Philippa Kaur
More stories
150 Shares129 Views
in EcologyControlling biodiversity impacts of future global hydropower reservoirs by strategic site selection
1.
Bogdanov, D. et al. Radical transformation pathway towards sustainable electricity via evolutionary steps. Nat. Commun. 10, 1077. https://doi.org/10.1038/s41467-019-08855-1 (2019).
ADS CAS Article PubMed PubMed Central Google Scholar
2.
UNEP. Green Energy Choices: The benefits, risks and trade-offs of low-carbon technologies for electricity production. Report of the International Resource Panel (2016).3.
United Nations. Transforming our world: The 2030 agenda for sustainable development—A/RES/70/1. (2015).4.
Intergovernmental Panel on Climate Change. Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. (2018).5.
Gernaat, D. E. H. J., Bogaart, P. W., Vuuren, D. P. V., Biemans, H. & Niessink, R. High-resolution assessment of global technical and economic hydropower potential. Nature Energy 2, 821–828. https://doi.org/10.1038/s41560-017-0006-y (2017).
ADS Article Google Scholar6.
IEA. Hydropower. (Paris, 2020).7.
Intergovernmental Panel on Climate Change. Hydropower. In IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation (2011).8.
Almeida, R. M. et al. Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning. Nat. Commun. 10, 4281. https://doi.org/10.1038/s41467-019-12179-5 (2019).
ADS CAS Article PubMed PubMed Central Google Scholar9.
Fuso Nerini, F. et al. Mapping synergies and trade-offs between energy and the sustainable development goals. Nat. Energy 3, 10–15. https://doi.org/10.1038/s41560-017-0036-5 (2018).
ADS Article Google Scholar10.
Muller, M. Hydropower dams can help mitigate the global warming impact of wetlands. Nature 566, 315–317. https://doi.org/10.1038/d41586-019-00616-w (2019).
CAS Article PubMed Google Scholar11.
Pehl, M. et al. Understanding future emissions from low-carbon power systems by integration of life-cycle assessment and integrated energy modelling. Nat. Energy 2, 939–945. https://doi.org/10.1038/s41560-017-0032-9 (2017).
ADS CAS Article Google Scholar12.
Wu, H. et al. Effects of dam construction on biodiversity: a review. J. Clean. Prod. 221, 480–489. https://doi.org/10.1016/j.jclepro.2019.03.001 (2019).
Article Google Scholar13.
Turgeon, K., Turpin, C., Gregory-Eaves, I. & Lawler, J. Dams have varying impacts on fish communities across latitudes: a quantitative synthesis. Ecol. Lett. 22, 1501–1516. https://doi.org/10.1111/ele.13283 (2019).
Article PubMed Google Scholar14.
Gracey, E. O. & Verones, F. Impacts from hydropower production on biodiversity in an LCA framework—review and recommendations. Int. J. Life Cycle Assess. 21, 412–428. https://doi.org/10.1007/s11367-016-1039-3 (2016).
Article Google Scholar15.
Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9, 494–502. https://doi.org/10.1890/100125 (2011).
Article Google Scholar16.
Dorber, M., May, R. & Verones, F. Modeling net land occupation of hydropower reservoirs in Norway for use in life cycle assessment. Environ. Sci. Technol. 52, 2375–2384. https://doi.org/10.1021/acs.est.7b05125 (2018).
ADS CAS Article PubMed Google Scholar17.
Strachan, I. B. et al. Does the creation of a boreal hydroelectric reservoir result in a net change in evaporation?. J. Hydrol. 540, 886–899. https://doi.org/10.1016/j.jhydrol.2016.06.067 (2016).
ADS Article Google Scholar18.
Mekonnen, M. M. & Hoekstra, A. Y. The blue water footprint of electricity from hydropower. Hydrol. Earth Syst. Sci. 16, 179–187. https://doi.org/10.5194/hess-16-179-2012 (2012).
ADS Article Google Scholar19.
Poff, N. L. & Zimmerman, J. K. H. Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshw. Biol. 55, 194–205. https://doi.org/10.1111/j.1365-2427.2009.02272.x (2010).
Article Google Scholar20.
Gillespie, B. R., Desmet, S., Kay, P., Tillotson, M. R. & Brown, L. E. A critical analysis of regulated river ecosystem responses to managed environmental flows from reservoirs. Freshw. Biol. 60, 410–425. https://doi.org/10.1111/fwb.12506 (2015).
Article Google Scholar21.
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573. https://doi.org/10.1126/science.aaa4984 (2015).
ADS CAS Article PubMed Google Scholar22.
Hermoso, V., Clavero, M. & Green, A. J. Don’t let damage to wetlands cancel out the benefits of hydropower. Nature 568, 171–171. https://doi.org/10.1038/d41586-019-01140-7 (2019).
CAS Article PubMed Google Scholar23.
McAllister, D. E., Craig, J. F., Davidson, N., Delany, S. & Seddon, M. Biodiversity impacts of large dams. Background Paper Nr. 1 – Prepared for IUCN/UNEP/WCD (2001).24.
Crook, D. A. et al. Human effects on ecological connectivity in aquatic ecosystems: Integrating scientific approaches to support management and mitigation. Sci. Total Environ. 534, 52–64. https://doi.org/10.1016/j.scitotenv.2015.04.034 (2015).
ADS CAS Article PubMed Google Scholar25.
Alho, C. J. Environmental effects of hydropower reservoirs on wild mammals and freshwater turtles in Amazonia: a review. Oecologia Australis 15, 593–604 (2011).
Article Google Scholar26.
Kitzes, J. & Shirley, R. Estimating biodiversity impacts without field surveys: a case study in northern Borneo. Ambio 45, 110–119. https://doi.org/10.1007/s13280-015-0683-3 (2016).
CAS Article PubMed Google Scholar27.
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26. https://doi.org/10.1016/j.tree.2011.08.006 (2012).
Article PubMed Google Scholar28.
Secretariat of the Convention on Biological Diversity. Global Biodiversity Outlook 4. (Montreal, 2014).29.
Bennett, E. M. et al. Linking biodiversity, ecosystem services, and human well-being: three challenges for designing research for sustainability. Curr. Opin. Environ. Sustain. 14, 76–85. https://doi.org/10.1016/j.cosust.2015.03.007 (2015).
Article Google Scholar30.
Opoku, A. Biodiversity and the built environment: Implications for the sustainable development goals (SDGs). Resour. Conserv. Recycl. 141, 1–7. https://doi.org/10.1016/j.resconrec.2018.10.011 (2019).
Article Google Scholar31.
Blicharska, M. et al. Biodiversity’s contributions to sustainable development. Nat. Sustain. 2, 1083–1093. https://doi.org/10.1038/s41893-019-0417-9 (2019).
Article Google Scholar32.
Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129. https://doi.org/10.1126/science.aac7082 (2016).
ADS CAS Article PubMed Google Scholar33.
Nilsson, M., Griggs, D. & Visbeck, M. Policy: map the interactions between sustainable development goals. Nature 534, 320–322. https://doi.org/10.1038/534320a (2016).
ADS Article PubMed Google Scholar34.
Bhaduri, A. et al. Achieving sustainable development goals from a water perspective. Front. Environ. Sci. 4, 64. https://doi.org/10.3389/fenvs.2016.00064 (2016).
Article Google Scholar35.
Liu, J. et al. Nexus approaches to global sustainable development. Nat. Sustain. 1, 466–476. https://doi.org/10.1038/s41893-018-0135-8 (2018).
Article Google Scholar36.
Shin, S. et al. High resolution modeling of river-floodplain-reservoir inundation dynamics in the Mekong River Basin. Water Resour. Res. 56, e2019WR026449. https://doi.org/10.1029/2019wr026449 (2020).
ADS Article Google Scholar37.
Schmitt, R. J. P., Bizzi, S., Castelletti, A. & Kondolf, G. M. Improved trade-offs of hydropower and sand connectivity by strategic dam planning in the Mekong. Nat. Sustain. 1, 96–104. https://doi.org/10.1038/s41893-018-0022-3 (2018).
Article Google Scholar38.
Pokhrel, Y., Shin, S., Lin, Z., Yamazaki, D. & Qi, J. Potential disruption of flood dynamics in the Lower Mekong River Basin due to upstream flow regulation. Sci. Rep. 8, 17767. https://doi.org/10.1038/s41598-018-35823-4 (2018).
ADS CAS Article PubMed PubMed Central Google Scholar39.
Ashraf, F. B. et al. Changes in short term river flow regulation and hydropeaking in Nordic rivers. Sci. Rep. 8, 17232. https://doi.org/10.1038/s41598-018-35406-3 (2018).
ADS CAS Article PubMed PubMed Central Google Scholar40.
Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl. Acad. Sci. 117, 3648. https://doi.org/10.1073/pnas.1912776117 (2020).
ADS CAS Article PubMed Google Scholar41.
Scherer, L. & Pfister, S. Hydropower’s biogenic carbon footprint. PLoS ONE 11, e0161947. https://doi.org/10.1371/journal.pone.0161947 (2016).
CAS Article PubMed PubMed Central Google Scholar42.
Scherer, L. & Pfister, S. Global water footprint assessment of hydropower. Renew. Energy 99, 711–720. https://doi.org/10.1016/j.renene.2016.07.021 (2016).
Article Google Scholar43.
Evans, A., Strezov, V. & Evans, T. J. Assessment of sustainability indicators for renewable energy technologies. Renew. Sustain. Energy Rev. 13, 1082–1088. https://doi.org/10.1016/j.rser.2008.03.008 (2009).
Article Google Scholar44.
Laborde, A., Habit, E., Link, O. & Kemp, P. Strategic methodology to set priorities for sustainable hydropower development in a biodiversity hotspot. Sci. Total Environ. 714, 136735. https://doi.org/10.1016/j.scitotenv.2020.136735 (2020).
ADS CAS Article PubMed Google Scholar45.
Haga, C. et al. Scenario analysis of renewable energy-biodiversity nexuses using a forest landscape model. Front. Ecol. Evol. 8, 155. https://doi.org/10.3389/fevo.2020.00155 (2020).
ADS Article Google Scholar46.
Zarfl, C. et al. Future large hydropower dams impact global freshwater megafauna. Sci. Rep. 9, 18531. https://doi.org/10.1038/s41598-019-54980-8 (2019).
ADS CAS Article PubMed PubMed Central Google Scholar47.
Gibon, T., Hertwich, E. G., Arvesen, A., Singh, B. & Verones, F. Health benefits, ecological threats of low-carbon electricity. Environ. Res. Lett. 12, 034023. https://doi.org/10.1088/1748-9326/aa6047 (2017).
ADS CAS Article Google Scholar48.
Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800. https://doi.org/10.1016/j.rse.2011.02.019 (2011).
ADS Article Google Scholar49.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).
Article Google Scholar50.
Dorber, M., Mattson, K. R., Sandlund, O. T., May, R. & Verones, F. Quantifying net water consumption of Norwegian hydropower reservoirs and related aquatic biodiversity impacts in life cycle assessment. Environ. Impact Assess. Rev. 76, 36–46. https://doi.org/10.1016/j.eiar.2018.12.002 (2019).
Article Google Scholar51.
Verones, F. et al. LCIA framework and cross-cutting issues guidance within the UNEP-SETAC Life Cycle Initiative. J. Clean. Prod. 161, 957–967 (2017).
Article Google Scholar52.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
ADS CAS Article Google Scholar53.
Critical Ecosystem Partnership Fund. Biodiversity Hotspot Shapefile. https://www.cepf.net/our-work/biodiversity-hotspots/hotspots-defined (2016).54.
Le Blanc, D. Towards integration at last? The sustainable development goals as a network of targets. Sustain. Dev. 23, 176–187. https://doi.org/10.1002/sd.1582 (2015).
Article Google Scholar55.
Mutel, C. et al. Overview and recommendations for regionalized life cycle impact assessment. Int. J. Life Cycle Assess. 24, 856–865. https://doi.org/10.1007/s11367-018-1539-4 (2019).
CAS Article PubMed PubMed Central Google Scholar56.
Popescu, V. D. et al. Quantifying biodiversity trade-offs in the face of widespread renewable and unconventional energy development. Sci. Rep. 10, 7603. https://doi.org/10.1038/s41598-020-64501-7 (2020).
ADS CAS Article PubMed PubMed Central Google Scholar57.
Oliver, T. H. How much biodiversity loss is too much?. Science 353, 220. https://doi.org/10.1126/science.aag1712 (2016).
ADS CAS Article PubMed Google Scholar58.
Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50. https://doi.org/10.1146/annurev-environ-042911-093511 (2012).
Article Google Scholar59.
Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 12, 7–21. https://doi.org/10.1038/s41561-018-0262-x (2019).
ADS CAS Article Google Scholar60.
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288. https://doi.org/10.1126/science.aaf2201 (2016).
ADS CAS Article PubMed Google Scholar61.
Eloranta, A. P., Finstad, A. G., Helland, I. P., Ugedal, O. & Power, M. Hydropower impacts on reservoir fish populations are modified by environmental variation. Sci. Total Environ. 618, 313–322. https://doi.org/10.1016/j.scitotenv.2017.10.268 (2018).
ADS CAS Article PubMed Google Scholar62.
Worrall, T. P. et al. The identification of hydrological indices for the characterization of macroinvertebrate community response to flow regime variability. Hydrol. Sci. J. 59, 645–658. https://doi.org/10.1080/02626667.2013.825722 (2014).
CAS Article Google Scholar63.
Holt, C. R., Pfitzer, D., Scalley, C., Caldwell, B. A. & Batzer, D. P. Macroinvertebrate community responses to annual flow variation from river regulation: an 11-year study. River Res. Appl. 31, 798–807. https://doi.org/10.1002/rra.2782 (2015).
Article Google Scholar64.
International Organisation for Standardization. ISO 14044:2006 Environmental management—Life cycle assessment—Principles and framework (2006).65.
Jolliet, O. et al. Global guidance on environmental life cycle impact assessment indicators: impacts of climate change, fine particulate matter formation, water consumption and land use. Int. J. Life Cycle Assess 23, 2189–2207. https://doi.org/10.1007/s11367-018-1443-y (2018).
CAS Article Google Scholar66.
Hirsch, P. E., Schillinger, S., Weigt, H. & Burkhardt-Holm, P. A hydro-economic model for water level fluctuations: combining limnology with economics for sustainable development of hydropower. PLoS ONE 9, e114889–e114889. https://doi.org/10.1371/journal.pone.0114889 (2014).
ADS CAS Article PubMed PubMed Central Google Scholar67.
Gagnon, L., Bélanger, C. & Uchiyama, Y. Life-cycle assessment of electricity generation options: the status of research in year 2001. Energy Policy 30, 1267–1278. https://doi.org/10.1016/s0301-4215(02)00088-5 (2002).
Article Google Scholar68.
George, M. W., Hotchkiss, R. H. & Huffaker, R. Reservoir sustainability and sediment management. J. Water Resour. Plann. Manag. https://doi.org/10.1061/(asce)wr.1943-5452.0000720 (2017).
Article Google Scholar69.
Yüksel, I. Hydropower for sustainable water and energy development. Renew. Sustain. Energy Rev. 14, 462–469. https://doi.org/10.1016/j.rser.2009.07.025 (2010).
Article Google Scholar70.
Hertwich, E. G. Addressing biogenic greenhouse gas emissions from hydropower in LCA. Environ. Sci. Technol. 47, 9604–9611. https://doi.org/10.1021/es401820p (2013).
ADS CAS Article PubMed Google Scholar71.
Bakken, T. H., Modahl, I. S., Raadal, H. L., Bustos, A. A. & Arnoy, S. Allocation of water consumption in multipurpose reservoirs. Water Policy 18, 932–947. https://doi.org/10.2166/wp.2016.009 (2016).
Article Google Scholar72.
Hanafiah, M. M., Xenopoulos, M. A., Pfister, S., Leuven, R. S. E. W. & Huijbregts, M. A. J. Characterization factors for water consumption and greenhouse gas emissions based on freshwater fish species extinction. Environ. Sci. Technol. 45, 5272–5278. https://doi.org/10.1021/es1039634 (2011).
ADS CAS Article PubMed Google Scholar73.
Tendall, D. M., Hellweg, S., Pfister, S., Huijbregts, M. A. J. & Gaillard, G. Impacts of river water consumption on aquatic biodiversity in life cycle assessment—a proposed method, and a case study for Europe. Environ. Sci. Technol. 48, 3236–3244. https://doi.org/10.1021/es4048686 (2014).
ADS CAS Article PubMed Google Scholar74.
Wang, J. et al. Assessing the water and carbon footprint of hydropower stations at a national scale. Sci. Total Environ. 676, 595–612. https://doi.org/10.1016/j.scitotenv.2019.04.148 (2019).
ADS CAS Article PubMed Google Scholar75.
Bakken, T. H., Modahl, I. S., Engeland, K., Raadal, H. L. & Arnøy, S. The life-cycle water footprint of two hydropower projects in Norway. J. Clean. Prod. 113, 241–250. https://doi.org/10.1016/j.jclepro.2015.12.036 (2016).
Article Google Scholar76.
Song, C., Gardner, K. H., Klein, S. J. W., Souza, S. P. & Mo, W. Cradle-to-grave greenhouse gas emissions from dams in the United States of America. Renew. Sustain. Energy Rev. 90, 945–956. https://doi.org/10.1016/j.rser.2018.04.014 (2018).
Article Google Scholar77.
Aung, T. S., Fischer, T. B. & Azmi, A. S. Are large-scale dams environmentally detrimental? Life-cycle environmental consequences of mega-hydropower plants in Myanmar. Int. J. Life Cycle Assess. 25, 1749–1766. https://doi.org/10.1007/s11367-020-01795-9 (2020).
CAS Article Google Scholar78.
Moran, E. F., Lopez, M. C., Moore, N., Müller, N. & Hyndman, D. W. Sustainable hydropower in the 21st century. Proc. Natl. Acad. Sci. 115, 11891. https://doi.org/10.1073/pnas.1809426115 (2018).
CAS Article PubMed Google Scholar79.
United Nation Environmental Program. Green energy choices: The benefits, risks, and trade-offs of low-carbon technologies for electricity production. (2016).80.
Edenhofer, O. et al. IPCC special report on renewable energy sources and climate change mitigation. (Prepared By Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 2011).81.
Laranjeiro, T., May, R. & Verones, F. Impacts of onshore wind energy production on birds and bats: recommendations for future life cycle impact assessment developments. Int. J. Life Cycle Assess 23, 2007–2023. https://doi.org/10.1007/s11367-017-1434-4 (2018).
CAS Article Google Scholar82.
Bakken, T. H., Killingtveit, Å., Engeland, K., Alfredsen, K. & Harby, A. Water consumption from hydropower plants—review of published estimates and an assessment of the concept. Hydrol. Earth Syst. Sci. 17, 3983–4000. https://doi.org/10.5194/hess-17-3983-2013 (2013).
ADS Article Google Scholar83.
Dorber, M., Kuipers, K. & Verones, F. Global characterization factors for terrestrial biodiversity impacts of future land inundation in life cycle assessment. Sci. Total Environ. 712, 134582. https://doi.org/10.1016/j.scitotenv.2019.134582 (2020).
ADS CAS Article PubMed Google Scholar84.
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51, 933. https://doi.org/10.1641/0006-3568(2001)051[0933:teotwa]2.0.co;2 (2001).
Article Google Scholar85.
Kuipers, K. J. J., Hellweg, S. & Verones, F. Potential consequences of regional species loss for global species richness: a quantitative approach for estimating global extinction probabilities. Environ. Sci. Technol. 53, 4728–4738. https://doi.org/10.1021/acs.est.8b06173 (2019).
ADS CAS Article PubMed Google Scholar86.
University of Montana. MODIS Global Evapotranspiration Project (MOD16), http://www.ntsg.umt.edu/project/modis/mod16.php.87.
Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111, 519–536. https://doi.org/10.1016/j.rse.2007.04.015 (2007).
ADS Article Google Scholar88.
Xenopoulos, M. A. & Lodge, D. M. Going with the flow: using species-discharge relationships to forecast losses in fish biodiversity. Ecology 87, 1907–1914. https://doi.org/10.1890/0012-9658(2006)87[1907:gwtfus]2.0.co;2 (2006).
Article PubMed Google Scholar89.
Abell, R. et al. Freshwater ecoregions of the world: a new map of biogeographic units for freshwater biodiversity conservation. BioScience 58(5), 403–414 (2008).
Article Google Scholar90.
Myhre, G. et al. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2013).91.
Verones, F. et al. LC-IMPACT: A regionalized life cycle damage assessment method. J. Ind. Ecol. 24, 1201–1219. https://doi.org/10.1111/jiec.13018 (2020).
Article Google Scholar92.
Thematic Mapping API. World Borders Dataset. http://thematicmapping.org/downloads/world_borders.php (2009).93.
ESRI. ArcGis Desktop—ArcMap Version 10.8. https://desktop.arcgis.com/en/arcmap/ (2020). More175 Shares149 Views
in EcologyDifferential longitudinal establishment of human fecal bacterial communities in germ-free porcine and murine models
Identifying core microbiotas in the human donors
To compare the establishment of human fecal bacterial communities in HMA mice and piglets, we inoculated GF mice and piglets maintained in gnotobiotic isolators with fecal matter from four separate human donors. The donors selected had diverse microbial communities (Fig. 1) and represented different stages of human development (see “Methods” for donor information). All animals in a given isolator (for both mice and piglets) were inoculated with the inocula obtained from a single donor. Both recipient species of animals were inoculated twice during the study—the initial round of inoculations were performed after weaning and the second round of inoculations occurred two weeks after the first round of inoculations. All inocula were prepared at the same time under the same conditions and both mice and piglets were fed the exact same sterile solid diet.
Fig. 1: Box-whisker plots comparing the alpha diversity of the inoculum aliquots among the different donors using the Shannon index.Statistical comparisons were performed using the Wilcoxon rank-sum test. Boxes with different letters indicate statistically significant differences (p More
75 Shares99 Views
in EcologyBrazil’s Amazon Soy Moratorium reduced deforestation
1.
Schwartzman, S. & Zimmerman, B. Conservation alliances with indigenous peoples of the Amazon. Conserv. Biol. 19, 721–727 (2005).
Google Scholar
2.
Fearnside, P. M. Deforestation in Brazilian Amazonia: history, rates, and consequences. Conserv. Biol. 19, 680–688 (2005).
Google Scholar3.
Malhi, Y. et al. Climate change, deforestation, and the fate of the amazon. Science 319, 169–172 (2008).
ADS CAS PubMed Google Scholar4.
Nepstad, D. et al. Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science 344, 1118–1123 (2014).
ADS CAS PubMed Google Scholar5.
Assunção, J., Gandour, C. & Rocha, R. Deforestation slowdown in the Brazilian Amazon: prices or policies? Environ. Dev. Econ. 20, 697–722 (2015).
Google Scholar6.
Assunção, J., Gandour, C. & Rocha, R. DETERring Deforestation in the Amazon: Environmental Monitoring and Law Enforcement (Climate Policy Initiative, 2017).7.
Cisneros, E., Zhou, S. L. & Börner, J. Naming and shaming for conservation: evidence from the Brazilian Amazon. PLoS ONE 10, e0136402 (2015).
PubMed PubMed Central Google Scholar8.
Arima, E. Y., Barreto, P., Araújo, E. & Soares-Filho, B. Public policies can reduce tropical deforestation: lessons and challenges from Brazil. Land Use Policy 41, 465–473 (2014).
Google Scholar9.
Soares-Filho, B. et al. Role of Brazilian Amazon protected areas in climate change mitigation. Proc. Natl Acad. Sci. USA 107, 10821–10826 (2010).
ADS CAS PubMed Google Scholar10.
Soares-Filho, B. et al. Cracking Brazil’s Forest Code. Science 344, 363–364 (2014).
ADS CAS PubMed Google Scholar11.
Assunção, J. & Rocha, R. Getting Greener by Going Black: The Priority Municipalities in Brazil (Climate Policy Initiative, 2014).12.
Assunção, J., Gandour, C., Rocha, R. & Rocha, R. The effect of rural credit on deforestation: evidence from the Brazilian Amazon. Econ. J. 130, 290–330 (2020).
Google Scholar13.
Gibbs, H. K. et al. Brazil’s soy moratorium. Science 347, 377–378 (2015).
ADS CAS PubMed Google Scholar14.
Nepstad, D. C., Stickler, C. M. & Almeida, O. T. Globalization of the Amazon soy and beef industries: opportunities for conservation. Conserv. Biol. 20, 1595–1603 (2006).
PubMed Google Scholar15.
Gibbs, H. K. et al. Did ranchers and slaughterhouses respond to zero-deforestation agreements in the Brazilian Amazon? Brazil’s zero-deforestation pacts. Conserv. Lett. 9, 32–42 (2016).
Google Scholar16.
Monitoramento do Desmatamento da Floresta Amazônica Brasileira por Satélite (INPE, 2018); http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes17.
Eating up the Amazon (Greenpeace, 2006); https://www.greenpeace.org/usa/wp-content/uploads/legacy/Global/usa/report/2010/2/eating-up-the-amazon.pdf18.
Soy Moratorium Announcement (ABIOVE, ANEC, 2006).19.
Rudorff, B. F. T. et al. Remote sensing images to detect soy plantations in the Amazon biome—the Soy Moratorium Initiative. Sustainability 4, 1074–1088 (2012).
Google Scholar20.
Trase Yearbook 2018: Sustainability in Forest-Risk Supply Chains: Spotlight on Brazilian Soy (Trase, 2018).21.
Zu Ermgassen, E. K. H. J. et al. Using supply chain data to monitor zero deforestation commitments: an assessment of progress in the Brazilian soy sector. Environ. Res. Lett. 15, 035003 (2020).
ADS Google Scholar22.
Lambin, E. F. et al. The role of supply-chain initiatives in reducing deforestation. Nat. Clim. Change 8, 109–116 (2018).
ADS Google Scholar23.
Soy Moratorium: 2016/2017 Crop Year (ABIOVE, Agrosatelite, GTS, INPE, 2017).24.
Rudorff, B. F. T. et al. The Soy Moratorium in the Amazon biome monitored by remote sensing images. Remote Sens. 3, 185–202 (2011).
ADS Google Scholar25.
Miranda, J., Börner, J., Kalkuhl, M. & Soares-Filho, B. Land speculation and conservation policy leakage in Brazil. Environ. Res. Lett. 14, 045006 (2019).
ADS Google Scholar26.
Ferrante, L. & Fearnside, P. M. Brazil’s new president and ‘ruralists’ threaten Amazonia’s environment, traditional peoples and the global climate. Environ. Conserv. 46, 261–263 (2019).
Google Scholar27.
Abessa, D., Famá, A. & Buruaem, L. The systematic dismantling of Brazilian environmental laws risks losses on all fronts. Nat. Ecol. Evol. 3, 510–511 (2019).
PubMed Google Scholar28.
Dauvergne, P. & Lister, J. The prospects and limits of eco-consumerism: shopping our way to less deforestation? Organ. Environ. 23, 132–154 (2010).
Google Scholar29.
Macedo, M. N. et al. Decoupling of deforestation and soy production in the southern Amazon during the late 2000s. Proc. Natl Acad. Sci. USA 109, 1341–1346 (2012).
ADS CAS PubMed Google Scholar30.
Kastens, J. H., Brown, J. C., Coutinho, A. C., Bishop, C. R. & Esquerdo, J. C. D. M. Soy moratorium impacts on soybean and deforestation dynamics in Mato Grosso, Brazil. PLoS ONE 12, e0176168 (2017).
PubMed PubMed Central Google Scholar31.
Svahn, J., Brunner, D. & Harding, T. Did the Soy Moratorium Reduce Deforestation in the Brazilian Amazon? A Counterfactual Analysis of the Impact of the Soy Moratorium on Deforestation in the Amazon Biome. MSc thesis, Norwegian School of Economics (2018).32.
West, T. A. P., Börner, J. & Fearnside, P. M.Climatic benefits from the 2006–2017 avoided deforestation in Amazonian Brazil. Front. For. Glob. Change 2, 52 (2019).
Google Scholar33.
Sy, V. D. et al. Land use patterns and related carbon losses following deforestation in South America. Environ. Res. Lett. 10, 124004 (2015).
ADS Google Scholar34.
Moratatória da Soja: Monitoramento por Imagens de Satélites dos Plantios de Soja no Bioma Amazonia (ABIOVE & Agrosatélite, 2018); https://abiove.org.br/wp-content/uploads/2019/05/30012019-165924-portugues.pdf35.
Alix-Garcia, J., Rausch, L. L., L’Roe, J., Gibbs, H. K. & Munger, J. Avoided deforestation linked to environmental registration of properties in the Brazilian Amazon: environmental registration in the Amazon. Conserv. Lett. 11, e12414 (2018).
Google Scholar36.
Burgess, R., Costa, F. J. M. & Olken, B. A. Wilderness Conservation and the Reach of the State: Evidence from National Borders in the Amazon Working Paper 24861 (2018); https://doi.org/10.3386/w2486137.
Silva Junior, C. H. L. et al. Fire responses to the 2010 and 2015/2016 Amazonian droughts. Front. Earth Sci. 7, 97 (2019).
ADS Google Scholar38.
Rudorff, B. F. T. & Risso, J. Geospatial Analyses of the Annual Crops Dynamic in the Brazilian Cerrado Biome: 2000 to 2014 (Agrosatélite Applied Geotechnology, 2015).39.
Gollnow, F., Hissa, L., de, B. V., Rufin, P. & Lakes, T. Property-level direct and indirect deforestation for soybean production in the Amazon region of Mato Grosso, Brazil. Land Use Policy 78, 377–385 (2018).
Google Scholar40.
Zalles, V. et al. Near doubling of Brazil’s intensive row crop area since 2000. Proc. Natl Acad. Sci. USA 116, 428–435 (2019).
ADS CAS PubMed Google Scholar41.
Arima, E. Y., Richards, P., Walker, R. & Caldas, M. M. Statistical confirmation of indirect land use change in the Brazilian Amazon. Environ. Res. Lett. 6, 024010 (2011).
ADS Google Scholar42.
Börner, J., Wunder, S., Wertz-Kanounnikoff, S., Hyman, G. & Nascimento, N. Forest law enforcement in the Brazilian Amazon: costs and income effects. Glob. Environ. Change 29, 294–305 (2014).
Google Scholar43.
Sills, E. O. et al. Estimating the impacts of local policy innovation: the synthetic control method applied to tropical deforestation. PLoS ONE 10, e0132590 (2015).
PubMed PubMed Central Google Scholar44.
Börner, J., Kis-Katos, K., Hargrave, J. & König, K. Post-crackdown effectiveness of field-based forest law enforcement in the Brazilian Amazon. PLoS ONE 10, e0121544 (2015).
PubMed PubMed Central Google Scholar45.
L’Roe, J., Rausch, L., Munger, J. & Gibbs, H. K. Mapping properties to monitor forests: landholder response to a large environmental registration program in the Brazilian Amazon. Land Use Policy 57, 193–203 (2016).
Google Scholar46.
Azevedo, A. A. et al. Limits of Brazil’s Forest Code as a means to end illegal deforestation. Proc. Natl Acad. Sci. USA 114, 7653–7658 (2017).
ADS CAS PubMed Google Scholar47.
Brown, J. C. & Koeppe, M. in Environment and the Law in Amazonia: A Plurilateral Encounter (eds Cooper, J. M. & Hunefeldt, C.) 110–126 (Sussex Academic Press, 2013).48.
Lambin, E. F. et al. Effectiveness and synergies of policy instruments for land use governance in tropical regions. Glob. Environ. Change 28, 129–140 (2014).
Google Scholar49.
Garrett, R. D., Carlson, K. M., Rueda, X. & Noojipady, P. Assessing the potential additionality of certification by the Round Table on Responsible Soybeans and the Roundtable on Sustainable Palm Oil. Environ. Res. Lett. 11, 045003 (2016).
ADS Google Scholar50.
Le Polain de Waroux, Y. et al. The restructuring of South American soy and beef production and trade under changing environmental regulations. World Dev. 121, 188–202 (2019).
Google Scholar51.
Heilmayr, R., Carlson, K. M. & Benedict, J. J. Deforestation spillovers from oil palm sustainability certification. Environ. Res. Lett. 15, 075002 (2020).
ADS CAS Google Scholar52.
Dou, Y., da Silva, R. F. B., Yang, H. & Liu, J. Spillover effect offsets the conservation effort in the Amazon. J. Geogr. Sci. 28, 1715–1732 (2018).
Google Scholar53.
Moffette, F. & Gibbs, H. Agricultural displacement and deforestation leakage in the Brazilian Legal Amazon. Land Econ. (in the press).54.
Baylis, K. et al. Mainstreaming impact evaluation in nature conservation. Conserv. Lett. 9, 58–64 (2016).
Google Scholar55.
Noojipady, P. et al. Forest carbon emissions from cropland expansion in the Brazilian Cerrado biome. Environ. Res. Lett. 12, 025004 (2017).
ADS Google Scholar56.
Rausch, L. L. et al. Soy expansion in Brazil’s Cerrado. Conserv. Lett. 12, e12671 (2019).
Google Scholar57.
S. Garcia, A. et al. Assessing land use/cover dynamics and exploring drivers in the Amazon’s Arc of Deforestation through a hierarchical, multi-scale and multi-temporal classification approach. Remote Sens. Appl. Soc. Environ. 15, 100233 (2019).
Google Scholar58.
Richards, P. D., Walker, R. T. & Arima, E. Y. Spatially complex land change: the indirect effect of Brazil’s agricultural sector on land use in Amazonia. Glob. Environ. Change 29, 1–9 (2014).
PubMed PubMed Central Google Scholar59.
Richards, P. What drives indirect land use change? How Brazil’s agriculture sector influences frontier deforestation. Ann. Assoc. Am. Geogr. 105, 1026–1040 (2015).
PubMed PubMed Central Google Scholar60.
Silva, C. A. & Lima, M. Soy Moratorium in Mato Grosso: deforestation undermines the agreement. Land Use Policy 71, 540–542 (2018).
Google Scholar61.
Rausch, L. & Gibbs, H. Property arrangements and soy governance in the Brazilian state of Mato Grosso: implications for deforestation-free production. Land 5, 7 (2016).
Google Scholar62.
Garrett, R. D. et al. Intensification in agriculture–forest frontiers: land use responses to development and conservation policies in Brazil. Glob. Environ. Change 53, 233–243 (2018).
Google Scholar63.
Koch, N., zu Ermgassen, E. K. H. J., Wehkamp, J., Oliveira Filho, F. J. B. & Schwerhoff, G.Agricultural productivity and forest conservation: evidence from the Brazilian Amazon. Am. J. Agric. Econ. 101, 919–940 (2019).
Google Scholar64.
Le Polain de Waroux, Y., Garrett, R. D., Heilmayr, R. & Lambin, E. F. Land-use policies and corporate investments in agriculture in the Gran Chaco and Chiquitano. Proc. Natl Acad. Sci. USA 113, 4021–4026 (2016).
ADS CAS PubMed Google Scholar65.
Garrett, R. D. et al. Criteria for effective zero-deforestation commitments. Glob. Environ. Change 54, 135–147 (2019).
Google Scholar66.
Soterroni, A. C. et al. Expanding the Soy Moratorium to Brazil’s Cerrado. Sci. Adv. 5, eaav7336 (2019).
ADS PubMed PubMed Central Google Scholar67.
Governo alega ameaça à soberania nacional e apoia fim da Moratória da Soja. Aprosoja http://www.aprosoja.com.br/comunicacao/noticia/governo-alega-ameaca-a-soberania-nacional-e-apoia-fim-da-moratoria-da-soja (2019).68.
Barona, E., Ramankutty, N., Hyman, G. & Coomes, O. T. The role of pasture and soybean in deforestation of the Brazilian Amazon. Environ. Res. Lett. 5, 024002 (2010).
ADS Google Scholar69.
Project MapBiomas—Collection 2.3 of Brazilian Land Cover & Use Map Series (MapBiomas, 2018); http://mapbiomas.org/70.
Richards, P. D., Myers, R. J., Swinton, S. M. & Walker, R. T. Exchange rates, soybean supply response, and deforestation in South America. Glob. Environ. Change 22, 454–462 (2012).
Google Scholar71.
Wing, C., Simon, K. & Bello-Gomez, R. A. Designing difference in difference studies: best practices for public health policy research. Annu. Rev. Public Health 39, 453–469 (2018).
PubMed Google Scholar72.
Freyaldenhoven, S., Hansen, C. & Shapiro, J. M. Pre-event trends in the panel event-study design. Am. Econ. Rev. 109, 3307–3338 (2019).
Google Scholar73.
Lechner, M. The estimation of causal effects by difference-in-difference methods estimation of spatial panels. Found. Trends Econom. 4, 165–224 (2010).
MATH Google Scholar74.
Clarke, D. Estimating Difference-in-Differences in the Presence of Spillovers MPRA Paper 81604 (Univ, Library of Munich, 2017).75.
Zu Ermgassen, E. K. H. J. et al. Using supply chain data to monitor zero deforestation commitments: an assessment of progress in the Brazilian soy sector. Environ. Res. Lett. 15, 035003 (2019).
ADS Google Scholar76.
Alix-Garcia, J. M., Shapiro, E. N. & Sims, K. R. E. Forest conservation and slippage: evidence from Mexico’s National Payments for Ecosystem Services program. Land Econ. 88, 613–638 (2012).
Google Scholar77.
Hertel, T. W. Economic perspectives on land use change and leakage. Environ. Res. Lett. 13, 075012 (2018).
ADS Google Scholar78.
Hertel, T. W., West, T. A. P., Börner, J. & Villoria, N. B. A review of global–local–global linkages in economic land-use/cover change models. Environ. Res. Lett. 14, 053003 (2019).
ADS Google Scholar More200 Shares109 Views
in EcologyOptofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions
1.
Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).
CAS Article PubMed PubMed Central Google Scholar
2.
Blainey, P. C., Mosier, A. C., Potanina, A., Francis, C. A. & Quake, S. R. Genome of a low-salinity ammonia-oxidizing archaeon determined by single-cell and metagenomic analysis. PLoS ONE 6, e16626 (2011).
CAS PubMed PubMed Central Article Google Scholar3.
Thomas, T., Gilbert, J. & Meyer, F. Metagenomics -– a guide from sampling to data analysis. Microb. Inform. Exp. 2, 3 (2012).
PubMed PubMed Central Article Google Scholar4.
Horgan, R. P. & Kenny, L. C. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet. Gynaecol 13, 189–195 (2011).
Google Scholar5.
Prosser, J. I. Dispersing misconceptions and identifying opportunities for the use of ‘omics’ in soil microbial ecology. Nat. Rev. Microbiol. 13, 439–446 (2015).
CAS PubMed Article Google Scholar6.
Yu, F. B. et al. Microfluidic-based mini-metagenomics enables discovery of novel microbial lineages from complex environmental samples. eLife 6, e26580 (2017).
PubMed PubMed Central Article Google Scholar7.
Mukherjee, S. et al. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res 47, D649–D659 (2019).
CAS PubMed Article Google Scholar8.
Woyke, T., Doud, D. F. R. & Schulz, F. The trajectory of microbial single-cell sequencing. Nat. Methods 14, 1045–1054 (2017).
CAS PubMed Article Google Scholar9.
Berry, D. & Loy, A. Stable-isotope probing of human and animal microbiome function. Trends Microbiol 26, 999–1007 (2018).
CAS PubMed PubMed Central Article Google Scholar10.
Manefield, M., Whiteley, A. S., Griffiths, R. I. & Bailey, M. J. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl. Environ. Microbiol. 68, 5367–5373 (2002).
CAS PubMed PubMed Central Article Google Scholar11.
Dumont, M. G. & Murrell, J. C. Stable isotope probing—linking microbial identity to function. Nat. Rev. Microbiol. 3, 499–504 (2005).
CAS PubMed Article Google Scholar12.
Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J 13, 413–429 (2019).
CAS PubMed Article Google Scholar13.
Wang, Y., Huang, W. E., Cui, L. & Wagner, M. Single cell stable isotope probing in microbiology using Raman microspectroscopy. Curr. Opin. Biotechnol. 41, 34–42 (2016).
CAS PubMed Article Google Scholar14.
Haider, S. et al. Raman microspectroscopy reveals long-term extracellular activity of chlamydiae. Mol. Microbiol 77, 687–700 (2010).
CAS PubMed Article Google Scholar15.
Huang, W. E. et al. Raman-FISH: combining stable-isotope Raman spectroscopy and fluorescence in situ hybridization for the single cell analysis of identity and function. Environ. Microbiol. 9, 1878–1889 (2007).
CAS PubMed Article Google Scholar16.
Wagner, M. Single-cell ecophysiology of microbes as revealed by Raman microspectroscopy or secondary ion mass spectrometry imaging. Annu. Rev. Microbiol. 63, 411–429 (2009).
CAS PubMed Article Google Scholar17.
Berry, D. et al. Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells. Proc. Natl Acad. Sci. USA 112, E194–E203 (2015).
CAS PubMed Article Google Scholar18.
Malmstrom, R. R. & Eloe-Fadrosh, E. A. Advancing genome-resolved metagenomics beyond the shotgun. mSystems 4, e00118–e00119 (2019).
CAS PubMed PubMed Central Article Google Scholar19.
Neufeld, J. D. et al. DNA stable-isotope probing. Nat. Protoc. 2, 860–866 (2007).
CAS PubMed Article Google Scholar20.
Jing, X. et al. Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean. Environ. Microbiol. 20, 2241–2255 (2018).
CAS PubMed PubMed Central Article Google Scholar21.
Wang, Y. et al. Raman activated cell ejection for isolation of single cells. Anal. Chem. 85, 10697–10701 (2013).
CAS PubMed Article Google Scholar22.
Singer, E., Wagner, M. & Woyke, T. Capturing the genetic makeup of the active microbiome in situ. ISME J 11, 1949–1963 (2017).
CAS PubMed PubMed Central Article Google Scholar23.
Huang, W. E., Ward, A. D. & Whiteley, A. S. Raman tweezers sorting of single microbial cells. Environ. Microbiol. Rep 1, 44–49 (2009).
CAS PubMed Article Google Scholar24.
Lee, K. S. et al. An automated Raman-based platform for the sorting of live cells by functional properties. Nat. Microbiol. 4, 1035–1048 (2019).
CAS PubMed Article Google Scholar25.
Lee, K. S., Wagner, M. & Stocker, R. Raman-based sorting of microbial cells to link functions to their genes. Microb. Cell 7, 62–65 (2020).
CAS PubMed PubMed Central Article Google Scholar26.
Premvardhan, L., Bordes, L., Beer, A., Büchel, C. & Robert, B. Carotenoid structures and environments in trimeric and oligomeric fucoxanthin chlorophyll a/c2 proteins from resonance Raman spectroscopy. J. Phys. Chem. B 113, 12565–12574 (2009).
CAS PubMed Article Google Scholar27.
Takano, H. The regulatory mechanism underlying light-inducible production of carotenoids in nonphototrophic bacteria. Biosci. Biotechnol. Biochem. 80, 1264–1273 (2016).
CAS PubMed Article Google Scholar28.
Wagstaff, K., Cardie, C., Rogers, S. & Schrödl, S. Constrained k-means clustering with background knowledge. in Proc. 18th International Conference on Machine Learning (eds Brodley, C. E. & Danyluk, A. P.) 577–584 (Morgan Kaufmann, 2001).29.
Kanungo, T. et al. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Patt. Anal. Mach. Intell. 24, 881–892 (2002).
Article Google Scholar30.
Rinke, C. et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat. Protoc. 9, 1038–1048 (2014).
CAS PubMed Article Google Scholar31.
Bonner, W. A., Hulett, H. R., Sweet, R. G. & Herzenberg, L. A. Fluorescence activated cell sorting. Rev. Sci. Instrum. 43, 404–409 (1972).
CAS PubMed Article Google Scholar32.
Ha, B. H., Lee, K. S., Jung, J. H. & Sung, H. J. Three-dimensional hydrodynamic flow and particle focusing using four vortices Dean flow. Microfluid. Nanofluid. 17, 647–655 (2014).
CAS Article Google Scholar33.
Chu, H., Doh, I. & Cho, Y.-H. A three-dimensional (3D) particle focusing channel using the positive dielectrophoresis (pDEP) guided by a dielectric structure between two planar electrodes. Lab Chip 9, 686–691 (2009).
CAS PubMed Article Google Scholar34.
Gao, C. et al. Single-cell bacterial transcription measurements reveal the importance of dimethylsulfoniopropionate (DMSP) hotspots in ocean sulfur cycling. Nat. Commun. 11, 1942 (2020).
CAS PubMed PubMed Central Article Google Scholar35.
Kitzinger, K. et al. Single cell analyses reveal contrasting life strategies of the two main nitrifiers in the ocean. Nat. Commun. 11, 767 (2020).
CAS PubMed PubMed Central Article Google Scholar36.
Majed, N., Chernenko, T., Diem, M. & Gu, A. Z. Identification of functionally relevant populations in enhanced biological phosphorus removal processes based on intracellular polymers profiles and insights into the metabolic diversity and heterogeneity. Environ. Sci. Technol. 46, 5010–5017 (2012).
CAS PubMed Article Google Scholar37.
Fernando, E. Y. et al. Resolving the individual contribution of key microbial populations to enhanced biological phosphorus removal with Raman–FISH. ISME J 13, 1933–1946 (2019).
CAS PubMed PubMed Central Article Google Scholar38.
Milucka, J. et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546 (2012).
CAS PubMed Article Google Scholar39.
Hatzenpichler, R. et al. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal–bacterial consortia. Proc. Natl Acad. Sci. USA 113, E4069–E4078 (2016).
CAS PubMed Article PubMed Central Google Scholar40.
Schiessl, K. T. et al. Phenazine production promotes antibiotic tolerance and metabolic heterogeneity in Pseudomonas aeruginosa biofilms. Nat. Commun. 10, 762 (2019).
CAS PubMed PubMed Central Article Google Scholar41.
Gleizer, S. et al. Conversion of Escherichia coli to generate all biomass carbon from CO2. Cell 179, 1255–1263 (2019).
CAS PubMed PubMed Central Article Google Scholar42.
Dong, T. G., Ho, B. T., Yoder-Himes, D. R. & Mekalanos, J. J. Identification of T6SS-dependent effector and immunity proteins by Tn-seq in Vibrio cholerae. Proc. Natl Acad. Sci. USA 110, 2623–2628 (2013).
CAS PubMed Article Google Scholar43.
Dolinšek, J., Lagkouvardos, I., Wanek, W., Wagner, M. & Daims, H. Interactions of nitrifying bacteria and heterotrophs: identification of a Micavibrio-like putative predator of Nitrospira spp. Appl. Environ. Microbiol. 79, 2027–2037 (2013).
PubMed PubMed Central Article CAS Google Scholar44.
Pätzold, R. et al. In situ mapping of nitrifiers and anammox bacteria in microbial aggregates by means of confocal resonance Raman microscopy. J. Microbiol. Methods 72, 241–248 (2008).
PubMed Article CAS Google Scholar45.
Wei, L. & Min, W. Electronic preresonance stimulated Raman scattering microscopy. J. Phys. Chem. Lett. 9, 4294–4301 (2018).
CAS PubMed PubMed Central Article Google Scholar46.
Gruber-Vodicka, H. R. et al. Paracatenula, an ancient symbiosis between thiotrophic Alphaproteobacteria and catenulid flatworms. Proc. Natl Acad. Sci. USA. 108, 12078–12083 (2011).
CAS PubMed Article Google Scholar47.
Lenz, R., Enders, K., Stedmon, C. A., MacKenzie, D. M. A. & Nielsen, T. G. A critical assessment of visual identification of marine microplastic using Raman spectroscopy for analysis improvement. Mar. Pollut. Bull. 100, 82–91 (2015).
CAS PubMed Article Google Scholar48.
Gillibert, R. et al. Raman tweezers for small microplastics and nanoplastics identification in seawater. Environ. Sci. Technol. 53, 9003–9013 (2019).
CAS PubMed Article Google Scholar49.
Choy, C. A. et al. The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column. Sci. Rep. 9, 7843 (2019).
PubMed PubMed Central Article CAS Google Scholar50.
Zhang, P. et al. Raman-activated cell sorting based on dielectrophoretic single-cell trap and release. Anal. Chem. 87, 2282–2289 (2015).
CAS PubMed Article Google Scholar51.
McIlvenna, D. et al. Continuous cell sorting in a flow based on single cell resonance Raman spectra. Lab Chip 16, 1420–1429 (2016).
CAS PubMed Article Google Scholar52.
Folick, A., Min, W. & Wang, M. C. Label-free imaging of lipid dynamics using coherent anti-stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) microscopy. Curr. Opin. Genet. Dev. 21, 585–590 (2011).
CAS PubMed PubMed Central Article Google Scholar53.
Hiramatsu, K. et al. High-throughput label-free molecular fingerprinting flow cytometry. Sci. Adv. 5, eaau0241 (2019).
PubMed PubMed Central Article CAS Google Scholar54.
Suzuki, Y. et al. Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering. Proc. Natl Acad. Sci. USA 116, 15842–15848 (2019).
CAS PubMed Article Google Scholar55.
Nitta, N. et al. Raman image-activated cell sorting. Nat. Commun. 11, 3452 (2020).
CAS PubMed PubMed Central Article Google Scholar56.
Eek, K. M., Sessions, A. L. & Lies, D. P. Carbon-isotopic analysis of microbial cells sorted by flow cytometry. Geobiology 5, 85–95 (2007).
CAS Article Google Scholar57.
Dyksma, S. et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J 10, 1939–1953 (2016).
CAS PubMed PubMed Central Article Google Scholar58.
Ling, L., Zhou, F., Huang, L. & Li, Z.-Y. Optical forces on arbitrary shaped particles in optical tweezers. J. Appl. Phys. 108, 073110 (2010).
Article CAS Google Scholar59.
Bonessi, D., Bonin, K. & Walker, T. Optical forces on particles of arbitrary shape and size. J. Opt. A Pure Appl. Opt. 9, S228–S234 (2007).
Article Google Scholar60.
Ashkin, A. Forces of a single-beam gradient laser trap on a dielectric sphere in the ray optics regime. Biophys. J. 61, 569–582 (1992).
CAS PubMed PubMed Central Article Google Scholar61.
Novotny, L., Bian, R. X. & Xie, X. S. Theory of nanometric optical tweezers. Phys. Rev. Lett. 79, 645–648 (1997).
CAS Article Google Scholar62.
Dholakia, K. & Reece, P. Optical micromanipulation takes hold. Nano Today 1, 18–27 (2006).
Article Google Scholar63.
Kim, S., Kang, I., Seo, J.-H. & Cho, J.-C. Culturing the ubiquitous freshwater actinobacterial acI lineage by supplying a biochemical ‘helper’ catalase. ISME J 13, 2252–2263 (2019).
CAS PubMed PubMed Central Article Google Scholar64.
Li, T. et al. Simultaneous analysis of microbial identity and function using NanoSIMS. Environ. Microbiol. 10, 580–588 (2008).
CAS PubMed PubMed Central Article Google Scholar65.
Huang, W. E., Griffiths, R. I., Thompson, I. P., Bailey, M. J. & Whiteley, A. S. Raman microscopic analysis of single microbial cells. Anal. Chem. 76, 4452–4458 (2004).
CAS PubMed Article Google Scholar66.
McDonald, J. C. et al. Fabrication of microfluidic systems in poly(dimethylsiloxane). Electrophoresis 21, 27–40 (2000).
CAS PubMed Article Google Scholar67.
Schuster, K. C., Reese, I., Urlaub, E., Gapes, J. R. & Lendl, B. Multidimensional information on the chemical composition of single bacterial cells by confocal Raman microspectroscopy. Anal. Chem. 72, 5529–5534 (2000).
CAS PubMed Article Google Scholar68.
Dochow, S. et al. Quartz microfluidic chip for tumour cell identification by Raman spectroscopy in combination with optical traps. Anal. Bioanal. Chem. 405, 2743–2746 (2013).
CAS PubMed Article Google Scholar69.
Kodinariya, T. M. & Makwana, P. R. Review on determining number of Cluster in K-Means Clustering. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1, 90–95 (2013).
Google Scholar70.
Bjerg, J. T. et al. Long-distance electron transport in individual, living cable bacteria. Proc. Natl Acad. Sci. USA. 115, 5786–5791 (2018).
CAS PubMed Article Google Scholar71.
Zhao, J., Lui, H., McLean, D. I. & Zeng, H. Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy. Appl. Spectrosc. 61, 1225–1232 (2007).
CAS PubMed Article Google Scholar72.
Beier, B. D. & Berger, A. J. Method for automated background subtraction from Raman spectra containing known contaminants. Analyst 134, 1198–1202 (2009).
CAS PubMed Article Google Scholar73.
Hehemann, J.-H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat. Commun. 7, 12860 (2016).
CAS PubMed PubMed Central Article Google Scholar74.
Taheri-Araghi, S. et al. Cell-size control and homeostasis in bacteria. Curr. Biol. 25, 385–391 (2015).
CAS PubMed Article Google Scholar75.
Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–891 (2013).
CAS PubMed PubMed Central Article Google Scholar76.
Wang, Y. et al. Reverse and multiple stable isotope probing to study bacterial metabolism and interactions at the single cell level. Anal. Chem. 88, 9443–9450 (2016).
CAS PubMed Article Google Scholar77.
Yuan, X. et al. Effect of laser irradiation on cell function and its implications in Raman spectroscopy. Appl. Environ. Microbiol. 84, e02508–e02517 (2018).
CAS PubMed PubMed Central Google Scholar More
