Ecology

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

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 Scholar 

3.
Malhi, Y. et al. Climate change, deforestation, and the fate of the amazon. Science 319, 169–172 (2008).
ADS  CAS  PubMed  Google Scholar 

4.
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 Scholar 

5.
Assunção, J., Gandour, C. & Rocha, R. Deforestation slowdown in the Brazilian Amazon: prices or policies? Environ. Dev. Econ. 20, 697–722 (2015).
Google Scholar 

6.
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 Scholar 

8.
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 Scholar 

9.
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 Scholar 

10.
Soares-Filho, B. et al. Cracking Brazil’s Forest Code. Science 344, 363–364 (2014).
ADS  CAS  PubMed  Google Scholar 

11.
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 Scholar 

13.
Gibbs, H. K. et al. Brazil’s soy moratorium. Science 347, 377–378 (2015).
ADS  CAS  PubMed  Google Scholar 

14.
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 Scholar 

15.
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 Scholar 

16.
Monitoramento do Desmatamento da Floresta Amazônica Brasileira por Satélite (INPE, 2018); http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes

17.
Eating up the Amazon (Greenpeace, 2006); https://www.greenpeace.org/usa/wp-content/uploads/legacy/Global/usa/report/2010/2/eating-up-the-amazon.pdf

18.
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 Scholar 

20.
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 Scholar 

22.
Lambin, E. F. et al. The role of supply-chain initiatives in reducing deforestation. Nat. Clim. Change 8, 109–116 (2018).
ADS  Google Scholar 

23.
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 Scholar 

25.
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 Scholar 

26.
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 Scholar 

27.
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 Scholar 

28.
Dauvergne, P. & Lister, J. The prospects and limits of eco-consumerism: shopping our way to less deforestation? Organ. Environ. 23, 132–154 (2010).
Google Scholar 

29.
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 Scholar 

30.
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 Scholar 

31.
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 Scholar 

33.
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 Scholar 

34.
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.pdf

35.
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 Scholar 

36.
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/w24861

37.
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 Scholar 

38.
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 Scholar 

40.
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 Scholar 

41.
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 Scholar 

42.
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 Scholar 

43.
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 Scholar 

44.
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 Scholar 

45.
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 Scholar 

46.
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 Scholar 

47.
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 Scholar 

49.
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 Scholar 

50.
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 Scholar 

51.
Heilmayr, R., Carlson, K. M. & Benedict, J. J. Deforestation spillovers from oil palm sustainability certification. Environ. Res. Lett. 15, 075002 (2020).
ADS  CAS  Google Scholar 

52.
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 Scholar 

53.
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 Scholar 

55.
Noojipady, P. et al. Forest carbon emissions from cropland expansion in the Brazilian Cerrado biome. Environ. Res. Lett. 12, 025004 (2017).
ADS  Google Scholar 

56.
Rausch, L. L. et al. Soy expansion in Brazil’s Cerrado. Conserv. Lett. 12, e12671 (2019).
Google Scholar 

57.
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 Scholar 

58.
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 Scholar 

59.
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 Scholar 

60.
Silva, C. A. & Lima, M. Soy Moratorium in Mato Grosso: deforestation undermines the agreement. Land Use Policy 71, 540–542 (2018).
Google Scholar 

61.
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 Scholar 

62.
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 Scholar 

63.
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 Scholar 

64.
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 Scholar 

65.
Garrett, R. D. et al. Criteria for effective zero-deforestation commitments. Glob. Environ. Change 54, 135–147 (2019).
Google Scholar 

66.
Soterroni, A. C. et al. Expanding the Soy Moratorium to Brazil’s Cerrado. Sci. Adv. 5, eaav7336 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

67.
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 Scholar 

69.
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 Scholar 

71.
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 Scholar 

72.
Freyaldenhoven, S., Hansen, C. & Shapiro, J. M. Pre-event trends in the panel event-study design. Am. Econ. Rev. 109, 3307–3338 (2019).
Google Scholar 

73.
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 Scholar 

74.
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 Scholar 

76.
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 Scholar 

77.
Hertel, T. W. Economic perspectives on land use change and leakage. Environ. Res. Lett. 13, 075012 (2018).
ADS  Google Scholar 

78.
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  More

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 Scholar 

3.
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 Scholar 

4.
Horgan, R. P. & Kenny, L. C. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet. Gynaecol 13, 189–195 (2011).
Google Scholar 

5.
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 Scholar 

6.
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 Scholar 

7.
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 Scholar 

8.
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 Scholar 

9.
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 Scholar 

10.
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 Scholar 

11.
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 Scholar 

12.
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 Scholar 

13.
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 Scholar 

14.
Haider, S. et al. Raman microspectroscopy reveals long-term extracellular activity of chlamydiae. Mol. Microbiol 77, 687–700 (2010).
CAS  PubMed  Article  Google Scholar 

15.
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 Scholar 

16.
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 Scholar 

17.
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 Scholar 

18.
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 Scholar 

19.
Neufeld, J. D. et al. DNA stable-isotope probing. Nat. Protoc. 2, 860–866 (2007).
CAS  PubMed  Article  Google Scholar 

20.
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 Scholar 

21.
Wang, Y. et al. Raman activated cell ejection for isolation of single cells. Anal. Chem. 85, 10697–10701 (2013).
CAS  PubMed  Article  Google Scholar 

22.
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 Scholar 

23.
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 Scholar 

24.
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 Scholar 

25.
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 Scholar 

26.
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 Scholar 

27.
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 Scholar 

28.
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 Scholar 

30.
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 Scholar 

31.
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 Scholar 

32.
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 Scholar 

33.
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 Scholar 

34.
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 Scholar 

35.
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 Scholar 

36.
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 Scholar 

37.
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 Scholar 

38.
Milucka, J. et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546 (2012).
CAS  PubMed  Article  Google Scholar 

39.
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 Scholar 

40.
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 Scholar 

41.
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 Scholar 

42.
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 Scholar 

43.
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 Scholar 

44.
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 Scholar 

45.
Wei, L. & Min, W. Electronic preresonance stimulated Raman scattering microscopy. J. Phys. Chem. Lett. 9, 4294–4301 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
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 Scholar 

47.
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 Scholar 

48.
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 Scholar 

49.
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 Scholar 

50.
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 Scholar 

51.
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 Scholar 

52.
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 Scholar 

53.
Hiramatsu, K. et al. High-throughput label-free molecular fingerprinting flow cytometry. Sci. Adv. 5, eaau0241 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

54.
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 Scholar 

55.
Nitta, N. et al. Raman image-activated cell sorting. Nat. Commun. 11, 3452 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
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 Scholar 

57.
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 Scholar 

58.
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 Scholar 

59.
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 Scholar 

60.
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 Scholar 

61.
Novotny, L., Bian, R. X. & Xie, X. S. Theory of nanometric optical tweezers. Phys. Rev. Lett. 79, 645–648 (1997).
CAS  Article  Google Scholar 

62.
Dholakia, K. & Reece, P. Optical micromanipulation takes hold. Nano Today 1, 18–27 (2006).
Article  Google Scholar 

63.
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 Scholar 

64.
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 Scholar 

65.
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 Scholar 

66.
McDonald, J. C. et al. Fabrication of microfluidic systems in poly(dimethylsiloxane). Electrophoresis 21, 27–40 (2000).
CAS  PubMed  Article  Google Scholar 

67.
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 Scholar 

68.
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 Scholar 

69.
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 Scholar 

70.
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 Scholar 

71.
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 Scholar 

72.
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 Scholar 

73.
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 Scholar 

74.
Taheri-Araghi, S. et al. Cell-size control and homeostasis in bacteria. Curr. Biol. 25, 385–391 (2015).
CAS  PubMed  Article  Google Scholar 

75.
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 Scholar 

76.
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 Scholar 

77.
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

1.
van Zanten, H. H. E., van Ittersum, M. K. & de Boer, I. J. M. Glob. Food Secur. 21, 18–22 (2019).
Article  Google Scholar 
2.
Duarte, C. M. et al. Bioscience 59, 967–976 (2009).
Article  Google Scholar 

3.
Marra, J. Nature 436, 175–176 (2005).
ADS  CAS  Article  Google Scholar 

4.
Costello, C. et al. Nature https://doi.org/10.1038/s41586-020-2616-y (2020).

5.
Jouray, J.-B., Blasiak, R., Nörström, A. V., Österblom, H. & Nyström, M. One Earth 2, 43–54 (2020).
Article  Google Scholar 

6.
Costello, C. et al. The Future of Food from the Sea (World Resources Institute, 2019).

7.
Pharo, P. & Oppenheim, J. Growing Better: Ten Critical Transitions to Transform Food and Land Use (The Food and Land Use Coalition, 2019).

8.
Gentry, R. R. et al. Nat. Ecol. Evol. 1, 1317–1324 (2017).
Article  Google Scholar 

9.
Froehlich, H. E., Afflerbach, J. C., Frazier, M. & Halpern, B. S. Curr. Biol. 29, 3087–3093 (2019).
CAS  Article  Google Scholar 

10.
Field, C., Behrenfeld, M., Randerson, J. & Falkowski, P. Science 281, 237–240 (1998).
ADS  CAS  Article  Google Scholar 

11.
Shurin, J., Gruner, D. & Hillebrand, H. Proc. Royal Soc. B 273, 1–9 (2006).
Article  Google Scholar 

12.
Tucker, M. A. & Rogers, T. L. Proc. Royal Soc. B 281, 20142103 (2014).
Article  Google Scholar 

13.
Kolding, J., Bundy, A., van Zwieten, P. A. M. & Plank, M. J. ICES J. Mar. Sci. 73, 1697–1713 (2016).
Article  Google Scholar 

14.
Rossiter, W., King, G. & Johnson, B. Am. Midl. Nat. 177, 1–14 (2017).
Article  Google Scholar 

15.
Chapin, F. S., Matson, P. A. & Mooney, H. A. Principles of Terrestrial Ecosystem Ecology (Springer, 2002).

16.
Stebbins, G. L. Ann. Missouri Bot. Gard. 68, 75–86 (1981).
Article  Google Scholar 

17.
Cyr, H. & Pace, M. Nature 361, 148–150 (1993).
ADS  Article  Google Scholar 

18.
Cebrian, J. & Lartigue, J. Ecol. Monogr. 74, 237–259 (2004).
Article  Google Scholar 

19.
Humphreys, W. F. J. Anim. Ecol. 48, 427–453 (1979).
Article  Google Scholar 

20.
Conti, L. & Scardi, M. Mar. Ecol. Prog. Ser. 410, 233–244 (2010).
ADS  Article  Google Scholar 

21.
Robinson, J. & Bodmer, R. J. Wildl. Manage. 63, 1–13 (1999).
Article  Google Scholar 

22.
Greater North Sea Ecoregion — Fisheries Overview (ICES, 2018).

23.
Oesterheld, M., Sala, O. E. & McNaughton, S. J. Nature 356, 234–236 (1992).
ADS  CAS  Article  Google Scholar 

24.
Coe, M. J., Cumming, D. H. & Phillipson, J. Oecologia 22, 341–354 (1976).
ADS  CAS  Article  Google Scholar 

25.
Niedertscheider, M. et al. Environ. Res. Lett. 11, 014008 (2016).
ADS  Article  Google Scholar 

26.
Fry, J. P., Mailloux, N. A., Love, D. C., Milli, M. C. & Cao, L. Environ. Res. Lett. 13, 024017 (2018).
ADS  Article  Google Scholar 

27.
Kemp, W., Brooks, M. & Hood, R. Mar. Ecol. Prog. Ser. 223, 73–87 (2001).
ADS  Article  Google Scholar 

28.
Smil, V. Annu. Rev. Energy Environ. 25, 53–88 (2000).
Article  Google Scholar 

29.
Cordell, D., Drangert, J.-O. & White, S. Glob. Environ. Chang. 19, 292–305 (2009).
Article  Google Scholar 

30.
Zhou, S. & Flynn, P. Clim. Change 71, 203–220 (2005).
ADS  CAS  Article  Google Scholar 

31.
Nicol, S., Foster, J. & Kawaguchi, S. Fish Fish. 13, 30–40 (2012).
Article  Google Scholar 

32.
McCauley, D. J. et al. Ecol. Lett. 21, 439–454 (2018).
Article  Google Scholar 

33.
Bar-On, Y. M., Phillips, R. & Milo, R. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
CAS  Article  Google Scholar 

34.
Ytrestøyl, T., Aas, T. S. & Åsgård, T. Aquaculture 448, 365–374 (2015).
Article  Google Scholar 

35.
Ryther, J. H. Science 166, 72–76 (1969).
ADS  CAS  Article  Google Scholar 

36.
McNaughton, S. J., Oesterheld, M., Frank, D. A. & Williams, K. J. Nature 341, 142–144 (1989).
ADS  CAS  Article  Google Scholar  More

1.
Donnelly, C. A. et al. Four principles to make evidence synthesis more useful for policy. Nature 558, 361–364 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
McKinnon, M. C., Cheng, S. H., Garside, R., Masuda, Y. J. & Miller, D. C. Sustainability: map the evidence. Nature 528, 185–187 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Rubin, D. B. For objective causal inference, design trumps analysis. Ann. Appl. Stat. 2, 808–840 (2008).
MathSciNet  MATH  Article  Google Scholar 

4.
Peirce, C. S. & Jastrow, J. On small differences in sensation. Mem. Natl Acad. Sci. 3, 73–83 (1884).

5.
Fisher, R. A. Statistical methods for research workers. (Oliver and Boyd, 1925).

6.
Angrist, J. D. & Pischke, J.-S. Mostly harmless econometrics: an empiricist’s companion. (Princeton University Press, 2008).

7.
de Palma, A. et al. Challenges with inferring how land-use affects terrestrial biodiversity: study design, time, space and synthesis. in Next Generation Biomonitoring: Part 1 163–199 (Elsevier Ltd., 2018).

8.
Sagarin, R. & Pauchard, A. Observational approaches in ecology open new ground in a changing world. Front. Ecol. Environ. 8, 379–386 (2010).
Article  Google Scholar 

9.
Shadish, W. R., Cook, T. D. & Campbell, D. T. Experimental and quasi-experimental designs for generalized causal inference. (Houghton Mifflin, 2002).

10.
Rosenbaum, P. R. Design of observational studies. vol. 10 (Springer, 2010).

11.
Light, R. J., Singer, J. D. & Willett, J. B. By design: Planning research on higher education. By design: Planning research on higher education. (Harvard University Press, 1990).

12.
Ioannidis, J. P. A. Why most published research findings are false. PLOS Med. 2, e124 (2005).
PubMed  PubMed Central  Article  Google Scholar 

13.
Open Science Collaboration. Estimating the reproducibility of psychological science. Science 349, aac4716 (2015).
Article  CAS  Google Scholar 

14.
John, L. K., Loewenstein, G. & Prelec, D. Measuring the prevalence of questionable research practices with incentives for truth telling. Psychol. Sci. 23, 524–532 (2012).
PubMed  Article  PubMed Central  Google Scholar 

15.
Kerr, N. L. HARKing: hypothesizing after the results are known. Personal. Soc. Psychol. Rev. 2, 196–217 (1998).
CAS  Article  Google Scholar 

16.
Zhao, Q., Keele, L. J. & Small, D. S. Comment: will competition-winning methods for causal inference also succeed in practice? Stat. Sci. 34, 72–76 (2019).
MATH  Article  Google Scholar 

17.
Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning. vol. 1 (Springer series in statistics, 2001).

18.
Underwood, A. J. Beyond BACI: experimental designs for detecting human environmental impacts on temporal variations in natural populations. Mar. Freshw. Res. 42, 569–587 (1991).
Article  Google Scholar 

19.
Stewart-Oaten, A. & Bence, J. R. Temporal and spatial variation in environmental impact assessment. Ecol. Monogr. 71, 305–339 (2001).
Article  Google Scholar 

20.
Eddy, T. D., Pande, A. & Gardner, J. P. A. Massive differential site-specific and species-specific responses of temperate reef fishes to marine reserve protection. Glob. Ecol. Conserv. 1, 13–26 (2014).
Article  Google Scholar 

21.
Sher, A. A. et al. Native species recovery after reduction of an invasive tree by biological control with and without active removal. Ecol. Eng. 111, 167–175 (2018).
Article  Google Scholar 

22.
Imbens, G. W. & Rubin, D. B. Causal Inference in Statistics, Social, and Biomedical Sciences. (Cambridge University Press, 2015).

23.
Greenhalgh, T. How to read a paper: the basics of Evidence Based Medicine. (John Wiley & Sons, Ltd, 2019).

24.
Salmond, S. S. Randomized Controlled Trials: Methodological Concepts and Critique. Orthopaedic Nursing 27, (2008).

25.
Geijzendorffer, I. R. et al. How can global conventions for biodiversity and ecosystem services guide local conservation actions? Curr. Opin. Environ. Sustainability 29, 145–150 (2017).
Article  Google Scholar 

26.
Dimick, J. B. & Ryan, A. M. Methods for evaluating changes in health care policy. JAMA 312, 2401 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Ding, P. & Li, F. A bracketing relationship between difference-in-differences and lagged-dependent-variable adjustment. Political Anal. 27, 605–615 (2019).
Article  Google Scholar 

28.
Christie, A. P. et al. Simple study designs in ecology produce inaccurate estimates of biodiversity responses. J. Appl. Ecol. 56, 2742–2754 (2019).
Article  Google Scholar 

29.
Watson, M. et al. An analysis of the quality of experimental design and reliability of results in tribology research. Wear 426–427, 1712–1718 (2019).
Article  CAS  Google Scholar 

30.
Kilkenny, C. et al. Survey of the quality of experimental design, statistical analysis and reporting of research using animals. PLoS ONE 4, e7824 (2009).

31.
Christie, A. P. et al. The challenge of biased evidence in conservation. Conserv, Biol. 13577, https://doi.org/10.1111/cobi.13577 (2020).

32.
Christie, A. P. et al. Poor availability of context-specific evidence hampers decision-making in conservation. Biol. Conserv. 248, 108666 (2020).
Article  Google Scholar 

33.
Moscoe, E., Bor, J. & Bärnighausen, T. Regression discontinuity designs are underutilized in medicine, epidemiology, and public health: a review of current and best practice. J. Clin. Epidemiol. 68, 132–143 (2015).
Article  Google Scholar 

34.
Goldenhar, L. M. & Schulte, P. A. Intervention research in occupational health and safety. J. Occup. Med. 36, 763–778 (1994).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Junker, J. et al. A severe lack of evidence limits effective conservation of the World’s primates. BioScience https://doi.org/10.1093/biosci/biaa082 (2020).

36.
Altindag, O., Joyce, T. J. & Reeder, J. A. Can Nonexperimental Methods Provide Unbiased Estimates of a Breastfeeding Intervention? A Within-Study Comparison of Peer Counseling in Oregon. Evaluation Rev. 43, 152–188 (2019).
Article  Google Scholar 

37.
Chaplin, D. D. et al. The Internal And External Validity Of The Regression Discontinuity Design: A Meta-Analysis Of 15 Within-Study Comparisons. J. Policy Anal. Manag. 37, 403–429 (2018).
Article  Google Scholar 

38.
Cook, T. D., Shadish, W. R. & Wong, V. C. Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons. J. Policy Anal. Manag. 27, 724–750 (2008).
Article  Google Scholar 

39.
Ioannidis, J. P. A. et al. Comparison of evidence of treatment effects in randomized and nonrandomized studies. J. Am. Med. Assoc. 286, 821–830 (2001).
CAS  Article  Google Scholar 

40.
dos Santos Ribas, L. G., Pressey, R. L., Loyola, R. & Bini, L. M. A global comparative analysis of impact evaluation methods in estimating the effectiveness of protected areas. Biol. Conserv. 246, 108595 (2020).
Article  Google Scholar 

41.
Benson, K. & Hartz, A. J. A Comparison of Observational Studies and Randomized, Controlled Trials. N. Engl. J. Med. 342, 1878–1886 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Smokorowski, K. E. et al. Cautions on using the Before-After-Control-Impact design in environmental effects monitoring programs. Facets 2, 212–232 (2017).
Article  Google Scholar 

43.
França, F. et al. Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles. J. Appl. Ecol. 53, 1098–1105 (2016).
Article  Google Scholar 

44.
Duvendack, M., Hombrados, J. G., Palmer-Jones, R. & Waddington, H. Assessing ‘what works’ in international development: meta-analysis for sophisticated dummies. J. Dev. Effectiveness 4, 456–471 (2012).
Article  Google Scholar 

45.
Sutherland, W. J. et al. Building a tool to overcome barriers in research-implementation spaces: The Conservation Evidence database. Biol. Conserv. 238, 108199 (2019).
Article  Google Scholar 

46.
Gusenbauer, M. & Haddaway, N. R. Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Res. Synth. Methods 11, 181–217 (2020).
PubMed  PubMed Central  Article  Google Scholar 

47.
Konno, K. & Pullin, A. S. Assessing the risk of bias in choice of search sources for environmental meta‐analyses. Res. Synth. Methods 11, 698–713 (2020).
PubMed  PubMed Central  Google Scholar 

48.
Butsic, V., Lewis, D. J., Radeloff, V. C., Baumann, M. & Kuemmerle, T. Quasi-experimental methods enable stronger inferences from observational data in ecology. Basic Appl. Ecol. 19, 1–10 (2017).

49.
Brownstein, N. C., Louis, T. A., O’Hagan, A. & Pendergast, J. The role of expert judgment in statistical inference and evidence-based decision-making. Am. Statistician 73, 56–68 (2019).
MathSciNet  Article  Google Scholar 

50.
Hahn, J., Todd, P. & Klaauw, W. Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica 69, 201–209 (2001).
Article  Google Scholar 

51.
Slavin, R. E. Best evidence synthesis: an intelligent alternative to meta-analysis. J. Clin. Epidemiol. 48, 9–18 (1995).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Slavin, R. E. Best-evidence synthesis: an alternative to meta-analytic and traditional reviews. Educ. Researcher 15, 5–11 (1986).
Article  Google Scholar 

53.
Shea, B. J. et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ (Online) 358, 1–8 (2017).
Google Scholar 

54.
Sterne, J. A. C. et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355, i4919 (2016).
PubMed  PubMed Central  Article  Google Scholar 

55.
Guyatt, G. et al. GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes. J. Clin. Epidemiol. 66, 151–157 (2013).
PubMed  Article  PubMed Central  Google Scholar 

56.
Davies, G. M. & Gray, A. Don’t let spurious accusations of pseudoreplication limit our ability to learn from natural experiments (and other messy kinds of ecological monitoring). Ecol. Evolution 5, 5295–5304 (2015).
Article  Google Scholar 

57.
Lortie, C. J., Stewart, G., Rothstein, H. & Lau, J. How to critically read ecological meta-analyses. Res. Synth. Methods 6, 124–133 (2015).
PubMed  Article  PubMed Central  Google Scholar 

58.
Gutzat, F. & Dormann, C. F. Exploration of concerns about the evidence-based guideline approach in conservation management: hints from medical practice. Environ. Manag. 66, 435–449 (2020).
Article  Google Scholar 

59.
Greenhalgh, T. Will COVID-19 be evidence-based medicine’s nemesis? PLOS Med. 17, e1003266 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

61.
Gurevitch, J. & Hedges, L. V. Statistical issues in ecological meta‐analyses. Ecology 80, 1142–1149 (1999).
Article  Google Scholar 

62.
Stone, J. C., Glass, K., Munn, Z., Tugwell, P. & Doi, S. A. R. Comparison of bias adjustment methods in meta-analysis suggests that quality effects modeling may have less limitations than other approaches. J. Clin. Epidemiol. 117, 36–45 (2020).
PubMed  Article  Google Scholar 

63.
Rhodes, K. M. et al. Adjusting trial results for biases in meta-analysis: combining data-based evidence on bias with detailed trial assessment. J. R. Stat. Soc.: Ser. A (Stat. Soc.) 183, 193–209 (2020).
MathSciNet  CAS  Article  Google Scholar 

64.
Efthimiou, O. et al. Combining randomized and non-randomized evidence in network meta-analysis. Stat. Med. 36, 1210–1226 (2017).
MathSciNet  PubMed  Article  Google Scholar 

65.
Welton, N. J., Ades, A. E., Carlin, J. B., Altman, D. G. & Sterne, J. A. C. Models for potentially biased evidence in meta-analysis using empirically based priors. J. R. Stat. Soc. Ser. A (Stat. Soc.) 172, 119–136 (2009).
Article  Google Scholar 

66.
Turner, R. M., Spiegelhalter, D. J., Smith, G. C. S. & Thompson, S. G. Bias modelling in evidence synthesis. J. R. Stat. Soc.: Ser. A (Stat. Soc.) 172, 21–47 (2009).
MathSciNet  Article  Google Scholar 

67.
Shackelford, G. E. et al. Dynamic meta-analysis: a method of using global evidence for local decision making. bioRxiv 2020.05.18.078840, https://doi.org/10.1101/2020.05.18.078840 (2020).

68.
Sutherland, W. J., Pullin, A. S., Dolman, P. M. & Knight, T. M. The need for evidence-based conservation. Trends Ecol. evolution 19, 305–308 (2004).
Article  Google Scholar 

69.
Ioannidis, J. P. A. Meta-research: Why research on research matters. PLOS Biol. 16, e2005468 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

70.
LaLonde, R. J. Evaluating the econometric evaluations of training programs with experimental data. Am. Econ. Rev. 76, 604–620 (1986).

71.
Long, Q., Little, R. J. & Lin, X. Causal inference in hybrid intervention trials involving treatment choice. J. Am. Stat. Assoc. 103, 474–484 (2008).
MathSciNet  CAS  MATH  Article  Google Scholar 

72.
Thomson Reuters. ISI Web of Knowledge. http://www.isiwebofknowledge.com (2019).

73.
Stroup, W. W. Generalized linear mixed models: modern concepts, methods and applications. (CRC press, 2012).

74.
Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evolution 24, 127–135 (2009).
Article  Google Scholar 

75.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2019).

76.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

77.
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002).

78.
Stan Development Team. RStan: the R interface to Stan. R package version 2.19.3 (2020). More

1.
Newton, I. The Migration Ecology of Birds (Academic Press, Cambridge, 2008).
Google Scholar 
2.
Alerstam, T. Bird Migration (Cambridge University Press, Cambridge, 1990).
Google Scholar 

3.
Alerstam, T. Detours in bird migration. J. Theor. Biol. 209, 319–331 (2001).
CAS  PubMed  Article  Google Scholar 

4.
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: Evolution and determinants. Oikos 103, 247–260 (2003).
Article  Google Scholar 

5.
Alves, J. A., Dias, M. P., Méndez, V., Katrínardóttir, B. & Gunnarsson, T. Very rapid long-distance sea crossing by a migratory bird. Sci. Rep. 6, 38154 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Schmaljohann, H., Liechti, F. & Bruderer, B. Songbird migration across the Sahara: The non-stop hypothesis rejected!. Proc. R. Soc. B 274, 735–739 (2007).
PubMed  Article  PubMed Central  Google Scholar 

7.
Gill, R. E. Jr. et al. Extreme endurance flights by landbirds crossing the Pacific Ocean: Ecological corridor rather than barrier?. Proc. R. Soc. B. 276, 447–457 (2009).
PubMed  Article  PubMed Central  Google Scholar 

8.
Léandri-Breton, D. J., Lamarre, J. F. & Bêty, J. Seasonal variation in migration strategies used to cross ecological barriers in a nearctic migrant wintering in Africa. J. Avian Biol. 50, e02101 (2019).
Article  Google Scholar 

9.
Donald, C. H. Bird migration across Himalayas. J. Bombay Nat. Hist. Soc. 51, 269–271 (1953).
Google Scholar 

10.
Kinnear, N. B. On the birds collected by Mr. A.F.R. Wollaston during the first Mount Everest Expedition. Ibis 64, 495–526 (1922).
Article  Google Scholar 

11.
Ali, S. & Ripley, S. D. Compact Handbook of the Birds of India and Pakistan Together with those of Bangladesh, Nepal, Bhutan and Sri Lanka. 2nd edn. (Oxford University Press, Oxford, 1987)

12.
Balachandran, S., Katti, T. & Manakadan, R. Indian Bird Migration Atlas (Bombay Natural History Society & Oxford University Press, Oxford, 2018).
Google Scholar 

13.
Prins, H. H. T. & Namgail, T. Bird Migration Across the Himalayas: Wetland Functioning amidst Mountains and Glaciers (Cambridge University Press, Cambridge, 2017).
Google Scholar 

14.
Kanai, Y., Minton, J. & Nagendran, M. Migration of Demoiselle Cranes in Asia based on satellite tracking and field work. Glob. Environ. Res. 4, 143–153 (2000).
Google Scholar 

15.
Parr, N. et al. High altitude flights by Ruddy Shelduck Tadorna ferruginea during trans-Himalayan migrations. J. Avian Biol. 48, 1310–1315 (2017).
Article  Google Scholar 

16.
Namgail, T. et al. Himalayan Thoroughfare: Migratory Routes of Ducks over the Rooftop of the World. In Bird Migration Across the Himalayas: Wetland Functioning amidst Mountains and Glaciers (eds. Prins, H. H. T. & Namgail, T.) 30–44 (Cambridge University Press, Cambridge, 2017).

17.
Hawkes, L. A. et al. The trans-Himalayan flights of Bar-headed Geese (Anser indicus). Proc. Natl. Acad. Sci. U. S. A. 108, 9516–9518 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Hawkes, L. A. et al. The paradox of extreme high-altitude migration in bar-headed geese Anser indicus. Proc. R. Soc. B 280, 20122114 (2012).
PubMed  Article  PubMed Central  Google Scholar 

19.
Veen, J. et al. An Atlas of Movements of Southwest Siberian Waterbirds (Wetlands International, Wageningen, 2005).
Google Scholar 

20.
Pavlov, D. S. Migrations of Birds of Eastern Europe and Northern Asia Gruiformes and Charadriiformes (in Russian). (Academy of Sciences of the USSR, 1985).

21.
McClure, H. E. Migration and Survival of the Birds of Asia. (US Army Medical Component SEATO Medical Project, 1974).

22.
Delany, D., Williams, C., Sulston, C., Norton, J. & Garbutt, D. Wader migration across the Himalayas. In Bird Migration Across the Himalayas: Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H. H. T. & Namgail, T.) 82–97 (Cambridge University Press, Cambridge, 2017).

23.
Bamford, M., Watkins, D., Bancroft, W., Tischler, G. & Wahl, J. Migratory Shorebirds of the East Asian—Australasian Flyway: Population Estimates and Internationally Important Sites. (Wetlands International, Oceania, 2008).

24.
Cao, W. H. et al. Tracking the migration of Whimbrels along the East Asian-Australasian Flyway (in Chinese). Chin. J. Zool. 54, 775–783. https://doi.org/10.13859/j.cjz.201906000 (2019).
Article  Google Scholar 

25.
Higgins, P. J. & Davies, S. J. J. F. Handbook of Australian, New Zealand and Antarctic Birds. Volumes 3: Snipe to Pigeons. (Oxford University Press, Oxford, 1996).

26.
Gan, J., Tan, M. & Li, D. Migratory Birds of Sungei Buloh Wetland Reserve. 2nd edn. (Singapore National Parks Board, Singapore, 2012).

27.
Zhang, F. Y. & Yang, R. L. China Bird Migration Research (in Chinese). (Beijing Forestry Press, Beijing, 1997).

28.
Wells, D. R. The Birds of Thai-Malay Peninsula, Volume 1: Non Passerines. (Academic Press, Cambridge, 1999).

29.
Yatim, S. H. Short notes on band recovery of waders in 1991/1992. J. Wildlife Parks 11, 58–59 (1991).
Google Scholar 

30.
Chia, A. A. ‘Ringing’ endorsement for Singapore migrant’s flight of wonder. Nat. Watch. 21, 17 (2013).
Google Scholar 

31.
Standen, R. & Londo, I. Sumatran-flagged Common Redshank seen on the breeding grounds. Tattler 37, 7–8 (2015).
Google Scholar 

32.
Bellio, M. & Kaluthota, C. Australian Curlew Sandpiper on passage through Sri Lanka. Wader Study 110, 66 (2006).
Google Scholar 

33.
Tiwari, J. K. An Australian ringed bird seen in Kutch, India. Tattler 31, 19 (2013).
Google Scholar 

34.
Zöckler, C., Moses, S. & Lwin, S. T. The importance of the Myeik mangroves and mudflats, Tanintharyi, Myanmar for migratory waders and other waterbirds. Wader Study 126, 129–141 (2019).
Article  Google Scholar 

35.
Ratanakorn, P. et al. Satellite tracking on the flyways of Brown-headed Gulls and their potential role in the spread of highly pathogenic avian influenza H5N1 virus. PLoS ONE 7, e49939 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

36.
Hayman, P., Marchant, J. & Prater, A. J. Shorebirds. (Croom Helm, 1986).

37.
Summers, R. W., Underhill, L. G. & Prys-Jones, R. P. Why do young waders in southern Africa delay their first return migration to the breeding grounds?. Ardea 83, 351–357 (1995).
Google Scholar 

38.
Battley, P. F. et al. Interacting roles of breeding geography and early-life settlement in godwit migration timing. Front. Ecol. Evol. 8, 52 (2020).
Article  Google Scholar 

39.
Kuang, F. et al. Seasonal and population differences in migration of Whimbrels in the East Asian–Australasian Flyway. Avian Res. 11, 24 (2020).
Article  Google Scholar 

40.
Dolnik, V. R. Bird migration across arid and mountainous regions of Middle Asia and Kazakhstan. In Bird Migration (ed. Gwinner E.) 368–386 (Springer, New York, 1990).

41.
Senner, N. R. et al. High-altitude shorebird migration in the absence of topographical barriers: Avoiding high air temperatures and searching for profitable winds. Proc. R. Soc. B. 285, 20180569 (2018).
PubMed  Article  PubMed Central  Google Scholar 

42.
Alerstam, T. et al. A polar system of intercontinental bird migration. Proc. Biol. Sci. 274, 2523–2530 (2007).
PubMed  PubMed Central  Google Scholar 

43.
Duijns, S. et al. Long-distance migratory shorebirds travel faster towards their breeding grounds, but fly faster post breeding. Sci. Rep. 9, 9420 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Lague, S. L. et al. Divergent respiratory and cardiovascular responses to hypoxia in bar-headed geese and Andean birds. J. Exp. Biol. 220, 4186–4194 (2017).
PubMed  Article  PubMed Central  Google Scholar 

45.
Parr, N., Wilkes, M. & Hawkes, L. A. Natural climbers: Insights from avian physiology at high altitude. High Alt. Med. Biol. 20, 427–437 (2019).
PubMed  Article  PubMed Central  Google Scholar 

46.
Scott, G. R. Elevated performance: The unique physiology of birds that fly at high altitudes. J. Exp. Biol. 214, 2455–2462 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Landys-Ciannelli, M. M., Jukema, J. & Piersma, T. Blood parameter changes during stopover in a long-distance migratory shorebird, the bar-tailed godwit Limosa lapponica taymyrensis. J. Avian Biol. 33, 451–455 (2002).
Article  Google Scholar 

48.
Guglielmo, C. G., Haunerland, N. H., Hochachka, P. W. & Williams, T. D. Seasonal dynamics of flight muscle fatty acid binding protein and catabolic enzymes in a migratory shorebird. Am. J. Physiol. Regul. Integr. Comp. Physiol. 282, 1405–1413 (2002).
Article  Google Scholar 

49.
Piersma, T., Gudmundsson, G. A. & Lilliendahl, K. Rapid changes in the size of different functional organ and muscle groups during refueling in a long-distance migrating shorebird. Physiol. Biochem. Zool. 72, 405–415 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Lu, X. The Birds of Qinghai-Tibet Plateau of China (in Chinese). (Hunan Science and Technology Press, Hunan, 2018).

51.
Liu, N. F., Bao, X. K. & Liao, J. C. Bird Classification and Distribution on Qinghai-Tibet Plateau (in Chinese). (Beijing Science Press, Beijing, 2013).

52.
Clark, N. A. et al. The use of light-level geolocators to study wader movements. Wader Study 117, 173–178 (2010).
Google Scholar 

53.
Minton, C. et al. Geolocator studies on Ruddy Turnstones Arenaria interpres and Greater Sandplovers Charadrius leschenaultii in the East Asian-Australasia Flyway reveal widely different migration strategies. Wader Study 118, 87–96 (2011).
Google Scholar 

54.
Buxton, N. Redshanks in the Western Isles of Scotland. Ringing Migr. 9, 146–152 (1988).
Article  Google Scholar 

55.
Burton, N. H. K. Winter site-fidelity and survival of Redshank Tringa totanus at Cardiff, south Wales. Bird Study 47, 102–112 (2000).
Article  Google Scholar 

56.
Lisovski, S. et al. Light-level geolocator analyses: A user’s guide. J. Anim. Ecol. 89, 221–236. https://doi.org/10.1111/1365-2656.13036 (2020).
Article  PubMed  PubMed Central  Google Scholar 

57.
Lisovski, S., Sumner, M. & Wotherspoon, S. TwGeos: Basic data processing for light-level geolocation archival tags. GitHub repository. https://github.com/slisovski/TwGeos. (2016).

58.
Lisovski, S. Define movements in light-level geolocator data. GitHub repository: https://github.com/slisovski/invMovement. (2019).

59.
Wotherspoon, S.J., Sumner, D.A., Lisovski, S. R Package SGAT: Solar/Satellite Geolocation for Animal Tracking. GitHub repository. https://github.com/SWotherspoon/SGAT. (2013).

60.
Battley, P. F. & Conklin, J. R. Geolocator wetness data accurately detect periods of migratory flight in two species of shorebird. Wader Study 124, 112–119 (2017).
Article  Google Scholar 

61.
Rappole, J. H. & Tipton, A. R. New harness design for attachment of radio transmitters to small passerines. J. Field Ornithol. 62, 335–337 (1991).
Google Scholar 

62.
Phillips, R. A., Xavier, J. C. & Croxall, J. P. Effects of satellite transmitters on Albatrosses and Petrels. Auk 120(4), 1082–1090 (2003).
Article  Google Scholar 

63.
Davidson, N. C. & Evans, P. R. Prebreeding accumulation of fat and muscle protein by Arctic-breeding shorebirds. Proc. Int. Ornithol. Congr. 19, 342–352 (1988).
Google Scholar 

64.
Kranstauber, B., Smolla, M. & Scharf, A. K. Move: Visualizing and Analyzing Animal Track Data. R package version 3.3.0. https://CRAN.R-project.org/package=move. (2020).

65.
Dodge, S. et al. The environmental-data automated track annotation (Env-DATA) system: Linking animal tracks with environmental data. Mov. Ecol. 1(1), 3 (2013).
PubMed  PubMed Central  Article  Google Scholar 

66.
Safi, K. et al. Flying with the wind: Scale dependency of speed and direction measurements in modelling wind support in avian flight. Mov. Ecol. 1(1), 4 (2013).
PubMed  PubMed Central  Article  Google Scholar 

67.
Amante, C. & Eakins, B.W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA. (2009). https://doi.org/10.7289/V5C8276M

68.
BirdLife International and Handbook of the Birds of the World. Bird species distribution maps of the world. Version 2018.1. http://datazone.birdlife.org/species/requestdis (2018).

69.
R Core Team. R: A Language of Environment and Statistical Computing, Vienna Austria. https://www.R-project.org (2019). More