More stories

  • in

    The impact of the first United Kingdom COVID-19 lockdown on environmental air pollution, digital display device use and ocular surface disease symptomatology amongst shielding patients

    Knight, H. et al. Impacts of the COVID-19 Pandemic and Self-Isolation on Students and Staff in Higher Education: A Qualitative Study. Int. J. Environ. Res. Public Health 18, 10675 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Higham, J. E., Ramírez, C. A., Green, M. A. & Morse, A. P. UK COVID-19 lockdown: 100 days of air pollution reduction? Air Quality. Atmosphere & Health https://doi.org/10.1007/s11869-020-00937-0 (2020).Article 

    Google Scholar 
    Office, P. M. s. Slides and datasets to accompany coronavirus press conference. (2020).Organization, W. H. WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide: executive summary. (2021).Singh, A. et al. Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford UK. Environ. Pollut. 293, 118584. https://doi.org/10.1016/j.envpol.2021.118584 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Lee, J. D., Drysdale, W. S., Finch, D. P., Wilde, S. E. & Palmer, P. I. UK surface NO2 levels dropped by 42% during the COVID-19 lockdown: impact on surface O3. Atmos. Chem. Phys. 20, 15743–15759. https://doi.org/10.5194/acp-20-15743-2020 (2020).Article 
    CAS 

    Google Scholar 
    Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Ropkins, K. & Tate, J. E. Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK. Sci. Total Environ. 754, 142374. https://doi.org/10.1016/j.scitotenv.2020.142374 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nwanaji-Enwerem, J. C., Allen, J. G. & Beamer, P. I. Another invisible enemy indoors: COVID-19, human health, the home, and United States indoor air policy. J Expo Sci Environ Epidemiol 30, 773–775. https://doi.org/10.1038/s41370-020-0247-x (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rasha, A., Karan Jetly, J. & Shqran, S. Indoor Air Quality Monitoring Systems: A Comprehensive Review of Different IAQM Systems. International Journal of Knowledge-Based Organizations (IJKBO) 11, 1–14, doi:https://doi.org/10.4018/ijkbo.2021070101 (2021).World Health Organization. Regional Office for, E. WHO guidelines for indoor air quality: selected pollutants. xxv, 454 p. (World Health Organization. Regional Office for Europe, 2010).Stafoggia, M. et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events: Results from 11 European cohorts within the ESCAPE project. Environ. Health Perspect 122, 919–925. https://doi.org/10.1289/ehp.1307301 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brook, R. D. et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American heart association. Circulation 121, 2331–2378. https://doi.org/10.1161/CIR.0b013e3181dbece1 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Raaschou-Nielsen, O. et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European study of cohorts for air pollution effects (ESCAPE). Lancet Oncol. 14, 813–822. https://doi.org/10.1016/s1470-2045(13)70279-1 (2013).Article 
    PubMed 

    Google Scholar 
    Guan, W. J., Zheng, X. Y., Chung, K. F. & Zhong, N. S. Impact of air pollution on the burden of chronic respiratory diseases in China: Time for urgent action. Lancet 388, 1939–1951. https://doi.org/10.1016/s0140-6736(16)31597-5 (2016).Article 
    PubMed 

    Google Scholar 
    Atkinson, R. W. et al. Acute effects of particulate air pollution on respiratory admissions: Results from APHEA 2 project. Air pollution and health: A European approach. Am. J. Respir. Crit. Care Med. 164, 1860–1866. https://doi.org/10.1164/ajrccm.164.10.2010138 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Stapleton, F. et al. TFOS DEWS II epidemiology report. Ocular Surf. 15, 334–365. https://doi.org/10.1016/j.jtos.2017.05.003 (2017).Article 

    Google Scholar 
    Starr, C. E. et al. Dry eye disease flares: A rapid evidence assessment. Ocul. Surf. 22, 51–59. https://doi.org/10.1016/j.jtos.2021.07.001 (2021).Article 
    PubMed 

    Google Scholar 
    Torricelli, A. A. et al. Correlation between signs and symptoms of ocular surface dysfunction and tear osmolarity with ambient levels of air pollution in a large metropolitan area. Cornea 32, e11-15. https://doi.org/10.1097/ICO.0b013e31825e845d (2013).Article 
    PubMed 

    Google Scholar 
    Hwang, S. H. et al. Potential importance of ozone in the association between outdoor air pollution and dry eye disease in South Korea. JAMA Ophthalmol. 134, 503–510. https://doi.org/10.1001/jamaophthalmol.2016.0139 (2016).Article 
    PubMed 

    Google Scholar 
    Wiwatanadate, P. Acute air pollution-related symptoms among residents in Chiang Mai Thailand. J. Environ. Health 76, 76–84 (2014).CAS 
    PubMed 

    Google Scholar 
    Alves, M., Novaes, P., Morraye Mde, A., Reinach, P. S. & Rocha, E. M. Is dry eye an environmental disease? Arq. Bras. Oftalmol. 77, 193–200 https://doi.org/10.5935/0004-2749.20140050 (2014).Bourcier, T. et al. Effects of air pollution and climatic conditions on the frequency of ophthalmological emergency examinations. Br. J. Ophthalmol. 87, 809–811. https://doi.org/10.1136/bjo.87.7.809 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hao, R. et al. Impact of air pollution on the ocular surface and tear cytokine levels: A multicenter prospective cohort study. Front. Med. (Lausanne) 9, 909330. https://doi.org/10.3389/fmed.2022.909330 (2022).Article 
    PubMed 

    Google Scholar 
    Vehof, J., Snieder, H., Jansonius, N. & Hammond, C. J. Prevalence and risk factors of dry eye in 79,866 participants of the population-based lifelines cohort study in the Netherlands. Ocul. Surf. 19, 83–93. https://doi.org/10.1016/j.jtos.2020.04.005 (2021).Article 
    PubMed 

    Google Scholar 
    Wolffsohn, J. S. et al. Demographic and lifestyle risk factors of dry eye disease subtypes: A cross-sectional study. Ocul. Surf. 21, 58–63. https://doi.org/10.1016/j.jtos.2021.05.001 (2021).Article 
    PubMed 

    Google Scholar 
    Núñez-Álvarez, C. & Osborne, N. N. Enhancement of corneal epithelium cell survival, proliferation and migration by red light: Relevance to corneal wound healing. Exp. Eye Res. 180, 231–241. https://doi.org/10.1016/j.exer.2019.01.003 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Marek, V. et al. Blue light phototoxicity toward human corneal and conjunctival epithelial cells in basal and hyperosmolar conditions. Free Radic. Biol. Med. 126, 27–40. https://doi.org/10.1016/j.freeradbiomed.2018.07.012 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Talens-Estarelles, C., García-Marqués, J. V., Cerviño, A. & García-Lázaro, S. Determining the best management strategy for preventing short-term effects of digital display use on dry eyes. Eye Contact Lens 48, 416–423. https://doi.org/10.1097/icl.0000000000000921 (2022).Article 
    PubMed 

    Google Scholar 
    GOV.UK. COVID-19: guidance on protecting people defined on medical grounds as extremely vulnerable, (2020).Joy, M. et al. Reorganisation of primary care for older adults during COVID-19: A cross-sectional database study in the UK. Br. J. Gen. Pract. 70, e540–e547. https://doi.org/10.3399/bjgp20X710933 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D. & Reis, B. L. Reliability and validity of the ocular surface disease index. Arch. Ophthalmol. 118, 615–621. https://doi.org/10.1001/archopht.118.5.615 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Amparo, F. & Dana, R. Web-based longitudinal remote assessment of dry eye symptoms. Ocul. Surf. 16, 249–253. https://doi.org/10.1016/j.jtos.2018.01.002 (2018).Article 
    PubMed 

    Google Scholar 
    Inomata, T. et al. Characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using a smartphone application. JAMA Ophthalmol. 138, 58–68. https://doi.org/10.1001/jamaophthalmol.2019.4815 (2020).Article 
    PubMed 

    Google Scholar 
    Toth, M. & Jokić-Begić, N. Psychological contribution to understanding the nature of dry eye disease: A cross-sectional study of anxiety sensitivity and dry eyes. Health Psychol. Behav. Med. 8, 202–219. https://doi.org/10.1080/21642850.2020.1770093 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mehra, D. & Galor, A. Digital screen use and dry eye: A review. Asia-Pacific J. Ophthalmol. 9, 491–497. https://doi.org/10.1097/apo.0000000000000328 (2020).Article 

    Google Scholar 
    Galor, A., Kumar, N., Feuer, W. & Lee, D. J. Environmental factors affect the risk of dry eye syndrome in a United States veteran population. Ophthalmology 121, 972–973. https://doi.org/10.1016/j.ophtha.2013.11.036 (2014).Article 
    PubMed 

    Google Scholar 
    Courtin, R. et al. Prevalence of dry eye disease in visual display terminal workers: A systematic review and meta-analysis. BMJ Open 6, e009675. https://doi.org/10.1136/bmjopen-2015-009675 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Torricelli, A. A. et al. Effects of ambient levels of traffic-derived air pollution on the ocular surface: Analysis of symptoms, conjunctival goblet cell count and mucin 5AC gene expression. Environ. Res. 131, 59–63. https://doi.org/10.1016/j.envres.2014.02.014 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gupta, S. K., Gupta, V., Joshi, S. & Tandon, R. Subclinically dry eyes in urban Delhi: An impact of air pollution?. Ophthalmologica 216, 368–371. https://doi.org/10.1159/000066183 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    Berg, E. J. et al. Climatic and environmental correlates of dry eye disease severity: A report from the dry eye assessment and management (DREAM) study. Trans. Vision Sci. Technol. 9, 25–25. https://doi.org/10.1167/tvst.9.5.25 (2020).Article 

    Google Scholar 
    Lang, S.-J., Abel, G. A., Mant, J. & Mullis, R. Impact of socioeconomic deprivation on screening for cardiovascular disease risk in a primary prevention population: A cross-sectional study. BMJ Open 6, e009984. https://doi.org/10.1136/bmjopen-2015-009984 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Denniston, A. K. et al. United Kingdom diabetic retinopathy electronic medical record (UK DR EMR) users group: Report 4, real-world data on the impact of deprivation on the presentation of diabetic eye disease at hospital services. Br. J. Ophthalmol. 103, 837–843. https://doi.org/10.1136/bjophthalmol-2018-312568 (2019).Article 
    PubMed 

    Google Scholar 
    Nessim, M., Denniston, A. K., Nolan, W., Holder, R. & Shah, P. Research into Glaucoma and Ethnicity (ReGAE) 8: Is there a relationship between social deprivation and acute primary angle closure?. Br. J. Ophthalmol. 94, 1304–1306. https://doi.org/10.1136/bjo.2009.160721 (2010).Article 
    PubMed 

    Google Scholar 
    Sharma, H. E. et al. The role of social deprivation in severe neovascular age-related macular degeneration. Br. J. Ophthalmol. 98, 1625–1628. https://doi.org/10.1136/bjophthalmol-2014-304959 (2014).Article 
    PubMed 

    Google Scholar 
    Bo, M., Salizzoni, P., Clerico, M. & Buccolieri, R. Assessment of indoor-outdoor particulate matter air pollution: A review. Atmosphere 8, 136 (2017).Article 

    Google Scholar 
    Strøm-Tejsen, P., Zukowska, D., Fang, L., Space, D. R. & Wyon, D. P. Advantages for passengers and cabin crew of operating a gas-phase adsorption air purifier in 11-h simulated flights. Indoor Air 18, 172–181. https://doi.org/10.1111/j.1600-0668.2007.00511.x (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mandell, J. T., Idarraga, M., Kumar, N. & Galor, A. Impact of air pollution and weather on dry eye. J. Clin. Med. https://doi.org/10.3390/jcm9113740 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Navarro, D. Learning Statistics with R. (Daniel Joseph Navarro, 2015). More

  • in

    Multi-proxy dentition analyses reveal niche partitioning between sympatric herbivorous dinosaurs

    Versluys, J. Die Kaubewegungen von Trachodon. Palaontol. Z. 4, 80–87 (1922).
    Google Scholar 
    Kripp, D. Die Kaubewegung und Lebensweise von Edmontosaurus spec. auf Grund der mechanischkonstruktiven analyse. Palaeobiologica 5, 409–422 (1933).
    Google Scholar 
    Ostrom, J. H. Cranial morphology of the hadrosaurian dinosaurs of North America. Bull. Am. Mus. Nat. Hist. 122, 39–186 (1961).
    Google Scholar 
    Ostrom, J. H. A functional analysis of jaw mechanics in the dinosaur. Triceratops. Postilla. 88, 1–35 (1964).MathSciNet 

    Google Scholar 
    Galton, P. M. The cheeks of ornithischian dinosaurs. Lethaia 6, 67–89. https://doi.org/10.1111/j.1502-3931.1973.tb00873.x (1973).Article 

    Google Scholar 
    Galton, P. M. Herbivorous adaptations of Late Triassic and Early Jurassic dinosaurs. In The Beginning of the Age of Dinosaurs (ed. Padian, K.) 203–221 (Cambridge University Press, 1986).
    Google Scholar 
    Weishampel, D. B. Hadrosaurid jaw mechanics. Acta Palaeontol. Pol. 28, 271–280 (1983).
    Google Scholar 
    Weishampel, D. B. The evolution of jaw mechanisms in ornithopod dinosaurs. Adv. Anat. Embryol. Cell. Biol. 87, 1–2 (1984).Article 
    CAS 
    PubMed 

    Google Scholar 
    Weishampel, D. B. Interactions between Mesozoic plants and vertebrates: fructifications and seed predation. Neues Jahrb. Geol. Paläontol. Abh. 167, 224–250 (1984).
    Google Scholar 
    Weishampel, D. B. & Norman, D. B. Vertebrate herbivory in the Mesozoic: Jaws, plants, and evolutionary metrics. In Paleobiology of the Dinosaurs Special Papers 238 (ed. Farlow, J. O.) 87–100 (Geological Society of America, 1989).Chapter 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Feeding mechanisms in some small herbivorous dinosaurs: processes and patterns. In Biomechanics and Evolution (eds Rayner, J. M. V. & Wooton, R. J.) 161–181 (Cambridge University Press, 1991).
    Google Scholar 
    Sereno, P., Zijin, Z. & Lin, T. A new psittacosaur from Inner Mongolia and the parrot-like structure and function of the psittacosaur skull. Proc. Roy. Soc. B. 277, 199–209. https://doi.org/10.1098/rspb.2009.0691 (2010).Article 

    Google Scholar 
    Barrett, P. M. Paleobiology of herbivorous dinosaurs. Annu. Rev. Earth Planet. Sci. 42(1), 207–230. https://doi.org/10.1146/annurev-earth-042711-105515 (2014).Article 
    CAS 

    Google Scholar 
    Erickson, G. M. et al. Wear biomechanics in the slicing dentition of the giant horned dinosaur Triceratops. Sci. Adv. 1(5), e1500055. https://doi.org/10.1126/sciadv.1500055 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nabavizadeh, A. Hadrosauroid jaw mechanics and the functionalsignificance of the predentary bone. In The hadrosaurs: Proceedings of the International Hadrosaur Symposium (eds Evans, D. & Eberth, D.) 467–482 (Indiana University Press, 2014).
    Google Scholar 
    Nabavizadeh, A. Evolutionary trends in the jaw adductor mechanics of ornithischian dinosaurs. Anat. Rec. 299(3), 271–294. https://doi.org/10.1002/ar.23306 (2016).Article 

    Google Scholar 
    Nabavizadeh, A. new reconstruction of cranial musculature in ceratopsian dinosaurs: Implications for jaw mechanics and ‘cheek’anatomy. FASEB J. 30, lb27–lb27. https://doi.org/10.1096/fasebj.30.1_supplement.lb27 (2016).Article 

    Google Scholar 
    Nabavizadeh, A. new reconstruction of cranial musculature in ornithischian dinosaurs: Implications for feeding mechanismsand buccal anatomy. Anat. Rec. 303, 347–362. https://doi.org/10.1002/ar.23988 (2020).Article 

    Google Scholar 
    Varriale, F. J. Dental microwear reveals mammal-like chewing in the neoceratopsian dinosaur Leptoceratops gracilis. PeerJ 4, e2132. https://doi.org/10.7717/peerj.2132 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Melstrom, K. M., Chiappe, L. M. & Smith, N. D. Exceptionally simple, rapidly replaced teeth in sauropod dinosaurs demonstrate a novel evolutionary strategy for herbivory in Late Jurassic ecosystems. BMC Evol. Biol. 21(1), 1–12. https://doi.org/10.1186/s12862-021-01932-4 (2021).Article 

    Google Scholar 
    Norman, D. B. On the cranial morphology and evolution of ornithopod dinosaurs. Proc. Zool. Soc. Lond. 52, 521–547 (1984).
    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Ornithopod feeding mechanisms: Their bearing on the evolution of herbivory. Am. Nat. 126, 151–164. https://doi.org/10.1086/284406 (1985).Article 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Vegetarian dinosaurs chew it differently-living mammals can chew plants for more effectively than reptiles. Yet some dinosaurs were surprisingly adept chewers. This unexpected ability may have been crucial in their evolution. New Sci. 114(1559), 42–45 (1987).
    Google Scholar 
    Rybczynski, N., Tirabasso, A., Bloskie, P., Cuthbertson, R. & Holliday, C. A three-dimensional animation model of Edmontosaurus (Hadrosauridae) for testing chewing hypotheses. Palaeontol. Electron. 11(2), 9A (2008).
    Google Scholar 
    Williams, V. S., Barrett, P. M. & Purnell, M. A. Quantitative analysis of dental microwear in hadrosaurid dinosaurs, and the implications for hypotheses of jaw mechanics and feeding. PNAS 106(27), 11194–11199. https://doi.org/10.1073/pnas.0812631106 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cuthbertson, R. S., Tirabasso, A., Rybczynski, N. & Holmes, R. B. Kinetic limitations of intracranial joints in Brachylophosaurus canadensis and Edmontosaurus regalis (Dinosauria: Hadrosauridae), and their implications for the chewing mechanics of hadrosaurids. Anat. Rec. 295, 968–979. https://doi.org/10.1002/ar.22458 (2012).Article 

    Google Scholar 
    Erickson, G. M. & Zelenitsky, D. K. Osteohistology and occlusal morphology of Hypacrosaurus stebengeri teeth throughout ontogeny with comments on wear-induced form and function. In Hadrosaurs (eds Eberth, D. A. & Evans, D. C.) 422–432 (Indiana University Press, 2014).
    Google Scholar 
    Barrett, P. M. Tooth wear and possible jaw action of Scelidosaurus harrisonii Owen and a review of feeding mechanisms in other thyreophoran dinosaurs. In The Armored Dinosaurs (ed. Carpenter, K.) 25–52 (Indiana University Press, 2001).
    Google Scholar 
    Rybczynski, N. & Vickaryous, M. K. Evidence of complex jaw movement in the Late Cretaceous ankylosaurid Euoplocephalus tutus (Dinosauria: Thyreophora). In The Armored Dinosaurs (ed. Carpenter, K.) 299–317 (Indiana University Press, 2001).
    Google Scholar 
    Mallon, J. C. & Anderson, J. S. The functional and palaeoecological implications of tooth morphology and wear for the megaherbivorous dinosaurs from the Dinosaur Park Formation (Upper Campanian) of Alberta, Canada. PLoS ONE 9(6), e98605. https://doi.org/10.1371/journal.pone.0098605 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mallon, J. C. & Anderson, J. S. Implications of beak morphology for the evolutionary paleoecology of the megaherbivorous dinosaurs from the Dinosaur Park Formation (upper Campanian) of Alberta, Canada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 394, 29–41. https://doi.org/10.1016/j.palaeo.2013.11.014 (2014).Article 

    Google Scholar 
    Ősi, A., Barrett, P. M., Földes, T. & Tokai, R. Wear pattern, dental function, and jaw mechanism in the Late Cretaceous ankylosaur Hungarosaurus. Anat. Rec. 297(7), 1165–1180. https://doi.org/10.1002/ar.22910 (2014).Article 

    Google Scholar 
    Ősi, A., Prondvai, E., Mallon, J. & Bodor, E. R. Diversity and convergences in the evolution of feeding adaptations in ankylosaurs (Dinosauria: Ornithischia). Hist. Biol. 29(4), 539–570. https://doi.org/10.1080/08912963.2016.1208194 (2017).Article 

    Google Scholar 
    Hill, R. V., D’Emic, M. D., Bever, G. S. & Norell, M. A. A complex hyobranchial apparatus in a Cretaceous dinosaur and the antiquity of avian paraglossalia. Zool. J. Linn. Soc. 175(4), 892–909. https://doi.org/10.1111/zoj.12293 (2015).Article 

    Google Scholar 
    Lautenschlager, S., Brassey, C. A., Button, D. J. & Barrett, P. M. Decoupled form and function in disparate herbivorous dinosaur clades. Sci. Rep. 6(1), 1–10. https://doi.org/10.1038/srep26495 (2016).Article 
    CAS 

    Google Scholar 
    Skutschas, P. P. et al. Wear patterns and dental functioning in an Early Cretaceous stegosaur from Yakutia, Eastern Russia. PLoS ONE 16(3), e0248163. https://doi.org/10.1371/journal.pone.0248163 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Strickson, E., Prieto-Márquez, A., Benton, M. J. & Stubbs, T. L. Dynamics of dental evolution in ornithopod dinosaurs. Sci. Rep. 6, 28904. https://doi.org/10.1038/srep28904 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Virág, A. & Ősi, A. Morphometry, microstructure, and wear pattern of neornithischian dinosaur teeth from the Upper Cretaceous Iharkút locality (Hungary). Anat. Rec. 300(8), 1439–1463. https://doi.org/10.1002/ar.23592 (2017).Article 

    Google Scholar 
    Mallon, J. C. & Anderson, J. S. Skull ecomorphology of megaherbivorous dinosaurs from the Dinosaur Park Formation (Upper Campanian) of Alberta, Canada. PLoS ONE 8(7), e67182. https://doi.org/10.1371/journal.pone.0067182 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Botfalvai, G., Ősi, A. & Mindszenty, A. Taphonomic and paleoecologic investigations of the Late Cretaceous (Santonian) Iharkút vertebrate assemblage (Bakony Mts, northwestern Hungary). Palaeogeogr. Palaeoclimatol. Palaeoecol. 417, 379–405. https://doi.org/10.1016/j.palaeo.2014.09.032 (2015).Article 

    Google Scholar 
    Botfalvai, G., Haas, J., Bodor, E. R., Mindszenty, A. & Ősi, A. Facies architecture and palaeoenvironmental implications of the upper Cretaceous (Santonian) Csehbánya formation at the Iharkút vertebrate locality (Bakony Mountains, Northwestern Hungary). Palaeogeogr. Palaeoclimatol. Palaeoecol. 441, 659–678. https://doi.org/10.1016/j.palaeo.2015.10.018 (2016).Article 

    Google Scholar 
    Ősi, A. et al. The Late Cretaceous continental vertebrate fauna from Iharkút, western Hungary: A review. In Bernissart Dinosaurs and Early Cretaceous Terrestrial Ecosystems (ed. Godefroit, P.) 532–569 (Indiana University Press, 2012).
    Google Scholar 
    Wells, N. A. Making thin sections. In Paleotechniques (eds Feldmann, R. M. et al.) 120–129 (University of Tennessee, 1989).
    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evans, A. R. Surfer Manipulator. http://evomorph.org/surfermanipulator (2011).Evans, A. R., Wilson, G. P., Fortelius, M. & Jernvall, J. High-level similarity of dentitions in carnivorans and rodents. Nature 445, 78–81. https://doi.org/10.1038/nature05433 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wilson, G. P. et al. Adaptive radiation of multituberculate mammals before the extinction of dinosaurs. Nature 483, 457–460. https://doi.org/10.1038/nature10880 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ungar, P. S. Dental microwear of European Miocene catarrhines: Evidence for diets and tooth use. J. Hum. Evol. 31, 355–366. https://doi.org/10.1006/jhev.1996.0065 (1996).Article 

    Google Scholar 
    Ungar, P. S. A semiautomated image analysis procedure for the quantification of dental microwear II. Scanning. 17, 57–59. https://doi.org/10.1002/sca.4950170108 (1995).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ungar, P. S., Brown, C. A., Bergstrom, T. S. & Walker, A. Quantification of dental microwear by tandem scanning confocal microscopy and scale-sSensitive fractal analyses. Scanning 25, 185–193. https://doi.org/10.1002/sca.4950250405 (2003).Article 
    PubMed 

    Google Scholar 
    Ungar, P. S., Merceron, G. & Scott, R. S. Dental microwear texture analysis of Varswater bovids and Early Pliocene paleoenvironments of langebaanweg, Western Cape Province, South Africa. J. Mammal. Evol. 14, 163–181. https://doi.org/10.1007/s10914-007-9050-x (2007).Article 

    Google Scholar 
    Scott, J. R. Dental microwear texture analysis of extant African Bovidae. Mammalia 76, 157–217. https://doi.org/10.1515/mammalia-2011-0083 (2012).Article 

    Google Scholar 
    Merceron, G., Hofman-Kaminska, E. & Kowalczyk, R. 3D dental microwear texture analysis of feeding habits of sympatric ruminants in the Białowieza Primeval Forest, Poland. For. Ecol. Manag. 328, 262–269. https://doi.org/10.1016/j.foreco.2014.05.041 (2014).Article 

    Google Scholar 
    Caporale, S. S. & Ungar, P. S. Rodent incisor microwear as a proxy for ecological reconstruction. Palaeogeog. Palaeocl. Palaeoecol. 446, 225–233. https://doi.org/10.1016/j.palaeo.2016.01.013 (2016).Article 

    Google Scholar 
    R Core Team. R. A language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2021).Erickson, G. M. Incremental lines of von Ebner in dinosaurs and the assessment of tooth replacement rates using growth line counts. PNAS 93(25), 14623–14627. https://doi.org/10.1073/pnas.93.25.14623 (1996).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Godefroit, P. et al. Extreme tooth enlargement in a new Late Cretaceous rhabdodontid dinosaur from Southern France. Sci. Rep. 7(1), 1–9. https://doi.org/10.1038/s41598-017-13160-2 (2017).Article 
    CAS 

    Google Scholar 
    Edmund, G. Tooth replacement phenomena in the lower vertebrates. Life. Sci. Contrib. R. Ont. Mus. 52, 1–190 (1960).
    Google Scholar 
    D’Emic, M. D., Whitlock, J. A., Smith, K. M., Fisher, D. C. & Wilson, J. A. Evolution of high tooth replacement rates in sauropod dinosaurs. PLoS ONE 8(7), e69235. https://doi.org/10.1371/journal.pone.0069235 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ősi, A., Prondvai, E., Butler, R. & Weishampel, D. B. Phylogeny, histology and inferred body size evolution in a new rhabdodontid dinosaur from the Late Cretaceous of Hungary. PLoS ONE 7(9), e44318. https://doi.org/10.1371/journal.pone.0044318 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weishampel, D. B., Jianu, C. M., Csiki, Z. & Norman, D. B. Osteology and phylogeny of Zalmoxes (ng), an unusual euornithopod dinosaur from the latest Cretaceous of Romania. J. Syst. Palaeontol. 1(2), 65–123. https://doi.org/10.1017/S1477201903001032 (2003).Article 

    Google Scholar 
    Melstrom, K. M. The relationship between diet and tooth complexity in living dentigerous saurians. J. Morphol. 278, 500–522 (2017).Article 
    PubMed 

    Google Scholar 
    LeBlanc, A. R. H., Reisz, R. R., Evans, D. C. & Bailleul, A. M. Ontogeny reveals function and evolution of the hadrosaurid dinosaur dental battery. BMC Evol. Biol. 16(1), 1–13. https://doi.org/10.1186/s12862-016-0721-1 (2016).Article 

    Google Scholar 
    Erickson, G. M. et al. Complex dental structure and wear biomechanics in hadrosaurid dinosaurs. Science 338(6103), 98–101. https://doi.org/10.1126/science.1224495 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Iguanodontidae and related Ornithopoda. In The Dinosauria (eds Weishampel, D. B. et al.) 510–533 (University of California Press, 1990).
    Google Scholar 
    Hulke, J. W. An attempt at a complete osteology of Hypsilophodon foxii, a British Wealden dinosaur. Philos. Trans. R. Soc. Lond. 172, 1035–1062. https://doi.org/10.1098/rstl.1882.0025 (1882).Article 

    Google Scholar 
    Sternberg, C. H. Thescelosaurus edmontonensis, n. sp., and classification of the Hypsilophodontidae. J. Paleontol. 14, 481–494 (1940).
    Google Scholar 
    Galton, P. M. The ornithischian dinosaur Hypsilophodon from the Wealden of the Isle of Wight. Bull. Br. Mus. Nat. Hist. 25(1), 1–152 (1974).
    Google Scholar 
    Norman, D. B. On the anatomy of Iguanodon atherfieldensis (Ornithischia: Ornithopoda). Bull. Inst. Roy. Sci. Nat. Belgique 56, 281–372 (1986).
    Google Scholar 
    Norman, D. B. & Barrett, P. M. Ornithischian dinosaurs from the lower Cretaceous (Berriasian) of England. Spec. Pap. Palaeontol. 68, 161–190 (2002).
    Google Scholar 
    Kosch, J. C. & Zanno, L. E. Sampling impacts the assessment of tooth growth and replacement rates in archosaurs: Implications for paleontological studies. PeerJ 8, e9918. https://doi.org/10.7717/peerj.9918 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Janis, C. M. & Fortelius, M. On the means whereby mammals achieve increased functional durability of their dentitions with special reference to limiting factors. Biol. Rev. 63, 197–230. https://doi.org/10.1111/j.1469-185X.1988.tb00630.x (1988).Article 
    CAS 
    PubMed 

    Google Scholar 
    You, H., Ji, Q. & Li, D. Lanzhousaurus magnidens gen. et sp. nov. from Gansu Province, China: The largest-toothed herbivorous dinosaur in the world. Geol. Bull. Chi 24(9), 785–794 (2005).
    Google Scholar 
    Suarez, C. A., You, H. L., Suarez, M. B., Li, D. Q. & Trieschmann, J. B. Stable isotopes reveal rapid enamel elongation (amelogenesis) rates for the Early Cretaceous iguanodontian dinosaur Lanzhousaurus magnidens. Sci. Rep. 7, 15319. https://doi.org/10.1038/s41598-017-15653-6 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Upchurch, P. & Barrett, P. M. The evolution of sauropod feeding mechanisms. In Evolution of Herbivory in Terrestrial Vertebrates: Perspectives from the Fossil Record (ed. Sues, H. D.) 79–122 (Cambridge University Press, 2000).Chapter 

    Google Scholar 
    Sereno, P. C. & Wilson, J. A. Structure and evolution of a sauropod tooth battery in Curry. In The Sauropods: Evolution and Paleobiology (eds Rogers, K. A. & Wilson, J. A.) 157–177 (University of California Press, 2005).
    Google Scholar 
    Brown, B. & Schlaikjer, E. M. The structure and relationships of Protoceratops. Ann. N. Y. Acad. Sci. 40(3), 133–265. https://doi.org/10.1111/j.1749-6632.1940.tb57047.x (1940).Article 

    Google Scholar 
    Solounias, N., Teaford, M. & Walker, A. Interpreting the diet of extinct ruminants-the case of a non-browsing giraffid. Paleobiology 14, 287–300. https://doi.org/10.1017/S009483730001201X (1988).Article 

    Google Scholar 
    Walker, A. & Teaford, M. Inferences from quantitative analysis of dental microwear. Folia Primatol. 53, 177–189. https://doi.org/10.1159/000156415 (1989).Article 
    CAS 

    Google Scholar 
    Ungar, P. S. Mammalian dental function and wear: A review. Biosurf. Biotribol. 1(1), 25–41. https://doi.org/10.1016/j.bsbt.2014.12.001 (2015).Article 
    MathSciNet 

    Google Scholar 
    Janis, C. M. An estimation of tooth volume and hypsodonty indices in ungulate mammals, and the correlation of these factors with dietary preferences. Mém. Mus. Natl. Hist. Nat. Sér. C Géol. 53, 367–387 (1988).
    Google Scholar 
    Lucas, P. W. et al. The role of dust, grit and phytoliths in tooth wear. Ann. Zool. Fenn. 51(1–2), 143–152. https://doi.org/10.5735/086.051.0215 (2014).Article 

    Google Scholar 
    Winkler, D. E. et al. Shape, size, and quantity of ingested external abrasives influence dental microwear texture formation in guinea pigs. Proc. Nat. Acad. Sci. 117, 22264–22273. https://doi.org/10.1073/pnas.2008149117 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kaiser, T. M. et al. Nano-indentation of native phytoliths and dental tissues: Implications for herbivore-plant combat and dental wear proxies. Evol. Syst. 2, 55–63. https://doi.org/10.3897/evolsyst.2.22678 (2018).Article 

    Google Scholar 
    Winkler, D. E. et al. Forage silica and water content control dental surface texture in guinea pigs and provide implications for dietary reconstruction. Proc. Nat. Acad. Sci. 116, 1325–1330. https://doi.org/10.1073/pnas.1814081116 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ősi, A. & Makádi, L. New remains of Hungarosaurus tormai (Ankylosauria, Dinosauria) from the Upper Cretaceous of Hungary: Skeletal reconstruction and body mass estimation. Palaontol. Z. 83(2), 227–245. https://doi.org/10.1007/s12542-009-0017-5 (2009).Article 

    Google Scholar 
    Winkler, D. E., Schulz-Kornas, E., Kaiser, T. M. & Tütken, T. Dental microwear texture reflects dietary tendencies in extant Lepidosauria despite their limited use of oral food processing. Proc. R. Soc. B 286, 20190544. https://doi.org/10.1098/rspb.2019.0544 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bestwick, J., Unwin, D. M., Butler, R. J. & Purnell, M. A. Dietary diversity and evolution of the earliest flying vertebrates revealed by dental microwear texture analysis. Nat. Commun. 11, 1–9. https://doi.org/10.1038/s41467-020-19022-2 (2020).Article 
    CAS 

    Google Scholar 
    Sakaki, H. et al. Non-occlusal dental microwear texture analysis of a titanosauriform sauropod dinosaur from the Upper Cretaceous (Turonian) Tamagawa Formation, northeastern Japan. Cret. Res. 136, 105218. https://doi.org/10.1016/j.cretres.2022.105218 (2022).Article 

    Google Scholar 
    Fiorillo, A. R. Dental microwear on the teeth of Camarasaurus and Diplodocus; implications for sauropod paleoecology. In Fifth Symposium on Mesozoic Terrestrial Ecosystems and Biota (eds Kielan-Jaworowska, Z. et al.) 23–24 (Paleontologisk Museum, 1991).
    Google Scholar 
    Mallon, J. C., Cuthbertson, R. S. & Tirabasso, A. Hadrosaurid jaw mechanics as revealed by cranial joint limitations and dental microwear analysis. In Hadrosaur Symposium Abstract Volume (eds Braman, D. R. et al.) 87–90 (Royal Tyrrell Museum of Palaeontology, 2011).
    Google Scholar 
    Fiorillo, A. R. Dental microwear patterns of the sauropod dinosaurs Camarasaurus and Diplodocus: Evidence for resource partitioning in the Late Jurassic of North America. Hist. Biol. 13, 1–16. https://doi.org/10.1080/08912969809386568 (1998).Article 

    Google Scholar 
    Sereno, P. C. et al. Structural extremes in a Cretaceous dinosaur. PLoS ONE 2(11), e1230. https://doi.org/10.1371/journal.pone.0001230 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Whitlock, J. A. Inferences of diplodocoid (Sauropoda: Dinosauria) feeding behavior from snout shape and microwear analyses. PLoS ONE 6(4), e18304. https://doi.org/10.1371/journal.pone.0018304 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fiorillo, A. R. Microwear patterns on the teeth of northern high latitude hadrosaurs with comments on microwear patterns in hadrosaurs as a function of latitude and seasonal ecological constraints. Palaeontol. Electron. 14(3), 20A (2011).
    Google Scholar 
    Bell, P. R., Snively, E. & Shychoski, L. A comparison of the jaw mechanics in hadrosaurid and ceratopsid dinosaurs using finite element analysis. Anat. Rec. 292(9), 1338–1351. https://doi.org/10.1002/ar.20978 (2009).Article 

    Google Scholar 
    Chin, K. & Gill, B. D. Dinosaurs, dung beetles, and conifers: Participants in a Cretaceous food web. Palaios 11, 280–285. https://doi.org/10.2307/3515235 (1996).Article 

    Google Scholar 
    Brown, C. M. et al. Dietary palaeoecology of an early Cretaceous armoured dinosaur (Ornithischia; Nodosauridae) based on floral analysis of stomach contents. Roy. Soc. Open Sci. 7(6), 200305. https://doi.org/10.1098/rsos.200305 (2020).Article 
    CAS 

    Google Scholar 
    Crane, P. C., Friis, E. M. & Pedersen, K. R. The origin and early diversification of angiosperms. Nature 374, 27–33 (1995).Article 
    CAS 

    Google Scholar 
    Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution 1–596 (Cambridge University Press, 2011). https://doi.org/10.1017/CBO9780511980206.Book 

    Google Scholar 
    Benson, R. B., Hunt, G., Carrano, M. T. & Campione, N. Cope’s rule and the adaptive landscape of dinosaur body size evolution. Palaeontology 61, 13–48. https://doi.org/10.1111/pala.12329 (2018).Article 

    Google Scholar 
    Hummel, J. et al. In vitro digestibility of fern and gymnosperm foliage: Implications for sauropod feeing ecology and diet selection. Proc. Royal Soc. B 275, 1015–1021. https://doi.org/10.1098/rspb.2007.1728 (2008).Article 

    Google Scholar 
    Gee, C. T. Dietary options for the sauropod dinosaurs from an integrated botanical and paleobotanical perspective. In Biology of the Sauropod Dinosaurs: Understanding the Life of Giants (eds Klein, K. et al.) 34–56 (Indiana University Press, 2011).
    Google Scholar 
    Peters, R. H. The Ecological Implications of Body Size 1–329 (Cambridge University Press, 1983).Book 

    Google Scholar 
    Jarman, P. J. The social organisation of antelope in relation to their ecology. Behaviour 48, 215–267 (1974).Article 

    Google Scholar  More

  • in

    Photosynthetic usable energy explains vertical patterns of biodiversity in zooxanthellate corals

    Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281, 237–240. https://doi.org/10.1126/science.281.5374.237 (1998).Article 
    CAS 
    PubMed 

    Google Scholar 
    Valladares, F. In Progress in Botany Vol. 64 (eds Esser, K. et al.) 439–471 (Springer, 2003).Chapter 

    Google Scholar 
    Anthony, K. R. N., Ridd, P. V., Orpin, A. R., Larcombe, P. & Lough, J. Temporal variation of light availability in coastal benthic habitats: Effects of clouds, turbidity, and tides. Limnol. Oceanogr. 49, 2201–2211. https://doi.org/10.4319/lo.2004.49.6.2201 (2004).Article 

    Google Scholar 
    Gattuso, J. P. et al. Light availability in the coastal ocean: Impact on the distribution of benthic photosynthetic organisms and their contribution to primary production. Biogeosciences 3, 489–513. https://doi.org/10.5194/bg-3-489-2006 (2006).Article 

    Google Scholar 
    Wright, D. H. Species-energy theory: An extension of species-area theory. Oikos 41, 496–506 (1983).Article 

    Google Scholar 
    Cusens, J., Wright, S. D., McBride, P. D. & Gillman, L. N. What is the form of the productivity–animal-species-richness relationship? A critical review and meta-analysis. Ecology 93, 2241–2252. https://doi.org/10.1890/11-1861.1 (2012).Article 
    PubMed 

    Google Scholar 
    Rosenzweig, M. L. & Abramsky, Z. in Species Diversity in Ecological Communities. Historical and Geographical Perspectives (eds Ricklefs, R. E. & Schluter, D.) Ch. 5, 52–65 (The University of Chicago Press, 1993).Abrams, P. A. Monotonic or unimodal diversity-productivity gradients: What does competition theory predict?. Ecology 76, 2019–2027 (1995).Article 

    Google Scholar 
    Huston, M. A. Disturbance, productivity, and species diversity: Empiricism vs. logic in ecological theory. Ecology 95, 2382–2396 (2014).Article 

    Google Scholar 
    Roberts, T. E. et al. Testing biodiversity theory using species richness of reef-building corals across a depth gradient. Biol. Lett. 15, 20190493. https://doi.org/10.1098/rsbl.2019.0493 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Frankowiak, K. et al. Photosymbiosis and the expansion of shallow-water corals. Sci. Adv. 2, e1601122. https://doi.org/10.1126/sciadv.1601122 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goreau, T. F. & Goreau, N. I. The physiology of skeleton formation in corals. II. Calcium deposition by hermatypic corals under various conditions in the reef. Biol. Bull. 117, 239–250. https://doi.org/10.2307/1538903 (1959).Article 
    CAS 

    Google Scholar 
    Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems 3rd edn. (Cambridge University Press, 2011).
    Google Scholar 
    Stoddart, D. R. Ecology and morphology of recent coral reefs. Biol. Rev. 44, 433–498. https://doi.org/10.1111/j.1469-185X.1969.tb00609.x (1969).Article 

    Google Scholar 
    Lesser, M. P., Slattery, M. & Leichter, J. J. Ecology of mesophotic coral reefs. J. Exp. Mar. Biol. Ecol. 375, 1–8 (2009).Article 

    Google Scholar 
    Ackleson, S. G. Light in shallow waters: A brief research review. Limnol. Oceanogr. 48, 323–328. https://doi.org/10.4319/lo.2003.48.1_part_2.0323 (2003).Article 

    Google Scholar 
    Connell, J. H. Diversity in tropical rain forests and coral reefs. High diversity of trees and corals is maintained only in a nonequilibrium state. Science 199, 1302–1310. https://doi.org/10.1126/science.199.4335.1302 (1978).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dollar, S. J. Wave stress and coral community structure in Hawaii. Coral Reefs 1, 71–81. https://doi.org/10.1007/BF00301688 (1982).Article 

    Google Scholar 
    Hughes, T. P. Community structure and diversity of coral reefs: The role of history. Ecology 70, 275–279. https://doi.org/10.2307/1938434 (1989).Article 

    Google Scholar 
    Fraser, R. H. & Currie, D. J. The species richness-energy hypothesis in a system where historical factors are thought to prevail: Coral reefs. Am. Nat. 148, 138–159 (1996).Article 

    Google Scholar 
    Cornell, H. V. & Karlson, R. H. Coral species richness: Ecological versus biogeographical influences. Coral Reefs 19, 37–49 (2000).Article 

    Google Scholar 
    Bellwood, D. R., Hughes, T., Connolly, S. & Tanner, J. Environmental and geometric constraints on Indo-Pacific coral reef biodiversity. Ecol. Lett. 8, 643–651. https://doi.org/10.1111/j.1461-0248.2005.00763.x (2005).Article 

    Google Scholar 
    Brown, B. E. et al. Diurnal changes in photochemical efficiency and xanthophyll concentrations in shallow water reef corals: Evidence for photoinhibition and photoprotection. Coral Reefs 18, 99–105 (1999).Article 

    Google Scholar 
    Hoegh-Guldberg, O. & Jones, R. J. Photoinhibition and photoprotection in symbiotic dinoflagellates from reef-building corals. Mar. Ecol. Prog. Ser. 183, 73–86. https://doi.org/10.3354/meps183073 (1999).Article 

    Google Scholar 
    Lesser, M. P. & Gorbunov, M. Y. Diurnal and bathymetric changes in chlorophyll fluorescence yields of reef corals measured in situ with a fast repetition rate fluorometer. Mar. Ecol. Prog. Ser. 212, 69–77. https://doi.org/10.3354/meps212069 (2001).Article 
    CAS 

    Google Scholar 
    Hoogenboom, M. O., Anthony, K. R. N. & Connolly, S. R. Energetic cost of photoinhibition in corals. Mar. Ecol. Prog. Ser. 313, 1–12. https://doi.org/10.3354/meps313001 (2006).Article 
    CAS 

    Google Scholar 
    Huot, Y. & Babin, M. Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications 31–74 (Springer, 2010).Book 

    Google Scholar 
    Warner, M. E., Lesser, M. P. & Ralph, P. J. Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications Ch. Chapter 10, 209–222 (Springer Science+Business Media B.V., 2010).Skirving, W. et al. Remote sensing of coral bleaching using temperature and light: Progress towards an operational algorithm. Remote Sens. 10, 18 (2018).Article 

    Google Scholar 
    Enríquez, S., Merino, M. & Iglesias-Prieto, R. Variations in the photosynthetic performance along the leaves of the tropical seagrass Thalassia testudinum. Mar. Biol. 140, 891–900. https://doi.org/10.1007/s00227-001-0760-y (2002).Article 
    CAS 

    Google Scholar 
    Sundby, C., McCaffery, S. & Anderson, J. M. Turnover of the photosystem II D1 protein in higher plants under photoinhibitory and nonphotoinhibitory irradiance. J. Biol. Chem. 268, 25476–25482 (1993).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tyystjärvi, E. & Aro, E. M. The rate constant of photoinhibition, measured in lincomycin-treated leaves, is directly proportional to light intensity. Proc. Natl. Acad. Sci. U. S. A. 93, 2213–2218. https://doi.org/10.1073/pnas.93.5.2213 (1996).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Iglesias-Prieto, R., Beltrán, V. H., LaJeunesse, T. C., Reyes-Bonilla, H. & Thomé, P. E. Different algal symbionts explain the vertical distribution of dominant reef corals in the eastern Pacific. Proc. R. Soc. Lond. B 271, 1757–1763. https://doi.org/10.1098/rspb.2004.2757 (2004).Article 
    CAS 

    Google Scholar 
    Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).Article 
    CAS 

    Google Scholar 
    Long, S. P., Humphries, S. & Falkowski, P. G. Photoinhibition of photosynthesis in nature. Annu. Rev. Plant Physiol. Plant Mol. Biol. 45, 633–662. https://doi.org/10.1146/annurev.pp.45.060194.003221 (1994).Article 
    CAS 

    Google Scholar 
    Huner, N. P. A., Öuist, G. & Sarhan, F. Energy balance and acclimation to light and cold. Trends Plant Sci. 3, 224–230 (1998).Article 

    Google Scholar 
    Sheppard, C. R. C. Coral cover, zonation and diversity on reef slopes of Chagos Atolls, and population structures of the major species. Mar. Ecol. Prog. Ser. 2, 193–205 (1980).Article 

    Google Scholar 
    Huston, M. A. Patterns of species diversity in relation to depth at Discovery Bay, Jamaica. Bull. Mar. Sci. 37, 928–935 (1985).
    Google Scholar 
    Loya, Y. Community structure and species diversity of hermatypic corals at Eilat, Red Sea. Mar. Biol. 13, 100–123. https://doi.org/10.1007/BF00366561 (1972).Article 

    Google Scholar 
    Chow, G. S. E., Chan, Y. K. S., Jain, S. S. & Huang, D. Light limitation selects for depth generalists in urbanised reef coral communities. Mar. Environ. Res. 147, 101–112. https://doi.org/10.1016/j.marenvres.2019.04.010 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kahng, S. E. et al. Community ecology of mesophotic coral reef ecosystems. Coral Reefs 29, 255–275. https://doi.org/10.1007/s00338-010-0593-6 (2010).Article 

    Google Scholar 
    Iglesias-Prieto, R. Temperature-dependent inactivation of Photosystem II in symbiotic dinoflagellates. in Proc. 8th Int. Coral Reef Sym, 1313–1318 (1997).Jones, R. J., Hoegh-Guldberg, O., Larkum, A. W. D. & Schreiber, U. Temperature-induced bleaching of corals begins with impairment of the CO2 fixation mechanism in zooxanthellae. Plant Cell Environ. 21, 1219–1230. https://doi.org/10.1046/j.1365-3040.1998.00345.x (1998).Article 
    CAS 

    Google Scholar 
    Hennige, S. J., Suggett, D. J., Warner, M. E., McDougall, K. E. & Smith, D. J. Photobiology of Symbiodinium revisited: Bio-physical and bio-optical signatures. Coral Reefs 28, 179–195. https://doi.org/10.1007/s00338-008-0444-x (2008).Article 

    Google Scholar 
    Quigg, A. & Beardall, J. Protein turnover in relation to maintenance metabolism at low photon flux in two marine microalgae. Plant Cell Environ. 26, 693–703. https://doi.org/10.1046/j.1365-3040.2003.01004.x (2003).Article 
    CAS 

    Google Scholar 
    Järvi, S., Suorsa, M. & Aro, E. M. Photosystem II repair in plant chloroplasts—Regulation, assisting proteins and shared components with photosystem II biogenesis. Biochim. Biophys. Acta Bioenerg. 900–909, 2015. https://doi.org/10.1016/j.bbabio.2015.01.006 (1847).Article 
    CAS 

    Google Scholar 
    Jokiel, P. L. Solar ultraviolet radiation and coral reef epifauna. Science 207, 1069–1071 (1980).Article 
    CAS 
    PubMed 

    Google Scholar 
    López-Londoño, T. et al. Physiological and ecological consequences of the water optical properties degradation on reef corals. Coral Reefs 40, 1243–1256. https://doi.org/10.1007/s00338-021-02133-7 (2021).Article 

    Google Scholar 
    Vermeij, M. J. A. & Bak, R. P. M. How are coral populations structured by light? Marine light regimes and the distribution of Madracis. Mar. Ecol. Prog. Ser. 233, 105–116. https://doi.org/10.3354/meps233105 (2002).Article 

    Google Scholar 
    Hoogenboom, M. O., Connolly, S. R. & Anthony, K. R. N. Interactions between morphological and physiological plasticity optimize energy acquisition in corals. Ecology 89, 1144–1154. https://doi.org/10.1890/07-1272.1 (2008).Article 
    PubMed 

    Google Scholar 
    Kaniewska, P., Anthony, K., Sampayo, E., Campbell, P. & Hoegh-Guldberg, O. Implications of geometric plasticity for maximizing photosynthesis in branching corals. Mar. Biol. 161, 313–328 (2014).Article 
    CAS 

    Google Scholar 
    Kramer, N., Tamir, R., Eyal, G. & Loya, Y. Coral morphology portrays the spatial distribution and population size-structure along a 5–100 m depth gradient. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00615 (2020).Article 

    Google Scholar 
    Lesser, M. P., Mobley, C. D., Hedley, J. D. & Slattery, M. Incident light on mesophotic corals is constrained by reef topography and colony morphology. Mar. Ecol. Prog. Ser. 670, 49–60. https://doi.org/10.3354/meps13756 (2021).Article 

    Google Scholar 
    Prada, C. et al. Linking photoacclimation responses and microbiome shifts between depth-segregated sibling species of reef corals. R. Soc. Open Sci. 9, 211591. https://doi.org/10.1098/rsos.211591 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rowan, R., Knowlton, N., Baker, A. & Jara, J. Landscape ecology of algal symbionts creates variation in episodes of coral bleaching. Nature 388, 265–269. https://doi.org/10.1038/40843 (1997).Article 
    CAS 
    PubMed 

    Google Scholar 
    Warner, M. E., LaJeunesse, T. C., Robison, J. D. & Thur, R. M. The ecological distribution and comparative photobiology of symbiotic dinoflagellates from reef corals in Belize: Potential implications for coral bleaching. Limnol. Oceanogr. 51, 1887–1897. https://doi.org/10.4319/lo.2006.51.4.1887 (2006).Article 

    Google Scholar 
    Anthony, K. R. N. & Fabricius, K. E. Shifting roles of heterotrophy and autotrophy in coral energetics under varying turbidity. J. Exp. Mar. Biol. Ecol. 252, 221–253 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hoogenboom, M., Rodolfo-Metalpa, R. & Ferrier-Pagès, C. Co-variation between autotrophy and heterotrophy in the Mediterranean coral Cladocora caespitosa. J. Exp. Biol. 213, 2399–2409 (2010).Article 
    PubMed 

    Google Scholar 
    Carlson, R. R., Foo, S. A. & Asner, G. P. Land use impacts on coral reef health: A ridge-to-reef perspective. Front. Mar. Sci 6, 562. https://doi.org/10.3389/fmars.2019.00562 (2019).Article 

    Google Scholar 
    Wang, M. et al. The great Atlantic Sargassum belt. Science 365, 83–87. https://doi.org/10.1126/science.aaw7912 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Alvarez-Filip, L., González-Barrios, F. J., Pérez-Cervantes, E., Molina-Hernández, A. & Estrada-Saldívar, N. Stony coral tissue loss disease decimated Caribbean coral populations and reshaped reef functionality. Commun. Biol. 5, 440. https://doi.org/10.1038/s42003-022-03398-6 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muscatine, L., McCloskey, L. R. & Marian, R. E. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611. https://doi.org/10.4319/lo.1981.26.4.0601 (1981).Article 
    CAS 

    Google Scholar 
    Jørgensen, S. E. & Bendoricchio, G. Fundamentals of Ecological Modelling 3rd edn, Vol. 21 (Elsevier Sceince B. V., 2001).
    Google Scholar 
    Hennige, S. J. et al. Acclimation and adaptation of scleractinian coral communities along environmental gradients within an Indonesian reef system. J. Exp. Mar. Biol. Ecol. 391, 143–152. https://doi.org/10.1016/j.jembe.2010.06.019 (2010).Article 

    Google Scholar 
    Scheufen, T., Iglesias-Prieto, R. & Enríquez, S. Changes in the number of symbionts and Symbiodinium cell pigmentation modulate differentially coral light absorption and photosynthetic performance. Front. Mar. Sci 4, 309. https://doi.org/10.3389/fmars.2017.00309 (2017).Article 

    Google Scholar 
    Veron, J. E. N. Corals in Space and Time. The Biogeography and Evolution of the Scleractinia 321 (Cornell University Press, 1995).
    Google Scholar 
    Nelder, J. A. & Mead, R. A simplex method for function minimization. J. Comput. 7, 308–313. https://doi.org/10.1093/comjnl/7.4.308 (1965).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    R: A languate and environment for statistical computing. Retrieved from http://www.R-project.org (R Foundation for Statistical Computing, Vienna, Austria, 2010). More

  • in

    Alternative stable ecological states observed after a biological invasion

    Study systemOur focal ecosystem is in Selvíria, state of Mato Grosso do Sul, Brazil ((hbox {20}^{circ }) (22′) (41.86”) S, (hbox {51}^{circ }) (24′) (58.90”) W), on a property owned by the São Paulo State University (UNESP). The location covers 350 ha of pasture composed of liverseed grass (Urochloa decumbens). The native vegetation was removed, pasture areas were implemented, and livestock was introduced in the 1970s, maintaining this configuration during the following 50 years. The climate of this area is categorized as equatorial savanna, with dry periods concentrated mostly during the winter, from April to August. During our sampling period (from November 23th, 1989, to November 19th, 2015), no vermifuges and insecticides that could affect negatively the community of dung beetles associated with cow pads were used1.The native dung beetle community at this site is composed of dwellers and tunnelers. Dwellers comprise the Aphodiinae subfamily, whereas all the tunnelers belong to the Scarabaeinae subfamily31. In total, there were eight species classified as dwellers (Ataenius crenulatus, A. picinus and Atanius aequalis-platensis grouped as one species, Blackburneus furcatus, Genieridium bidens, Labarrus pseudolividus, Nialaphodius nigrita and Trichillum externepunctatum) and ten native tunnelers (Ateuchus nr. puncticollis, A. vividus, Canthidium nr. pinotoides, Dichotomius bos, D. semiaeneus, D. sexdentatus, Ontherus appendiculatus, O. dentatus, O. sulcator). These species were chosen for our study because, as the invasive tunneler D. gazella (also from the Scarabaeinae subfamily), they all co-occur in pasture and exploit the same resource (cow pad)32. The initial establishment of D. gazella caused the loss of most of the native tunnelers from the community, with the invader becoming the overwhelming representative of the functional group, and an initial decrease of abundance for dwellers. Differently from native tunnelers, however, dwellers were able to recover their number a few years after invasion (Fig. 1a, Fig. S1).As reported in1, the abundance of dung beetles was significantly affected by both local minimum temperature and relative humidity. The influence of these two factors is expected, as they determine egg and larval survival and development of dung beetles. For example, because dung beetles are poikilotherms, environmental temperature is key to their development and fecundity33. One of the main dweller species, Labarrus pseudolividus, is widely found in locations with temperature averages ranging between (hbox {12},^{circ }hbox {C}) and (hbox {18},^{circ }hbox {C})34, making it tolerant to colder local temperatures. On the other hand, for D. gazella the lower developmental threshold is (hbox {15.5},^{circ }hbox {C}) (individuals cannot survive below this temperature), and the optimum temperature for population growth is (hbox {28},^{circ }hbox {C})35. For both groups, physiological growth and reproduction rates are maintained even when outside temperatures are close to the lower developmental threshold; dwellers, for example, live inside the dung pile, where temperature is higher and less variable than outside36,37. However, while tunnelers oviposit deep in the soil to protect the eggs, warmer and drier conditions reduce dweller egg viability on dung piles since they are exposed38. Low humidity conditions lead to drier dung and can cause egg and insect dessication. In addition, dwellers from our focal system have Palearctic evolutionary origins39; D. gazella’s natural distribution ranges from central to southern Africa40, presenting high physiological plasticity that allows it to tolerate high temperatures and low relative humidity better than other tunneler species41.Functional-group data collection and community structure characterizationDung beetles were collected once a week in a black-light flight intercept trap42, which guarantees the collection of coprophagic beetles. During all collection periods, climate variables were also collected from a meteorological station located within 2 km of our collecting site. See1 for the complete description of the collection process and database. For our purposes, we retained the species, number of individuals per species, and climate variables for each week sampled (Supplementary Information, SI, Figs. S1–S2).We focused first on the weekly abundance data, which we needed to process in order to avoid spurious results in our analyses stemming from the measurement protocol. Specifically, we filtered out seasonal low values associated with sampling in the coldest periods, when few beetles are captured because the reduced activity in all functional groups restricts their spatio-temporal distribution43. Including such samples would not be representative of the community and could bias the analysis since we are investigating community composition (i.e. proportions, very sensitive to low sampling). Thus, we considered only samples with a total number of beetles (that is, summing up all groups together) higher than the value of the median of all data, a conservative threshold that retains observations that allow for as much representation of the community as possible. As will become evident in the Results section and Supplementary Information, less conservative choices for the threshold did not alter our main conclusions.Following Mesquita -Filho et al.1, we categorized all sampled species into either dwellers or tunnelers. D. gazella is a tunneler and, as explained above, the native tunneler species experienced massive declines in abundance after its establishment, leaving D. gazella as almost the single representative in the tunneler functional group during the period of observation1. Thus, given the sharp contrast in community composition, we also separated the data into before and after invasion using to that end the 200th week, when D. gazella was first observed at the study site (September 11th, 1993, starting date for what we will call “after invasion”, our focal period henceforth).To describe community functional composition (i.e. system state) through time, we derived a normalized functional group ratio. First, because the abundance of each functional group spanned up to four orders of magnitude, we performed a logarithmic transformation of the number of captured insects from each group i, (log _{10}(N_{i}+K)), following  Yamamura44. Here, we chose (K=1), but the value of K did not alter our results qualitatively. In addition, the original data showed random mismatches in the phenology of each group, which gave the wrong impression of extreme short-term shifts in functional group dominance within the community. To avoid such artifacts, we used nonparametric local regression (LOESS)45 to smooth the dynamics of each group46. For this smoothing, we employed the loess function in the R software 3.6.147 with a smooth parameter equal to 0.25, but other moderate values (or an optimal value calculated with Bayesian inference by the R function optimal_span) did not alter our conclusions. Finally, we extracted back from the smoothed curve the number of beetles within each functional group to calculate the fraction (f_{dwell}) that measures the relative abundance of dwellers:$$begin{aligned} f_{dwell} = frac{N_D}{N_D+N_T} end{aligned}$$
    (1)
    where (N_D) corresponds to the number of dwellers per week and (N_T) corresponds to the number of native tunnelers (for the period before invasion), or only the number of D. gazella observed per week (after invasion), using their corresponding smoothed curves. Including also native tunnelers after invasion did not alter our conclusions.Climate driverWe devised a single climatic driver variable that merges the weekly measurement of temperature and relative humidity over the years, abiotic factors key to the survival and reproduction of both groups (see above). We first converted minimum temperatures and relative humidity to normalized climate variables using a min-max normalization (a feature scaling that uses the total range of temperatures or relative humidity, respectively, as normalization factor):$$begin{aligned} T = frac{T_{week} – T_{min}}{T_{max}-T_{min}};;,~ ~ ~ ~ ~ ~ RH = frac{RH_{week} – RH_{min}}{RH_{max}-RH_{min}};;, end{aligned}$$
    (2)
    where T corresponds to the normalized temperature, (T_{week}) is the weekly temperature, and (T_{max}) and (T_{min}) are the absolute maximum and minimum temperatures observed during the whole sampling period, respectively. We used a similar notation for relative humidity, RH. Based on the information above regarding beetle response to climate, the merged climate factor c was defined as the relationship:$$begin{aligned} c = frac{T}{RH};;, end{aligned}$$
    (3)
    for (RHne 0). That is, higher temperatures and/or drier conditions (expected to favor D. gazella) lead to higher values for c. On the other hand, lower temperatures and/or more humid conditions (expected to favor dwellers) imply lower values for c. Intermediate values of c can represent either moderate or extreme values for both T and RH.Identifying ecological states and quantifying resilienceWith our (f_{dwell}) data as an index of community composition (i.e. system state), we calculated kernel density functions to interpolate a continuous probability distribution of the relative fraction of dwellers in the community, (p_{n}(f_{dwell})) (function density, R software 3.1.647) for a given range of climatic driver c values. We grouped the (f_{dwell}) data using ranges for c of size 0.4, to ensure a significant amount of weekly samples that allowed for the reconstruction of these probability distributions (see Table S1, first column). Note that bins with extreme values showed few data points (see first and last rows in Table S1), and thus were rejected to prevent misleading results due to reduced sampling. Also note that, for the density function, we used the default Gaussian kernel with a smoothing bandwidth adjusted to be (50%) larger than the default value (“adjust” argument set to 1.5). This conservative choice aims to reduce the effect of the different sampling across c bins and to ensure that differences among distributions across c values are not the result of spurious sampling noise.Further, we transformed the kernel density function:$$begin{aligned} V(f_{dwell}) = -ln (p_{n}(f_{dwell})) end{aligned}$$
    (4)
    This (V(f_{dwell})) function, called potential (e.g.48), shows by design well-defined minima for the most frequently observed values of (f_{dwell}) (i.e. configurations most frequently observed for the community, which conform the modes of the probability distribution) in a given group of data. At these points, the potential exhibits a change of trend from decreasing to increasing, and therefore its derivative shows a change of sign. Eq. (4), thus, provides a simple criterion to identify possible system states, which is a reason why potentials have been used extensively across disciplines49,50,51. Nonetheless, because the position of extrema is invariant under the transformation, using probability distributions instead would not alter our conclusions.Representing the potential obtained from all the (f_{dwell}) system states associated with a same range of climatic driver c values allowed us to identify stable community configurations associated with a specific climate. The comparison of the potentials obtained for different c ranges enabled the description of how the community changed in response to climatic variation. The location of the minima revealed which states were stable for a given value of the climatic driver; the presence of two minima, then, flagged the existence of bistability (i.e. two different community compositions possible for the same c value).These minima are materialized as wells in the potential’s landscape, which provides an easy way to understand the concept of stability: the dynamics of the system for the given value of the driver will eventually “fall” into a well (either a state dominated by dwellers or a state dominated by tunnelers), with the shape of the well (e.g. its depth) determining how difficult it is for the system to “escape” that state. Therefore, the area inside a well provides quantification of the tendency of a system to stay in that specific state, i.e. the resilience of the associated ecological state or how strong a perturbation has to be to move the system from such an ecological state to another2,3,50,51,52,53. Thus, in addition to number and location of wells, measuring their associated area allowed us to further characterize the resilience of the community. To this end, we first set a visualization window common to all potentials. Specifically, we plotted the potentials within a range for the vertical variable (the potential, V) given by ([-1.5,1.5]); the horizontal variable (fraction of dwellers, (f_{dwell})) is by definition bounded between 0 and 1. For potentials that showed one single well, the area of the well was measured as the area above the potential curve within this visualization window. For potentials that showed two wells (bistability), we measured the value of the potential at the local maximum separating the two wells, and established that value as the upper (horizontal) line closing the area of each well. To ensure all cases were comparable and eliminate any arbitrariness of the choices above, we expressed resilience as a relative area; in other words, we further normalized the well area by the total area across wells for that potential, which means that any single-well case will show a resilience (or relative area) of 1, and the resilience of the two wells when there is bistability adds up to 1.Figure 1Left: Community composition by functional group for all weeks of observation1. Green represents dwellers, blue represents tunnelers, and orange represents the invader D. gazella. Right: Sketch of responses of the community composition to the climatic driver (i.e. phase diagram) expected from the physiological and behavioral characteristics of the functional groups in the community as described in text: linear (red), or non-linear but monotonic without (blue) or with (brown) hysteresis.Full size imageIdentifying ecological transitionsMeasuring a state variable, (f_{dwell}), and a driver, c (order and control parameter, respectively, in the jargon of regime shift theory), allowed us to study how their observed behavior over time materializes in a driver-state relationship (the so-called phase diagram) defining the possible shifts in dominance (i.e. regime shifts) that the community may undergo as climate changes12. The non-monotonic temporal behavior of the components of the order parameter (i.e. dwellers and tunneler availability) and the components of the control parameter (i.e. temperature and relative humidity) makes it difficult to predict the shape of the phase diagram, and therefore whether we can expect alternative stable states in the focal example. For such cases, the dominance of the dung beetle community could (1) shift in a linear fashion toward the functional group favored by climatic conditions; (2) shift between functional groups in non-linear threshold response to climatic conditions without hysteresis; or (3) shift between functional groups in non-linear threshold response to climatic conditions with hysteresis –and thus showing bistability (see Fig. 1b, or12). Other possibilities, e.g. a non-linear shift between functional groups where one group is favored at intermediate climatic conditions12 are discarded as the invader is better suited for warmer and drier conditions. To evaluate which of these possibilities occurred, we represented (f_{dwell}) as a function of c, as well as the location of the minima shown by the potentials above. In addition to the emerging shape of this relationship, this plot can reveal the presence of alternative stable states if two or more different points occur for the same value of the control parameter, c. More

  • in

    A global roadmap to seize the opportunities of healthy longevity

    Building from this background the NAM took on these issues as its first-ever grand challenge, as a critical issue of import and urgency for us all. In 2018, the NAM empaneled an international, independent and multidisciplinary commission to create a global roadmap for healthy longevity, complete with evidence-based, targeted and actionable recommendations to move societies forward from an almost-exclusive focus on ‘coping with aging populations’ toward enabling individuals and societies to age successfully, and to reap the economic and societal benefits of longevity. The commission offers a way forward for governments and societies by beginning with recommendations for the next five years, and how these solutions can be financially sustainable through the creation of a virtuous cycle.To support these goals, the commission was to “(1) comprehensively address the challenges and opportunities presented by global aging population; (2) catalyze breakthrough ideas and research that will extend the human healthspan; and (3) generate transformative and scalable innovations world wide”8. The resulting comprehensive report, which was delayed in good measure by the COVID-19 pandemic, was released in June 2022 (ref. 8). We report here a summary of the high-level vision, goals, findings and recommendations of this global roadmap.The evidence for opportunities of longevity and the costs of inactionWe are seeing longer lives with increasing years spent in ill health (that is, the decompression of morbidity)9. The implications of longevity without health are costly ones for the individual, their families and for society. By contrast, scientific evidence shows that the majority of chronic diseases are preventable, and that prevention works at every age and stage of life. Further, the subset of individuals who are the beneficiaries of cumulative health-promoting conditions across the life course are demonstrating healthy longevity, defined as “the state in which years in good health approach the biological lifespan, with physical, cognitive and social functioning, enabling well-being across populations”8. However, only a minority of people in any country have the benefit of the necessary investments that promote health, and disparities in access to these investments across the life course are a major cause of unhealthy longevity. The costs of inaction in the face of widening disparities include the high risk of young people aging with more ill health, and the attendant costs to them and society.Further, the commission reports that when people have health and function in older age, the considerable cognitive and socioemotional capabilities and expertise that accrue with aging, and the prosocial goals of older age, constitute human and social capital assets that are unprecedented in both nature and scale. Contrary to disproven myths, workforce participation not only brings these valuable capabilities (such that intergenerational teams in the workplace are more productive and innovative than single-age-group teams), but older people working is also associated with more jobs for younger individuals10. In the USA and EU, it has been shown that older adults contribute 7% of gross domestic product (GDP) through paid work and the economic value of volunteering and caregiving11, even before opportunities are specifically expanded for the increasing older population. Societies that recognize this potential and invest to create both healthy longevity and the societal organizations and policies through which older adults can contribute to societal good will develop the opportunity for all ages to thrive. The return on investment will be to create older ages with health, function, dignity, meaning, purpose and opportunities — for those who desire it — to work longer, care for others or contribute in ways that they value to their community and future generations.The definition, principles and vision of ‘Vision 2050’ for healthy longevityThe global roadmap builds on the WHO ‘Decade of Healthy Ageing’, the UN Sustainable Development Goals for 2030 and other reports. It sets out principles for achieving healthy longevity using data and meaningful metrics to track achievement of outcomes and guide decision making. The report offers a vision empowered by the evidence: that, by 2050, societies will value the capabilities and assets of older people; all people will have the opportunity to live long lives with health and function; barriers to full participation by older people in society will have been solved; and that older people, with such health, will have the opportunity to engage in meaningful and productive activities. In turn, this societal engagement will create unprecedented social, human and economic capital, contributing to intergenerational well-being and cohesion, and to GDP.Implementing Vision 2050Accomplishing this vision demands ‘all-of-society’ intent — with aligned goals for healthy longevity and transformative action across public, private and academic sectors, and all of civil society and communities — and the implementation of evidence across the full and extending life course. Transforming only one component or sector (for example, health systems) will not be sufficient to create healthy longevity or its full opportunities. Rather, given that nations are complex systems, this vision for our future requires governmental leadership and transformation of all sectors of our complex societal system (Fig. 1).Fig. 1: Relevant actors for an all-of-society approach to healthy longevity.Healthy longevity requires government leadership and cooperation across all sectors. Adapted with permission from figure S-2 of ref. 8.Full size imageInvestment for healthy longevity — across the enabling sectors of health systems, social infrastructure and protections, the physical environment, and work and volunteering contributions — will require intentional planning and leadership to transform those components in tandem, and to resolve disrupters such as ageism, the social determinants of health and inequity, and pollution. These investments across all sectors will create the conditions for achieving healthy longevity and build new capital (human, social and economic) that will benefit all of society. As a result of these investments, society will see younger people thrive and move into a position to age with healthy longevity; those individuals who are already older will be recognized as valuable contributors to society in a ‘pay-it-forward’ stage of life. The underpinning social compact between citizens and government will support valuing each age group’s capabilities and goals, and the building of a society of well-being and cohesion across generations. This is at the center of the virtuous cycle for healthy longevity (Fig. 2)Fig. 2: The virtuous cycle of healthy longevity.Healthy longevity (top) is an outcome of a virtuous cycle, itself contributing to capital development (bottom left). Bottom right, capital (human, financial and social) supports enablers (work, physical environment, health systems and social infrastructure). The enablers propel the cycle, contributing to healthy longevity. Intentional investment for healthy longevity across all enabling sectors will create new capital that will benefit all of society. Adapted with permission from figure 1-4 of ref. 8.Full size imageGoals for initiating the transformation to healthy longevityThe commission identified the following changes that should occur from now to 2027 to start transformation of all of society, towards Vision 2050 and the creation of healthy longevity for all:

    Creating social cohesion, social engagement and addressing the social determinants of health through social infrastructure are among the most effective determinants of slowed aging and the prevention of chronic conditions across the life course. Financial security in older age is essential for all.

    Governments, the private sector and civil society should partner to design physical environments and infrastructure that are user-centered, and function as cohesion-enabling intergenerational communities for healthy longevity. Initiatives should focus on the inclusion of older people in the design, creating public spaces that promote social cohesion and intergenerational connection as well as mobility, physical activity and access to food, transportation, social services and engagement.

    By 2027, governments should develop strategies and plans to arrive at adequately sized, geriatrically knowledgeable public health, clinical and long-term care workforces, and an integration of the pillars of the health system and social services. Together, these dimensions would foster and extend years of good health and support the diverse health needs and well-being of older people.

    Governments should work to build the dividend of health longevity in collaboration with the business sector and civil society, to develop policies, incentives, and supportive systems that enable and encourage lifelong learning, and greater opportunities and necessary skills to engage in meaningful work or community volunteering across the lifespan.

    We summarize the commission’s recommended goals for each of these sectors in brief in Box 1. Across all sectors, the key first steps that the commission identified are ones that can resolve obstacles to change and plan the change needed to shift multiple complex systems through both top-down and bottom-up approaches, in ways appropriate to each country and context. These initiatives should create enough momentum to foster early returns on investment and optimism to propel sustained investment for subsequent stages. This would need to begin for all governments by 2023, establishing calls to action to develop and implement data-driven, all-of-society plans to build the systems, policies, organizations and infrastructure needed, and for tracking change.Box 1 Goals for 2022–2027 to initiate the transformation to healthy longevityThese goals are reproduced from Global Roadmap for Healthy Longevity8.
    Social infrastructure

    Develop evidence-based multipronged strategies to reduce ageism against all groups.

    Develop plans for ensuring basic financial security for all older people.

    Develop strategies to increase financial literacy and mechanisms for promoting working longer, pension options and savings over the life course.

    Plan opportunities for purposeful and meaningful engagement by older people at the family, community and societal levels.

    Physical environment

    At the societal level, improve broadband accessibility to reduce the digital divide and develop public transportation solutions that address first- and last-mile transportation.

    At the city level, implement mitigation strategies to reduce the negative effects of the physical environment and related emergencies on older people (for example, air pollution and climate-induced events, including extreme heat and flooding) and design environments for connection and cohesion.

    At the neighborhood level, promote and measure innovative policy solutions for healthy longevity, including affordable housing and intergenerational living, zoning and design for connection and cohesion, and the enabling of social capital.

    At the home level, update physical infrastructure and policies to address affordability, provide coliving arrangements that match people’s goals and needs, and resolve insufficiencies and inefficiencies in housing stock.

    Health systems

    Establish healthy longevity as a major goal.

    Increase investments in public health systems, which are needed to promote health and prevent disease, disability and injury at the population level, across the full life course. This may require rebalancing investments between this type of public health and medical care, recognizing that such public health is a public good and, as such, tends to be underinvested in.

    Provide adequate primary care that includes preventive screening, addresses risk factors for chronic conditions and promotes positive health behaviors, and offers a continuum of medical care, including geriatrically knowledgeable care for older adults.

    Make culturally sensitive, person-centered and equitable long-term care systems available, which (to the degree possible) offer dignity and honor people’s preferences about care settings.

    Building the healthy longevity dividend

    Governments, in collaboration with the business sector and civil society, should design (1) work environments and develop new policies that enable and encourage older adults who want or need to remain in the work force longer, and (2) engagement opportunities that strengthen communities at every stage of life.

    Governments, employers and educational institutions should prioritize redesigning education systems to support lifelong learning and training, and invest in the science of learning and training for middle-aged and older adults.

    Pilot innovations that incentivize and allow middle-aged and older adults to retool for multiple careers and/or participate as volunteers across their lifespan in roles with meaning and purpose. More

  • in

    Growth of alpine grassland will start and stop earlier under climate warming

    Körner, C. Alpine Plant Life: Functional plant ecology of high mountain ecosystems. (Springer, 2021). https://doi.org/10.1007/978-3-030-59538-8.Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Change 5, 424–430 (2015).Article 

    Google Scholar 
    Pepin, N. C. et al. Climate changes and their elevational patterns in the mountains of the world. Rev. Geophys. 60, e2020RG000730 (2022).Stewart, I. T. Changes in snowpack and snowmelt runoff for key mountain regions. Hydrol. Process 23, 78–94 (2009).Article 

    Google Scholar 
    Vorkauf, M., Marty, C., Kahmen, A. & Hiltbrunner, E. Past and future snowmelt trends in the Swiss Alps: the role of temperature and snowpack. Clim. Change 165, 44–62 (2021).Article 

    Google Scholar 
    Inouye, D. W. Effects of climate change on phenology, frost damage, and floral abundance of montane wildflowers. Ecology 89, 353–362 (2008).Article 
    PubMed 

    Google Scholar 
    Vorkauf, M., Kahmen, A., Körner, C. & Hiltbrunner, E. Flowering phenology in alpine grassland strongly responds to shifts in snowmelt but weakly to summer drought. Alp. Bot. 131, 73–88 (2021).Article 

    Google Scholar 
    Wipf, S. & Rixen, C. A review of snow manipulation experiments in Arctic and alpine tundra ecosystems. Polar Res. 29, 95–109 (2010).Article 

    Google Scholar 
    Collins, C. G. et al. Experimental warming differentially affects vegetative and reproductive phenology of tundra plants. Nat. Commun. 12, 3442 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Choler, P. Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration. Biogeosciences 12, 3885–3897 (2015).Article 

    Google Scholar 
    Xie, J. et al. Land surface phenology and greenness in alpine grasslands driven by seasonal snow and meteorological factors. Sci. Total Environ. 725, 138380 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nord, E. A. & Lynch, J. P. Plant phenology: a critical controller of soil resource acquisition. J. Exp. Bot. 60, 1927–1937 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gallinat, A. S., Primack, R. B. & Wagner, D. L. Autumn, the neglected season in climate change research. Trends Ecol. Evol. 30, 169–176 (2015).Article 
    PubMed 

    Google Scholar 
    Rosa, R. K. et al. Plant phenological responses to a long‐term experimental extension of growing season and soil warming in the tussock tundra of Alaska. Glob. Change Biol. 21, 4520–4532 (2015).Article 

    Google Scholar 
    Livensperger, C. et al. Earlier snowmelt and warming lead to earlier but not necessarily more plant growth. AoB Plants 8, plw021 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Körner, C. & Hiltbrunner, E. Why is the alpine flora comparatively robust against climatic warming? Diversity 13, 383–397 (2021).Article 

    Google Scholar 
    Ma, H. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat. Ecol. Evol. 5, 1110–1122 (2021).Article 
    PubMed 

    Google Scholar 
    Iversen, C. M. et al. The unseen iceberg: plant roots in arctic tundra. N. Phytol. 205, 34–58 (2015).Article 

    Google Scholar 
    Abramoff, R. Z. & Finzi, A. C. Are above‐ and below‐ground phenology in sync? N. Phytol. 205, 1054–1061 (2015).Article 

    Google Scholar 
    Liu, H. et al. Phenological mismatches between above- and belowground plant responses to climate warming. Nat. Clim. Change 12, 97–102 (2022).Article 
    CAS 

    Google Scholar 
    Rixen, C. et al. Winters are changing: snow effects on Arctic and alpine tundra ecosystems. Arct. Sci. 1–37 (2022) https://doi.org/10.1139/as-2020-0058.Johnson, M. G., Tingey, D. T., Phillips, D. L. & Storm, M. J. Advancing fine root research with minirhizotrons. Environ. Exp. Bot. 45, 263–289 (2001).Article 
    PubMed 

    Google Scholar 
    Atkinson, J. A., Pound, M. P., Bennett, M. J. & Wells, D. M. Uncovering the hidden half of plants using new advances in root phenotyping. Curr. Opin. Biotech. 55, 1–8 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Radville, L., McCormack, M. L., Post, E. & Eissenstat, D. M. Root phenology in a changing climate. J. Exp. Bot. 67, 3617–3628 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Blume-Werry, G. The belowground growing season. Nat. Clim. Change 12, 11–12 (2022).Article 

    Google Scholar 
    Wipf, S., Stoeckli, V. & Bebi, P. Winter climate change in alpine tundra: plant responses to changes in snow depth and snowmelt timing. Clim. Change 94, 105–121 (2009).Article 

    Google Scholar 
    Baptist, F., Flahaut, C., Streb, P. & Choler, P. No increase in alpine snowbed productivity in response to experimental lengthening of the growing season. Plant Biol. 12, 755–764 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Vitasse, Y. et al. ‘Hearing’ alpine plants growing after snowmelt: ultrasonic snow sensors provide long-term series of alpine plant phenology. Int J. Biometeorol. 61, 349–361 (2017).Article 
    PubMed 

    Google Scholar 
    Blume‐Werry, G., Jansson, R. & Milbau, A. Root phenology unresponsive to earlier snowmelt despite advanced above‐ground phenology in two subarctic plant communities. Funct. Ecol. 31, 1493–1502 (2017).Article 

    Google Scholar 
    Darrouzet‐Nardi, A. et al. Limited effects of early snowmelt on plants, decomposers, and soil nutrients in Arctic tundra soils. Ecol. Evol. 9, 1820–1844 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ernakovich, J. G. et al. Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob. Change Biol. 20, 3256–3269 (2014).Article 

    Google Scholar 
    Keller, F. & Körner, C. The role of photoperiodism in alpine plant development. Arct. Antarct. Alp. Res 35, 361–368 (2003).Article 

    Google Scholar 
    Hiltbrunner, E., Arnaiz, J. & Körner, C. Biomass allocation and seasonal non-structural carbohydrate dynamics do not explain the success of tall forbs in short alpine grassland. Oecologia 1–15 (2021) https://doi.org/10.1007/s00442-021-04950-7.Inauen, N., Körner, C. & Hiltbrunner, E. No growth stimulation by CO2 enrichment in alpine glacier forefield plants. Glob. Change Biol. 18, 985–999 (2012).Article 

    Google Scholar 
    Möhl, P., Hiltbrunner, E. & Körner, C. Halving sunlight reveals no carbon limitation of aboveground biomass production in alpine grassland. Glob. Change Biol. 26, 1857–1872 (2020).Article 

    Google Scholar 
    Porter, J. R. & Gawith, M. Temperatures and the growth and development of wheat: a review. Eur. J. Agron. 10, 23–36 (1999).Article 

    Google Scholar 
    Parent, B., Turc, O., Gibon, Y., Stitt, M. & Tardieu, F. Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. J. Exp. Bot. 61, 2057–2069 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Körner, C. H. & Woodward, F. I. The dynamics of leaf extension in plants with diverse altitudinal ranges. Oecologia 72, 279–283 (1987).Article 
    PubMed 

    Google Scholar 
    Nagelmüller, S., Hiltbrunner, E. & Körner, C. Low temperature limits for root growth in alpine species are set by cell differentiation. AoB Plants 9, plx054 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Starr, G., Oberbauer, S. F. & Pop, E. W. Effects of lengthened growing season and soil warming on the phenology and physiology of Polygonum bistorta. Glob. Change Biol. 6, 357–369 (2000).Article 

    Google Scholar 
    Yoshie, F. Vegetative phenology of alpine plants at Tateyama Murodo-daira in central Japan. J. Plant Res. 123, 675–688 (2010).Article 
    PubMed 

    Google Scholar 
    Jonas, T., Rixen, C., Sturm, M. & Stoeckli, V. How alpine plant growth is linked to snow cover and climate variability. J. Geophys. Res. 113, G03013 (2008).
    Google Scholar 
    Wang, H. et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 23, 701–710 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Frei, E. R. & Henry, G. H. R. Long-term effects of snowmelt timing and climate warming on phenology, growth, and reproductive effort of Arctic tundra plant species. Arct. Sci. 1–22 (2021) https://doi.org/10.1139/as-2021-0028.Schäppi, B. & Körner, C. Growth responses of an alpine grassland to elevated CO2. Oecologia 105, 43–52 (1996).Article 
    PubMed 

    Google Scholar 
    Aloni, R. Role of hormones in controlling vascular differentiation and the mechanism of lateral root initiation. Planta 238, 819–830 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sloan, V. L., Fletcher, B. J. & Phoenix, G. K. Contrasting synchrony in root and leaf phenology across multiple sub‐Arctic plant communities. J. Ecol. 104, 239–248 (2016).Article 
    CAS 

    Google Scholar 
    Nagelmüller, S., Hiltbrunner, E. & Körner, C. Critically low soil temperatures for root growth and root morphology in three alpine plant species. Alp. Bot. 126, 11–21 (2016).Article 

    Google Scholar 
    Woo, H. R., Kim, H. J., Lim, P. O. & Nam, H. G. Leaf senescence: systems and dynamics aspects. Annu. Rev. Plant Biol. 70, 1–30 (2019).Article 

    Google Scholar 
    Liu, Z., Marella, C. B. N., Hartmann, A., Hajirezaei, M. R. & Wirén, Nvon An age-dependent sequence of physiological processes defines developmental root senescence. Plant Physiol. 181, 993–1007 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ryser, P., Puig, S., Müller, M. & Munné-Bosch, S. Abscisic acid responses match the different patterns of autumn senescence in roots and leaves of Iris versicolor and Sparganium emersum. Environ. Exp. Bot. 176, 104097 (2020).Article 
    CAS 

    Google Scholar 
    Budge, K., Leifeld, J., Hiltbrunner, E. & Fuhrer, J. Alpine grassland soils contain large proportion of labile carbon but indicate long turnover times. Biogeosciences 8, 1911–1923 (2011).Article 
    CAS 

    Google Scholar 
    Solly, E. F. et al. Unravelling the age of fine roots of temperate and boreal forests. Nat. Commun. 9, 3006 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Trumbore, S. E., Sierra, C. A. & Pries, C. E. H. Radiocarbon and climate change, mechanisms, applications and laboratory techniques. 45–82 (2016) https://doi.org/10.1007/978-3-319-25643-6_3.Windmaißer, T. & Reisch, C. Long-term study of an alpine grassland: local constancy in times of global change. Alp. Bot. 123, 1–6 (2013).Article 

    Google Scholar 
    De Witte, L. C. D., Armbruster, G. F. J., Gielly, L., Taberlet, P. & Stöcklin, J. AFLP markers reveal high clonal diversity and extreme longevity in four key arctic‐alpine species. Mol. Ecol. 21, 1081–1097 (2012).Article 
    PubMed 

    Google Scholar 
    Landolt, E. Unsere Alpenflora. (SAC-Verlag, 2012).Puşcaş, M. & Choler, P. A biogeographic delineation of the European Alpine System based on a cluster analysis of Carex curvula-dominated grasslands. Flora – Morphol. Distrib. Funct. Ecol. Plants 207, 168–178 (2012).Article 

    Google Scholar 
    Grabherr, G., Mahr, E. & Reisigl, H. Nettoprimärproduktion und Reproduktion in einem Krummseggenrasen (Caricetum curvulae) der Otztaler Alpen, Tirol. Oecologia Plant. 13, 227–251 (1978).
    Google Scholar 
    Chiang, C., Bånkestad, D. & Hoch, G. Reaching natural growth: light quality effects on plant performance in indoor growth facilities. Plants 9, 1273 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chiang, C., Olsen, J. E., Basler, D., Bånkestad, D. & Hoch, G. Latitude and weather influences on sun light quality and the relationship to tree growth. Forests 10, 610–621 (2019).Article 

    Google Scholar 
    Richardson, A. D. et al. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152, 323–334 (2007).Article 
    PubMed 

    Google Scholar 
    Jiang, Y. & Li, C. Convolutional neural networks for image-based high-throughput plant phenotyping: a review. Plant Phenomics 2020, 4152816 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Möller, B. et al. rhizoTrak: a flexible open source Fiji plugin for user-friendly manual annotation of time-series images from minirhizotrons. Plant Soil 444, 519–534 (2019).Article 

    Google Scholar 
    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Smith, A. G., Petersen, J., Selvan, R. & Rasmussen, C. R. Segmentation of roots in soil with U-Net. Plant Methods 16, 13–27 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Seethepalli, A. et al. Rhizovision crown: an integrated hardware and software platform for root crown phenotyping. Plant Phenomics 2020, 3074916 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 3–36 (2011).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.6.2-1. (2021).Möhl P., von Büren R. S. & Hiltbrunner E. Data from: Growth of alpine grassland will start and stop earlier under climate warming figshare. https://doi.org/10.6084/m9.figshare.20440497 (2022). More

  • in

    Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance

    Research Group for Genomic Epidemiology, Technical University of Denmark, Kgs, Lyngby, DenmarkPatrick Munk, Christian Brinch, Frederik Duus Møller, Thomas N. Petersen, Rene S. Hendriksen, Anne Mette Seyfarth, Jette S. Kjeldgaard, Christina Aaby Svendsen & Frank M. AarestrupCentre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh, UKBram van Bunnik & Mark WoolhouseCentre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, SwedenFanny Berglund & D. G. Joakim LarssonDepartment of Viroscience, Erasmus MC, Rotterdam, The NetherlandsMarion KoopmansInstitute of Public Health, Tirana, AlbaniaArtan BegoUniversidad de Buenos Aires, Buenos Aires, ArgentinaPablo PowerMelbourne Water Corporation, Melbourne, AustraliaCatherine Rees & Kris CoventryCharles Darwin University, Darwin, AustraliaDionisia LambrinidisUniversity of Copenhagen, Frederiksberg C, DenmarkElizabeth Heather Jakobsen Neilson & Yaovi Mahuton Gildas HounmanouCharles Darwin University, Darwin Northern Territory, AustraliaKaren GibbCanberra Hospital, Canberra, AustraliaPeter CollignonALS Water, Scoresby, AustraliaSusan CassarAustrian Agency for Health and Food Safety (AGES), Vienna, AustriaFranz AllerbergerUniversity of Dhaka, Dhaka, BangladeshAnowara Begum & Zenat Zebin HossainEnvironmental Protection Department, Bridgetown, St. Michael, BarbadosCarlon WorrellLaboratoire Hospitalier Universitaire de Bruxelles (LHUB-ULB), Brussels, BelgiumOlivier VandenbergAQUAFIN NV, Aartselaar, BelgiumIlse PietersPolytechnic School of Abomey-Calavi, Abomey-Calavi, BeninDougnon Tamègnon VictorienUniversidad Catσlica Boliviana San Pablo, La Paz, BoliviaAngela Daniela Salazar Gutierrez & Freddy SoriaPublic Health Institute of the Republic of Srpska, Faculty of Medicine University of Banja Luka, Banja Luka, Bosnia and HerzegovinaVesna Rudić GrujićPublic Health Institute of the Republic of Srpska, Banja Luka, Bosnia and HerzegovinaNataša MazalicaBotswana International University of Science and Technology, Palapye, BotswanaTeddie O. RahubeUniversidade Federal de Minas Gerais, Belo Horizonte, BrazilCarlos Alberto Tagliati & Larissa Camila Ribeiro de SouzaOswaldo Cruz Institute, Rio de Janeiro, BrazilDalia RodriguesVale Institute of Technology, Belιm, PA, BrazilGuilherme OliveiraNational Center of Infectious and Parasitic Diseases, Sofia, BulgariaIvan IvanovUniversity of Ouagadougou, Ouagadougou, Burkina FasoBonkoungou Isidore Juste & Traoré OumarInstitut Pasteur du Cambodge, Phnom Penh, CambodiaThet Sopheak & Yith VuthyCentre Pasteur du Cameroun, Yaoundι, CameroonAntoinette Ngandijo, Ariane Nzouankeu & Ziem A. Abah Jacques OlivierUniversity of Regina, Regina, CanadaChristopher K. YostEau Terre Environnement Research Centre (INRS-ETE), Quebec City G1K 9A9, Canada and Indian Institute of Technology, Jammu, IndiaPratik KumarEau Terre Environnement Research Centre (INRS-ETE), Quebec City G1K 9A9, Canada and Lassonde School of Enginerring, York University, Toronto, CanadaSatinder Kaur BrarUniversity of N’Djamena, N’Djamena, ChadDjim-Adjim TaboEscuela de Medicina Veterinaria, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, ChileAiko D. AdellInstitute of Public Health, Santiago, ChileEsteban Paredes-Osses & Maria Cristina MartinezCentro de Biotecnologνa de los Recursos Naturales, Facultad de Ciencias Agrarias y Forestales, Talca, ChileSara Cuadros-OrellanaGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaChangwen Ke, Huanying Zheng & Li BaishengThe Hong Kong Polytechnic University, Hong Kong, ChinaLok Ting Lau & Teresa ChungShantou University Medical College, Shantou, ChinaXiaoyang JiaoNanjing University of Information Science and Technology, Nanjing, ChinaYongjie YuCenter for Disease Control and Prevention of Henan province, Zhengzhou, ChinaZhao JiaYongColombian Integrated Program for Antimicrobial Resistance Surveillance – Coipars, CI Tibaitatα, Corporaciσn Colombiana de Investigaciσn Agropecuaria (AGROSAVIA), Tibaitatα – Mosquera, Cundinamarca, ColombiaJohan F. Bernal Morales, Maria Fernanda Valencia & Pilar Donado-GodoyInstitut Pasteur de Côte d’Ivoire, Abidjan, Côte d’IvoireKalpy Julien CoulibalyUniversity of Zagreb, Zagreb, CroatiaJasna HrenovicAndrija Stampar Teaching Institute of Public Health, Zagreb, CroatiaMatijana JergovićVeterinary Research Institute, Brno, Czech RepublicRenáta KarpíškováCentre de Recherche en Sciences Naturelles de Lwiro (CRSN-LWIRO), Bukavu, Democratic Republic of CongoZozo Nyarukweba DeogratiasBIOFOS A/S, Copenhagen K, DenmarkBodil ElsborgTechnical University of Denmark, Kgs., Lyngby, DenmarkLisbeth Truelstrup Hansen & Pernille Erland JensenSuez Canal University, Ismailia, EgyptMohamed AbouelnagaUniversity of Sadat City, Sadat City, EgyptMohamed Fathy SalemMinistry of Health, Environmental Microbiology, Tallinn, EstoniaMarliin KoolmeisterAddis Ababa University, Addis Ababa, EthiopiaMengistu Legesse & Tadesse EgualeUniversity of Helsinki, Helsinki, FinlandAnnamari HeikinheimoFrench Institute Search Pour L’exploitation De La Mer (Ifremer), Nantes, FranceSoizick Le Guyader & Julien SchaefferInstituto Nacional de Investigaciσn en Salud Pϊblica-INSPI (CRNRAM), Galαpagos, Quito, EcuadorJose Eduardo VillacisNational Public Health Laboratories, Ministry of Health and Social Welfare, Kotu, GambiaBakary SannehNational Center for Disease Control and Public Health, Tbilisi, GeorgiaLile MalaniaRobert Koch Institute, Berlin, GermanyAndreas Nitsche & Annika BrinkmannTechnische Universitδt Dresden, Institute of Hydrobiology, Dresden, GermanySara Schubert, Sina Hesse & Thomas U. BerendonkUniversity for Development Studies, Tamale, GhanaCourage Kosi Setsoafia SabaUniversity of Ghana, Accra, GhanaJibril MohammedKwame Nkrumah University of Science and Technology, Kumasi, PMB, GhanaPatrick Kwame FegloCouncil for Scientific and Industrial Research Water Research Institute, Accra, GhanaRegina Ama BanuVeterinary Research Institute of Thessaloniki, Hellenic Agricultural Organisation-DEMETER, Thermi, GreeceCharalampos KotzamanidisAthens Water Supply and Sewerage Company (EYDAP S.A.), Athens, GreeceEfthymios LytrasUniversidad de San Carlos de Guatemala, Guatemala City, GuatemalaSergio A. LickesSemmelweis University, Institute of Medical Microbiology, Budapest, HungaryBela KocsisUniversity of Veterinary Medicine, Budapest, HungaryNorbert SolymosiUniversity of Iceland, Reykjavνk, IcelandThorunn R. ThorsteinsdottirCochin University of Science and Technology, Cochin, IndiaAbdulla Mohamed HathaKasturba Medical College, Manipal, IndiaMamatha BallalApollo Diagnostics, Mangalore, IndiaSohan Rodney BangeraShiraz University of Medical Sciences, Shiraz, IranFereshteh FaniShahid Beheshti University of Medical Sciences, Tehran, IranMasoud AlebouyehNational University of Ireland Galway, Galway, IrelandDearbhaile Morris, Louise O’Connor & Martin CormicanBen Gurion University of the Negev and Ministry of Health, Beer-Sheva, IsraelJacob Moran-GiladIstituto Zooprofilattico Sperimentale del Lazio e della Toscana, Rome, ItalyAntonio Battisti, Elena Lavinia Diaconu & Patricia AlbaCNR – Water Research Institute, Verbania, ItalyGianluca Corno & Andrea Di CesareNational Institute of Infectious Diseases, Tokyo, JapanJunzo Hisatsune, Liansheng Yu, Makoto Kuroda, Motoyuki Sugai & Shizuo KayamaNational Center of Expertise, Taldykorgan, KazakhstanZeinegul ShakenovaMount Kenya University, Thika, KenyaCiira KiiyukiaKenya Medical Research Institute, Nairobi, KenyaEric Ng’enoUniversity of Prishtina “Hasan Prishtina” & National Institute of Public Health of Kosovo, Pristina, KosovoLul RakaKuwait Institute for Scientific Research, Kuwait City, KuwaitKazi Jamil, Saja Adel Fakhraldeen & Tareq AlaatiInstitute of Food Safety, Riga, LatviaAivars Bērziņš, Jeļena Avsejenko, Kristina Kokina, Madara Streikisa & Vadims BartkevicsAmerican University of Beirut, Beirut, LebanonGhassan M. MatarCentral Michigan University & Michigan Health Clinics, Saginaw, MI, USAZiad DaoudNational Food and Veterinary Risk Assessment Institute, Vilnius, LithuaniaAsta Pereckienė & Ceslova Butrimaite-AmbrozevicieneLuxembourg Institute of Science and Technology, Belvaux, LuxembourgChristian PennyInstitut Pasteur de Madagascar, Antananarivo, MadagascarAlexandra Bastaraud & Jean-Marc CollardUniversity of Antananarivo, Centre d’Infectiologie Charles Mιrieux, Antananarivo, MadagascarTiavina Rasolofoarison, Luc Hervé Samison & Mala Rakoto AndrianariveloUniversity of Malawi, Blantyre, MalawiDaniel Lawadi BandaMalaysian Genomics Resource Centre Berhad, Kuala Lumpur, MalaysiaArshana AminAIMST University, COMBio, Kedah, MalaysiaHeraa Rajandas & Sivachandran ParimannanWater Services Corporation, Luqa, MaltaDavid SpiteriEnvironmental Health Directorate, St. Venera, MaltaMalcolm Vella HaberUniversity of Mauritius, Reduit, MauritiusSunita J. SantchurnInstitute for Public Health Montenegro, Podgorica, MontenegroAleksandar Vujacic & Dijana DjurovicInstitut Pasteur du Maroc, Casablanca, MoroccoBrahim Bouchrif & Bouchra KarraouanCentro de Investigaηγo em Saϊde de Manhiηa (CISM), Maputo, MozambiqueDelfino Carlos VubilAgriculture and Forestry University, Kathmandu, NepalPushkar PalNational Institute for Public, Health and the Environment (RIVM), Bilthoven, The NetherlandsHeike Schmitt & Mark van PasselUniversity of Otago, Dunedin, New ZealandGert-Jan Jeunen & Neil GemmellUniversity of Otago, Christchurch, New ZealandStephen T. ChambersUniversity of Central America, Managua, NicaraguaFania Perez Mendoza & Jorge Huete-PιrezUniversidad Nacional Autσnoma de Nicaragua-Leσn, Leσn, NicaraguaSamuel VilchezUniversity of Ilorin, Ilorin, NigeriaAkeem Olayiwola Ahmed, Ibrahim Raufu Adisa & Ismail Ayoade OdetokunUniversity of Ibadan, Ibadan, NigeriaKayode FashaeNorwegian Institute of Public Health, Oslo, NorwayAnne-Marie Sørgaard & Astrid Louise WesterVEAS, Slemmestad, NorwayPia Ryrfors & Rune HolmstadUniversity of Agriculture, Faisalabad, PakistanMashkoor MohsinAga Khan University, Karachi, PakistanRumina Hasan & Sadia ShakoorLaboratorio Central de Salud Publica, Asuncion, ParaguayNatalie Weiler Gustafson & Claudia Huber SchillInstituto Nacional de Salud, Lima, PeruMaria Luz Zamudio RojasUniversidad de Piura, Piura, PeruJorge Echevarria Velasquez & Felipe Campos YauceWHO Environmental and Occupational Health, Manila, PhilippinesBonifacio B. MagtibayMaynilad Water Services, Inc., Quezon City, PhilippinesKris Catangcatang & Ruby SibuloNational Veterinary Research Institute, Pulawy, PolandDariusz WasylUniversidade Catσlica Portuguesa, CBQF – Centro de Biotecnologia e Quνmica Fina – Laboratσrio Associado, Escola Superior de Biotecnologia, Porto, PortugalCelia Manaia & Jaqueline RochaAguas do Tejo Atlantico, Lisboa, PortugalJose Martins & Pedro ÁlvaroGwangju Institute of Science and Technology, Gwangju, Republic of KoreaDoris Di Yoong Wen, Hanseob Shin & Hor-Gil HurKorea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaSukhwan YoonInstitute of Public Health of the Republic of North Macedonia, Skopje, Republic of North MacedoniaGolubinka Bosevska & Mihail KochubovskiState Medical and Pharmaceutical University, Chișinău, Republic of MoldovaRadu CojocaruNational Agency for Public Health, Chișinău, Republic of MoldovaOlga BurduniucKing Abdullah University of Science and Technology, Thuwal, Saudi ArabiaPei-Ying HongUniversity of Edinburgh, Edinburgh, Scotland, UKMeghan Rose PerryInstitut Pasteur de Dakar, Dakar, SenegalAmy GassamaInstitute of Veterinary Medicine of Serbia, Belgrade, SerbiaVladimir RadosavljevicNanyang Technological University, Singapore, SingaporeMoon Y. F. Tay, Rogelio Zuniga-Montanez & Stefan WuertzPublic Health Authority of the Slovak Republic, Bratislava, SlovakiaDagmar Gavačová, Katarína Pastuchová & Peter TruskaNational Laboratory of Health, Environment and Food, Ljubljana, SloveniaMarija TrkovIndependent consultant, Johannesburg, South AfricaKaren KeddyDaspoort Waste Water Treatment Works, Pretoria, South AfricaKerneels EsterhuyseKorea Advanced Institute of Science and Technology, Daejeon, South KoreaMin Joon SongSchool of Veterinary Sciences, Lugo, SpainMarcos Quintela-BalujaLabaqua, Santiago de Compostela, SpainMariano Gomez LopezIRTA, Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autonoma de Barcelona, Bellaterra, SpainMarta Cerdà-CuéllarUniversity of Kelaniya, Ragama, Sri LankaR. R. D. P. Perera, N. K. B. K. R. G. W. Bandara & H. I. PremasiriMedical Research Institute, Colombo, Sri LankaSujatha PathirageCaribbean Public Health Agency, Catries, Saint LuciaKareem CharlemagneThe Sahlgrenska Academy at the University of Gothenburg, Gothenburg, SwedenCarolin RutgerssonSwedish University of Agricultural Sciences, Uppsala, SwedenLeif Norrgren & Stefan ÖrnFederal Food Safety and Veterinary Office (FSVO), Bern, SwitzerlandRenate BossAra Region Bern AG, Herrenschwanden, SwitzerlandTanja Van der HeijdenCenters for Disease Control, Taipei, TaiwanYu-Ping HongKilimanjaro Clinical Research Institute, Moshi, TanzaniaHappiness Houka KumburuSokoine University of Agriculture, Morogoro, TanzaniaRobinson Hammerthon MdegelaFaculty of Science and Technology, Suratthani Rajabhat University, Surat Thani, ThailandKaknokrat ChonsinFaculty of Public Health, Mahidol University, Bangkok, ThailandOrasa SuthienkulFaculty of Medicine Siriraj Hospital, Bangkok, ThailandVisanu ThamlikitkulNational Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsAna Maria de Roda HusmanNational Institute of Hygiene, Lomι, TogoBawimodom BidjadaAgence de Mιdecine Prιventive, Dapaong, TogoBerthe-Marie Njanpop-LafourcadeDivision of Integrated Surveillance of Health Emergencies and Response, Lomι, TogoSomtinda Christelle Nikiema-PessinabaPublic Health Institution of Turkey, Ankara, TurkeyBelkis LeventHatay Mustafa Kemal University, Hatay, TurkeyCemil KurekciMakerere University, Kampala, UgandaFrancis Ejobi & John Bosco KaluleAbu Dhabi Public Health Center, Abu Dhai, United Arab EmiratesJens ThomsenDubai municipality, WWTP Al Aweer, Dubai, UAEOuidiane ObaidiRashid Hospital, Dubai, UAELaila Mohamed JassimNorthumbrian Water, Northumbria House, Abbey Road, Pity Me, Durham, UKAndrew MooreUniversity of Exeter Medical School, Cornwall, UKAnne Leonard, Lihong Zhang & William H. GazeNewcastle University, Newcastle upon Tyne, UKDavid W. Graham & Joshua T. BunceBrightwater Treatment Plant, Woodinville, WA, USABrett LeforDepartment of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USADrew Capone & Joe BrownUniversity of North Carolina, Chapel Hill, USAEmanuele Sozzi & Mark D. SobseyUniversity of Washington, Seattle, WA, USAJohn Scott Meschke, Nicola Koren Beck, Pardi Sukapanpatharam & Phuong TruongBaylor University, Waco, USAMichael DavisColumbia Boulevard WWTP, Portland, USARonald LilienthalEastern Illinois University, Charleston, USASanghoon KangThe Ohio State University, Columbus Ohio, USAThomas E. WittumLaboratorio Tecnolσgico del Uruguay, Montevideo, UruguayNatalia Rigamonti & Patricia BaklayanInstitute of Public Health in Ho Chi Minh City, Ho Chi Minh, VietnamChinh Dang Van, Doan Minh Nguyen Tran & Nguyen Do PhucUniversity of Zambia, Lusaka, ZambiaGeoffrey KwendaF.M.A., M.K., and M.W. conceived the study and secured funding. R.S.H., A.M.S., C.A.A.S., and J.S.K. organized sample collection, material transfer, and logistics. F.D.M., P.M., and C.B. did quality control, sample selection, and outlier detection. P.M., C.B., F.D.M., T.N.P., and F.B. performed bioinformatics analyses. P.M. and C.B. carried out data and statistical analyses and visualization. P.M. and F.M.A. drafted the initial manuscript with input from C.B., B.v.B., D.G.J.L., M.W., and M.K. The Global Sewage Consortium authors carried out sewage sampling, filled in metadata and shipped the samples to DTU. All authors helped to review and improve the manuscript. More

  • in

    Non-inversion conservation tillage as an underestimated driver of tillage erosion

    Montgomery, D. R. Soil erosion and agricultural sustainability. Proc. Natl. Acad. Sci. 104, 13268–13272 (2007).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evans, D. L., Quinton, J. N., Davies, J. A. C., Zhao, J. & Govers, G. Soil lifespans and how they can be extended by land use and management change. Environ. Res. Lett. 15, 1. https://doi.org/10.1088/1748-9326/aba2fd (2020).Adhikari, K. & Hartemink, A. E. Linking soils to ecosystem services—A global review. Geoderma 262, 101–111. https://doi.org/10.1016/j.geoderma.2015.08.009 (2016).Article 
    CAS 

    Google Scholar 
    Gao, Y. et al. Effects of tillage methods on soil carbon and wind erosion. Land Degrad. Dev. 27, 583–591. https://doi.org/10.1002/ldr.2404 (2016).Article 

    Google Scholar 
    Klik, A. & Rosner, J. Long-term experience with conservation tillage practices in Austria: Impacts on soil erosion processes. Soil Till. Res. 203, 1. https://doi.org/10.1016/j.still.2020.104669 (2020).Seitz, S. et al. Conservation tillage and organic farming reduce soil erosion. Agron. Sustain. Dev. 39, 1. https://doi.org/10.1007/s13593-018-0545-z (2018).Lal, R., Reicosky, D. C. & Hanson, J. D. Evolution of the plow over 10,000 years and the rationale for no-till farming. Soil Till. Res. 93, 1–12. https://doi.org/10.1016/j.still.2006.11.004 (2007).Article 

    Google Scholar 
    Mal, P., Schmitz, M. & Hesse, J. W. Economic and environmental effects of conservation tillage with glyphosate use: A case study of Germany. Outlooks Pest Manag. 26, 24–27. https://doi.org/10.1564/v26_feb_07 (2015).Article 

    Google Scholar 
    Statistisches Bundesamt. Land- und Forstwirtschaft, Fischerei. Bodenbearbeitung, Bewässerung, Landschaftselemente. Erhebung über landwirtschaftliche Produktionsmethoden (ELPM). 2010. (2011).Quinton, J. N., Govers, G., Van Oost, K. & Bardgett, R. D. The impact of agricultural soil erosion on biogeochemical cycling. Nat. Geosci. 3, 311–314. https://doi.org/10.1038/ngeo838 (2010).Article 
    CAS 

    Google Scholar 
    Öttl, L. K. et al. Tillage erosion as an important driver of in-field biomass patterns in an intensively used hummocky landscape. Land Degrad. Dev. 32, 3077–3091. https://doi.org/10.1002/ldr.3968 (2021).Article 

    Google Scholar 
    Wilken, F., Ketterer, M., Koszinski, S., Sommer, M. & Fiener, P. Understanding the role of water and tillage erosion from 239+240Pu tracer measurements using inverse modelling. SOIL 6, 549–564. https://doi.org/10.5194/soil-6-549-2020 (2020).Article 
    CAS 

    Google Scholar 
    Van Oost, K., Govers, G., De Alba, S. & Quine, T. A. Tillage erosion: A review of controlling factors and implications for soil quality. Prog. Phys. Geogr. 30, 443–466. https://doi.org/10.1191/0309133306pp487ra (2006).Article 

    Google Scholar 
    Winnige, B. Ergebnisse zur Bodenverlagerung durch Bearbeitungserosion in der Jungmoränenlandschaft Nordostdeutschlands—Investigations of soil movement by tillage as a type of soil erosion in the young moraine soil landscape of Northeast Germany. Arch. Agron. Soil Sci. 50, 319–327. https://doi.org/10.1080/03650340410001663864 (2004).Article 

    Google Scholar 
    Fiener, P., Wilken, F. & Auerswald, K. Filling the gap between plot and landscape scale—eight years of soil erosion monitoring in 14 adjacent watersheds under soil conservation at Scheyern, Southern Germany. Adv. Geosci. 48, 31–48. https://doi.org/10.5194/adgeo-48-31-2019 (2019).Article 

    Google Scholar 
    Fiener, P. et al. Uncertainties in assessing tillage erosion—How appropriate are our measuring techniques?. Geomorphology 304, 214–225. https://doi.org/10.1016/j.geomorph.2017.12.031 (2018).Article 

    Google Scholar 
    Kimaro, D. N., Deckers, J. A., Poesen, J., Kilasara, M. & Msanya, B. M. Short and medium term assessment of tillage erosion in the Uluguru Mountains Tanzania. Soil Till. Res. 81, 97–108. https://doi.org/10.1016/j.still.2004.05.006 (2005).Article 

    Google Scholar 
    Sadowski, H. & Sorge, B. Der Normalhöhenpunkt von 1912 – Datumspunkt des DHHN 2012? Vermessung Brandenburg (2005).Lobb, D. A., Kachanoski, R. G. & Miller, M. H. Tillage translocation and tillage erosion in the complex upland landscapes of southwestern Ontario Canada. Soil Till. Res. 51, 1. https://doi.org/10.1016/S0167-1987(99)00037-9 (1999).Article 

    Google Scholar 
    Zhang, J. H. & Li, F. C. An appraisal of two tracer methods for estimating tillage erosion rates under hoeing tillage. Proc. Environ. Sci. 11, 1227–1233. https://doi.org/10.1016/j.proenv.2011.12.184 (2011).Article 

    Google Scholar 
    Turkelboom, F. et al. Assessment of tillage erosion rates on steep slopes in northern Thailand. CATENA 29, 29–44 (1997).Article 
    CAS 

    Google Scholar 
    Van Muysen, W., Govers, G., Van Oost, K. & Van Rompaey, A. The effect of tillage depth, tillage speed, and soil condition on chisel tillage erosivity. J. Soil Water Conserv. 55, 355–364 (2000).
    Google Scholar 
    Quine, T. A., Desmet, P. J. J., Govers, G., Vandaele, K. & Walling, D. E. A comparison of the roles of tillage and water erosion in landform development and sediment export on agricultural land near Leuven, Belgium. IAHS Publ. 224, 77–86 (1994).CAS 

    Google Scholar 
    Heckrath, G. et al. Tillage erosion and its effect on soil properties and crop yield in Denmark. J. Environ. Qual. 34, 312–324. https://doi.org/10.2134/jeq2005.0312a (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Carter, M. R. Conservation tillage. Encyclop. Soils Environ. 1, 306–311. https://doi.org/10.1016/B0-12-348530-4/00270-8 (2005).Article 

    Google Scholar 
    Govers, G., Vandaele, K., Desmet, P., Poesen, J. & Bunte, K. The role of tillage in soil redistribution on hillslopes. Eur. J. Soil Sci. 45, 469–478. https://doi.org/10.1111/j.1365-2389.1994.tb00532.x (1994).Article 

    Google Scholar 
    Marques da Silva, J. R. & Alexandre, C. Soil carbonation processes as evidence of tillage-induced erosion. Soil Till. Res. 78, 217–224. https://doi.org/10.1016/j.still.2004.02.008 (2004).Mech, S. J. & Free, G. R. Movement of soil during tillage operations. Agric. Eng. 1, 379–382 (1942).
    Google Scholar 
    Tiessen, K. H. D., Mehuys, G. R., Lobb, D. A. & Rees, H. W. Tillage erosion within potato production systems in Atlantic Canada: I. Measurement of tillage translocation by implements used in seedbed preparation. Soil Till. Res. 95, 308–319. https://doi.org/10.1016/j.still.2007.02.003 (2007).Article 

    Google Scholar 
    Marques da Silva, J. R., Soares, J. M. C. N. & Karlen, D. L. Implement and soil condition effects on tillage-induced erosion. Soil Till. Res. 78, 207–216. https://doi.org/10.1016/j.still.2004.02.009 (2004).Article 

    Google Scholar 
    Kietzer, B. Aufklärung der Bodenverlagerung durch Bearbeitungserosion in Jungmoränenlandschaften—Elucidation of soil displacement by tillage erosion in young moraine landscapes PhD thesis, Technical University of Berlin, (2007).Lüthgens, C., Böse, M. & Preusser, F. Age of the Pomeranian ice-marginal position in northeastern Germany determined by Optically Stimulated Luminescence (OSL) dating of glaciofluvial sediments. Boreas 40, 598–615. https://doi.org/10.1111/j.1502-3885.2011.00211.x (2011).Article 

    Google Scholar 
    Deumlich, D., Schmidt, R. & Sommer, M. A multiscale soil-landform relationship in the glacial-drift area based on digital terrain analysis and soil attributes. J. Plant Nutr. Soil Sci. 173, 843–851. https://doi.org/10.1002/jpln.200900094 (2010).Article 
    CAS 

    Google Scholar 
    Koszinski, S., Gerke, H. H., Hierold, W. & Sommer, M. Geophysical-based modeling of a kettle hole catchment of the morainic soil landscape. Vadose Zone J. 12, 1. https://doi.org/10.2136/vzj2013.02.0044 (2013).Article 

    Google Scholar 
    Sommer, M., Gerke, H. H. & Deumlich, D. Modelling soil landscape genesis: A “time split” approach for hummocky agricultural landscapes. Geoderma 145, 480–493. https://doi.org/10.1016/j.geoderma.2008.01.012 (2008).Article 
    CAS 

    Google Scholar 
    DWD Climate Data Center (CDC). Historical hourly station observations of 2m air temperature and humidity for Germany, version v006. (2018).DWD Climate Data Center (CDC). Historical hourly station observations of precipitation for Germany, version v21.3. (2021).Zhang, H. et al. Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection. Earth Surf. Dyn. 7, 807–827. https://doi.org/10.5194/esurf-7-807-2019 (2019).Article 

    Google Scholar 
    Lindstrom, M. J., Nelson, W. W., Schumacher, T. E. & Lemme, G. D. Soil movement by tillage as affected by slope. Soil Till. Res. 17, 255–264. https://doi.org/10.1016/0167-1987(90)90040-K (1990).Article 

    Google Scholar 
    Crawley, M. J. The R book. 2nd edn, (Wiley, 2013).Wickham, H. ggplot2: Elegant graphics for data analysis (Springer, 2016).Book 
    MATH 

    Google Scholar 
    R Core Team. A language and environment for statistical computing. (2021).De Alba, S. Modelling the effects of complex topography and patterns of tillage on soil translocation by tillage with mouldboard plough. J. Soil Water Conserv. 1, 335–345 (2001).
    Google Scholar 
    Gerontidis, D. V. S. et al. The effect of moldboard plow on tillage erosion along a hillslope. J. Soil Water Conserv. 56, 147–152 (2001).
    Google Scholar 
    Heckrath, G., Halekoh, U., Djurhuus, J. & Govers, G. The effect of tillage direction on soil redistribution by mouldboard ploughing on complex slopes. Soil Tillage Res. 88, 225–241. https://doi.org/10.1016/j.still.2005.06.001 (2006).Article 

    Google Scholar 
    Kosmas, C. et al. The effects of tillage displaced soil on soil properties and wheat biomass. Soil Till Res. 58, 31–44. https://doi.org/10.1016/S0167-1987(00)00175-6 (2001).Article 

    Google Scholar 
    Lindstrom, M. J., Nelson, W. W. & Schumacher, T. E. Quantifying tillage erosion rates due to moldboard plowing. Soil Till Res. 24, 243–255. https://doi.org/10.1016/0167-1987(92)90090-X (1992).Article 

    Google Scholar 
    Lobb, D. A., Kachanoski, R. G. & Miller, M. H. Tillage translocation and tillage erosion on shoulder slope landscape positions measured using 137Cs as a tracer. Can. J. Soil Sci. 75, 211–218. https://doi.org/10.4141/cjss95-029 (1995).Article 

    Google Scholar 
    Quine, T. A. & Zhang, Y. Re-defining tillage erosion: Quantifying intensity–direction relationships for complex terrain: 1. Derivation of an adirectional soil transport coefficient. Soil Use Manag. 20, 114–123. https://doi.org/10.1111/j.1475-2743.2004.tb00346.x (2004).Article 

    Google Scholar 
    Quine, T. A., Basher, L. R. & Nicholas, A. P. Tillage erosion intensity in the South Canterbury Downlands, New Zealand. Aust. J. Soil Res. 41, 789–807. https://doi.org/10.1071/SR02063 (2003).Article 

    Google Scholar 
    Revel, J. C. & Guiresse, M. Erosion due to cultivation of calcareous clay soils on the hillsides of south west France: I. Effect of former farming practices. Soil Till Res. 35, 147–155. https://doi.org/10.1016/0167-1987(95)00482-3 (1995).Article 

    Google Scholar 
    Van Muysen, W. & Govers, G. Soil displacement and tillage erosion during secondary tillage operations: The case of rotary harrow and seeding equipment. Soil Till Res. 65, 185–191. https://doi.org/10.1016/S0167-1987(01)00284-7 (2002).Article 

    Google Scholar 
    Van Muysen, W., Govers, G., Bergkamp, G., Roxo, M. & Poesen, J. Measurement and modelling of the effects of initial soil conditions and slope gradient on soil translocation by tillage. Soil Till Res. 51, 303–316. https://doi.org/10.1016/S0167-1987(99)00044-6 (1999).Article 

    Google Scholar 
    Poesen, J. et al. Patterns of rock fragment cover generated by tillage erosion. Geomorphology 18, 183–197. https://doi.org/10.1016/S0169-555X(96)00025-6 (1997).Article 

    Google Scholar 
    Quine, T. A. et al. Fine-earth translocation by tillage in stony soils in the Guadalentin, south-east Spain: An investigation using caesium-134. Soil Till Res. 51, 279–301. https://doi.org/10.1016/S0167-1987(99)00043-4 (1999).Article 
    MathSciNet 

    Google Scholar 
    Kemper, W. D. & Rosenau, R. C. Soil cohesion as affected by time and water content. Soil Sci. Soc. Am. J. 1, 1001–1006. https://doi.org/10.2136/sssaj1984.03615995004800050009x (1984).Article 

    Google Scholar 
    Reinermann, S., Gessner, U., Asam, S., Kuenzer, C. & Dech, S. The effect of droughts on vegetation condition in Germany: An analysis based on two decades of satellite earth observation time series and crop yield statistics. Rem. Sens. 11, 1. https://doi.org/10.3390/rs11151783 (2019).Article 

    Google Scholar 
    Lüttger, A. B. & Feike, T. Development of heat and drought related extreme weather events and their effect on winter wheat yields in Germany. Theor. Appl. Climatol. 1, 15–29. https://doi.org/10.1007/s00704-017-2076-y (2018).Article 

    Google Scholar 
    Madarász, B. et al. Conservation tillage vs. conventional tillage: Long-term effects on yields in continental, sub-humid Central Europe. Hungary. Int. J. Agric. Sustain. 14, 408–427. https://doi.org/10.1080/14735903.2016.1150022 (2016).Article 

    Google Scholar 
    Lowder, S. K., Skoet, J. & Raney, T. The number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Dev. 87, 16–29. https://doi.org/10.1016/j.worlddev.2015.10.041 (2016).Article 

    Google Scholar 
    Napoli, M., Altobelli, F. & Orlandini, S. Effect of land set up systems on soil losses. Ital. J. Agron. 15, 306–314. https://doi.org/10.4081/ija.2020.1768 (2020).Article 

    Google Scholar 
    Dumanski, J., Peiretti, R., Benites, J. R., McGarry, D. & Pieri, C. The paradigm of conservation agriculture. In Proceedings of World Association of Soil and Water Conservation, 58–64 (2006). More