath. Wildl. Res. 38(4), 290–298. https://doi.org/10.1071/WR11007 (2011).Article 

    Google Scholar 
    Grazzini, M. T. Comportamento riproduttivo e accrescimento post-natale in Hystrix cristata L. (Rodentia, Hystricidae). Dissertation, University of Pisa (1992).Capizzi, D. & Santini, L. Hystrix cristata Linnaeus, 1758. In Fauna d’Italia, Mammalia II: Erinaceomorpha, Soricomorpha, Lagomorpha, Rodentia (eds Amori, G. et al.) 695–706 (Edizione Calderini de il Sole 24 Ore, 2008).
    Google Scholar 
    Coppola, F. New knowledge tools for crested porcupine (Hystrix cristata L., 1758) management in the wild: First census model, new behavioural ecology aspects and preliminary investigation on health status. University of Pisa, PhD thesis (2021).Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017).Book 

    Google Scholar 
    Wood, S. N. A simple test for random effects in regression models. Biometrika 100, 1005–1010 (2013).MathSciNet 
    Article 

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

    Google Scholar  More

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    Small brains predisposed Late Quaternary mammals to extinction

    Martin, P. S. & Klein, R. G. Quaternary extinctions: a prehistoric revolution. (University of Arizona Press, 1984).Waguespack, N. M. & Surovell, T. A. Clovis hunting strategies, or how to make out on plentiful resources. Am. Antiq. 68, 333–352 (2003).
    Google Scholar 
    Surovell, T. A., Pelton, S. R., Anderson-Sprecher, R. & Myers, A. D. Test of Martin’s overkill hypothesis using radiocarbon dates on extinct megafauna. Proc. Natl. Acad. Sci. 113, 886–891 (2016).CAS 
    PubMed 
    ADS 

    Google Scholar 
    Martin, P. S. Prehistoric overkill: the global model. In Quaternary extinctions: a prehistoric revolution (eds. Martin, P. S. & Klein, R. G.) 355–403 (University of Arizona Press, 1984).Barnosky, A. D. & Lindsey, E. L. Timing of Quaternary megafaunal extinction in South America in relation to human arrival and climate change. Quatern. Int. 217, 10–29 (2010).
    Google Scholar 
    Prescott, G. W., Williams, D. R., Balmford, A., Green, R. E. & Manica, A. Quantitative global analysis of the role of climate and people in explaining late Quaternary megafaunal extinctions. Proc. Natl. Acad. Sci. 109, 4527–4531 (2012).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Sandom, C., Faurby, S., Sandel, B. & Svenning, J.-C. Global late Quaternary megafauna extinctions linked to humans, not climate change. Proc. R. Soc. B Biol. Sci. 281, 20133254 (2014).
    Google Scholar 
    Wolfe, A. L. & Broughton, J. M. A foraging theory perspective on the associational critique of North American Pleistocene overkill. J. Archaeol. Sci. 119, 105162 (2020).
    Google Scholar 
    Berger, J., Swenson, J. E. & Persson, I. L. Recolonizing carnivores and naïve prey: Conservation lessons from pleistocene extinctions. Science 291, 1036–1039 (2001).CAS 
    PubMed 
    ADS 

    Google Scholar 
    Brook, B. W. & Bowman, D. M. J. S. The uncertain blitzkrieg of Pleistocene megafauna. J. Biogeogr. 31, 517–523 (2004).
    Google Scholar 
    Johnson, C. N. Determinants of loss of mammal species during the Late Quaternary ‘megafauna’ extinctions: life history and ecology, but not body size. Proc. R. Soc. London. Ser. B Biol. Sci. 269, 2221–2227 (2002).CAS 

    Google Scholar 
    Bourgon, N. et al. Trophic ecology of a Late Pleistocene early modern human from tropical Southeast Asia inferred from zinc isotopes. J. Hum. Evol. 161, 103075 (2021).PubMed 

    Google Scholar 
    Meltzer, D. J. Overkill, glacial history, and the extinction of North America’s Ice Age megafauna. Proc. Natl. Acad. Sci. 117, 28555–28563 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stewart, M., Carleton, W. C. & Groucutt, H. S. Climate change, not human population growth, correlates with Late Quaternary megafauna declines in North America. Nat. Commun. 12, 965 (2021).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Nogués-Bravo, D., Rodríguez, J., Hortal, J., Batra, P. & Araújo, M. B. Climate change, humans, and the extinction of the woolly mammoth. PLoS Biol. 6, e79 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    Koch, P. L. & Barnosky, A. D. Late quaternary extinctions: State of the debate. Annu. Rev. Ecol. Evol. Syst. 37, 215–250 (2006).
    Google Scholar 
    Cardillo, M. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).CAS 
    PubMed 
    ADS 

    Google Scholar 
    Meiri, S. & Liang, T. Rensch’s rule—Definitions and statistics. Glob. Ecol. Biogeogr. 30, 573–577 (2021).
    Google Scholar 
    Lyons, S. K. et al. The changing role of mammal life histories in Late Quaternary extinction vulnerability on continents and islands. Biol. Lett. 12, 20160342 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Alroy, J. A multispecies overkill simulation of the end-pleistocene megafaunal mass extinction. Science 292, 1893–1896 (2001).CAS 
    PubMed 
    ADS 

    Google Scholar 
    Smaers, J. B. et al. The evolution of mammalian brain size. Sci. Adv. 7, 1–12 (2021).
    Google Scholar 
    Jerison, H. J. Evolution of the Brain and Intelligence (Academic Press, 1973). https://doi.org/10.2307/4512058.Book 

    Google Scholar 
    Sol, D., Bacher, S., Reader, S. M. & Lefebvre, L. Brain size predicts the success of mammal species introduced into novel environments. Am. Nat. 172, S63–S71 (2008).PubMed 

    Google Scholar 
    Møller, A. P. & Erritzøe, J. Brain size in birds is related to traffic accidents. R. Soc. Open Sci. 4, 161040 (2017).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Sayol, F., Sol, D. & Pigot, A. L. Brain size and life history interact to predict urban tolerance in birds. Front. Ecol. Evol. 8, 58 (2020).
    Google Scholar 
    Budd, G. E. & Jensen, S. The origin of the animals and a ‘Savannah’ hypothesis for early bilaterian evolution. Biol. Rev. 92, 446–473 (2017).PubMed 

    Google Scholar 
    Benoit, J. et al. Brain evolution in Proboscidea (Mammalia, Afrotheria) across the Cenozoic. Sci. Rep. 9, 9323 (2019).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Møller, A. P. & Erritzøe, J. Brain size and the risk of getting shot. Biol. Lett. 12, 20160647 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Di Febbraro, M. et al. Does the jack of all trades fare best? Survival and niche width in Late Pleistocene megafauna. J. Biogeogr. 44, 2828–2838 (2017).
    Google Scholar 
    Morris, S. D., Kearney, M. R., Johnson, C. N. & Brook, B. W. Too hot for the devil? Did climate change cause the mid-Holocene extinction of the Tasmanian devil Sacrophilus harrisii from mainland Australia? Ecography 2022, (2022).Fillios, M., Crowther, M. S. & Letnic, M. The impact of the dingo on the thylacine in Holocene Australia. World Archaeol. 44, 118–134 (2012).
    Google Scholar 
    González-Lagos, C., Sol, D. & Reader, S. M. Large-brained mammals live longer. J. Evol. Biol. 23, 1064–1074 (2010).PubMed 

    Google Scholar 
    Barton, R. A. & Capellini, I. Maternal investment, life histories, and the costs of brain growth in mammals. Proc. Natl. Acad. Sci. U.S.A. 108, 6169–6174 (2011).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Abelson, E. S. Brain size is correlated with endangerment status in mammals. Proc. R. Soc. B Biol. Sci. 283, 20152772 (2016).
    Google Scholar 
    Gonzalez-Voyer, A., González-Suárez, M., Vilà, C. & Revilla, E. Larger brain size indirectly increases vulnerability to extinction in mammals. Evolution (N.Y.) 70, 1364–1375 (2016).
    Google Scholar 
    Ives, A. R. & Helmus, M. R. Generalized linear mixed models for phylogenetic analyses of community structure. Ecol. Monogr. 81, 511–525 (2011).
    Google Scholar 
    Castiglione, S. et al. A new method for testing evolutionary rate variation and shifts in phenotypic evolution. Methods Ecol. Evol. 9, 974–983 (2018).
    Google Scholar 
    Billet, G. Phylogeny of the Notoungulata (Mammalia) based on cranial and dental characters. J. Syst. Palaeontol. 9, 481–497 (2011).
    Google Scholar 
    Shultz, S., Bradbury, R. B., Evans, K. L., Gregory, R. D. & Blackburn, T. M. Brain size and resource specialization predict long-term population trends in British birds. Proc. R. Soc. B Biol. Sci. 272, 2305–2311 (2005).
    Google Scholar 
    Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evol. 4, 788–793 (2020).PubMed 

    Google Scholar 
    Abelson, E. S. Big brains reduce extinction risk in Carnivora. Oecologia 191, 721–729 (2019).PubMed 
    ADS 

    Google Scholar 
    Lundgren, E. J. et al. Introduced herbivores restore Late Pleistocene ecological functions. Proceedings of the National Academy of Sciences 117, 7871–7878 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shultz, S. & Dunbar, R. Encephalization is not a universal macroevolutionary phenomenon in mammals but is associated with sociality. Proceedings of the National Academy of Sciences 107, 21582–21586 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gould, S. J. & Vrba, E. S. Exaptation—A missing term in the science of form. Paleobiology 8, 4–15 (1982).
    Google Scholar 
    Wroe, S. et al. Climate change frames debate over the extinction of megafauna in Sahul (Pleistocene Australia-New Guinea). Proc. Natl. Acad. Sci. U.S.A. 110, 8777–8781 (2013).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the Causes of Late Pleistocene Extinctions on the Continents. Science 306, 70–75 (2004).Article 
    PubMed 

    Google Scholar 
    Profico, A., Buzi, C., Melchionna, M., Veneziano, A. & Raia, P. Endomaker, a new algorithm for fully automatic extraction of cranial endocasts and the calculation of their volumes. Am. J. Phys. Anthropol. 172, 511–515 (2020).PubMed 

    Google Scholar 
    Damuth, J. & Macfadden, B. J. Body Size in Mammalian Paleobiology: Estimation and Biological Implications (Cambridge University Press, 1990).
    Google Scholar 
    Zagwijn, W. H. The beginning of the Ice Age in Europe and its major subdivisions. Quatern. Sci. Rev. 11, 583–591 (1992).ADS 

    Google Scholar 
    Hearty, P. J., Hollin, J. T., Neumann, A. C., O’Leary, M. J. & McCulloch, M. Global sea-level fluctuations during the Last Interglaciation (MIS 5e). Quatern. Sci. Rev. 26, 2090–2112 (2007).ADS 

    Google Scholar 
    Ashwell, K. W. S., Hardman, C. D. & Musser, A. M. Brain and behaviour of living and extinct echidnas. Zoology 117, 349–361 (2014).PubMed 

    Google Scholar 
    Castiglione, S. et al. The influence of domestication, insularity and sociality on the tempo and mode of brain size evolution in mammals. Biol. J. Linn. Soc. 132, 221–231 (2021).
    Google Scholar 
    Wilkins, A. S., Wrangham, R. W. & Tecumseh Fitch, W. The ‘domestication syndrome’ in mammals: A unified explanation based on neural crest cell behavior and genetics. Genetics 197, 795–808 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Sayol, F., Steinbauer, M. J., Blackburn, T. M., Antonelli, A. & Faurby, S. Anthropogenic extinctions conceal widespread evolution of flightlessness in birds. Sci. Adv. 6, eabb6095 (2020).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Fromm, A., Meiri, S. & McGuire, J. Big, flightless, insular and dead: Characterising the extinct birds of the Quaternary. J. Biogeogr. 48(9), 2350–2359. https://doi.org/10.1111/jbi.14206 (2021).Article 

    Google Scholar 
    Meiri, S., Dayan, T. & Simberloff, D. The generality of the island rule reexamined. J. Biogeogr. 33, 1571–1577 (2006).
    Google Scholar 
    Larramendi, A. & Palombo, M. R. Body Size, Structure, Biology and Encephalization Quotient of Palaeoloxodon ex gr. P. falconeri from Spinagallo Cave (Hyblean plateau, Sicily). Hystrix, the Italian Journal of Mammalogy 26, 102–109 (2015).Article 

    Google Scholar 
    Slavenko, A., Tallowin, O. J. S., Itescu, Y., Raia, P. & Meiri, S. Late Quaternary reptile extinctions: Size matters, insularity dominates. Glob. Ecol. Biogeogr. 25, 1308–1320 (2016).
    Google Scholar 
    Tracy, C. R. & George, T. L. On the determinants of extinction. Am. Nat. 139, 102–122 (1992).
    Google Scholar 
    Manne, L. L., Brooks, T. M. & Pimm, S. L. Relative risk of extinction of passerine birds on continents and islands. Nature 399, 258–261 (1999).CAS 
    ADS 

    Google Scholar 
    Turvey, S. T. In the shadow of the megafauna: prehistoric mammal and bird extinctions across the Holocene. in Holocene Extinctions 17–40 (Oxford University Press, 2009). https://doi.org/10.1093/acprof:oso/9780199535095.003.0002Ebinger, P. A cytoarchitectonic volumetric comparison of brains in wild and domestic sheep. Zeitschrift für Anat. und Entwicklungsgeschichte 144, 267–302 (1974).CAS 

    Google Scholar 
    Röhrs, M. & Ebinger, P. Welche quantitativen beziehungen bestehen bei säugetieren zwischen schädelkapazität und hirnvolumen? Mammalian Biology 66, 102–110 (2001).Köhler, M. & Moyà-Solà, S. Reduction of brain and sense organs in the fossil insular bovid Myotragus. Brain Behav. Evol. 63, 125–140 (2004).PubMed 

    Google Scholar 
    de Bello, F. et al. On the need for phylogenetic ‘corrections’ in functional trait-based approaches. Folia Geobot. 50, 349–357 (2015).
    Google Scholar 
    Bates, D., Sarkar, D., Bates, M. D. & Matrix, L. The lme4 Package. October (2007).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
    Google Scholar 
    Raia, P. & Meiri, S. The tempo and mode of evolution: Body sizes of island mammals. Evolution 65, 1927–1934 (2011).

    Google Scholar 
    Montgomery, S. H. et al. The evolutionary history of cetacean brain and body size. Evolution 67, 3339–3353 (2013).
    PubMed 

    Google Scholar 
    Li, D., Dinnage, R., Nell, L. A., Helmus, M. R. & Ives, A. R. phyr: An r package for phylogenetic species-distribution modelling in ecological communities. Methods Ecol. Evol. 11, 1455–1463 (2020).
    Google Scholar 
    Melchionna, M. et al. Macroevolutionary trends of brain mass in Primates. Biological Journal of the Linnean Society 129, 14–25 (2020).Article 

    Google Scholar 
    Serio, C. et al. Macroevolution of toothed whales exceptional relative brain size. Evol. Biol. 46, 332–342 (2019).
    Google Scholar 
    Wickham, H. et al. Welcome to the Tidyverse. Journal of Open Source Software 4, 1686 (2019).Barton, K. Package ‘MuMIn’ Title Multi-Model Inference. CRAN-R (2018). More

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    Tropical tree growth driven by dry-season climate variability

    Forest Ecology and Forest Management Group, Wageningen University, Wageningen, the NetherlandsPieter A. Zuidema & Ute Sass-KlaassenSchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USAFlurin BabstLaboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USAFlurin Babst, Valerie Trouet, Zakia Hassan Khamisi, Paul R. Sheppard & Ramzi TouchanDepartment of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, BrazilPeter Groenendijk & José Roberto Vieira AragãoWorld Agroforestry Centre (ICRAF), Addis Ababa, EthiopiaAbrham AbiyuDepartment of Microbiology and Parasitology, Universidad Nacional Autónoma de México, Mexico City, MexicoRodolfo Acuña-SotoLaboratory of Protection and Forest Management, Department of Forest Engineering, Universidade Regional de Blumenau, Santa Catarina, BrazilEduardo Adenesky-FilhoDepartment of Biology, Wilfrid Laurier University, Waterloo, Ontario, CanadaRaquel Alfaro-SánchezDepartment of Forest Sciences, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Piracicaba, BrazilGabriel Assis-Pereira, Claudia Fontana & Mario Tomazello-FilhoTree-Ring Laboratory, Forest Science Department, Federal University of Lavras, Lavras, BrazilGabriel Assis-Pereira & Ana Carolina BarbosaCAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, ChinaXue Bai, Ze-Xin Fan, Shankar Panthi & Zhe-Kun ZhouDepartment of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “L. Vanvitelli”, Caserta, ItalyGiovanna BattipagliaService of Wood Biology, Royal Museum for Central Africa, Tervuren, BelgiumHans Beeckman, Camille Couralet & Benjamin ToirambeBrazilian Agricultural Research Corporation (Embrapa), Embrapa Forestry, Colombo, BrazilPaulo Cesar BotossoU.S. Department of Agriculture, Forest Service, NWCG Member Agency, Washington, DC, USATim BradleyInstitute of Geography, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, GermanyAchim Bräuning, Mahmuda Islam, Mulugeta Mokria & Mizanur RahmanSchool of Geography, University of Leeds, Leeds, UKRoel Brienen & Emanuel GloorLamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USABrendan M. Buckley & Rosanne D’ArrigoInstituto Pirenaico de Ecología (IPE-CSIC), Zaragoza, SpainJ. Julio CamareroCentre for Functional Ecology, Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra, PortugalAna Carvalho & Cristina NabaisDepartment of Botany, Institute of Biosciences, University of São Paulo, São Paulo, BrazilGregório Ceccantini, Bruno Barçante Ladvocat Cintra & Giuliano Maselli LocosselliInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Centro Nacional de Investigación Disciplinaría en Relación Agua-Suelo-Planta-Atmósfera (CENID-RASPA), Gómez Palacio, MéxicoLibrado R. Centeno-Erguera, Julián Cerano-Paredes & Jose Villanueva-DiazInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Campo Experimental Centro – Altos de Jalisco, Tepatitlán de Morelos, MéxicoÁlvaro Agustín Chávez-DuránDepartment of Geosciences, University of Arkansas, Fayetteville, AR, USAMalcolm K. Cleaveland & Daniela Granato-SouzaDepartment of Forest Sciences, Universidad Nacional de Colombia – Sede Medellín, Medellín, ColombiaJorge Ignacio del ValleMaster School for Carpentry and Cabinetmaking, Ebern, GermanyOliver DünischDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USABrian J. EnquistSanta Fe Institute, Santa Fe, NM, USABrian J. EnquistDepartment of Biological Sciences, University of Joinville Region ‐ UNIVILLE, Joinville, BrazilKarin Esemann-QuadrosPostgraduate Program in Forestry, Regional University of Blumenau – FURB, Blumenau, BrazilKarin Esemann-QuadrosCollege of Life Science, Climate Science Center and Department of Earth Science, Addis Ababa University, Addis Ababa, EthiopiaZewdu EshetuDepartamento de Dendrocronología e Historia Ambiental, IANIGLA, CCT-CONICET-Mendoza, Mendoza, ArgentinaM. Eugenia Ferrero, Lidio Lopez, Fidel Alejandro Roig & Ricardo VillalbaLaboratorio de Dendrocronología, Universidad Continental, Huancayo, PerúM. Eugenia Ferrero, Janet G. Inga & Edilson Jimmy Requena-RojasDepartment of Crop Sciences, Tropical Plant Production and Agricultural Systems Modelling, Göttingen University, Göttingen, GermanyEsther FichtlerInstitute of Pacific Islands Forestry, USDA Forest Service Pacific Southwest Research Station, Hilo, HI, USAKainana S. Francisco & Mulugeta MokriaWorld Agroforestry Centre (ICRAF), Nairobi, KenyaAster GebrekirstosFlanders Heritage Agency, Brussels, BelgiumKristof HanecaDepartment of Geography and Geological Sciences, University of Idaho, Moscow, ID, USAGrant Logan HarleyGerman Archaeological Institute DAI, Berlin, GermanyIngo HeinrichGeography Department, Humboldt University Berlin, Berlin, GermanyIngo HeinrichGFZ German Research Centre for Geosciences, Potsdam, GermanyIngo Heinrich & Gerd HelleDepartment of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet, BangladeshMahmuda Islam & Mizanur RahmanFaculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech RepublicYu-mei JiangUS Fish and Wildlife Service, Albuquerque, NM, USAMark KaibDepartment of Ecology and Biogeography, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, Toruń, PolandMarcin KoprowskiCentre for Climate Change Research, Nicolaus Copernicus University, Toruń, PolandMarcin KoprowskiWater Systems and Global Change Group, Wageningen University and Research, Wageningen, the NetherlandsBart KruijtInstituto Nacional de Innovación Agraria, Programa Nacional de Investigación Forestal, Huancayo, PerúEva LaymeEnvironmental Systems Analysis Group, Wageningen University and Research, Wageningen, the NetherlandsRik LeemansDepartment of Natural Resource Management, South Dakota State University, Brookings, USA, SDA. Joshua LefflerLaboratory of Plant Anatomy and Dendrochronology, Department of Biology, Universidade Federal de Sergipe, Sergipe, BrazilClaudio Sergio Lisi, Mariana Alves Pagotto & Adauto de Souza Ribeiro Department of Geography, Swansea University, Swansea, UKNeil J. Loader & Iain RobertsonDepartamento Forestal, Universidad Autónoma Agraria Antonio Narro, Saltillo, MexicoMaría I. López-HernándezCITAB – Department of Forestry Sciences and Landscape (CIFAP), University of Trás-os-Montes and Alto Douro, Vila Real, PortugalJosé Luís Penetra Cerveira LousadaEscuela de Ciencias Biológicas, Universidad Pedagógica y Tecnológica de Colombia (UPTC), Tunja, ColombiaHooz A. MendivelsoBrazilian Agricultural Research Corporation (Embrapa), Embrapa Amazônia Ocidental, Manaus, BrazilValdinez Ribeiro MontóiaIHE Delft, Delft, the NetherlandsEddy MoorsVU University Amsterdam, Amsterdam, the NetherlandsEddy MoorsDepartment of Biomaterials Science and Technology, School of Natural Resources, The Copperbelt University, Kitwe, ZambiaJustine NgomaLaboratory of Ecology and Dendrology of the Federal Institute of Sergipe, São Cristovão, BrazilFrancisco de Carvalho Nogueira JúniorLaboratory of Plant Ecology, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, BrazilJuliano Morales Oliveira & Gabriela Morais OlmedoBIOAPLIC, Departamento de Botánica, Universidade de Santiago de Compostela, EPSE, Lugo, SpainGonzalo Pérez-De-LisLaboratorio de Dendrocronología, Carrera de Ingeniería Forestal, Universidad Nacional de Loja, Loja, EcuadorDarwin Pucha-CofrepFaculty of Environment and Resource studies, Mahidol University, Nakhon Pathom, ThailandNathsuda PumijumnongFacultad de Ciencias Agrarias, Universidad del Cauca, Popayán, ColombiaJorge Andres RamirezHémera Centro de Observación de la Tierra, Escuela de Ingeniería Forestal, Facultad de Ciencias, Universidad Mayor, Santiago, ChileFidel Alejandro Roig & Alejandro Venegas-GonzálezInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Centro de Investigación Regional Pacífico Centro – Campo Experimental, Centro Altos de Jalisco, MéxicoErnesto Alonso Rubio-CamachoNational Institute for Amazon Research, Petrópolis, Manaus, BrazilJochen SchöngartDepartment of Earth Sciences, Freie Universität Berlin, Berlin, GermanyFranziska SlottaDepartment of Earth and Environmental Systems, Indiana State University, Terre Haute, IN, USAJames H. SpeerDepartment of Geography, University of Alabama, Tuscaloosa, AL, USAMatthew D. TherrellDepartment of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USAMax C. A. TorbensonDepartment of Geography, Johannes Gutenberg University, Mainz, GermanyMax C. A. TorbensonDepartment of Plant and Environmental Sciences, School of Natural Resources, The Copperbelt University, Kitwe, ZambiaRoyd VinyaForest and Nature Management, Van Hall Larenstein University of Applied Sciences, Velp, the NetherlandsMart VlamSchool of Teacher Training for Secondary Education Tilburg, Fontys University of Applied Sciences, Tilburg, the NetherlandsTommy WilsP.A.Z., P.G. and V.T. initiated the tropical tree-ring network; P.A.Z., F.B., P.G. and V.T. designed the study; all co-authors except F.B. contributed tree-ring data; F.B. and P.G. analysed the data, with important contributions from P.A.Z.; P.A.Z. and V.T. wrote the manuscript, with important contributions from F.B. and P.G. All co-authors read and approved the manuscript. More

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    Individual experience as a key to success for the cuckoo catfish brood parasitism

    Study systemThe cuckoo catfish (Synodontis multipunctatus) belongs to the African catfish family Mochokidae. The genus Synodontis, with 131 species distributed across African freshwaters57, gave rise to a small radiation in Lake Tanganyika, with 10 described endemic species58. The taxonomy of the group is not well established59 and we use the name S. multipunctatus as this species is confirmed as a brood parasite30 and the name was used in previous studies4,30,32,37,42. Cuckoo catfish primarily parasitise mouthbrooding cichlids from the tribe Tropheini30, but species from other lineages can also be parasitised59.Experimental designAll experiments took place between January and August 2020 at the Institute of Vertebrate Biology, Czech Republic. Prior to experimental use, fish were housed in mixed-sex groups in tanks equipped with shelter and internal filtration. Cuckoo catfish were F1 generation of commercially imported wild-caught parents (10 pairs). Host cichlids were descendant of wild fish imported from Kalambo, Zambia. Experimental tanks (420 L; length 150 cm, depth 70 cm, height 40 cm) were equipped with internal filtration, fine gravel (2–4 mm diameter), half a clay pot as a shelter on each side of the tank, and one artificial plant in the centre of each tank. Water temperature was maintained at 27 °C (±1 °C) and the dark – light regime was set to 11 h:13 h. In total, we stocked 18 tanks with 4 males and 12 females of the mouthbrooding cichlid Astatotilapia burtoni and introduced 3 cuckoo catfish pairs of one of three different experience levels. Naïve catfish (n = 36 individuals) had no prior experience with cichlids. Experienced catfish (n = 36) were housed together with reproductive cichlids for 12 months prior to the experiment and were age-matched to naïve catfish (5 years old). Highly experienced catfish (n = 36) were raised, coexisted and reproduced with cichlids for 7 years (and were on average 7–8% larger than both naïve and experienced catfish; mean ± SE, naïve: 116.2 ± 1.9 mm, experienced: 117.1 ± 1.5 mm, highly experienced: 125.6 ± 1.4 mm; Linear Model (LM): experienced vs. highly experienced, estimate ± S.E = 8.44 ± 2.29, t = 3.68, P = 0.0004, experienced vs. naïve, estimate ± S.E = −0.94 ± 2.29, t = −0.41, P = 0.681, n = 108). Additionally, both naïve and experienced cuckoo catfish were bred using in-vitro fertilisation32 to avoid cichlid imprinting (i.e., priming with cichlid cues), while highly experienced catfish were bred under natural conditions within the buccal cavities of their hosts. Each experimental tank contained catfish with the same experience level. Due to space limitations, we split the experiment into two consecutive phases with 3 replicate tanks for each treatment within both phases (in total 9 experimental tanks per phase). Between the two experimental phases, host cichlids were placed together and haphazardly assigned to new experimental tanks. During the second phase, we removed some cichlids from the tanks because of injuries caused by their intraspecific aggression (3 males and 3 females in total), and those hosts were not replaced. Over an experimental phase, cuckoo catfish and cichlids freely interacted for 15–16 weeks. During this period, each tank was checked for mouthbrooding cichlids twice each week (Tuesday and Friday). We caught the mouthbrooding females, gently washed the eggs out of their mouths using a jet of water from a Pasteur pipette, measured their body size to the nearest mm, and released them back to their experimental tank. For each female, we counted the number of cichlid eggs and cuckoo catfish eggs (if present). At the end of each experimental phase, we measured body size of all cuckoo catfish to the nearest mm. There was no significant difference between the number of cichlid spawnings between naïve and experienced catfish treatments (Generalised Linear Models with negative binomial error distribution, estimate ± S.E.: −0.093 ± 0.145, z = −0.644, P = 0.519), nor between naïve and highly experienced catfish (estimate ± S.E.: −0.269 ± 0.148, z = −1.810, P = 0.070).Behavioural recordingOver the experimental period, we successfully recorded 18 videos of spawning events (Lamax x3.1 ATLAS cameras; naïve catfish treatment, n = 9; experienced catfish treatment, n = 6; highly experienced catfish treatment, n = 3). One camera was placed near the spawning site approximately 20 cm away from spawning activity and a second camera was placed outside the experimental tank to obtain an overall view. Nine spawnings were recorded from the start (n = 7 naïve catfish experiments and 2 experienced catfish experiments) and nine spawnings were recorded from the timepoint when we recognised ongoing spawning activity (n = 2 naïve, 4 experienced, and 3 highly experienced catfish experiments). From the video footage taken for each spawning, we scored all overt aggression that host cichlids directed towards cuckoo catfish, counted the number of intruding catfish during each distinct cichlid spawning behaviour (i.e., male and female cichlid interact in a repeated succession of quivering and T-positions), measured the delay of intruding catfish to each distinct spawning behaviour (i.e., the time from the start of spawning behaviour until the first catfish directly approaches the spawning cichlids), and recorded the presence or absence of catfish during each spawning behaviour. Additionally, we recorded whether cichlids used the available shelters for spawning as this might have impeded catfish recognition of the spawning activity. When spawning was recorded from the start, scoring started 100 s before we detected the first egg laid (cichlid or cuckoo catfish). When spawning was already ongoing, the scoring started immediately after the cameras were in place. Mounting of the cameras did not interrupt the normal behaviour of cichlids or catfish. For all video footage, scoring ended 100 s after the last male-female interaction within the spawning site. To estimate the duration of male T-positions during spawnings, we measured the time period from the start of male nuzzling near female genital papilla until the female turned around either to collect eggs or start nuzzling near the male´s genital papilla (n = 115 male T-positions from 12 cichlid spawnings).Statistical analysisWe used R v. 3.5.1 (R Development Core Team, 2018) for all statistical analyses. All statistical tests were two-sided. First, we compared body size among the three cuckoo catfish experience levels using a Linear Model with catfish size (mm) as response variable and ‘treatment’ (naïve, experienced, and highly experienced catfish) as predictor variable. Second, we formally tested whether the number of host spawnings varied between the treatment groups (total numbers: naïve = 191 spawnings, experienced = 174 spawnings, highly experienced = 146 spawnings). To obtain an insight into temporal dynamics of cichlid spawning, we calculated the number of cichlid spawnings for each treatment in each quarter of the duration of the experimental period. We fitted a GLM with a negative binomial error distribution (to account for slightly overdispersed data) with the number of cichlid spawnings as the response variable and our treatment groups as predictors.To test how experience with host spawning (treatment) affected cuckoo catfish ability to place their eggs in the care of the host, we compared (1) the number of parasitised cichlid clutches among the three catfish experience groups (prevalence of parasitism), (2) the mean number of catfish eggs introduced into cichlid clutches among the three treatment levels (mean parasite egg abundance, the mean number of catfish eggs calculated across all cichlid broods, (3) mean parasite clutch size (the number of catfish eggs calculated only across parasitised cichlid broods), and examined (4) temporal dynamics of all three measures of parasite success within each treatment group throughout the duration of the experiment.To test for differences in prevalence of parasitism among different cuckoo catfish experience treatments, we applied a Generalised Linear Mixed-effects Model (GLMM, R package glmmTMB)60 with a binomial error distribution. We fitted the occurrence of ‘catfish parasitism’ (1 = yes, 0 = no) as the binary response variable and ‘treatment effect’ (i.e., ‘catfish experience’), ‘time progress of experiment’ (1–113 days) and ‘host female body size’ (in mm) as predictor variables. We additionally fitted an interaction between treatment (‘catfish experience’) and ‘time progress of experiment’ to the model to test whether parasitism rate changed over time at treatment-specific rates. We included tank identity (‘tank ID’) as a random intercept to account for nonindependence of data obtained from the same tank.Next, we tested whether the mean number of parasite eggs that were accepted by host females during one spawning bout differed between catfish experience treatments. We applied two GLMMs (R package glmmTMB)60 with a negative binomial error distribution (i.e., nbinom1) to account for over-dispersed count data. We applied GLMMs on the mean abundance of catfish eggs (across all host clutches) and on mean clutch size of cuckoo catfish using a subset of clutches that were parasitised. For both GLMMs, we included the ‘number of cuckoo catfish eggs per clutch’ as the response variable and treatment (‘catfish experience’), ‘time progress of experiment’, and their interaction as predictor variables. We additionally fitted ‘host female body size’ as a predictor variable because larger female cichlids are capable of laying more eggs and may appear more attractive hosts to cuckoo catfish. Further, a higher number of host eggs may increase the number of opportunities for cuckoo catfish to deposit their own eggs in the host clutch. ‘Tank ID’ was included as random intercept to account for nonindependence of data.To test whether cuckoo catfish presence affected cichlid spawning activity, we applied a GLMM (R package glmmTMB)60 with Gaussian error distribution (which provided superior model fit compared to Poisson and negative binomial distributions by ‘simulateResiduals’ and ‘testDispersion’ functions in the R package DHARMa)61. We fitted the ‘number of host eggs’ per clutch as the response variable and treatment (‘catfish experience’), ‘host female body size’, ‘time progress of experiment’, and ‘experimental phase’ (1st or 2nd phase) as predictor variables. We also included ‘tank ID’ as random intercept to account for nonindependence of data. The full model further included an interaction between treatment and ‘time progress of experiment’ to accommodate the possibility that host egg numbers may be affected differently across catfish experience treatments over time. As this full model predicted no difference in temporal aspect of host clutch size among treatments (‘catfish experience’: ‘time progress’, experienced: z = 0.92, P = 0.360, highly experienced: z = 1.46, P = 0.143), we subsequently dropped the interaction term from the final model.We used data collected from video footage to investigate whether naïve, experienced and highly experienced cuckoo catfish differed in their response to host spawnings and, additionally, if catfish from the three treatments elicited different host reactions towards them by applying Linear Mixed-effect Models using the R packages lme462 and glmmTMB60. To account for different starting times of recordings, we calculated either the rate of behaviour per minute of observation (i.e., for aggression) or their relative values (i.e., for the number of host courtships that cuckoo catfish missed).First, we tested whether host spawning pairs increased their aggressions towards cuckoo catfish over the experimental period to rule out the presence of host adaptation to cuckoo catfish intrusions, which would interfere with our aim of understanding parasite learning. We fitted a Generalised Linear Mixed-effects Model (GLMM, R package glmmTMB) with a negative binomial error distribution. The number of overt aggressive behaviours that the spawning pair performed towards cuckoo catfish per minute of catfish presence at the spawning site (summed over male and female cichlid) was fitted as the response variable and treatment (‘catfish experience’) as the predictor variable. We further included ‘time progress of experiment’ and ‘experimental phase’ as predictors to account for their possible effect on host aggression. We additionally included ‘tank ID’ as random intercept in the model to account for individual variation in host aggression levels among experimental tanks.To investigate if naïve cuckoo catfish missed more opportunities to parasitise cichlids than experienced and highly experienced catfish, we fitted a GLMM (R package lme4) with a binomial error distribution. We included the proportion of missed spawning behaviours (coded as ‘missed spawnings behaviours’ versus ‘intruded spawning behaviours’, based on count data for each spawning) as the response variable (‘spawnings missed’) and treatment (‘catfish experience’) as a predictor variable. We fitted ‘tank ID’ as a random intercept to the model to account for nonindependence of data within tanks, and we additionally fitted a random intercept based on whether the spawning was covered by a shelter or not (‘sheltered spawn’, yes / no) since spawning in a shelter may have been less apparent to catfish.We tested whether cuckoo catfish experience played a role in the timing of their intrusion to specific spawning behaviours by fitting a GLMM (R package lme4) with a Gamma error distribution to account for a positive skew in the data distribution. We included the mean delay of catfish to the first appearance of cichlid T-position in seconds (‘catfish delay’, see main text and Supplementary Movie 1 for a detailed description of cichlid spawning sequence) as the response variable and ‘catfish experience’ as the predictor variable. We included ‘tank ID’ and ‘sheltered spawn’ as random intercepts.Finally, we fitted a GLMM with a Poisson error distribution to test whether cuckoo catfish learn to synchronise their intrusion behaviour as they gain experience through interactions with their hosts. We included the maximum number of catfish during a specific cichlid spawning behaviour (‘intruder number’, count data) as the response variable and ‘catfish experience’ as the predictor variable. To account for nonindependence of data within experimental tanks and spawnings, we included a random intercept where each spawning was nested within ‘tank ID’ in the model.Ethical complianceResearch adhered to all national and institutional animal care and use guidelines, was administered under permit No. CZ62760203 and was approved by ethical boards of the Institute of Vertebrate Biology and the Czech Academy of Sciences (approval No. 32-2019).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Funding battles stymie ambitious plan to protect global biodiversity

    NEWS
    31 March 2022

    Funding battles stymie ambitious plan to protect global biodiversity

    Researchers are disappointed with the progress — but haven’t lost hope.

    Natasha Gilbert

    Natasha Gilbert

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    Animals such as this orangutan in Indonesia are endangered because of illegal deforestation.Credit: Jami Tarris/Future Publishing via Getty

    Scientists are frustrated with countries’ progress towards inking a new deal to protect the natural world. Government officials from around the globe met in Geneva, Switzerland, on 14–29 March to find common ground on a draft of the deal, known as the post-2020 global biodiversity framework, but discussions stalled, mostly over financing. Negotiators say they will now have to meet again before a highly anticipated United Nations biodiversity summit later this year, where the deal was to be signed.The framework so far sets out 4 broad goals, including slowing species extinction, and 21 mostly quantitative targets, such as protecting at least 30% of the world’s land and seas. It is part of an international treaty known as the UN Convention on Biological Diversity, and aims to address the global biodiversity crisis, which could see one million plant and animal species go extinct in the next few decades because of factors such as climate change, human activity and disease.
    China takes centre stage in global biodiversity push
    The COVID-19 pandemic has already slowed discussions of the deal. Over the past two years, countries’ negotiators met only virtually; the Geneva meeting was the first in-person gathering since the pandemic began. When it ended, Basile van Havre, one of the chairs of the framework negotiations working group, said that because negotiators couldn’t agree on goals, additional discussions will need to take place in June in Nairobi. The convention’s pivotal summit — its Conference of the Parties (COP15) — is expected to be held in Kunming, China, in August and September, but no firm date has been set.Anne Larigauderie, executive secretary of the Intergovernmental Platform on Biodiversity and Ecosystem Services in Bonn, Germany, who attended the Geneva gathering, told Nature: “We are leaving the meeting with no quantitative elements. I was hoping for more progress.”Robert Watson, a retired environmental scientist at the University of East Anglia, UK, says the quantitative targets are crucial to conserving biodiversity and monitoring progress towards that goal. He calls on governments to “bite the bullet and negotiate an appropriate deal that both protects and restores biodiversity”.Finance fightMany who were at the meeting say that disagreements over funding for biodiversity conservation were the main hold-up to negotiations. For example, the draft deal proposed that US$10 billion of funding per year should flow from developed nations to low- and middle-income countries to help them to implement the biodiversity framework. But many think this is not enough. A group of conservation organizations has called for at least $60 billion per year to flow to poorer nations.
    Biodiversity moves beyond counting species
    The consumption habits of wealthy nations are among the key drivers of biodiversity loss. And poorer nations are often home to areas rich in biodiversity, but have fewer means to conserve them.“The most challenging aspect is the amount of financing that wealthy nations are committing to developing nations,” says Brian O’Donnell, director of the Campaign for Nature in Washington DC, a partnership of private charities and conservation organizations advocating a deal to safeguard biodiversity. “Nations need to up their level of financing to get progress in the COP.”Other nations, particularly low-income ones, probably don’t want to agree “unless they have assurances of resources to allow them to implement the new framework”, Larigauderie says.Countries including Argentina and Brazil are largely responsible for stalling the deal, several sources close to the negotiations told Nature. They asked to remain anonymous because the negotiations are confidential.
    The world’s species are playing musical chairs: how will it end?
    No agreement could be reached even on targets with broad international support, such as protecting at least 30% of the world’s land and seas by 2030. O’Donnell says that just one country blocked agreement on this target, questioning its scientific basis.Van Havre downplayed the lack of progress, saying that the brinksmanship at the meeting was part of a “normal negotiating process”. He told reporters: “We are happy with the progress made.” Further delays ‘unacceptable’A bright spot in the negotiations, van Havre said, was a last-minute “major step forward” in discussions on how to fairly and equitably share the benefits of digital sequence information (DSI). DSI consists of genetic data collected from plants, animals and other organisms.
    Why deforestation and extinctions make pandemics more likely
    When pressed, however, van Havre admitted that the progress was simply an agreement between countries to continue discussing a way forward.Thomas Brooks, chief scientist at the International Union for Conservation of Nature in Gland, Switzerland, says that DSI discussions have actually been fraught. Communities from biodiverse-rich regions where genetic material is collected have little control over the commercialization of the data that come from it, and no way to recoup financial and other benefits, he explains.Like biodiversity financing, DSI rights could hold up negotiations on the overall framework. Low-income countries want a fair and equitable share of the benefits from genetic material that originates in their lands, but rich nations don’t want unnecessary barriers to sharing the information.“We are a long way from a watershed moment, and there remain genuine disagreements,” Brooks says. However, he is optimistic that progress will eventually be made.
    The biodiversity leader who is fighting for nature amid a pandemic
    Some conservation organizations take hope from new provisional language in the deal that calls for halting all human-caused species extinctions. The previous draft of the deal proposed only a reduction in the rate and risk of extinctions, says Paul Todd, an environmental lawyer at the Natural Resources Defense Council, a non-profit group based in New York City.Given how much work governments must do to reach agreement on the deal, Watson says the extra Nairobi meeting is a “logical” move. But he warns: “Any further delay would be unacceptable.”“This isn’t even the hard work,” Todd says. “Implementing the deal will be the real work.”

    doi: https://doi.org/10.1038/d41586-022-00916-8

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    Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction

    Martin, G., Martin-Clouaire, R. & Duru, M. Farming system design to feed the changing world. A review. Agron. Sustain. Dev. 33, 131–149 (2013).
    Google Scholar 
    McElwee, G. & Bosworth, G. Exploring the strategic skills of farmers across a typology of farm diversification approaches. J. Farm Manag. 13, 819–838 (2010).
    Google Scholar 
    Maghrebi, M. et al. Iran’s agriculture in the anthropocene. Earth’s Future. https://doi.org/10.1029/2020EF001547 (2020).Article 

    Google Scholar 
    Raorane, A. A. & Kulkarni, R. V. Data mining: An effective tool for yield estimation in the agricultural sector. Int. J. Emerg. Trends Technol. Comput. Sci. 1, 1–4 (2012).
    Google Scholar 
    Gonzalez-Sanchez, A., Frausto-Solis, J. & Ojeda-Bustamante, W. Attribute selection impact on linear and nonlinear regression models for crop yield prediction. Sci. World J. 2014, 509429 (2014).
    Google Scholar 
    Salman, S. A. et al. Changes in climatic water availability and crop water demand for Iraq region. Sustainability 12, 3437 (2020).
    Google Scholar 
    Mahmood, N., Arshad, M., Kächele, H., Ullah, A. & Müller, K. Economic efficiency of rainfed wheat farmers under changing climate: Evidence from Pakistan. Environ. Sci. Pollut. Res. 27, 34453–34467 (2020).
    Google Scholar 
    Pracha, A. S. & Volk, T. A. An edible energy return on investment (EEROI) analysis of wheat and rice in Pakistan. Sustainability 3, 2358–2391 (2011).
    Google Scholar 
    Canadell, J. et al. Abberton, M., Conant, R., & Batello, C. (Eds.). (2010). Grassland carbon sequestration: Management, policy and economics. Food and Agriculture Organization of the United Nations, Integrated Crop Management, Vol. 11–2010. Ahlstrom, A., Raupach, M., Schurgers. Sensit. A Semi-Arid Grassl. To Extrem. Precip. Events 127, 6 (2021).
    Google Scholar 
    Canton, H. Food and Agriculture Organization of the United Nations—FAO. In The Europa Directory of International Organizations 2021 (ed. Canton, H.) 297–305 (Routledge, 2021).
    Google Scholar 
    Abdullah, A. et al. Potential for sustainable utilisation of agricultural residues for bioenergy production in Pakistan: An overview. J. Clean. Prod. 287, 125047 (2020).
    Google Scholar 
    Mughal, I. et al. Protein quantification and enzyme activity estimation of Pakistani wheat landraces. PLoS ONE 15, e0239375 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dorosh, P. & Salam, A. Wheat markets and price stabilisation in Pakistan: An analysis of policy options. Pak. Dev. Rev. 47, 71–87 (2008).
    Google Scholar 
    Fowke, V. The National Policy and the Wheat Economy (University of Toronto Press, 2019).
    Google Scholar 
    Hussain, S. et al. Study the effects of COVID-19 in Punjab, Pakistan using space-time scan statistic for policy measures in regional agriculture and food supply chain. Environ. Sci. Pollut. Res. Int. 20, 1–14 (2021).
    Google Scholar 
    Sajjad, S. A. Story of Pakistan’s Elite Wheat (The Express Tribune, 2017).
    Google Scholar 
    Durgun, Y. Ö., Gobin, A., Duveiller, G. & Tychon, B. A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time. Int. J. Appl. Earth Obs. Geoinf. 86, 101988 (2020).
    Google Scholar 
    Vannoppen, A. et al. Wheat yield estimation from NDVI and regional climate models in Latvia. Remote Sens. 12, 2206 (2020).ADS 

    Google Scholar 
    Irmak, A. et al. Artificial neural network model as a data analysis tool in precision farming. Trans. ASABE 49, 2027–2037 (2006).
    Google Scholar 
    Bannerjee, G., Sarkar, U., Das, S. & Ghosh, I. Artificial intelligence in agriculture: A literature survey. Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud. 7, 1–6 (2018).
    Google Scholar 
    Patrício, D. I. & Rieder, R. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Comput. Electron. Agric. 153, 69–81 (2018).
    Google Scholar 
    Yaseen, Z. M. et al. Prediction of evaporation in arid and semi-arid regions: A comparative study using different machine learning models. Eng. Appl. Comput. Fluid Mech. 14, 70–89 (2019).
    Google Scholar 
    Bauer, M. E. The role of remote sensing in determining the distribution and yield of crops. In Advances in Agronomy (ed. Sparks, D. L.) 271–304 (Elsevier, 1975). https://doi.org/10.1016/s0065-2113(08)70012-9.Chapter 

    Google Scholar 
    Dempewolf, J. et al. Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sens. 6, 9653–9675 (2014).ADS 

    Google Scholar 
    Hamid, N., Pinckney, T. C., Gnaegy, S. & Valdes, A. The Wheat Economy of Pakistan: Setting and Prospects (IFPRI, 2015).
    Google Scholar 
    Muhammad, K. Description of the Historical Background of Wheat Improvement in Baluchistan, Pakistan (Agriculture Research Institute (Sariab, Quetta, Baluchistan, Pakistan), 1989).
    Google Scholar 
    Iqbal, N., Bakhsh, K., Maqbool, A. & Abid Shohab, A. Use of the ARIMA model for forecasting wheat area and production in Pakistan. J. Agric. Soc. Sci. 1, 120–122 (2005).
    Google Scholar 
    Sher, F. & Ahmad, E. Forecasting wheat production in Pakistan. LAHORE J. Econ. 13, 57–85 (2008).
    Google Scholar 
    Khan, N. et al. Determination of cotton and wheat yield using the standard precipitation evaporation index in Pakistan. Arab. J. Geosci. 14, 1–16 (2021).
    Google Scholar 
    Rahman, M. M., Haq, N. & Rahman, R. M. Machine learning facilitated rice prediction in Bangladesh. In 2014 Annual Global Online Conference on Information and Computer Technology. https://doi.org/10.1109/gocict.2014.9 (2014).Chen, C. & Mcnairn, H. A neural network integrated approach for rice crop monitoring. Int. J. Remote Sens. 27, 1367–1393 (2006).
    Google Scholar 
    Kaul, M., Hill, R. L. & Walthall, C. Artificial neural networks for corn and soybean yield prediction. Agric. Syst. 85, 1–18 (2005).
    Google Scholar 
    Deo, R. C., Samui, P., Kisi, O. & Yaseen, Z. M. Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation: Theory and Practice of Hazard Mitigation (Springer Nature, 2020).
    Google Scholar 
    Sanikhani, H. et al. Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors. Comput. Electron. Agric. 152, 242–260 (2018).
    Google Scholar 
    Hai, T. et al. Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model. IEEE Access 8, 12026–12042 (2020).
    Google Scholar 
    Ramos, A. P. M. et al. A random forest ranking approach to predict yield in maize with UAV-based vegetation spectral indices. Comput. Electron. Agric. 178, 105791 (2020).
    Google Scholar 
    Suchithra, M. S. & Pai, M. L. Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf. Process. Agric. 7, 72–82 (2020).
    Google Scholar 
    Feng, Z., Huang, G. & Chi, D. Classification of the complex agricultural planting structure with a semi-supervised extreme learning machine framework. Remote Sens. 12, 3708 (2020).ADS 

    Google Scholar 
    Tur, R. & Yontem, S. A comparison of soft computing methods for the prediction of wave height parameters. Knowl. Based Eng. Sci. 2, 31–46 (2021).
    Google Scholar 
    Yaseen, Z. M., Ali, M., Sharafati, A., Al-Ansari, N. & Shahid, S. Forecasting standardized precipitation index using data intelligence models: Regional investigation of Bangladesh. Sci. Rep. 11, 1–25 (2021).
    Google Scholar 
    Sharafati, A., Asadollah, S. B. H. S. & Neshat, A. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125468 (2020).Article 

    Google Scholar 
    Huang, G.-B., Zhu, Q.-Y. & Siew, C.-K. Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006).
    Google Scholar 
    Adnan, R. M. et al. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowl. Based Syst. 230, 107379 (2021).
    Google Scholar 
    Yaseen, Z. M. et al. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. J. Hydrol. 542, 603–614 (2016).ADS 

    Google Scholar 
    Prasad, R., Deo, R. C., Li, Y. & Maraseni, T. Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors. Soil Tillage Res. https://doi.org/10.1016/j.still.2018.03.021 (2018).Article 

    Google Scholar 
    Tiyasha, T. et al. Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models. Mar. Pollut. Bull. 170, 112639 (2021).CAS 
    PubMed 

    Google Scholar 
    Ali, M. et al. Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology. Energy Rep. 7, 6700–6717 (2021).
    Google Scholar 
    Khozani, Z. S. et al. Determination of compound channel apparent shear stress: Application of novel data mining models. J. Hydroinform. 21, 798–811 (2019).MathSciNet 

    Google Scholar 
    Dorigo, M. & Di Caro, G. Ant colony optimization: A new meta-heuristic. In Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999. https://doi.org/10.1109/CEC.1999.782657 (1999).Mullen, R. J., Monekosso, D., Barman, S. & Remagnino, P. A review of ant algorithms. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2009.01.020 (2009).Article 

    Google Scholar 
    Sweetlin, J. D., Nehemiah, H. K. & Kannan, A. Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images. Comput. Methods Prog. Biomed. https://doi.org/10.1016/j.cmpb.2017.04.009 (2017).Article 

    Google Scholar 
    Cordon, O., Herrera, F. & Stützle, T. A review on the ant colony optimization metaheuristic: Basis, models and new trends. Mathw. Comput. 9, 2–3 (2002).MathSciNet 
    MATH 

    Google Scholar 
    Singh, G., Kumar, N. & Kumar Verma, A. Ant colony algorithms in MANETs: A review. J. Netw. Comput. Appl. https://doi.org/10.1016/j.jnca.2012.07.018 (2012).Article 

    Google Scholar 
    Kumar, S., Solanki, V. K., Choudhary, S. K., Selamat, A. & González Crespo, R. Comparative study on ant colony optimization (ACO) and K-means clustering approaches for jobs scheduling and energy optimization model in internet of things (IoT). Int. J. Interact. Multimed. Artif. Intell. 6, 107 (2020).
    Google Scholar 
    Paniri, M., Dowlatshahi, M. B. & Nezamabadi-pour, H. MLACO: A multi-label feature selection algorithm based on ant colony optimization. Knowl. Based Syst. 192, 105285 (2020).
    Google Scholar 
    Yaseen, Z. M., Sulaiman, S. O., Deo, R. C. & Chau, K.-W. An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J. Hydrol. 569, 387–408 (2019).ADS 

    Google Scholar 
    Manju Parkavi, R., Shanthi, M. & Bhuvaneshwari, M. C. Recent trends in ELM and MLELM: A review. Adv. Sci. Technol. Eng. Syst. https://doi.org/10.25046/aj020108 (2017).Article 

    Google Scholar 
    Araba, A. M., Memon, Z. A., Alhawat, M., Ali, M. & Milad, A. Estimation at completion in Civil engineering projects: Review of regression and soft computing models. Knowl. Based Eng. Sci. 2, 1–12 (2021).
    Google Scholar 
    Tamura, S. & Tateishi, M. Capabilities of a four-layered feedforward neural network: Four layers versus three. IEEE Trans. Neural Netw. 8, 251–255 (1997).CAS 
    PubMed 

    Google Scholar 
    Huang, G.-B. Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans. Neural Netw. 14, 274–281 (2003).PubMed 

    Google Scholar 
    Ali, M., Deo, R. C., Downs, N. J. & Maraseni, T. Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting. Atmos. Res. 213, 450–464 (2018).
    Google Scholar 
    Liang, N.-Y., Huang, G.-B., Saratchandran, P. & Sundararajan, N. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17, 1411–1423 (2006).PubMed 

    Google Scholar 
    Lan, Y., Soh, Y. C. & Huang, G.-B. Ensemble of online sequential extreme learning machine. Neurocomputing 72, 3391–3395 (2009).
    Google Scholar 
    Yadav, B., Ch, S., Mathur, S. & Adamowski, J. Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: A case study in Neckar River, Germany. Measurement 92, 433–445 (2016).ADS 

    Google Scholar 
    Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).MATH 

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

    Google Scholar 
    Al-Sulttani, A. O. et al. Proposition of new ensemble data-intelligence models for surface water quality prediction. IEEE Access 9, 108527–108541 (2021).
    Google Scholar 
    Carranza, C., Nolet, C., Pezij, M. & Van Der Ploeg, M. Root zone soil moisture estimation with random forest. J. Hydrol. 593, 125840 (2021).
    Google Scholar 
    Evans, J. S., Murphy, M. A., Holden, Z. A. & Cushman, S. A. Modeling species distribution and change using random forest. In Predictive Species and Habitat Modeling in Landscape Ecology (eds Ashton Drew, C. et al.) 139–159 (Springer, 2011).
    Google Scholar 
    Rahmati, O., Pourghasemi, H. R. & Melesse, A. M. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. CATENA 137, 360–372 (2016).
    Google Scholar 
    Prasad, R., Ali, M., Kwan, P. & Khan, H. Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Appl. Energy 236, 778–792 (2019).
    Google Scholar 
    Sharafati, A. et al. The potential of novel data mining models for global solar radiation prediction. Int. J. Environ. Sci. Technol. https://doi.org/10.1007/s13762-019-02344-0 (2019).Article 

    Google Scholar 
    Service, A. M. I. District-Wise Area of Wheat Crop. Available at: http://www.amis.pk/Agristatistics/DistrictWise/2010-2012/Wheat.html (2012).Service, A. M. I. District-Wise Area of Wheat Crop. Available at: http://www.amis.pk/Agristatistics/DistrictWise/2012-2014/Wheat.html (2014).Punjab, P. Population. Available at: https://en.wikipedia.org/wiki/Punjab_Pakistan (2015).Steiniger, S. & Hunter, A. J. S. The 2012 free and open source GIS software map—A guide to facilitate research, development, and adoption. Comput. Environ. Urban Syst. 39, 136–150 (2013).
    Google Scholar 
    Hsu, C.-W. et al. A practical guide to support vector classification. BJU Int. https://doi.org/10.1177/02632760022050997 (2008).Article 
    PubMed 

    Google Scholar 
    Bergmeir, C. & Benítez, J. M. On the use of cross-validation for time series predictor evaluation. Inf. Sci. (NY) 191, 192–213 (2012).
    Google Scholar 
    Xia, Y., Liu, C., Li, Y. Y. & Liu, N. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2017.02.017 (2017).Article 

    Google Scholar 
    Yen, B. C., ASCE Task Committee on Definition of Criteria for Evaluation of Watershed Models of the Watershed Management Committee Irrigation and Drainage Division. Discussion and closure: Criteria for evaluation of watershed models. J. Irrig. Drain. Eng. 121, 130–132 (1995).
    Google Scholar 
    Yaseen, Z. M. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere 277, 130126 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Dawson, C. W., Abrahart, R. J. & See, L. M. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ. Model. Softw. 22, 1034–1052 (2007).
    Google Scholar 
    Legates, D. R. & Mccabe, G. J. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241 (1999).ADS 

    Google Scholar 
    Willmott, C. J. & Willmott, C. J. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/1520-0477(1982)063%3c1309:SCOTEO%3e2.0.CO;2 (1982).Article 
    MATH 

    Google Scholar 
    Willmott, C. J. On the validation of models. Phys. Geogr. https://doi.org/10.1080/02723646.1981.10642213 (1981).Article 
    MATH 

    Google Scholar 
    Sharafati, A., Yasa, R. & Azamathulla, H. M. Assessment of stochastic approaches in prediction of wave-induced pipeline scour depth. J. Pipeline Syst. Eng. Pract. 9, 04018024 (2018).
    Google Scholar 
    Mohammadi, K. et al. A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation. Energy Convers. Manag. 92, 162–171 (2015).
    Google Scholar 
    Willmott, C. J., Robeson, S. M. & Matsuura, K. A refined index of model performance. Int. J. Climatol. 32, 2088–2094 (2012).
    Google Scholar 
    Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 10, 282–290 (1970).ADS 

    Google Scholar 
    Yaseen, Z. M. et al. Hourly river flow forecasting: Application of emotional neural network versus multiple machine learning paradigms. Water Resour. Manag. 34, 1075–1091 (2020).
    Google Scholar 
    Bhagat, S. K., Tung, T. M. & Yaseen, Z. M. Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia. J. Hazard. Mater. 403, 123492 (2021).CAS 
    PubMed 

    Google Scholar 
    Hora, J. & Campos, P. A review of performance criteria to validate simulation models. Expert Syst. 32, 578–595 (2015).
    Google Scholar 
    Nourani, V., Kisi, Ö. & Komasi, M. Two hybrid Artificial Intelligence approaches for modeling rainfall-runoff process. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2011.03.002 (2011).Article 

    Google Scholar 
    Ertekin, C. & Yaldiz, O. Comparison of some existing models for estimating global solar radiation for Antalya (Turkey). Energy Convers. Manag. 41, 311–330 (2000).
    Google Scholar 
    Li, M. F., Tang, X. P., Wu, W. & Liu, H. B. General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Convers. Manag. 70, 139–148. https://doi.org/10.1016/j.enconman.2013.03.004 (2013).Article 

    Google Scholar 
    Xu, Z., Hou, Z., Han, Y. & Guo, W. A diagram for evaluating multiple aspects of model performance in simulating vector fields. Geosci. Model Dev. 9, 4365–4380 (2016).ADS 

    Google Scholar 
    Dan Foresee, F. & Hagan, M. T. Gauss–Newton approximation to bayesian learning. In IEEE International Conference on Neural Networks—Conference Proceedings. https://doi.org/10.1109/ICNN.1997.614194 (1997).Akhtar, I. U. H. Pakistan needs a new crop forecasting system (2012).Stathakis, D., Savina, I. & Nègrea, T. Neuro-fuzzy modeling for crop yield prediction. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34, 1–4 (2006).
    Google Scholar 
    Kumar, P., Gupta, D. K., Mishra, V. N. & Prasad, R. Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data. Int. J. Remote Sens. 36, 1604–1617 (2015).
    Google Scholar 
    Sun, J., Xu, W. & Feng, B. A global search strategy of quantum-behaved particle swarm optimization. In 2004 IEEE Conference on Cybernetics and Intelligent Systems. https://doi.org/10.1109/iccis.2004.1460396 (2004)Naganna, S. et al. Dew point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water. https://doi.org/10.3390/w11040742 (2019).Article 

    Google Scholar 
    Gilles, J. Empirical wavelet transform. IEEE Trans. Signal Process. 61, 3999–4010 (2013).ADS 
    MathSciNet 
    MATH 

    Google Scholar 
    Bokde, N., Feijóo, A., Al-Ansari, N., Tao, S. & Yaseen, Z. M. The hybridization of ensemble empirical mode decomposition with forecasting models: Application of short-term wind speed and power modeling. Energies 13, 1666 (2020).
    Google Scholar 
    Chau, K. W. & Wu, C. L. A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J. Hydroinform. 12, 458–473 (2010).
    Google Scholar  More

  • in

    Cross-biome antibiotic resistance decays after millions of years of soil development

    Van Goethem MW, Pierneef R, Bezuidt OKI, Van De Peer Y, Cowan DA, Makhalanyane TP. A reservoir of ‘historical’ antibiotic resistance genes in remote pristine Antarctic soils. Microbiome. 2018;6:40.Article 

    Google Scholar 
    D’Costa VM, McGrann KM, Hughes DW, Wright GD. Sampling the antibiotic resistome. Science. 2006;311:374–7.Article 

    Google Scholar 
    Allen HK, Donato J, Wang HH, Cloud-Hansen KA, Davies J, Handelsman J. Call of the wild: antibiotic resistance genes in natural environments. Nat Rev Microbiol. 2010;8:251–9.CAS 
    Article 

    Google Scholar 
    Martinez JL, Coque TM, Baquero F. What is a resistance gene? Ranking risk in resistomes. Nat Rev Microbiol. 2015;13:116–23.CAS 
    Article 

    Google Scholar 
    Genilloud O. Actinomycetes: still a source of novel antibiotics. Nat Prod Rep. 2017;34:1203–32.CAS 
    Article 

    Google Scholar 
    Ochoa-Hueso R, Plaza C, Moreno-Jimenez E, Delgado-Baquerizo M. Soil element coupling is driven by ecological context and atomic mass. Ecol Lett. 2021;24:319–26.Article 

    Google Scholar 
    Wardle DA, Walker LR, Bardgett RD. Ecosystem properties and forest decline in contrasting long-term chronosequences. Science. 2004;305:509–13.CAS 
    Article 

    Google Scholar 
    Crews TE, Kitayama K, Fownes JH, Riley RH, Herbert DA, Mueller-Dombois D, et al. Changes in soil phosphorus fractions and ecosystem dynamics across a long chronosequence in Hawaii. Ecology. 1995;76:1407–24.Article 

    Google Scholar 
    Walker LR, Wardle DA, Bardgett RD, Clarkson BD. The use of chronosequences in studies of ecological succession and soil development. J Ecol. 2010;98:725–36.Article 

    Google Scholar 
    Delgado-Baquerizo M, Reich PB, Bardgett RD, Eldridge DJ, Lambers H, Wardle DA, et al. The influence of soil age on ecosystem structure and function across biomes. Nat Commun. 2020;11:4721.CAS 
    Article 

    Google Scholar 
    Andersson DI, Hughes D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat Rev Microbiol. 2010;8:260–71.CAS 
    Article 

    Google Scholar 
    Zhu YG, Johnson TA, Su JQ, Qiao M, Guo GX, Stedtfeld RD, et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc Natl Acad Sci USA. 2013;110:3435–40.CAS 
    Article 

    Google Scholar 
    Zhu YG, Zhao Y, Li B, Huang CL, Zhang SY, Yu S, et al. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat Microbiol. 2017;2:16270.CAS 
    Article 

    Google Scholar 
    Li J, Cao J, Zhu YG, Chen QL, Shen F, Wu Y, et al. Global survey of antibiotic resistance genes in air. Environ Sci Technol. 2018;52:10975–84.CAS 
    Article 

    Google Scholar 
    Delgado-Baquerizo M, Bardgett RD, Vitousek PM, Maestre FT, Williams MA, Eldridge DJ, et al. Changes in belowground biodiversity during ecosystem development. Proc Natl Acad Sci USA. 2019;116:6891–6.CAS 
    Article 

    Google Scholar 
    Ortiz-Álvarez R, Fierer N, de Los Ríos A, Casamayor EO, Barberán A. Consistent changes in the taxonomic structure and functional attributes of bacterial communities during primary succession. ISME J. 2018;12:1658–67.Article 

    Google Scholar 
    Shen J, Li Z-M, Hu H, Zeng J, Zhang L-M, He J, et al. Distribution and succession feature of antibiotic resistance genes along a soil development chronosequence in Urumqi No. 1 Glacier of China. Front Microbiol. 2019;10:1569.Article 

    Google Scholar 
    Drenovsky RE, Vo D, Graham KJ, Scow KM. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Micro Ecol. 2004;48:424–30.CAS 
    Article 

    Google Scholar 
    Bastida F, Eldridge DJ, Garcia C, Kenny Png G, Bardgett RD, Delgado-Baquerizo M. Soil microbial diversity-biomass relationships are driven by soil carbon content across global biomes. ISME J. 2021;15:2081–91.CAS 
    Article 

    Google Scholar  More

  • in

    Phage-encoded ribosomal protein S21 expression is linked to late-stage phage replication

    Discovery of closely related phage sequences with the conserved genetic context of bS21Multiple phage-related sequences with a conserved genomic context were detected from several freshwater metagenome-assembled datasets (see Methods). Genes for bS21, TerL, PVP, prohead core scaffolding, and protease protein (hereafter prohead protease for short), and MCP are encoded in the genomic region. BLASTp search of the TerL sequences against the ggKbase sequences (ggkbase.berkeley.edu) obtained a total of 47 unique scaffolds with the conserved genomic region (Supplementary Table 1). Two related phages were included as outgroups for comparative analyses. The corresponding samples were collected from freshwater lakes or reservoirs (one from a wastewater treatment plant), and all but three were from the oxic layer (see Methods for details).General features of manually curated genomesAll the 49 phage sequences were manually curated to fill scaffolding gaps and fix the assembly errors, and nine of them (including one outgroup phage) were curated to completion (circular and no gaps or local assembly errors) (Supplementary Table 1). A total of 14 related phage genomes from IMG/VR were also included for further analyses. The eight bS21-encoding complete genomes had genome lengths of 293–331 kbp, GC contents of 31.0–33.7% and encoded 350–413 protein-coding genes (coding density, 91.1–94.9%), with 5–25 (average 17) tRNA genes. No alternative coding signal (i.e., stop codon reassignment) was detected in any genome. In comparison, the outgroup complete genome has a size of 308 kbp (450 protein-coding genes, 6 tRNAs, 94.7% coding density) and GC content of 27.3%.Genomic context of bS21 in phagesGenomic context analyses for bS21 genes showed a highly conserved gene architecture across phage genomes in proximity to the region encoding bS21 (see Fig. 1a for example). Specifically, we found that bS21 was consistently located in between two hypothetical protein families (positions 1 and –1 in Fig. 1b and Supplementary Table 2), with core structural proteins—including the TerL, PVP, prohead protease, and MCP—generally located within five genes in both the upstream and downstream DNA. Other hypothetical proteins were also consistently found in this region, although their positions were more variable upstream (positions –4 through –10, Fig. 1b). Importantly, the bS21 gene was consistently encoded in the reverse strand relative to the conserved hypothetical and structural protein genes (Fig. 1a and Supplementary Fig. 1).Fig. 1: Genetic context of the genes encoding bS21 in the phage genomes.a Examples of genetic context of phage genomes with and without bS21. The annotation of protein-coding genes is the same as indicated in b by different colors. Those in white are genes not shown in subfigure (b). b Summary of genetic context of all phage genomes encoding bS21. The relative position of genes near the bS21 gene is shown, and the size of circles indicates the number of phages with a gene belonging to a given protein family (annotation shown on right) at that relative position. Only the 12 most frequent families are shown. The details of the genetic context are shown in Supplementary Fig. 1.Full size imagePhylogeny of bS21-encoding phagesPhylogenetic analyses based on TerL suggested the phages belonging to several groups, we thus assigned them to clades a–e (Fig. 2 and Supplementary Table 1). Most of the phages belong to clades c, d, and e, and they have a broader environmental distribution than clades a and b. Interestingly, we found that some phages within a single clade were from distant sampling sites. Closer inspection indicated they also shared large genomic fragments with high similarity (82–98% for nucleotide sequences; Supplementary Fig. 2). Comparative genome-wide analyses of the complete genomes from the same site but sampled at different time points showed sequence variations in some genes (Supplementary Fig. 3).Fig. 2: The phylogeny of bS21 phages based on the large terminal (TerL) protein sequences.Two closely related phages without bS21 encoded were included as outgroups (shown at the top of the tree). The genomes are assigned to five clades (a, b, c, d, and e) based on the topology of the phylogenetic tree. The numbers in the brackets following the scaffold names indicate the total counts of the same scaffold detected from the corresponding sampling sites. The genomes that were manually curated to completion (circular and no gap) are indicated by squares, and the genome sizes are shown in brackets.Full size imageTerL phylogeny, constructed using sequences from this study and NCBI RefSeq sequences, indicated the most closely related classified phages belong to Caudovirales of either the Myoviridae or Ackermannviridae (Supplementary Fig. 4). A phage baseplate assembly protein was encoded in most curated genomes. This is an important building block for members of Siphoviridae and Myoviridae [8], so we concluded that the bS21-encoding phages are myoviruses.Predicted bacterial hosts of bS21-encoding phagesTo predict host-phage relationships we first used CRISPR-Cas spacers targeting. While none of the 16.5k unique spacers from the relevant metagenomes targeted any of the curated phage genomes from the same sampling sites, a single cross-site target was detected. Specifically, MIW1_072018_0_1um_scaffold_78 was targeted by a spacer (24 nt and no mismatch) from a MIW2 Flavobacterium genome (affiliation: Bacteroidetes, Flavobacteria). We then predicted the bacterial hosts based on the bacterial taxonomic affiliations of the phage gene inventories as previously described [2] (Supplementary Table 3). The results indicated that all of the phages infect members of Bacteroidetes, which were detected in 43 out of 45 samples (Fig. 3 and Supplementary Table 4). The two metagenomic samples without Bacteroidetes identified were both collected via filtering through 0.2 μm and onto 0.1 μm pore size filters. Bacteroidetes were detected in both of the corresponding 0.2 μm fraction samples (Fig. 3).Fig. 3: The relative abundance of the Bacteroidetes classes in all the analyzed samples in this study.The microbial communities were profiled based on ribosomal protein S3 (rpS3) assigned to the Bacteroidetes classes. The sampling sites were indicated by colored names, and the filter sizes used during sampling are shown by circles. The three pairs of filter samples are indicated by colored stars.Full size imageWe profiled the co-detection of phage clades and Bacteroidetes classes to test for specific connections (Supplementary Fig. 5). However, this was uninformative because most samples contained more than one class. However, phages from clades a and b are unlikely to infect class Bacteroidia members, as they did not co-occur in any sample.Comparison of bacterial and phage-encoded bS21Phylogenetic analyses revealed that bS21 protein sequences from phages (this study) and the bacterial bS21 sequences (from the corresponding samples and NCBI RefSeq) clustered separately (Supplementary Fig. 6). The bacterial bS21 sequences that are most similar to phage bS21 were from Bacteroidetes, mostly from the Flavobacteriia class (Supplementary Table 5). We aligned and compared the Bacteroidetes and phage bS21 sequences and mapped the divergent and non-divergent residues to the model of the ribosome of Flavobacterium johnsoniae (Fig. 4a). Multiple divergent positions are located at the beginning of the bS21 sequences and four residues (Arg21, Phe23, Asp25, and Thr28) were significantly divergent (Fig. 4b).Fig. 4: Conservation and differences between phage and bacterial bS21.a Location of bS21 (blue) within the 16S rRNA (green) and the ASD (magenta) of the F. johnsoniae ribosome (PDB ID: 7JIL) [9]. bS21 is in the neck region of the 16S rRNA, interacting closely with the 3’ end of the 16S rRNA, where the ASD is located. The 16S rRNA is shown from the subunit interface direction. b Zebra2 divergency results from an alignment of phage and bacterial bS21 sequences mapped on F. johnsoniae bS21. Divergent positions between phage and bacterial bS21 are shown with red. c Zebra2 conservation results from the same alignment as in (b) mapped on F. johnsoniae bS21 with conserved residues shown in yellow. The stacking interaction between Tyr54 and Adenine 1534 is indicated. d The sequence logo and consensus sequences of phage and bacterial bS21 alignments and the corresponding position of Tyr54 in F. johnsoniae bS21 in the alignment are highlighted. The C-terminal parts are highlighted with gray backgrounds.Full size imageBacteroidetes usually lack the SD sequences. It was recently reported that the bS21 Tyr54 (numbering in F. johnsoniae) is an important residue for blocking the ASD in the 16S rRNA within the ribosome [9]. Our analyses predict that all the analyzed bacterial and phage bS21 in this study have an amino acid with an aromatic ring (often Tyr54 but in a few cases His54, and in one case Phe54) at the position of Tyr54 in F. johnsoniae (Fig. 4c, d and Supplementary Fig. 6). This conservation of the aromatic property in phage bS21 should ensure stacking interaction with Adenine 1534 (numbering in F. johnsoniae 16S) from the ASD. In that way, phage bS21 mimics Bacteroidetes bS21 in the region where it binds the ribosome but differs from it in the region where the mRNA would bind.In contrast, the C-terminal regions of both the bacterial and phage bS21 sets were highly divergent (Fig. 4d). However, the phage C-terminal regions are generally conserved within the clades defined based on TerL phylogeny (Fig. 2 and Supplementary Fig. 7).Metabolic potentials of bS21-encoding phagesFunctional annotation of the predicted protein-coding genes revealed that in addition to bS21, these phages carry other genes related to protein production and stability (Supplementary Table 6). Examples include protein folding chaperones and Clp protease, suggesting the importance of controlling the proteostasis network of the cell. Interestingly, we also identified many genes involved in sugar-related chemistry and polysaccharide biosynthesis. Many of these genes were predicted to perform chemical transformations related to the biosynthesis of lipopolysaccharide, a major component of the Gram-negative bacterial outer membrane. We interpret this as a potential mechanism to remodel the cell surface and prevent superinfection by competitor phages, a strategy common to the phage lysogenic cycle. These phages lack detectable integration machinery (no gene for integrase or resolvase was detected), suggesting the possibility of a non-integrative long-term infection state such as pseudolysogeny [10].Clustering analyses of 22 phages with a minimum genome size of 100 kbp (including the two outgroup genomes) based on the presence/absence of protein families indicated they shared a total of 16 protein families (Supplementary Fig. 8 and Supplementary Table 7). Phosphate starvation-inducible protein PhoH (“fam582”) was the only predicted protein detected in all 22 phages (excluding the shared predicted proteins in the conserved rpS21-encoding region described above). Other common protein families include those related to DNA replication (e.g., DNA primase/helicase, DNA polymerase, HNH endonuclease, thymidylate synthase (EC:2.1.1.45), deoxyuridine 5’-triphosphate nucleotidohydrolase (EC:3.6.1.23)), those associated with virion assembly (e.g., a phage tail sheath protein, phage baseplate assembly protein W), and those for other functions (e.g., chaperone ATPase, alpha-amylase, DegT/DnrJ/EryC1/StrS aminotransferase).Temporal and spatial distribution and activity of bS21-encoding phages in Lake RotseeTo reveal the spatial and temporal distribution of the bS21-encoding phages, we focused on the Lake Rotsee data and profiled phage occurrence based on the sequencing coverage in the metagenomic datasets. The Lake Rotsee samples were collected from the oxic (7 samples) and anoxic (3 samples) layers of the water column. The bS21-encoding phages were readily detected in oxic samples, especially in the under-ice samples when the whole water column was oxic (Fig. 5a).Fig. 5: The spatial and temporal distribution and activity of bS21 phages at Lake Rotsee.a The sequencing coverage of each phage genome in each metagenomic dataset is shown in the heatmaps. The phages are phylogenetically clustered based on their TerL protein sequences (bootstraps shown in numbers), the colored backgrounds are the same as shown in Fig. 2 for different clades. The sampling time points and depths are shown on the left, and the oxygen conditions are indicated by colored circles on the right. Two replicates were sequenced from the 15 m sample collected in 2018. b The percentage of mapped RNA reads to the phage genomes in the corresponding samples (rows labeled in (a)). The mapped RNA reads had a minimum similarity of 98% to the phage genomes. No RNA data were generated for the three samples collected on October 10, 2017. See the figure legend for each genome in the upper right, the circular genomes have names in bold font.Full size imageRotsee Lake RNA reads were mapped to the phage genomes curated from this site to reveal the transcriptional activities of bS21-encoding phages (Fig. 5b). In general, the phages were likely to be most transcriptionally active in the oxic water columns. A total of 736 genes were transcribed in at least one sample (Supplementary Table 8), those for MCP, an AAA ATPase, tail sheath protein, bS21, FKBP-type peptidyl-prolyl cis-trans isomerase, and a methyltransferase FkbM domain protein are among the top 100 most highly transcribed. The high transcriptional activities of MCP in five phages indicated they were in the late stage of replication at the time of sampling.The transcriptional behavior of phage bS21 genesTo seek evidence of a transcriptional relationship involving bS21 and other genes we focused on the three phages that were most active based on the transcriptional level of their 19 shared single-copy genes (Fig. 6a). bS21 had very similar (but slightly lower) transcriptional activities as a neighboring gene (hereafter, bS21_CN gene) encoded on the opposite strand. The bS21_CN gene encodes a hypothetical protein (protein family: fam498) and was not detected in the two outgroup phages without bS21 (Supplementary Table 6). Interestingly, a comparison of the phylogenies of bS21 and bS21_CN showed a very similar evolutionary pattern (Supplementary Fig. 9), likely suggesting their potential functional relationship in the bS21-encoding phages.Fig. 6: The transcription levels of bS21 and core structural protein genes.a The normalized transcriptional level (NTL) of shared single-copy protein families of three phages (indicated by arrows in Fig. 5b) with ≥1000 RNA reads mapped. Two families (including MCP) are listed on a different scale due to their much higher transcription levels. Refer to Fig. 5 for shape symbols that designate phage genomes and samples. b Examples of RNA mapping profiles indicating the co-transcription of some genes neighboring bS21. Hypothetical protein genes are shown in white.Full size imageInspection of the RNA reads mapping profiles indicated that the conserved region encoding bS21 and core structural proteins was not transcribed as an operon, whereas bS21 and bS21_CN, MCP and its upstream hypothetical protein gene, and prohead protease and its downstream hypothetical protein gene may each be transcribed together (Fig. 6b). Given the observed RNA expression patterns, we conclude that the phage-encoded bS21 genes were actively transcribed during late-stage replication, along with other core structural proteins.Genomic context of bS21 genes in published phage genomesTo determine whether the phage bS21 genes are generally co-located with those for core structural proteins in diverse phages, we profiled the genomic context of bS21 in 900 published bS21-encoding phages [2, 11] (Supplementary Table 9). Functional annotations were performed for the upstream and downstream ten genes of the bS21 genes using pVOG (Supplementary Table 10). Of the 20 most abundant pVOGs, 6 were related to core structural assembly (Fig. 7a), i.e., prohead protease (n = 310), MCP (n = 154), PVP (n = 120), TerL (n = 78), neck protein (n = 70), and a tail sheath protein (n = 29). A total of 388 genomes contained at least one of these genes within ten genes of bS21, and eight had all of these six core structural proteins in close proximity. Three pVOGs were related to DNA processing, i.e., an exonuclease (n = 37), an endonuclease (n = 32), DNA helicase (n = 30). Other pVOGs included Hsp20 heat shock protein (n = 127), two ATP-dependent CLP proteases (n = 50 and 47, respectively), and lysozyme (for lysis; n = 29). Interestingly, the prohead protease and the MCP pVOG genes are very close to the bS21 gene (generally 2–4 genes; Fig. 7b), as in the bS21-encoding phage genomes analyzed in this study (2–6 genes away; Fig. 1 and Supplementary Fig. 1).Fig. 7: Neighboring genes within 10 genes of bS21 in published bS21-encoding phage genomes.a The annotation and corresponding functional category (if assigned) of the 20 most commonly detected pVOG genes and their predicted functions are shown on the left, the total number of genomes with the gene are shown on the right. b The distribution of the distance of each gene to bS21 in the genomes. The position of genes next to bS21 (thus distance = 1) is highlighted using a red dashed line. The average distance of each gene to bS21 is shown on the left. c The predicted hosts of bS21-encoding phages with the top 4 most abundant genes detected within 10 genes of bS21. The total count of hosts is shown on the right.Full size imageWe respectively predicted the hosts of the bS21-encoding phages with the four most dominant pVOGs within ten genes of bS21 (Fig. 7c and Supplementary Table 11). The bacterial hosts are diverse and include Proteobacteria, Bacteroidetes, and Firmicutes. More