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    Underrated past herbivore densities could lead to misoriented sustainability policies

    Pausas, J. G. & Bond, W. J. On the three major recycling pathways in terrestrial ecosystems. Trends Ecol. Evol. 35, 767–775 (2020).
    Google Scholar 
    Manzano, P. & White, S. R. Intensifying pastoralism may not reduce greenhouse gas emissions: wildlife-dominated landscape scenarios as a baseline in life cycle analysis. Clim. Res. 77, 91–97 (2019).
    Google Scholar 
    Röös, E. et al. Greedy or needy? Land use and climate impacts of food in 2050 under different livestock futures. Glob. Environ. Change 47, 1–12 (2017).
    Google Scholar 
    Harwatt, H., Ripple, W. J., Chaudhary, A., Betts, M. G. & Hayek, M. N. Scientists call for renewed Paris pledges to transform agriculture. Lancet Planet. Health 4, E9–E10 (2020).
    Google Scholar 
    Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).CAS 

    Google Scholar 
    Barnosky, A. D. Megafauna biomass trade-off as a driver of Quaternary and future extinctions. Proc. Natl Acad. Sci. USA 105, 11543–11548 (2008).CAS 

    Google Scholar 
    Smith, F. A. et al. Exploring the influence of ancient and historic megaherbivore extirpations on the global methane budget. Proc. Natl Acad. Sci. USA 113, 874–879 (2016).CAS 

    Google Scholar 
    Zimov, S. A., Zimov, N. S., Tikhonov, A. N. & Chapin, F. S. III Mammoth steppe: a high-productivity phenomenon. Quat. Sci. Rev. 57, 26–45 (2012).
    Google Scholar 
    Adams, J. M., Faure, H., Faure-Denard, L., McGlade, J. M. & Woodward, F. I. Increases in terrestrial carbon storage from the Last Glacial Maximum to the present. Nature 348, 711–714 (1990).CAS 

    Google Scholar 
    Nogués-Bravo, D., Rodríguez, J., Hortal, J., Batra, P. & Araújo, M. B. Climate change, humans, and the extinction of the woolly mammoth. PLoS Biol. 6, e79 (2008).
    Google Scholar 
    Bond, W. J. Open Ecosystems: Ecology and Evolution Beyond the Forest Edge (Oxford Univ. Press, 2019).Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).CAS 

    Google Scholar 
    Carpio Camargo, A. J. et al. Assessing red deer hunting management in the Iberian Peninsula: the importance of longitudinal studies. PeerJ 9, e10872 (2021).
    Google Scholar 
    Gordon, I. J., Manning, A. D., Navarro, L. M. & Rouet-Leduc, J. Domestic livestock and rewilding: are they mutually exclusive? Front. Sustain. Food Syst. 5, 550410 (2021).
    Google Scholar 
    Bond, W. J. Large parts of the world are brown or black: a different view on the ‘Green World’ hypothesis. J. Veg. Sci. 16, 261–266 (2005).
    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Google Scholar 
    ESRI. ArcGIS Desktop: Release 10.3. (Environmental Systems Research Institute, Redlands, CA, 2014).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).
    Google Scholar 
    Fløjgaard, C., Pedersen, P. B. M., Sandom, C. J., Svenning, J.-C. & Ejrnæs, R. Exploring a natural baseline for large-herbivore biomass in ecological restoration. J. Appl. Ecol. 59, 18–24 (2022).
    Google Scholar 
    Haller, T. et al. Conflicts, security and marginalisation: institutional change of the pastoral commons in a ‘glocal’ world. Rev. Sci. Tech. Off. Int. Epiz. 35, 405–416 (2016).CAS 

    Google Scholar 
    Torrents-Ticó, M., Fernández-Llamazares, A., Burgas, D. & Cabeza, M. Convergences and divergences between scientific and Indigenous and Local Knowledge contribute to inform carnivore conservation. Ambio 50, 990–1002 (2021).
    Google Scholar 
    Griffith, E. F., Pius, L., Manzano, P. & Jost, C. C. COVID-19 in pastoral contexts in the greater Horn of Africa: implications and recommendations. Pastoralism 10, 1–12 (2020).
    Google Scholar 
    Schieltz, J. M. & Rubenstein, D. I. Evidence-based review: positive versus negative effects of livestock grazing on wildlife. What do we really know? Environ. Res. Lett. 11, 113003 (2016).
    Google Scholar 
    García Sanz, A. La ganadería española entre 1750–1865: los efectos de la reforma agraria liberal. Agricultura y Sociedad 72, 81–120 (1991).
    Google Scholar 
    San Miguel, A., Roig, S. & Perea, R. The pastures of Spain. Pastos 46, 6–39 (2016).
    Google Scholar 
    Epp, H. & Dyck, I. Early human-bison population interdependence in the Plains ecosystem. Gt. Plains Res. 12, 323–337 (2002).
    Google Scholar 
    Hristov, A. N. Historic, pre-European settlement, and present-day contribution of wild ruminants to enteric methane emissions in the United States. J. Anim. Sci 90, 1371–1375 (2012).CAS 

    Google Scholar 
    Bond, W. J. Ancient grasslands at risk. Science 351, 120–122 (2016).CAS 

    Google Scholar 
    Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. The trouble with trees: afforestation plans for Africa. Trends Ecol. Evol. 34, 963–965 (2019).
    Google Scholar 
    Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl Acad. Sci. USA 118, e2023483118 (2021).CAS 

    Google Scholar 
    Swette, B. & Lambin, E. F. Institutional changes drive land use transitions on rangelands: the case of grazing on public lands in the American West. Glob. Environ. Change 66, 102220 (2021).
    Google Scholar 
    Hayek, M. N., Harwatt, H., Ripple, W. J. & Mueller, N. D. The carbon opportunity cost of animal-sourced food production on land. Nat. Sustain. 4, 21–24 (2021).
    Google Scholar 
    Kristensen, J. A., Svenning, J.-C., Georgiou, K. & Mahli, Y. Can large herbivores enhance ecosystem carbon persistence? Trends Ecol. Evol. 37, 117–128 (2022).CAS 

    Google Scholar 
    Carmona, C. P., Azcárate, F. M., Oteros-Rozas, E., González, J. A. & Peco, B. Assessing the effects of seasonal grazing on holm oak regeneration: Implications for the conservation of Mediterranean dehesas. Biol. Cons. 159, 240–247 (2013).
    Google Scholar 
    García-Fernández, A. et al. Herbivore corridors sustain genetic footprint in plant populations: a case for Spanish drove roads. PeerJ 7, e7311 (2019).
    Google Scholar 
    Scoones, I. Living with Uncertainty: New Directions in Pastoralism Development in Africa, Ch. 1 (ITDG, 1995).Pardo, G., Casas, R., del Prado, A. & Manzano, P. Carbon footprint of transhumant sheep farms: accounting for natural baseline emissions in Mediterranean systems. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-1838904/v1 (2022).Odhiambo, M. & Manzano, P. Making Way. Developing National Legal and Policy Frameworks for Pastoral Mobility (FAO, 2022).del Prado, A., Manzano, P. & Pardo, G. The role of the European small ruminant dairy sector on stabilizing global temperatures: lessons from GWP* warming-equivalent emission metrics. J. Dairy Res. 8, 8–15 (2021).
    Google Scholar 
    Molina-Flores, B., Manzano-Baena, P. & Coulibaly, M. A. The Role of Livestock in Food Security, Poverty Reduction and Wealth Creation in West Africa (FAO, 2020).Lasanta, T., Cortijos-López, M., Errea, M. P., Khorchani, M. & Nadal-Romero, E. An environmental management experience to control wildfires in the mid-mountain Mediterranean area: shrub clearing to generate mosaic landscapes. Land Use Policy 118, 106147 (2022).
    Google Scholar 
    Torres-Miralles, M. et al. Contribution of High Nature Value farming systems to sustainable livestock production: a case from Finland. Sci. Total Environ. 839, 156267 (2022).CAS 

    Google Scholar 
    Manzano, P. et al. Towards a holistic understanding of pastoralism. One Earth 4, 651–665 (2021).
    Google Scholar 
    Karlsson, J. O., Parodi, A., Van Zanten, H. H., Hansson, P. A. & Röös, E. Halting European Union soybean feed imports favours ruminants over pigs and poultry. Nat. Food 2, 38–46 (2021).
    Google Scholar 
    Leroy, F. et al. Transformation of animal agriculture should be evidence-driven and respectful of livestock’s benefits and contextual aspects. Animal 16, 100644 (2022).
    Google Scholar 
    Jackson, R. D. Grazed perennial grasslands can match current beef production while contributing to climate mitigation and adaptation. Agric. Environ. Lett. 7, e20059 (2022).
    Google Scholar 
    Mahli, Y. et al. The role of large wild animals in climate change mitigation and adaptation. Curr. Biol. 32, R181–R196 (2022).
    Google Scholar 
    O’Bryan, C. J. et al. Unrecognized threat to global soil carbon by a widespread invasive species. Glob. Change Biol. 28, 877–882 (2022).
    Google Scholar 
    Karp, A. T., Faith, J. T., Marlon, J. R. & Straver, A. C. Global response of fire activity to late Quaternary grazer extinctions. Science 374, 1145–1148 (2021).CAS 

    Google Scholar 
    Ripple, W. J. et al. World scientists’ warning of a climate emergency 2021. Bioscience 71, 894–898 (2021).
    Google Scholar 
    Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 180227 (2018).
    Google Scholar 
    Mann, D. H., Groves, P., Kunz, M. L., Reanier, R. E. & Gaglioti, B. V. Ice-age megafauna in Arctic Alaska: extinction, invasion, survival. Quat. Sci. Rev. 70, 91–108 (2013).
    Google Scholar 
    Sandom, C., Faurby, S., Sandel, B. & Svenning, J. C. Global late Quaternary megafauna extinctions linked to humans, not climate change. Proc. Royal Soc. B 281, 20133254 (2014).
    Google Scholar 
    Fariña, R. A., Czerwonogora, A. & di Giacomo, M. Splendid oddness: revisiting the curious trophic relationships of South American Pleistocene mammals and their abundance. An. Acad. Bras. Ciênc. 86, 311–331 (2014).
    Google Scholar  More

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    Solar radiation, temperature and the reproductive biology of the coral Lobactis scutaria in a changing climate

    Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).Article 

    Google Scholar 
    Plaisance, L., Caley, M. J., Brainard, R. E. & Knowlton, N. The diversity of coral reefs: What are we missing?. PLoS ONE 6, e25026 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Frieler, K. et al. Limiting global warming to 2 °C is unlikely to save most coral reefs. Nat. Clim. Change 3, 165–170 (2013).Article 
    ADS 

    Google Scholar 
    Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 301, 929–933 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Lotze, H. K. et al. Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proc. Natl. Acad. Sci. 116, 12907–12912 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Doney, S. C. et al. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11–37 (2012).Article 
    ADS 

    Google Scholar 
    Van Oppen, M. J., Oliver, J. K., Putnam, H. M. & Gates, R. D. Building coral reef resilience through assisted evolution. Proc. Natl. Acad. Sci. 112, 2307–2313 (2015).Article 
    ADS 

    Google Scholar 
    Parrett, J. M. & Knell, R. J. The effect of sexual selection on adaptation and extinction under increasing temperatures. Proc. R. Soc. B. 285, 20180303 (2018).Article 

    Google Scholar 
    Hagedorn, M. et al. Assisted gene flow using cryopreserved sperm in critically endangered coral. Proc. Natl. Acad. Sci. 118, e2110559118 (2021).Article 
    CAS 

    Google Scholar 
    Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Epstein, N., Bak, R. & Rinkevich, B. Applying forest restoration principles to coral reef rehabilitation. Aquat. Conserv. Mar. Freshw. Ecosyst. 13, 387–395 (2003).Article 

    Google Scholar 
    West, J. M. & Salm, R. V. Resistance and resilience to coral bleaching: Implications for coral reef conservation and management. Conserv. Biol. 17, 956–967 (2003).Article 

    Google Scholar 
    Yeemin, T., Sutthacheep, M. & Pettongma, R. Coral reef restoration projects in Thailand. Ocean Coast. Manag. 49, 562–575 (2006).Article 

    Google Scholar 
    Anthony, K. et al. Operationalizing resilience for adaptive coral reef management under global environmental change. Glob. Chang. Biol. 21, 48–61 (2015).Article 
    ADS 

    Google Scholar 
    Randall, C. J. et al. Sexual production of corals for reef restoration in the Anthropocene. Mar. Ecol. Prog. Ser. 635, 203–232 (2020).Article 
    ADS 

    Google Scholar 
    Porter, J. W., Fitt, W. K., Spero, H. J., Rogers, C. S. & White, M. W. Bleaching in reef corals: Physiological and stable isotopic responses. Proc. Natl. Acad. Sci. 86, 9342–9346 (1989).Article 
    ADS 
    CAS 

    Google Scholar 
    Mendes, J. M. & Woodley, J. D. Effect of the 1995–1996 bleaching event on polyp tissue depth, growth, reproduction and skeletal band formation in Montastraea annularis. Mar. Ecol. Prog. Ser. 235, 93–102 (2002).Article 
    ADS 

    Google Scholar 
    Grottoli, A., Rodrigues, L. & Juarez, C. Lipids and stable carbon isotopes in two species of Hawaiian corals, Porites compressa and Montipora verrucosa, following a bleaching event. Mar. Biol. 145, 621–631 (2004).Article 
    CAS 

    Google Scholar 
    Rodrigues, L. J. & Grottoli, A. G. Energy reserves and metabolism as indicators of coral recovery from bleaching. Limnol. Oceanogr. 52, 1874–1882 (2007).Article 
    ADS 

    Google Scholar 
    Levas, S. J., Grottoli, A. G., Hughes, A., Osburn, C. L. & Matsui, Y. Physiological and biogeochemical traits of bleaching and recovery in the mounding species of coral Porites lobata: Implications for resilience in mounding corals. PLoS ONE 8, e63267 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Schoepf, V. et al. Annual coral bleaching and the long-term recovery capacity of coral. Proc. R. Soc. B. 282, 20151887 (2015).Article 

    Google Scholar 
    Dai, C., Fan, T. & Yu, J. Reproductive isolation and genetic differentiation of a scleractinian coral Mycedium elephantotus. Mar. Ecol. Prog. Ser. 201, 179–187 (2000).Article 
    ADS 

    Google Scholar 
    Vargas-Ángel, B., Colley, S. B., Hoke, S. M. & Thomas, J. D. The reproductive seasonality and gametogenic cycle of Acropora cervicornis off Broward County, Florida, USA. Coral Reefs 25, 110–122 (2006).Article 
    ADS 

    Google Scholar 
    Rosser, N. & Gilmour, J. New insights into patterns of coral spawning on Western Australian reefs. Coral Reefs 27, 345–349 (2008).Article 
    ADS 

    Google Scholar 
    Szmant, A. M. & Gassman, N. J. The effects of prolonged “bleaching” on the tissue biomass and reproduction of the reef coral Montastrea annularis. Coral Reefs 8, 217–224 (1990).Article 
    ADS 

    Google Scholar 
    Baird, A. H. & Marshall, P. A. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 237, 133–141 (2002).Article 
    ADS 

    Google Scholar 
    Levitan, D. R., Boudreau, W., Jara, J. & Knowlton, N. Long-term reduced spawning in Orbicella coral species due to temperature stress. Mar. Ecol. Prog. Ser. 515, 1–10 (2014).Article 
    ADS 

    Google Scholar 
    Ward, S., Harrison, P. & Hoegh-Guldberg, O. Coral bleaching reduces reproduction of scleractinian corals and increases susceptibility to future stress. In Proc. 9th Int. Coral Reef Symp. 1123–1128 (2002).Johnston, E. C., Counsell, C. W., Sale, T. L., Burgess, S. C. & Toonen, R. J. The legacy of stress: Coral bleaching impacts reproduction years later. Funct. Ecol. 34, 2315–2325 (2020).Article 

    Google Scholar 
    Hirose, M. & Hidaka, M. Reduced reproductive success in scleractinian corals that survived the 1998 bleaching in Okinawa. Galaxea 2000, 17–21 (2000).Article 

    Google Scholar 
    Omori, M., Fukami, H., Kobinata, H. & Hatta, M. Significant drop of fertilization of Acropora corals in 1999: An after-effect of heavy coral bleaching?. Limnol. Oceanogr. 46, 704–706 (2001).Article 
    ADS 

    Google Scholar 
    Hagedorn, M. et al. Potential bleaching effects on coral reproduction. Reprod. Fertil. Dev. 28, 1061–1071 (2016).Article 
    CAS 

    Google Scholar 
    Bassim, K., Sammarco, P. & Snell, T. Effects of temperature on success of (self and non-self) fertilization and embryogenesis in Diploria strigosa (Cnidaria, Scleractinia). Mar. Biol. 140, 479–488 (2002).Article 

    Google Scholar 
    Kenkel, C. D. et al. Development of gene expression markers of acute heat-light stress in reef-building corals of the genus Porites. PLoS ONE 6, e26914 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Louis, Y. D., Bhagooli, R., Kenkel, C. D., Baker, A. C. & Dyall, S. D. Gene expression biomarkers of heat stress in scleractinian corals: Promises and limitations. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 191, 63–77 (2017).Article 
    CAS 

    Google Scholar 
    Bonesso, J. L., Leggat, W. & Ainsworth, T. D. Exposure to elevated sea-surface temperatures below the bleaching threshold impairs coral recovery and regeneration following injury. PeerJ 5, e3719 (2017).Article 

    Google Scholar 
    Gierz, S., Ainsworth, T. D. & Leggat, W. Diverse symbiont bleaching responses are evident from 2-degree heating week bleaching conditions as thermal stress intensifies in coral. Mar. Freshw. Res. 71, 1149–1160 (2020).Article 

    Google Scholar 
    Baker, D. M., Freeman, C. J., Wong, J. C., Fogel, M. L. & Knowlton, N. Climate change promotes parasitism in a coral symbiosis. ISME J. 12, 921–930 (2018).Article 
    CAS 

    Google Scholar 
    Yee, S. H. & Barron, M. G. Predicting coral bleaching in response to environmental stressors using 8 years of global-scale data. Environ. Monit. Assess. 161, 423–438 (2010).Article 

    Google Scholar 
    Lesser, M. P. Coral bleaching: causes and mechanisms. In Coral Reefs: An Ecosystem in Transition (eds Riegl, B. M. & Purkis, S. J.) 405–419 (Springer, 2011).Chapter 

    Google Scholar 
    Barber, J. & Andersson, B. Too much of a good thing: Light can be bad for photosynthesis. Trends Biochem. Sci. 17, 61–66 (1992).Article 
    CAS 

    Google Scholar 
    Aro, E.-M., Virgin, I. & Andersson, B. Photoinhibition of photosystem II. Inactivation, protein damage and turnover. Biochim. Biophys. Acta Bioenergy 1143, 113–134 (1993).Article 
    CAS 

    Google Scholar 
    Lesser, M. P. & Farrell, J. H. Exposure to solar radiation increases damage to both host tissues and algal symbionts of corals during thermal stress. Coral Reefs 23, 367–377 (2004).Article 

    Google Scholar 
    Salih, A., Hoegh-Guldberg, O. & Cox, G. Bleaching responses of symbiotic dinoflagellates in corals: the effects of light and elevated temperature on their morphology and physiology. In Proceedings of the Australian Coral Reef Society 75th Anniversary Conference (eds Greenwood, J. G. & Hall, N. R.) 199–216 (1998).Smith, D. J., Suggett, D. J. & Baker, N. R. Is photoinhibition of zooxanthellae photosynthesis the primary cause of thermal bleaching in corals?. Glob. Chang. Biol. 11, 1–11 (2005).Article 
    ADS 

    Google Scholar 
    Downs, C. et al. Heat-stress and light-stress induce different cellular pathologies in the symbiotic dinoflagellate during coral bleaching. PLoS ONE 8, e77173 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Banaszak, A. T. & Lesser, M. P. Effects of solar ultraviolet radiation on coral reef organisms. Photochem. Photobiol. Sci. 8, 1276–1294 (2009).Article 
    CAS 

    Google Scholar 
    Jokiel, P. L. & York, R. H. Jr. Solar ultraviolet photobiology of the reef coral Pocillopora damicornis and symbiotic zooxanthellae. Bull. Mar. Sci. 32, 301–315 (1982).
    Google Scholar 
    Vareschi, E. & Fricke, H. Light responses of a scleractinian coral (Plerogyra sinuosa). Mar. Biol. 90, 395–402 (1986).Article 

    Google Scholar 
    Henley, E. M. et al. Reproductive plasticity of Hawaiian Montipora corals following thermal stress. Sci. Rep. 11, 12525 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Wellington, G. & Fitt, W. Influence of UV radiation on the survival of larvae from broadcast-spawning reef corals. Mar. Biol. 143, 1185–1192 (2003).Article 
    CAS 

    Google Scholar 
    Gleason, D. F. & Wellington, G. M. Ultraviolet radiation and coral bleaching. Nature 365, 836–838 (1993).Article 
    ADS 

    Google Scholar 
    Courtial, L., Roberty, S., Shick, J. M., Houlbrèque, F. & Ferrier-Pagès, C. Interactive effects of ultraviolet radiation and thermal stress on two reef-building corals. Limnol. Oceanogr. 62, 1000–1013 (2017).Article 
    ADS 

    Google Scholar 
    Bahr, K. D., Jokiel, P. L. & Rodgers, K. S. The 2014 coral bleaching and freshwater flood events in Kāneʻohe Bay. Hawaiʻi. PeerJ 3, e1136 (2015).Article 

    Google Scholar 
    Rodgers, K. S., Bahr, K. D., Jokiel, P. L. & Richards Donà, A. Patterns of bleaching and mortality following widespread warming events in 2014 and 2015 at the Hanauma Bay Nature Preserve, Hawai‘i. PeerJ 5, e3355 (2017).Article 

    Google Scholar 
    Ritson-Williams, R. & Gates, R. D. Coral community resilience to successive years of bleaching in Kāne‘ohe Bay, Hawai‘i. Coral Reefs 39, 757–769 (2020).Article 

    Google Scholar 
    Krupp, D. A. Sexual reproduction and early development of the solitary coral Fungia scutaria (Anthozoa: Scleractinia). Coral Reefs 2, 159–164 (1983).Article 
    ADS 

    Google Scholar 
    Kramarsky-Winter, E. & Loya, Y. Reproductive strategies of two fungiid corals from the northern Red Sea: Environmental constraints?. Mar. Ecol. Prog. Ser. 174, 175–182 (1998).Article 
    ADS 

    Google Scholar 
    Loya, Y. & Sakai, K. Bidirectional sex change in mushroom stony corals. Proc. R. Soc. B. 275, 2335–2343 (2008).Article 

    Google Scholar 
    Hagedorn, M. et al. Coral larvae conservation: Physiology and reproduction. Cryobiology 52, 33–47 (2006).Article 
    CAS 

    Google Scholar 
    Jokiel, P. L. & Brown, E. K. Global warming, regional trends and inshore environmental conditions influence coral bleaching in Hawaii. Glob. Chang. Biol. 10, 1627–1641 (2004).Article 
    ADS 

    Google Scholar 
    Tanaka, K., Guidry, M. W. & Gruber, N. Ecosystem responses of the subtropical Kaneohe Bay, Hawaii, to climate change: A nitrogen cycle modeling approach. Aquat. Geochem. 19, 569–590 (2013).Article 
    CAS 

    Google Scholar 
    Couch, C. S. et al. Mass coral bleaching due to unprecedented marine heatwave in Papahānaumokuākea Marine National Monument (Northwestern Hawaiian Islands). PLoS ONE 12, e0185121 (2017).Article 

    Google Scholar 
    Coles, S. L. et al. Evidence of acclimatization or adaptation in Hawaiian corals to higher ocean temperatures. PeerJ 6, e5347 (2018).Article 

    Google Scholar 
    Barnhill, K. A. & Bahr, K. D. Coral resilience at Malaukaa fringing reef, Kāneʻohe Bay, Oʻahu after 18 years. J. Mar. Sci. Eng. 7, 311 (2019).Article 

    Google Scholar 
    Lesser, M., Stochaj, W., Tapley, D. & Shick, J. Bleaching in coral reef anthozoans: Effects of irradiance, ultraviolet radiation, and temperature on the activities of protective enzymes against active oxygen. Coral Reefs 8, 225–232 (1990).Article 
    ADS 

    Google Scholar 
    Brown, B., Dunne, R., Scoffin, T. & Le Tissier, M. Solar damage in intertidal corals. Mar. Ecol. Prog. Ser. 219–230 (1994).Le Tissier, M. D. A. & Brown, B. E. Dynamics of solar bleaching in the intertidal reef coral Goniastrea aspera at Ko Phuket, Thailand. Mar. Ecol. Prog. Ser. 136, 235–244 (1996).Article 
    ADS 

    Google Scholar 
    Lesser, M. P. Elevated temperatures and ultraviolet radiation cause oxidative stress and inhibit photosynthesis in symbiotic dinoflagellates. Limnol. Oceanogr. 41, 271–283 (1996).Article 
    ADS 
    CAS 

    Google Scholar 
    Takahashi, S., Nakamura, T., Sakamizu, M., Woesik, R. V. & Yamasaki, H. Repair machinery of symbiotic photosynthesis as the primary target of heat stress for reef-building corals. Plant Cell Physiol. 45, 251–255 (2004).Article 
    CAS 

    Google Scholar 
    Coelho, V. et al. Shading as a mitigation tool for coral bleaching in three common Indo-Pacific species. J. Exp. Mar. Biol. Ecol. 497, 152–163 (2017).Article 

    Google Scholar 
    Marquis, R. J. Phenological variation in the neotropical understory shrub Piper arielanum: Causes and consequences. Ecology 69, 1552–1565 (1988).Article 

    Google Scholar 
    Bouwmeester, J. et al. Latitudinal variation in monthly-scale reproductive synchrony among Acropora coral assemblages in the Indo-Pacific. Coral Reefs 40, 1411–1418 (2021).Article 

    Google Scholar 
    Hagedorn, M. et al. Preserving and using germplasm and dissociated embryonic cells for conserving Caribbean and Pacific coral. PLoS ONE 7, e33354 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Zuchowicz, N. et al. Assessing coral sperm motility. Sci. Rep. 11, 61 (2021).Article 
    CAS 

    Google Scholar 
    Binet, M., Doyle, C., Williamson, J. & Schlegel, P. Use of JC-1 to assess mitochondrial membrane potential in sea urchin sperm. J. Exp. Mar. Biol. Ecol. 452, 91–100 (2014).Article 
    CAS 

    Google Scholar 
    Jokiel, P., Maragos, J. & Franzisket, L. Coral growth: buoyant weight technique. In Coral Reefs: Research Methods Vol. 5 (eds Stoddart, D. R. & Johannes, R. E.) 529–542 (UNESCO, 1978).
    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org (R Foundation for Statistical Computing, 2019).Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage Publications, 2019).
    Google Scholar 
    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 
    MATH 

    Google Scholar 
    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

    Google Scholar 
    Lenth, R. V. Least-squares means: The R package lsmeans. J. Stat. Softw. 69, 1–33 (2016).Article 

    Google Scholar 
    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. J. Math. Methods Biosci. 50, 346–363 (2008).MathSciNet 
    MATH 

    Google Scholar 
    Graves, S., Piepho, H.-P. & Selzer, M. L. multcompView: Visualizations of paired comparisons. R package version 0.1-7. https://CRAN.R-project.org/package=multcompView (2015).Christensen, R. H. B. ordinal-Regression models for ordinal data. R package version 2019.4-25. https://cran.r-project.org/package=ordinal/. (2019).Mangiafico, S. rcompanion: functions to support extension education program evaluation. R package version 2.3.7. https://cran.r-project.org/package=rcompanion (2019).Hope, R. M. Rmisc: Ryan Miscellaneous. R package version 1.5. https://cran.r-project.org/package=Rmisc (2013).Hervé, M. RVAideMemoire: Testing and plotting procedures for biostatistics, R package version 0.9-73. https://cran.r-project.org/package=RVAideMemoire (2019).Callaghan, J. A short note on the intensification and extreme rainfall associated with Hurricane Lane. Trop. Cyclone Res. Rev. 8, 103–107 (2019).Article 

    Google Scholar 
    Guest, J. R., Baird, A. H., Goh, B. P. L. & Chou, L. M. Seasonal reproduction in equatorial reef corals. Invertebr. Reprod. Dev. 48, 207–218 (2005).Article 

    Google Scholar 
    Lotterhos, K. E. & Levitan, D. Gamete release and spawning behavior in broadcast spawning marine invertebrates. In The Evolution of Primary Sexual Characters (eds Leonard, J. & Córdoba-Aguilar, A.) 99–120 (Oxford Univ. Press, 2010).
    Google Scholar 
    Ims, R. A. The ecology and evolution of reproductive synchrony. Trends Ecol. Evol. 5, 135–140 (1990).Article 
    CAS 

    Google Scholar 
    Shlesinger, T. & Loya, Y. Breakdown in spawning synchrony: A silent threat to coral persistence. Science 365, 1002–1007 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Guest, J. R., Baird, A. H., Bouwmeester, J. & Edwards, A. J. To assess temporal breakdown in spawning synchrony requires comparable temporal data. https://doi.org/10.1126/comment.737627/full/ (2020).Hartmann, D. L. et al. Observations: atmosphere and surface. In Climate change 2013 The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) 159–254 (Cambridge University Press, 2013).Pörtner, H. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC Intergovernmental Panel on Climate Change, 2019).
    Google Scholar 
    Cheng, L., Abraham, J., Hausfather, Z. & Trenberth, K. E. How fast are the oceans warming?. Science 363, 128–129 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Gorbunov, M. Y. & Falkowski, P. G. Photoreceptors in the cnidarian hosts allow symbiotic corals to sense blue moonlight. Limnol. Oceanogr. 47, 309–315 (2002).Article 
    ADS 

    Google Scholar 
    Boch, C. A., Ananthasubramaniam, B., Sweeney, A. M., Doyle Iii, F. J. & Morse, D. E. Effects of light dynamics on coral spawning synchrony. Biol. Bull. 220, 161–173 (2011).Article 

    Google Scholar 
    Sweeney, A. M., Boch, C. A., Johnsen, S. & Morse, D. E. Twilight spectral dynamics and the coral reef invertebrate spawning response. J. Exp. Biol. 214, 770–777 (2011).Article 

    Google Scholar 
    Nozawa, Y. Annual variation in the timing of coral spawning in a high-latitude environment: Influence of temperature. Biol. Bull. 222, 192–202 (2012).Article 

    Google Scholar 
    Babcock, R. C. et al. Synchronous spawnings of 105 scleractinian coral species on the Great Barrier Reef. Mar. Biol. 90, 379–394 (1986).Article 

    Google Scholar 
    Hunter, C. Environmental cues controlling spawning in two Hawaiian corals, Montipora verrucosa and M. dilatata. In Proc 6th Int Coral Reef Symp. vol. 2, 727–732.Levitan, D. R. et al. Mechanisms of reproductive isolation among sympatric broadcast spawning corals of the Montastraea annularis species complex. Evolution 58, 308–323 (2004).
    Google Scholar 
    Negri, A. P., Marshall, P. A. & Heyward, A. J. Differing effects of thermal stress on coral fertilization and early embryogenesis in four Indo Pacific species. Coral Reefs 26, 759–763 (2007).Article 
    ADS 

    Google Scholar 
    Humanes, A., Noonan, S. H., Willis, B. L., Fabricius, K. E. & Negri, A. P. Cumulative effects of nutrient enrichment and elevated temperature compromise the early life history stages of the coral Acropora tenuis. PLoS ONE 11, e0161616 (2016).Article 

    Google Scholar 
    Lesser, M. P., Kruse, V. A. & Barry, T. M. Exposure to ultraviolet radiation causes apoptosis in developing sea urchin embryos. J. Exp. Biol. 206, 4097–4103 (2003).Article 

    Google Scholar 
    Häder, D.-P. et al. Effects of UV radiation on aquatic ecosystems and interactions with other environmental factors. Photochem. Photobiol. Sci. 14, 108–126 (2015).Article 

    Google Scholar 
    Albright, R. & Mason, B. Projected near-future levels of temperature and pCO2 reduce coral fertilization success. PLoS ONE 8, e56468 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Espinoza, J., Schulz, M., Sanchez, R. & Villegas, J. Integrity of mitochondrial membrane potential reflects human sperm quality. Andrologia 41, 51–54 (2009).Article 
    CAS 

    Google Scholar 
    Paoli, D. et al. Mitochondrial membrane potential profile and its correlation with increasing sperm motility. Fertil. Steril. 95, 2315–2319 (2011).Article 
    CAS 

    Google Scholar 
    Gallo, A., Esposito, M. C., Tosti, E. & Boni, R. Sperm motility, oxidative status, and mitochondrial activity: Exploring correlation in different species. Antioxidants 10, 1131 (2021).Article 
    CAS 

    Google Scholar 
    Schlegel, P., Binet, M. T., Havenhand, J. N., Doyle, C. J. & Williamson, J. E. Ocean acidification impacts on sperm mitochondrial membrane potential bring sperm swimming behaviour near its tipping point. J. Exp. Biol. 218, 1084–1090 (2015).Article 

    Google Scholar 
    Gulko, D. Effects of ultraviolet radiation on fertilization and production of planula larvae in the Hawaiian coral Fungia scutaria. In Ultraviolet Radiation and Coral Reefs Vol. 41 (eds Gulko, D. & Jokiel, P. L.) 135–147 (University of Hawai’i, 1995).
    Google Scholar 
    Pruski, A. M., Nahon, S., Escande, M.-L. & Charles, F. Ultraviolet radiation induces structural and chromatin damage in Mediterranean sea-urchin spermatozoa. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 673, 67–73 (2009).Article 
    CAS 

    Google Scholar 
    Dahms, H.-U. & Lee, J.-S. UV radiation in marine ectotherms: Molecular effects and responses. Aquat. Toxicol. 97, 3–14 (2010).Article 
    CAS 

    Google Scholar 
    Nesa, B., Baird, A. H., Harii, S., Yakovleva, I. & Hidaka, M. Algal symbionts increase DNA damage in coral planulae exposed to sunlight. Zool. Stud. 51, 12–17 (2012).CAS 

    Google Scholar 
    Paxton, C. W., Baria, M. V. B., Weis, V. M. & Harii, S. Effect of elevated temperature on fecundity and reproductive timing in the coral Acropora digitifera. Zygote 24, 511 (2015).Article 

    Google Scholar 
    Jokiel, P. & Coles, S. Effects of temperature on the mortality and growth of Hawaiian reef corals. Mar. Biol. 43, 201–208 (1977).Article 

    Google Scholar 
    Cantin, N. E., Cohen, A. L., Karnauskas, K. B., Tarrant, A. M. & McCorkle, D. C. Ocean warming slows coral growth in the Central Red Sea. Science 329, 322–325. https://doi.org/10.1126/science.1190182 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Cooper, T. F., De’Ath, G., Fabricius, K. E. & Lough, J. M. Declining coral calcification in massive Porites in two nearshore regions of the northern Great Barrier Reef. Glob. Chang. Biol. 14, 529–538 (2008).Article 
    ADS 

    Google Scholar 
    Tanzil, J., Brown, B., Tudhope, A. & Dunne, R. Decline in skeletal growth of the coral Porites lutea from the Andaman Sea, South Thailand between 1984 and 2005. Coral Reefs 28, 519–528 (2009).Article 
    ADS 

    Google Scholar 
    Tanzil, J. T. I. et al. Regional decline in growth rates of massive Porites corals in Southeast Asia. Glob. Chang. Biol. 19, 3011–3023 (2013).Article 
    ADS 

    Google Scholar 
    Richmond, R. H., Tisthammer, K. H. & Spies, N. P. The effects of anthropogenic stressors on reproduction and recruitment of corals and reef organisms. Front. Mar. Sci. 5, 226 (2018).Article 

    Google Scholar 
    Chen, P.-Y., Chen, C.-C., Chu, L. & McCarl, B. Evaluating the economic damage of climate change on global coral reefs. Glob. Environ. Change 30, 12–20 (2015).Article 

    Google Scholar 
    Kaniewska, P., Alon, S., Karako-Lampert, S., Hoegh-Guldberg, O. & Levy, O. Signaling cascades and the importance of moonlight in coral broadcast mass spawning. Elife 4, e09991 (2015).Article 

    Google Scholar 
    Lin, C.-H., Takahashi, S., Mulla, A. J. & Nozawa, Y. Moonrise timing is key for synchronized spawning in coral Dipsastraea speciosa. Proc. Natl. Acad. Sci. 118, e2101985118 (2021).Article 
    CAS 

    Google Scholar 
    Anthony, K. R. et al. Interventions to help coral reefs under global change—A complex decision challenge. PLoS ONE 15, e0236399 (2020).Article 
    CAS 

    Google Scholar 
    Daly, J. et al. Cryopreservation can assist gene flow on the Great Barrier Reef. Coral Reefs 41, 455–462 (2022).Article 

    Google Scholar  More

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    Bald eagle mortality and nest failure due to clade 2.3.4.4 highly pathogenic H5N1 influenza a virus

    Sample collection and postmortem evaluationBald eagle carcasses, and/or oropharyngeal and cloacal swabs were collected in the field and submitted to the Southeastern Cooperative Wildlife Disease Study Research and Diagnostic Service. In some cases, live bald eagles were found moribund and transported to wildlife rehabilitation clinics and either died in transit or soon after arrival. Carcasses underwent postmortem evaluation, including gross and histopathology. Tissue samples [heart, brain, kidney, spleen, lung, adrenal gland, pancreas, liver, small and large intestine, and cloacal bursa (if present)] were fixed in 10% neutral buffered formalin and routinely processed for histopathology23 at the Athens Veterinary Diagnostic Laboratory. Histopathology was assessed by a board-certified veterinary pathologist.Additional bald eagle and waterfowl species mortality dataData on wild bird deaths attributed to highly pathogenic influenza A viruses were retrieved from the U.S. Department of Agriculture, Animal and Plant Health Inspection Service website, at: https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/animal-disease-information/avian/avian-influenza/hpai-2022/2022-hpai-wild-birds. These data are publicly available and include state, county, date detected, and species of individual birds that tested positive for HP IAV.ImmunohistochemistryImmunohistochemistry (IHC) for avian influenza virus was performed in select cases on brain, pancreas, spleen, liver, and/or adrenal gland at the Athens Veterinary Diagnostic Laboratory. IHC was performed on an automated stainer (Nemesis 3600, Biocare Medical). Polyclonal antiserum against influenza A virus was used as the primary antibody (ab155877, Abcam), diluted 1:3000, and incubated for 60 min at 37 °C with agent-positive control. Antigen retrieval was with Target Retrieval Solution (S2367, Dako) pH (10x) at 110 °C for 15 min. Enzyme blockage was via 3% H2O2 for 20 min (H324-500, Fisher Scientific); protein blockage was with Universal Blocking Reagent (10x) Power Block diluted at 1:10 for 5 min (HK085-5 K, BioGenex); link was by biotinylated rabbit anti-goat (BA-5000, Vector) at a 1:100 dilution for 10 min with 4 + streptavidin alkaline phosphatase label for 10 min (AP605H, BioCare Medical). Staining was with warp red chromogen kit for 5 min (WR8065, BioCare Medical). Known influenza A-virus positive control tissues were tested alongside each case.Polymerase chain reactionOropharyngeal and cloacal swabs from bald eagle carcasses were pooled for each individual eagle and tested by real-time reverse transcription polymerase chain reaction (rRT-PCR). Briefly, swabs samples were extracted with the KingFisher magnetic particle processer using the MagMAX-96 AI/ND Viral RNA isolation Kit (Ambion/Applied Biosystems, Foster City, CA) following a modified MagMAX-S protocol24. Resultant nucleic acids were screened against primers specific for H5 IAV in rRT-PCR; samples that yielded a cycle threshold value  More

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    Residential green environments are associated with human milk oligosaccharide diversity and composition

    Study populationThe study is based on data from mothers and children participating in a longitudinal Southwest Finland cohort, Steps to Healthy development of Children (the STEPS Study) (described in detail in Lagström et al.31). The STEPS study is an ongoing population-based and multidisciplinary study that investigates children’s physical, psychological and social development, starting from pregnancy and continuing until adolescence. All Finnish- and Swedish-speaking mothers delivering a child between 1 January, 2008 and 31 March, 2010 in the Hospital District of Southwest Finland formed the cohort population (in total, 9811 mothers and their 9936 children). Altogether, 1797 mothers with 1805 neonates volunteered as participants for the intensive follow-up group of the STEPS Study. Mothers were recruited by midwives either during the first trimester of pregnancy while visiting maternity health care clinics, or after delivery on the maternity wards of Turku University Hospital or Salo Regional Hospital, or by a letter mailed to the mothers. The participating mothers differ slightly from the whole cohort population in some background characteristics (being older, with first-born child and higher socioeconomic status)31. The ethics committee of the Hospital District of Southwest Finland has approved the STEPS Study (2/2007) and all methods were performed in accordance with relevant guidelines and regulations. Written informed consent was obtained from all the participants and, for children, from one parent for study participation. Subjects have been and are free to withdraw from the study at any time without any specific reason. The STEPS Study have the appropriate government authorization to the use of the National birth register (THL/974/5.05.00/2017).Breastmilk collection and HMO analysisMothers from the STEPS Study were asked to collect breastmilk samples when the infant was approximately 3 months old. In total, 812 of the 1797 mothers (45%) provided a breastmilk sample. There were only slight differences in maternal and child characteristics between the participants providing breastmilk samples and the total STEPS Study cohort40. Altogether, 795 breastmilk samples were included in this study (excluding the duplicate observations and the 2nd born twins, samples with technical unclarity or insufficient sample quantity, one breastmilk sample collected notably later than the other samples, at infant age of 14.5 months (range for the other breastmilk samples: 0.6–6.07 months), one sample with missing information on the date of collection and six mothers missing data on residential green environment) (Supplementary Fig. 2). Mothers received written instructions for the collection of breastmilk samples: samples were collected by manual expression in the morning from one single breast, first milking a few drops to waste before collecting the actual sample (~ 10 ml) into a plastic container (pre-feed sample). The samples were stored in the fridge and the mothers brought the samples to the research center or the samples were collected from their homes on the day of sampling. All samples were frozen and stored at − 70 °C until analysis.High Performance Liquid Chromatography (HPLC) was used to identify HMOs in breastmilk as previously described40,57,58 at the University of California, San Diego (methods described in detail in Berger et al.58). Milk samples were spiked with raffinose (a non-HMO carbohydrate) as an internal standard to allow absolute quantification. HMOs were extracted by high-throughput solid-phase extraction, fluorescently labelled, and measured using HPLC with fluorescent detection (HPLC-FLD)58. Absolute concentrations for the 19 HMOs were calculated based on standard retention times and corrected for internal standard recovery. Quantified HMOs included: 2′-fucosyllactose (2′FL), 3-fucosyllactose (3FL), lacto-N-neotetraose (LNnT), 3′-sialyllactose (3′SL), difucosyllactose (DFlac), 6′-sialyllactose (6′SL), lacto-N-tetraose (LNT), lacto-Nfucopentaose (LNFP) I, LNFP II, LNFP III, sialyl-LNT (LST) b, LSTc, difucosyllacto-LNT (DFLNT), lacto-N-hexaose (LNH), disialyllacto-N-tetraose (DSLNT), fucosyllacto-Nhexaose (FLNH), difucosyllacto-N-hexaose (DFLNH), fucodisialyllacto-lacto-N-hexaose (FDSLNH) and disialyllacto-N-hexaose (DSLNH). HMOs were also summed up to seven groups based on structural features: small HMOs (2′FL, 3FL, 3′SL, 6′SL, and DFLac), type 1 HMOs (LNT, LNFP I, LNFP II, LSTb, DSLNT), type 2 HMOs (LNnT, LNFP III, LSTc), α-1-2-fucosylated HMOs (2’FL, LNFP I), terminal α-2-6-sialylated HMOs (6′SL, LSTc), internal α-2-6-sialylated HMOs (DSLNT, LSTb), terminal α-2-3-sialylated HMOs (3′SL, DSLNT). The total concentration of HMOs was calculated as the sum of the 19 oligosaccharides. HMO-bound fucose and HMO-bound sialic acid were calculated on a molar basis. The proportion of each HMO comprising the total HMO concentration was also calculated. HMO Simpson’s diversity was calculated as Simpson’s Reciprocal Index 1/D, which is the reciprocal sum of the square of the relative abundance of each of the measured 19 HMOs57,59. The higher the diversity value, the more heterogenous is the HMO composition in the sample.Properties of the residential green environmentThe selected residential green environment variables measure the properties of the green environments surrounding the homes of the participants and do not include any measures of the house characteristics, indoor environment or the actual use of green spaces by the participants. The residential green environment variables were selected due to their previously observed associations with residential microbiota and health33,34,35. The variables of the residential green environments were derived from multispectral satellite images series, with a 30 m × 30 m of spatial resolution (Landsat TM 5, National Aeronautics and Space Administration—NASA) and land cover data (CORINE). We used Landsat TM images obtained over the summertime (June–August, greenest months in Finland), to minimize the seasonal variation of living vegetation and cloud cover as well as to better identify vegetation areas and maximise the contrast in our estimated exposure. In each selected Landsat TM 5 images, the cloud was masked out, and the Normalized Difference Vegetation Index (NDVI)36 was calculated. The final NDVI map was the mean of NDVI images collected over three consecutive years (2008–2010), to make an NDVI map with non-missing values due to cloud cover for the study area. NDVI map measures the vegetation cover, vitality and density. The NDVI can get values ranging from − 1 to 1 where values below zero represent water surfaces, values close to zero indicate areas with low intensity of living vegetation and values close to one indicate high abundance of living vegetation. For the analyses, areas covered by water were removed and the value ranged from 0 to 1, to prevent negative values for underestimating the greenness values of the residential area like in some prior studies60. We assumed that summertime NDVI identified the green space and vegetation density well, but greenness intensity might vary seasonally.Second, we used calculated indicators related to the diversity and naturalness of the land cover from CORINE Land Cover data sets of the year 201261. The 12 land cover types include: (1) Residential area, (2) Industrial/commercial area, (3) Transport network, (4) Sport/leisure, (5) Agriculture, (6) Broad-leave forest, (7) Coniferous forest, (8) Mixed forest, (9) Shrub/grassland, (10) Bare surface, (11) Wetland, and (12) Water bodies. From this information, we calculated two vegetation cover indexes. The Vegetation Cover Diversity Index (Simpson’s Diversity Index of Vegetation Cover, VCDI)37, only includes vegetation classes from CORINE land cover types (categories 5–9 and 11). VCDI approaches 1 as the number of different vegetation classes increases and the proportional distribution of area among the land use classes becomes more equitable. Furthermore, because we were particularly interested in the natural vegetation cover in the residential area, we calculated the area-weighted Naturalness Index (NI)38. This is an integrated indicator used to measure the human impact and degree of all human interventions on ecological components. The index is based on CORINE Land Cover data but reclassified to 15 classes. Residential areas have been divided to two classes: Continuous residential area (High density buildings) and Discontinuous residential area (Low density, mostly individual houses area). Agricultural area has also been divided to two classes: Agricultural area (Cropland) and Pasture as well as class 9 (Shrub/grassland) has been separated to Woodland and Natural grassland. Assignment of CORINE Land Cover classes to degrees of naturalness has been made based on Walz and Stein 201438. The area-weighted NI range from 1 to 7, where low values represent low human impact (≤ 3 = Natural), medium values moderate human impact (4–5 = Semi-natural) and high values strong human impact (6–7 = Non-Natural). To ease the interpretation of results and to correspond to the same direction than the other residential green environment variables, we have reverse-scaled the NI values, so that higher values illustrate more natural residential areas.Background factorsAs genetics is strongly linked to HMO composition, maternal secretor status was determined by high abundance (secretor) or near absence (non-secretor) of the HMO 2’FL in the breastmilk samples. Mothers with active secretor (Se) genes and FUT2 enzyme produce high amounts of α-1-2-fucosylated HMOs such as 2′-fucosyllactose (2′FL), whereas in the breastmilk of non-secretor mothers these HMOs are almost absent. Beyond genetics, other maternal and infant characteristics may influence HMO composition. So far, several associations have been reported, including lactation stage, maternal pre-pregnancy BMI, maternal age, parity, maternal diet, mode of delivery, infant gestational age and infant sex22,40. Information on the potential confounding factors, child sex, birth weight, maternal age at birth, number of previous births, marital status, maternal occupational status, smoking during pregnancy (before and during pregnancy), maternal pre-pregnancy BMI, mode of delivery, duration of pregnancy and maternal diseases [including both maternal disorders predominantly related to pregnancy such as pre-eclampsia and gestational diabetes and chronic diseases (diseases of the nervous, circulatory, respiratory, digestive, musculoskeletal and genitourinary systems, cancer and mental and behavioral disorders, according to ICD-10 codes, i.e. WHO International Classification of Diseases Tenth Revision)], were obtained from Medical Birth Registers. Self-administered questionnaires upon recruitment provided information on family net income and maternal education level. Those who had no professional training or a maximum of an intermediate level of vocational training (secondary education) were classified as “basic”. Those who had studied at a University of Applied Sciences or higher (tertiary education) were classified as “advanced”. The advanced level included any academic degree (bachelor’s, master’s, licentiate or doctoral degree). Maternal diet quality was assessed in late pregnancy using the Index of Diet Quality (IDQ62) which measures adherence to health promoting diet and nutrition recommendations. The IDQ score was used in its original form by setting the statistically defined cut-off value at 10, with scores below 10 points indicating unhealthy diets and non-adherence and scores of 10–15 points indicating a health-promoting diet and adherence dietary guidelines. Lactation time postpartum (child age) and season were received from the recorded breastmilk collection dates. Lactation status (exclusive/partial/unknown breastfeeding) at the time of breastmilk collection were gathered from follow-up diaries. From partially breastfeeding mothers (n = 277) 253 had started formula feeding and 28 solids at the time of milk collection. Last, a summary z score representing socio-economic disadvantage in the residential area was obtained from Statistics Finland grid database for the year 2009 and is based on the proportion of adults with low level of education, the unemployment rate, and proportion of people living in rented housing at each participant’s residential area55.Statistical analysesTo harmonize the residential green environment variables we calculated the mean values for NDVI, VCDI and NI in 750 × 750 m squares (and 250 × 250 m) around participant homes in a Geographical Information System (QGIS, www.qgis.org). The same grid sizes were used to calculate residential socioeconomic disadvantage in the residential area55 at the time of child birth. The geographical coordinates (latitude/longitude) of the cohort participants’ home address (795 mothers) were obtained from the Population Register Centre at the time of their child birth.The background characteristics of the mothers and children are given as means and standard deviations (SD) for continuous variables and percentages for categorical variables. Due to non-normal distribution, natural logarithmic transformation was performed for all HMO variables (19 individual components, sum of HMOs, HMO-bound sialic acid, HMO-bound fucose and HMO groups (all in nmol/mL)) except for HMO diversity. Associations between each background factor and HMO diversity and 19 individual HMO components were analysed with univariate generalized linear models to identify factors independently associated with HMO composition. All factors demonstrating a significant association (p  More

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    Plant traits and marsh fate

    Coleman, D. J. et al. Limnol. Oceanogr. Lett. 7, 140–149 (2022).Article 

    Google Scholar 
    Noyce, G. L. et al. https://doi.org/10.1038/s41561-022-01070-6 (2022).Kirwan, M. L. & Megonigal, J. P. Nature 504, 53–60 (2013).Article 

    Google Scholar 
    Morris, J. T., Sundareshwar, P. V., Nietch, C. T., Kjerve, B. & Cahoon, D. R. Ecology 83, 2869–2877 (2002).Article 

    Google Scholar 
    Noyce, G. L., Kirwan, M. L., Rich, R. L. & Megonigal, J. P. Proc. Natl Acad. Sci. 116, 21623–21628 (2019).Article 

    Google Scholar 
    Langley, J. A., McKee, K. L., Cahoon, D. R., Cherry, J. A. & Megonigal, J. P. Proc. Natl Acad. Sci. 106, 182–6186 (2009).Article 

    Google Scholar 
    Dean, J. F. et al. Rev. Geophys. 56, 207–250 (2018).Article 

    Google Scholar 
    IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge University Press, 2021).Lin, Y. et al. Water Res. 205, 117682 (2021).Article 

    Google Scholar 
    Zakharova, L., Meyer, K. M. & Seifan, M. Ecol. Modell. 407, 108703 (2019).Article 

    Google Scholar  More

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    Measuring the world’s cropland area

    Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).Article 

    Google Scholar 
    Land Use Statistics and Indicators. Global, Regional and Country Trends 2000–2020 FAOSTAT Analytical Brief Series No 48 https://www.fao.org/food-agriculture-statistics/data-release/data-release-detail/en/c/1599856/ (FAO, 2022).FAO. Land Statistics. Global, Regional and Country Trends, 1990–2018 FAOSTAT Analytical Brief Series No. 15 https://www.fao.org/3/cb2860en/cb2860en.pdf (FAO, 2021).Summary for policymakers in: Special Report on Climate Change and Land (eds Shukla, P. R. et al.) https://www.ipcc.ch/site/assets/uploads/sites/4/2020/02/SPM_Updated-Jan20.pdf (WMO, in the press).Sustainable Development Goals Indicator 2.4.1 (FAO, accessed); https://www.fao.org/sustainable-development-goals/indicators/241/en/Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IGES, 2006).Grassi, G. et al. Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting. Earth Syst. Sci. Data 14, 4643–4666 (2022).Article 
    ADS 

    Google Scholar 
    Tubiello, F. N. et al. Measuring Progress Towards Sustainable Agriculture FAO Statistical Working Papers Series No. 21–24 https://www.fao.org/3/cb4549en/cb4549en.pdf (FAO, 2021).Conchedda, G. & Tubiello, F. N. Drainage of organic soils and GHG emissions: validation with country data. Earth Syst. Sci. Data 12, 3113–3137 (2020).Article 
    ADS 

    Google Scholar 
    Hanson, C., Mazur, E., Stolle, F., Davis, C. & Searchinger, T. 5 takeaways on cropland expansion and what it means for people and the planet. WRI Insights https://www.wri.org/insights/cropland-expansion-impacts-people-planet (2022).Potapov, P. et al. The Global 2000–-2020 land cover and land use change dataset derived from the Landsat archive: first results. Front. Remote Sens. 3, 856903 (2022).Article 

    Google Scholar 
    Hansen, M. C. et al. Global land use extent and dispersion within natural land cover using Landsat data. Environ. Res. Lett. 17, 034050 (2022).Article 
    ADS 

    Google Scholar 
    Tubiello, F. N. et al. Carbon emissions and removals from forests: new estimates, 1990–2020. Earth Syst. Sci. Data. 13, 1681–1691 (2021).Article 
    ADS 

    Google Scholar  More

  • in

    Carbohydrate complexity limits microbial growth and reduces the sensitivity of human gut communities to perturbations

    Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schmidt, T. S. B., Raes, J. & Bork, P. The human gut microbiome: from association to modulation. Cell 172, 1198–1215 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tap, J. et al. Gut microbiota richness promotes its stability upon increased dietary fibre intake in healthy adults. Environ. Microbiol. 17, 4954–4964 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    De Filippo, C. et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl Acad. Sci. USA 107, 14691–14696 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morrison, K. E., Jašarević, E., Howard, C. D. & Bale, T. L. It’s the fiber, not the fat: significant effects of dietary challenge on the gut microbiome. Microbiome 8, 15 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maslowski, K. M. & Mackay, C. R. Diet, gut microbiota and immune responses. Nat. Immunol. 12, 5–9 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Reynolds, A. et al. Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. Lancet 393, 434–445 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Slavin, J. Fiber and prebiotics: mechanisms and health benefits. Nutrients 5, 1417–1435 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Desai, M. S. et al. A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167, 1339–1353.e21 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Makki, K., Deehan, E. C., Walter, J. & Bäckhed, F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705–715 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Cantu-Jungles, T. M. et al. Dietary fiber hierarchical specificity: the missing link for predictable and strong shifts in gut bacterial communities. mBio 12, e01028-21 (2022).
    Google Scholar 
    Murga-Garrido, S. M. et al. Gut microbiome variation modulates the effects of dietary fiber on host metabolism. Microbiome 9, 117 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cantu-Jungles, T. M. & Hamaker, B. R. New view on dietary fiber selection for predictable shifts in gut microbiota. mBio 11, e02179-19 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Singh, V. et al. Dysregulated microbial fermentation of soluble fiber induces cholestatic liver cancer. Cell 175, 679–694.e22 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Terrapon, N., Lombard, V., Gilbert, H. J. & Henrissat, B. Automatic prediction of polysaccharide utilization loci in Bacteroidetes species. Bioinformatics 31, 647–655 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Terrapon, N. et al. PULDB: the expanded database of Polysaccharide Utilization Loci. Nucleic Acids Res. 46, D677–D683 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kouzuma, A., Kato, S. & Watanabe, K. Microbial interspecies interactions: recent findings in syntrophic consortia. Front. Microbiol. 6, 477 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rakoff-Nahoum, S., Coyne, M. J. & Comstock, L. E. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr. Biol. 24, 40–49 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Luis, A. S. et al. Dietary pectic glycans are degraded by coordinated enzyme pathways in human colonic Bacteroides. Nat. Microbiol. 3, 210–219 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Cartmell, A. et al. A surface endogalactanase in Bacteroides thetaiotaomicron confers keystone status for arabinogalactan degradation. Nat. Microbiol. 3, 1314–1326 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rakoff-Nahoum, S., Foster, K. R. & Comstock, L. E. The evolution of cooperation within the gut microbiota. Nature 533, 255–259 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pichler, M. J. et al. Butyrate producing colonic Clostridiales metabolise human milk oligosaccharides and cross feed on mucin via conserved pathways. Nat. Commun. 11, 3285 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rogowski, A. et al. Glycan complexity dictates microbial resource allocation in the large intestine. Nat. Commun. 6, 7481 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Feng, J. et al. Polysaccharide utilization loci in Bacteroides determine population fitness and community-level interactions. Cell Host Microbe https://doi.org/10.1016/j.chom.2021.12.006 (2022).Pollak, S. et al. Public good exploitation in natural bacterioplankton communities. Sci. Adv. 7, eabi4717 (2022).Article 

    Google Scholar 
    Cuskin, F. et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature 517, 165–169 (2015).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Patnode, M. L. et al. Interspecies competition impacts targeted manipulation of human gut bacteria by fiber-derived glycans. Cell 179, 59–73.e13 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Walter, J., Maldonado-Gómez, M. X. & Martínez, I. To engraft or not to engraft: an ecological framework for gut microbiome modulation with live microbes. Curr. Opin. Biotechnol. 49, 129–139 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Jernberg, C., Löfmark, S., Edlund, C. & Jansson, J. K. Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. ISME J. 1, 56–66 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dethlefsen, L., Huse, S., Sogin, M. L. & Relman, D. A. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol. 6, e280 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Becattini, S., Taur, Y. & Pamer, E. G. Antibiotic-induced changes in the intestinal microbiota and disease. Trends Mol. Med. 22, 458–478 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shade, A. et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 3, 417 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Stone, L. The stability of mutualism. Nat. Commun. 11, 2648 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).Article 
    PubMed 

    Google Scholar 
    Butler, S. & O’Dwyer, J. P. Stability criteria for complex microbial communities. Nat. Commun. 9, 2970 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, W. & Stevens, M. H. H. Fluctuating resource availability increases invasibility in microbial microcosms. Oikos 121, 435–441 (2012).Article 

    Google Scholar 
    Nobuhiko, K. et al. Regulated virulence controls the ability of a pathogen to compete with the gut microbiota. Science 336, 1325–1329 (2012).Article 

    Google Scholar 
    Maltby, R., Leatham-Jensen, M. P., Gibson, T., Cohen, P. S. & Conway, T. Nutritional basis for colonization resistance by human commensal Escherichia coli strains HS and Nissle 1917 against E. coli O157:H7 in the mouse intestine. PLoS ONE 8, e53957 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leatham, M. P. et al. Precolonized human commensal Escherichia coli strains serve as a barrier to E. coli O157:H7 growth in the streptomycin-treated mouse intestine. Infect. Immun. 77, 2876–2886 (2009).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clark, R. L. et al. Design of synthetic human gut microbiome assembly and butyrate production. Nat. Commun. 12, 3254 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hromada, S. et al. Negative interactions determine Clostridioides difficile growth in synthetic human gut communities. Mol. Syst. Biol. 17, e10355 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    MacArthur, R. Species packing and competitive equilibrium for many species. Theor. Popul. Biol. 1, 1–11 (1970).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ndeh, D. et al. Complex pectin metabolism by gut bacteria reveals novel catalytic functions. Nature 544, 65–70 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grondin, J. M., Tamura, K., Déjean, G., Abbott, D. W. & Brumer, H. Polysaccharide utilization loci: fueling microbial communities. J. Bacteriol. 199, e00860-16 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Devendran, S. et al. Clostridium scindens ATCC 35704: integration of nutritional requirements, the complete genome sequence, and global transcriptional responses to bile acids. Appl. Environ. Microbiol. 85, e00052-19 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rey, F. E. et al. Metabolic niche of a prominent sulfate-reducing human gut bacterium. Proc. Natl Acad. Sci. USA 110, 13582–13587 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kaoutari, A. E., Armougom, F., Gordon, J. I., Raoult, D. & Henrissat, B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nat. Rev. Microbiol. 11, 497–504 (2013).Article 
    PubMed 

    Google Scholar 
    Pereira, F. C. & Berry, D. Microbial nutrient niches in the gut. Environ. Microbiol. 19, 1366–1378 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Despres, J. et al. Xylan degradation by the human gut Bacteroides xylanisolvens XB1A(T) involves two distinct gene clusters that are linked at the transcriptional level. BMC Genomics 17, 326 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Déjean, G. et al. Synergy between cell surface glycosidases and glycan-binding proteins dictates the utilization of specific beta(1,3)-glucans by human gut bacteroides. mBio 11, e00095-20 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hamaker, B. R. & Tuncil, Y. E. A perspective on the complexity of dietary fiber structures and their potential effect on the gut microbiota. J. Mol. Biol. 426, 3838–3850 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bishop, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer, 2006).Wasserman, L. All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) (Springer, 2003).Willing, B. P., Russell, S. L. & Finlay, B. B. Shifting the balance: antibiotic effects on host–microbiota mutualism. Nat. Rev. Microbiol. 9, 233–243 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Panda, S. et al. Short-term effect of antibiotics on human gut microbiota. PLoS ONE 9, e95476 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ng, K. M. et al. Recovery of the gut microbiota after antibiotics depends on host diet, community context, and environmental reservoirs. Cell Host Microbe 26, 650–665.e4 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Van der Waaij, D., Berghuis-de Vries, J. M. & Lekkerkerk-van der Wees, J. E. C. Colonization resistance of the digestive tract in conventional and antibiotic-treated mice. J. Hygiene 69, 405–411 (1971).Article 

    Google Scholar 
    Freter, R. In vivo and in vitro antagonism of intestinal bacteria against Shigella flexneri. II. The inhibitory mechanism. J. Infect. Dis. 110, 38–46 (1962).Article 
    CAS 
    PubMed 

    Google Scholar 
    Maldonado-Gómez, M. X. et al. Stable engraftment of Bifidobacterium longum AH1206 in the human gut depends on individualized features of the resident microbiome. Cell Host Microbe 20, 515–526 (2016).Article 
    PubMed 

    Google Scholar 
    Sorbara, M. T. & Pamer, E. G. Interbacterial mechanisms of colonization resistance and the strategies pathogens use to overcome them. Mucosal Immunol. 12, 1–9 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Litvak, Y. & Bäumler, A. J. The founder hypothesis: a basis for microbiota resistance, diversity in taxa carriage, and colonization resistance against pathogens. PLoS Pathog. 15, e1007563 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jenior, M. L., Leslie, J. L., Young, V. B. & Schloss, P. D. Clostridium difficile colonizes alternative nutrient niches during infection across distinct murine gut microbiomes. mSystems 2, e00063-17 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Momose, Y., Hirayama, K. & Itoh, K. Competition for proline between indigenous Escherichia coli and E. coli O157:H7 in gnotobiotic mice associated with infant intestinal microbiota and its contribution to the colonization resistance against E. coli O157:H7. Antonie van Leeuwenhoek 94, 165–171 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fabich, A. J. et al. Comparison of carbon nutrition for pathogenic and commensal Escherichia coli strains in the mouse intestine. Infect. Immun. 76, 1143–1152 (2008).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shepherd, E. S., DeLoache, W. C., Pruss, K. M., Whitaker, W. R. & Sonnenburg, J. L. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature 557, 434–438 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jenior, M. L., Leslie, J. L., Young, V. B. & Schloss, P. D. Clostridium difficilealters the structure and metabolism of distinct cecal microbiomes during initial infection to promote sustained colonization. mSphere 3, e00261-18 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, S., Tan, J., Yang, X., Ma, C. & Jiang, L. Niche and fitness differences determine invasion success and impact in laboratory bacterial communities. ISME J. 13, 402–412 (2019).Article 
    PubMed 

    Google Scholar 
    Deng, Y.-J. & Wang, S. Y. Synergistic growth in bacteria depends on substrate complexity. J. Microbiol. 54, 23–30 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Deng, Y.-J. & Wang, S. Y. Complex carbohydrates reduce the frequency of antagonistic interactions among bacteria degrading cellulose and xylan. FEMS Microbiol. Lett. 364, fnx019 (2017).Article 
    PubMed Central 

    Google Scholar 
    Wu, F. et al. Modulation of microbial community dynamics by spatial partitioning. Nat. Chem. Biol. 18, 394–402 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Åström, K. J. & Murray, R. Feedback Systems. An Introduction for Scientists and Engineers (Princeton Univ. Press, 2008).Hammarlund, S. P. & Harcombe, W. R. Refining the stress gradient hypothesis in a microbial community. Proc. Natl Acad. Sci. USA 116, 15760–15762 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat. Commun. 12, 2365 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dal Bello, M., Lee, H., Goyal, A. & Gore, J. Resource–diversity relationships in bacterial communities reflect the network structure of microbial metabolism. Nat. Ecol. Evol. 5, 1424–1434 (2021).Article 
    PubMed 

    Google Scholar 
    Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017).Article 
    PubMed 

    Google Scholar 
    Baranwal, M. et al. Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics. eLife 11, e73870 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108, 4554–4561 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ramirez, J. et al. Antibiotics as major disruptors of gut microbiota. Front. Cell. Infect. Microbiol. 10, 572912 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Raue, A. et al. Lessons learned from quantitative dynamical modeling in systems biology. PLoS ONE 8, e74335 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Babtie, A. C., Kirk, P. & Stumpf, M. P. H. Topological sensitivity analysis for systems biology. Proc. Natl Acad. Sci. USA 111, 18507–18512 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Munsky, B., Hlavacek, W. S. & Tsimring, L. S. Quantitative Biology. Theory, Computational Methods, and Models (MIT Press, 2018).Ashyraliyev, M., Fomekong-Nanfack, Y., Kaandorp, J. A. & Blom, J. G. Systems biology: parameter estimation for biochemical models. FEBS J. 276, 886–902 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ravcheev, D. A., Godzik, A., Osterman, A. L. & Rodionov, D. A. Polysaccharides utilization in human gut bacterium Bacteroides thetaiotaomicron: comparative genomics reconstruction of metabolic and regulatory networks. BMC Genomics 14, 873 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salyers, A. A., Vercelloitti, J. R., West, S. E. & Wilkins, T. D. Fermentation of mucin and plant polysaccharides by strains of Bacteroides from the human colon. Appl. Environ. Microbiol. 33, 319–322 (1977).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sun, X., Liu, Y., Jiang, P., Song, S. & Ai, C. Interaction of sulfated polysaccharides with intestinal Bacteroidales plays an important role in its biological activities. Int. J. Biol. Macromol. 168, 496–506 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Respondek, F. et al. Short-chain fructo-oligosaccharides modulate intestinal microbiota and metabolic parameters of humanized gnotobiotic diet induced obesity mice. PLoS ONE 8, e71026 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schwiertz, A. et al. Anaerostipes caccae gen. nov., sp. nov., a new saccharolytic, acetate-utilising, butyrate-producing bacterium from human faeces. Syst. Appl. Microbiol. 25, 46–51 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    Benítez-Páez, A., Moreno, F. J., Sanz, M. L. & Sanz, Y. Genome structure of the symbiont Bifidobacterium pseudocatenulatum CECT 7765 and gene expression profiling in response to lactulose-derived oligosaccharides. Front. Microbiol. 7, 624 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bernalier, A., Willems, A., Leclerc, M., Rochet, V. & Collins, M. D. Ruminococcus hydrogenotrophicus sp. nov., a new H2/CO2-utilizing acetogenic bacterium isolated from human feces. Arch. Microbiol. 166, 176–183 (1996).Article 
    CAS 
    PubMed 

    Google Scholar 
    Moshfegh, A. J., Friday, J. E., Goldman, J. P. & Ahuja, J. K. C. Presence of inulin and oligofructose in the diets of Americans. J. Nutr. 129, 1407S–1411S (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sonnenburg, E. D. et al. Specificity of polysaccharide use in intestinal bacteroides species determines diet-induced microbiota alterations. Cell 141, 1241–1252 (2010).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Devillé, C., Damas, J., Forget, P., Dandrifosse, G. & Peulen, O. Laminarin in the dietary fibre concept. J. Sci. Food Agric. 84, 1030–1038 (2004).Article 

    Google Scholar 
    Selvendran, R. R. The plant cell wall as a source of dietary fiber: chemistry and structure. Am. J. Clin. Nutr. 39, 320–337 (1984).Article 
    CAS 
    PubMed 

    Google Scholar  More