More stories

  • in

    Optofluidic 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 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

  • in

    Limits to food production from the sea

    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

  • in

    Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences

    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

  • in

    Shorebirds wintering in Southeast Asia demonstrate trans-Himalayan flights

    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

  • in

    The erosion of biodiversity and biomass in the Atlantic Forest biodiversity hotspot

    Study region
    The Atlantic Forest is a global biodiversity hotspot that once covered 1.63 million km2 mostly in Brazil (92% of the total area), but also in Paraguay (6%) and Argentina (2%—Supplementary Fig. 1). It covers a wide range of climatic and edaphic conditions, with forest types ranging from rainforests to seasonal forests, including cloud, swamp, and white-sand forests56. The Atlantic Forest has been suffering from deforestation and degradation for over 500 years. Today, it includes some of the largest cities in South-America, with over 148 million people currently living within the Atlantic Forest limits57. Less than 20% of the original Atlantic Forest remains and the remnants are characterized by small ( More

  • in

    River conservation by an Indigenous community

    Rivers are a major source of renewable water, and provide food, jobs and a sense of place and cultural identity for people living in the vicinity. For many Indigenous peoples, rivers are central to how they understand themselves, their origins and their relationships to the rest of nature. As a citizen of the Penobscot Nation in Maine put it1, “The river is us: the river is in our veins.” Writing in Nature, Koning et al.2 report ecological surveys that demonstrate how local Indigenous people in the Salween River basin on the border between Thailand and Myanmar have successfully managed the river for conservation purposes and to protect livelihoods.

    Both biodiversity and the people in river-associated communities are under severe stress the world over. Across the globe, 30% of freshwater fish (see go.nature.com/3ixfd9l) are classified as being at risk (in either the critically endangered, endangered or vulnerable categories) in the 2020 Red List of threatened species compiled by the International Union for the Conservation of Nature. Furthermore, it is projected3 that half the human population will live in water-insecure areas by 2050. Principal among the threats to rivers are pollution, climate change, invasive species, changes in surrounding land use, and the construction of dams and infrastructure that affect river flow. These issues need to be addressed on scales ranging from local to global, and solutions should draw on the knowledge, practices and aspirations of those whose lives are most closely entwined with river health.
    Koning et al. assessed the outcome of a network of small fishery no-take reserves (areas where fishing is not allowed), and found that there was an average 27% rise in species richness, 124% higher fish density and 2,247% higher fish biomass in the reserve-associated waters compared with the corresponding values for nearby areas open to fishing. The presence of larger species and more individuals in the reserves is what drives the much higher biomass there. The authors suggest that such networks of locally managed, small, protected river areas could be used in other river systems to enhance fisheries and to conserve biodiversity.
    The authors’ work highlights the importance of inland waters to food and livelihood systems, demonstrates the value of community-led conservation, and points out commonalities between protected-area conservation strategies in marine and freshwater ecosystems. Marine-protected areas, which are usually created by governments, are used widely in ocean conservation and fisheries, but much less commonly in fresh waters4. The authors characterize the reserves studied as being created by the S’gaw Karen (also known as Pwak’nyaw) Indigenous people who live in the river catchment areas. The paper thus also supports the growing recognition5 among scientists and conservationists of the effectiveness of Indigenous resource-management practices.

    Koning and colleagues’ study draws on natural sciences — limnology (the freshwater equivalent of oceanography) and fish ecology — but also discusses how river management operates at a community level. Their natural-sciences disciplinary lens allows them to rigorously evaluate the benefits that protected areas confer on fish conservation and on the sustainability of local fish catches. In the area studied, Indigenous communities had planned and implemented local no-take reserves that complement other community-based conservation initiatives, including the management of adjacent land.
    However, the context in which this management system evolved, the knowledge and politics involved in its creation, and how local forms of knowledge and practice can be supported and valued are less in focus in Koning and colleagues’ study. Pwak’nyaw communities have been profoundly transformed as a result of colonization in Myanmar, the arrival of foreign missionaries in Myanmar and Thailand, and state modernization projects in both countries. Supporting river conservation here and elsewhere at locations where other Indigenous peoples live will require a reckoning with such legacies and a willingness to make space for local and Indigenous voices to be heard, alongside those of scientists, in river-basin planning.
    One of us (V.C.) is a Pwak’nyaw person, born in Hpa’an, Myanmar, on the banks of the Salween River, and believes that it is crucial that science conducted in Indigenous territory incorporates Indigenous systems of knowledge and beliefs, and for Indigenous people to have ownership over data that involve them. Although, during a period of 8 years of research, Koning et al. worked with local people for more than 18 months when living in the study area, there is scope for furthering these relationships so that Indigenous perspectives have increased visibility. An absence of Indigenous agency and control in the production of knowledge is a key issue, leading to calls for Indigenous data sovereignty and the decolonization of science6.

    Koning and colleagues’ study positively recognizes Pwak’nyaw involvement in conservation, and includes some cultural context, although Pwak’nyaw perspectives are lacking. One consequence of this might be the study’s focus on what the Pwak’nyaw would regard as only part of their integrated system of land and water management. For example, Pwak’nyaw don’t commonly identify themselves by categories that are familiar to those in Western culture, such as being a farmer or a fisher. Rather, rotational farming, growing rice, gardening, hunting, gathering and fishing are integrated parts of a Pwak’nyaw livelihood.
    Community-based research on Pwak’nyaw livelihoods in northern Thailand has found that fish conservation is also integrated into rotational farming practices. For instance, the concept nya pla htau, meaning fish surface, prohibits the clearing of a field on adjacent sides of a river bank in successive years to conserve fish-breeding grounds, and knowledge about fish is a factor in the selection of farmland7. In this sense, farming cannot be separated from fishing, which cannot be separated from conservation, because they are all part of a whole — and it is beneficial for them to be studied as such.
    Future studies, which should involve collaboration with Indigenous researchers, could adopt approaches to integrate Indigenous and scientific knowledge and Indigenous and Western legal and management approaches in ways that recognize and draw on both8. This would help to address some of the unanswered questions in Koning and colleagues’ valuable study on the origins, sustainability and future of this successful network of reserves.
    Conflict can arise in Thailand and elsewhere when there is confrontation between Indigenous people and the state, or other groups, regarding competing conservation models. Indigenous lives are in danger — around the world in 2019, more than 200 environmental activists died, 40% of whom were Indigenous people (see go.nature.com/36w68di). In the past decade, the deaths of prominent Pwak’nyaw environmental activists in Myanmar (see go.nature.com/2vspujn) and in Thailand (see go.nature.com/3mwjqm1) have hit the headlines.

    Figure 1 | The Salween Peace Park. Pwak’nyaw (also known as S’gaw Karen) people living at this site in Myanmar, located on a tributary of the Salween River, use their Indigenous knowledge to obtain food. For example, the basket-style nets in this image are a traditional way to catch fish and shellfish in shallow waters. Koning et al.2 report that conservation efforts by the Pwak’nyaw community in the Salween River basin area have substantially boosted fish diversity and might increase the yields of fishing catches.Credit: Paul Sein Twa/KESAN

    Indigenous resource-management systems can persist despite difficult circumstances. On the Myanmar side of the Salween River, Pwak’nyaw communities, whose livelihoods are affected by ongoing civil war, displacement and militarized development, have created a large-scale conservation project named the Salween Peace Park (Fig. 1), based on kaw (country), a holistic concept that encompasses the localized practice of social and environmental governance, based on Indigenous sovereignty. Pwak’nyaw living there conserve the environment using Indigenous knowledge (see go.nature.com/36tigxg), and are working to revive Indigenous practices lost through decades of conflict.
    Without such contextual cultural and political knowledge, it is difficult to say how easily the successes in the Salween River basin, convincingly enumerated by Koning and colleagues’ study, can be achieved elsewhere by trying to transfer this approach. The key insight here may be that the small reserves are potentially useful conservation measures that need to be understood from the perspectives of those who created them. Such reserves should be supported and legitimized where they exist, revived where they existed previously, and perhaps tried out where they haven’t been used before, as part of efforts to meet global river-conservation challenges. This would support a growing movement led by Indigenous peoples to focus on putting rivers at the centre of conservation efforts — including by assigning legal personhood to rivers, as part of a ‘rights of nature’ approach to environmental governance9. More

  • in

    In situ observations show vertical community structure of pelagic fauna in the eastern tropical North Atlantic off Cape Verde

    1.
    Robison, B. H. Deep pelagic biology. J. Exp. Mar. Biol. Ecol. 300, 253–272 (2004).
    Article  Google Scholar 
    2.
    Ramirez-Llodra, E. et al. Deep, diverse and definitely different: unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).
    ADS  Article  Google Scholar 

    3.
    Thurber, A. R. et al. Ecosystem function and services provided by the deep sea. Biogeosciences 11, 3941–3963 (2014).
    ADS  Article  Google Scholar 

    4.
    Keeling, R. F., Körtzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Ann. Rev. Mar. Sci. 2, 199–229 (2010).
    PubMed  Article  Google Scholar 

    5.
    Levin, L. A. & Le Bris, N. The deep ocean under climate change. Science 350, 766–768 (2015).
    ADS  CAS  PubMed  Article  Google Scholar 

    6.
    Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335–339 (2017).
    ADS  CAS  PubMed  Article  Google Scholar 

    7.
    Breitburg, D. L. et al. Declining oxygen in the global ocean and coastal waters. Science 359, 1–11 (2018).
    Article  CAS  Google Scholar 

    8.
    Robison, B. H. Conservation of deep pelagic biodiversity. Conserv. Biol. 23, 847–858 (2009).
    PubMed  Article  Google Scholar 

    9.
    Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548 (2013).
    ADS  CAS  Article  Google Scholar 

    10.
    Paulmier, A., Ruiz-Pino, D., Garçon, V. & Farías, L. Maintaining of the eastern south pacific oxygen minimum zone (OMZ) off Chile. Geophys. Res. Lett. 33, 1–6 (2006).
    Article  CAS  Google Scholar 

    11.
    Gilly, W. F., Beman, J. M., Litvin, S. Y. & Robison, B. H. Oceanographic and biological effects of shoaling of the oxygen minimum zone. Ann. Rev. Mar. Sci. 5, 393–420 (2013).
    PubMed  Article  Google Scholar 

    12.
    Chavez, F. P. & Messié, M. A comparison of eastern boundary upwelling ecosystems. Prog. Oceanogr. 83, 80–96 (2009).
    ADS  Article  Google Scholar 

    13.
    Karstensen, J., Stramma, L. & Visbeck, M. Oxygen minimum zones in the eastern tropical Atlantic and Pacific oceans. Prog. Oceanogr. 77, 331–350 (2008).
    ADS  Article  Google Scholar 

    14.
    Deutsch, C., Ferrel, A., Seibel, B., Pörtner, H.-O. & Huey, R. B. Climate change tightens a metabolic constraint on marine habitats. Science 348, 1132–1135 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Ekau, W., Auel, H., Pörtner, H.-O. & Gilbert, D. Impacts of hypoxia on the structure and processes in pelagic communities (zooplankton, macro-invertebrates and fish). Biogeosciences 7, 1669–1699 (2010).
    ADS  CAS  Article  Google Scholar 

    16.
    Childress, J. J. & Seibel, B. A. Life at stable low oxygen levels: adaptations of animals to oceanic oxygen minimum layers. J. Exp. Biol. 201, 1223–1232 (1998).
    CAS  PubMed  PubMed Central  Google Scholar 

    17.
    Gallo, N. D. & Levin, L. A. Fish ecology and evolution in the world’s oxygen minimum zones and implications of ocean deoxygenation. Adv. Mar. Biol. 74, 117–198 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Seibel, B. A. et al. Metabolic suppression during protracted exposure to hypoxia in the jumbo squid, Dosidicus gigas, living in an oxygen minimum zone. J. Exp. Biol. 217, 2555–2568 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    19.
    Lampert, W. The adaptive significance of diel vertical migration of zooplankton. Funct. Ecol. 3, 21–27 (1989).
    Article  Google Scholar 

    20.
    Longhurst, A. R., Bedo, A. W., Harrison, W. G., Head, E. J. H. & Sameoto, D. D. Vertical flux of respiratory carbon by oceanic diel migrant biota. Deep. Res. Part A. 37, 685–694 (1990).
    ADS  CAS  Article  Google Scholar 

    21.
    Kiko, R. et al. Zooplankton-mediated fluxes in the eastern tropical North Atlantic. Front. Mar. Sci. 7, 1–21 (2020).
    ADS  Article  Google Scholar 

    22.
    Christiansen, S. et al. Particulate matter flux interception in oceanic mesoscale eddies by the polychaete Poeobius sp. Limnol. Oceanogr. 63, 2093–2109 (2018).
    ADS  CAS  Article  Google Scholar 

    23.
    Robison, B. H., Sherlock, R. E., Reisenbichler, K. R. & Mcgill, P. R. Running the gauntlet: assessing the threats to vertical migrators. Front. Mar. Sci. 7, 1–10 (2020).
    CAS  Article  Google Scholar 

    24.
    NogueiraJúnior, M., PereiraBrandini, F. & UgazCodina, J. C. Diel vertical dynamics of gelatinous zooplankton (Cnidaria, Ctenophora and Thaliacea) in a subtropical stratified ecosystem (South Brazilian Bight). PLoS ONE 10, 1–28 (2015).
    Google Scholar 

    25.
    Wishner, K. F., Outram, D. M., Seibel, B. A., Daly, K. L. & Williams, R. L. Zooplankton in the eastern tropical north Pacific: boundary effects of oxygen minimum zone expansion. Deep Sea Res. Part I(79), 122–140 (2013).
    Article  CAS  Google Scholar 

    26.
    Hoving, H. J. T. & Robison, B. H. Vampire squid: detritivores in the oxygen minimum zone. Proc. R. Soc. B Biol. Sci. 279, 4559–4567 (2012).
    Article  Google Scholar 

    27.
    Seibel, B. A. Cephalopod susceptibility to asphyxiation via ocean incalescence, deoxygenation and acidification. Physiology 31, 418–429 (2016).
    CAS  PubMed  Article  Google Scholar 

    28.
    Gilly, W. F. et al. Vertical and horizontal migrations by the jumbo squid Dosidicus gigas revealed by electronic tagging. Mar. Ecol. Prog. Ser. 324, 1–17 (2006).
    ADS  Article  Google Scholar 

    29.
    Zuyev, G., Nigmatullin, C., Chesalin, M. & Nesis, K. Main results of long-term worldwide studies on tropical nektonic oceanic squid genus Sthenoteuthis: an overview of the Soviet investigations. Bull. Mar. Sci. 71, 1019–1060 (2002).
    Google Scholar 

    30.
    Prince, E. D. et al. Ocean scale hypoxia-based habitat compression of Atlantic istiophorid billfishes. Fish. Oceanogr. 19, 448–462 (2010).
    Article  Google Scholar 

    31.
    Stramma, L. et al. Expansion of oxygen minimum zones may reduce available habitat for tropical pelagic fishes. Nat. Clim. Chang. 2, 33–37 (2012).
    ADS  CAS  Article  Google Scholar 

    32.
    Thuesen, E. V. et al. Intragel oxygen promotes hypoxia tolerance of scyphomedusae. J. Exp. Biol. 208, 2475–2482 (2005).
    PubMed  Article  Google Scholar 

    33.
    Thuesen, E. V. & Childress, J. J. Oxygen consumption rates and metabolic enzyme activities of oceanic California Medusae in relation to body size and habitat depth. Biol. Bull. 187, 84–98 (1994).
    CAS  PubMed  Article  Google Scholar 

    34.
    Mills, C. E. Jellyfish blooms: are populations increasing globally in response to changing ocean conditions?. Hydrobiologia 451, 55–68 (2001).
    Article  Google Scholar 

    35.
    Hauss, H. et al. Dead zone or oasis in the open ocean? Zooplankton distribution and migration in low-oxygen modewater eddies. Biogeosciences 13, 1977–1989 (2016).
    ADS  CAS  Article  Google Scholar 

    36.
    Russell, F. S. On a remarkable new scyphomedusan. J. Mar. Biol. Assoc. UK 47, 469–473 (1967).
    Article  Google Scholar 

    37.
    Matsumoto, G. I. & Robison, B. H. Kiyohimea usagi, a new species of lobate ctenophore from the Monterey Submarine Canyon. Bull. Mar. Sci. 51, 19–29 (1992).
    Google Scholar 

    38.
    Matsumoto, G. I., Raskoff, K. A. & Lindsay, D. J. Tiburonia granrojo n. sp., a mesopelagic scyphomedusa from the Pacific Ocean representing the type of a new subfamily (class Scyphozoa: order Semaeostomeae: family Ulmaridae: subfamily Tiburoniinae subfam. nov.). Mar. Biol. 143, 73–77 (2003).
    Article  Google Scholar 

    39.
    Lindsay, D. J. & Hunt, J. C. Biodiversity in midwater cnidarians and ctenophores: submersible-based results from deep-water bays in the Japan Sea nand north-western Pacific. J. Mar. Biol. Assoc. UK 85, 503–517 (2005).
    Article  Google Scholar 

    40.
    Robison, B. H., Raskoff, K. A. & Sherlock, R. E. Ecological substrate in midwater: Doliolula equus, a new mesopelagic tunicate. J. Mar. Biol. Assoc. UK 85, 655–663 (2005).
    Article  Google Scholar 

    41.
    Robison, B. H., Sherlock, R. E. & Reisenbichler, K. R. The bathypelagic community of Monterey Canyon. Deep. Res. Part II(57), 1551–1556 (2010).
    Article  Google Scholar 

    42.
    Robison, B. H., Reisenbichler, K. R., Sherlock, R. E., Silguero, J. M. B. & Chavez, F. P. Seasonal abundance of the siphonophore, Nanomia bijuga, Monterey Bay. Deep. Res. II(45), 1741–1751 (1998).
    ADS  Google Scholar 

    43.
    Choy, C. A., Haddock, S. H. D. & Robison, B. H. Deep pelagic food web structure as revealed by in situ feeding observations. Proc. R. Soc. B Biol. Sci. 284, 1–10 (2017).
    Google Scholar 

    44.
    Lindsay, D. J. et al. The perils of online biogeographic databases: a case study with the ‘monospecific’ genus Aegina (Cnidaria, Hydrozoa, Narcomedusae). Mar. Biol. Res. 13, 494–512 (2017).
    Article  Google Scholar 

    45.
    Raskoff, K., Hopcroft, R. R., Kosobokova, K., Purcell, J. & Youngbluth, M. Jellies under ice: ROV observations from the Arctic 2005 hidden ocean expedition. Deep. Res. Part II(57), 111–126 (2010).
    Article  Google Scholar 

    46.
    Hosia, A., Falkenhaug, T., Baxter, E. J. & Pagès, F. Abundance, distribution and diversity of gelatinous predators along the northern Mid-Atlantic Ridge: a comparison of different sampling methodologies. PLoS ONE 12, 1–18 (2017).
    Article  CAS  Google Scholar 

    47.
    Lindsay, D. J. et al. Submersible observations on the deep-sea fauna of the south-west Indian Ocean: preliminary results for the mesopelagic and near-bottom communities. JAMSTEC J. Deep Sea Res. 16, 1–10 (2000).
    Google Scholar 

    48.
    Robison, B. H., Reisenbichler, K. R. & Sherlock, R. E. The coevolution of midwater research and ROV technology at MBARI. Oceanography 30, 26–37 (2017).
    Article  Google Scholar 

    49.
    Hays, G. C., Doyle, T. K. & Houghton, J. D. R. A Paradigm Shift in the Trophic Importance of Jellyfish?. Trends Ecol. Evol. 33, 874–884 (2018).
    PubMed  Article  Google Scholar 

    50.
    Hoving, H. J. T. et al. the pelagic in situ observation system (PELAGIOS) to reveal biodiversity, behavior, and ecology of elusive oceanic fauna. Ocean Sci. 15, 1327–1340 (2019).
    ADS  CAS  Article  Google Scholar 

    51.
    Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).
    ADS  CAS  Article  Google Scholar 

    52.
    Breitburg, D. L. et al. The pattern and influence of low dissolved oxygen in the Patuxent River, a seasonally hypoxic estuary. Estuaries 26, 280–297 (2003).
    CAS  Article  Google Scholar 

    53.
    Bailey, T. G., Youngbluth, M. J. & Owen, G. P. Chemical composition and metabolic rates of gelatinous zooplankton from midwater and benthic boundary layer environments off Cape Hatteras, North Carolina, USA. Mar. Ecol. Prog. Ser. 122, 121–134 (1995).
    ADS  CAS  Article  Google Scholar 

    54.
    Maas, A. E., Frazar, S. L., Outram, D. M., Seibel, B. A. & Wishner, K. F. Fine-scale vertical distribution of macroplankton and micronekton in the Eastern Tropical North Pacific in association with an oxygen minimum zone. J. Plankton Res. 36, 1557–1575 (2014).
    Article  Google Scholar 

    55.
    Morrison, J. M. et al. The oxygen minimum zone in the Arabian Sea during 1995. Deep. Res. Part II(46), 1903–1931 (1999).
    Article  Google Scholar 

    56.
    Tecchio, S. et al. Food web structure and vulnerability of a deep-sea ecosystem in the NW Mediterranean Sea. Deep. Res. Part I(75), 1–15 (2013).
    Google Scholar 

    57.
    Toda, R., Lindsay, D. J., Fuentes, V. L. & Moteki, M. Community structure of pelagic cnidarians off Adélie Land, East Antarctica, during austral summer 2008. Polar Biol. 37, 269–289 (2014).
    Article  Google Scholar 

    58.
    Licandro, P. et al. Biogeography of jellyfish in the North Atlantic, by traditional and genomic methods. Earth Syst. Sci. Data 7, 173–191 (2015).
    ADS  Article  Google Scholar 

    59.
    Lindsay, D., Umetsu, M., Grossmann, M., Miyake, H. & Yamamoto, H. The Gelatinous Macroplankton Community at the Hatoma Knoll Hydrothermal Vent, in Subseafloor Biosph. Linked to Hydrothermal Syst. TAIGA Concept (J.-i. Ishibashi, eds.) 639–666 (2015). https://doi.org/10.1007/978-4-431-54865-2.

    60.
    Johnsen, S. & Widder, E. A. Ultraviolet absorption in transparent zooplankton and its implications for depth distribution and visual predation. Mar. Biol. 138, 717–730 (2001).
    Article  Google Scholar 

    61.
    Lüskow, F. et al. Distribution and biomass of gelatinous zooplankton in relation to an oxygen minimum zone and a shallow seamount in the Eastern Tropical Atlantic Ocean. Reg. Stud. Mar. Sci. Submitt. (2020)  

    62.
    Raskoff, K. A. Distributions and trophic interactions of mesopelagic hydromedusae in Monterey Bay, CA: In situ studies with the MBARI ROVs Ventana and Tiburon. Ocean Sci. Diego, CA. Eos, Trans. Am. Geophys. Union. 79, 1, (1998).

    63.
    Youngbluth, M., Sørnes, T., Hosia, A. & Stemmann, L. Vertical distribution and relative abundance of gelatinous zooplankton, in situ observations near the Mid-Atlantic Ridge. Deep. Res. II Top. Stud. Oceanogr. 55, 119–125 (2008).
    ADS  Article  Google Scholar 

    64.
    Grossmann, M. M., Nishikawa, J. & Lindsay, D. J. Diversity and community structure of pelagic cnidarians in the Celebes and Sulu Seas, southeast Asian tropical marginal seas. Deep. Res. Part I(100), 54–63 (2015).
    Article  Google Scholar 

    65.
    Swift, H. F., Hamner, W. M., Robison, B. H. & Madin, L. P. Feeding behavior of the ctenophore Thalassocalyce inconstans: revision of anatomy of the order Thalassocalycida. Mar. Biol. 156, 1049–1056 (2009).
    Article  Google Scholar 

    66.
    Hoving, H. J., Neitzel, P. & Robison, B. In situ observations lead to the discovery of the large ctenophore Kiyohimea usagi (Lobata: Eurhamphaeidae) in the eatern tropical Atlantic. Zootaxa 4526, 232–238 (2018).
    PubMed  Article  Google Scholar 

    67.
    Kiko, R., Hauss, H., Buchholz, F. & Melzner, F. Ammonium excretion and oxygen respiration of tropical copepods and euphausiids exposed to oxygen minimum zone conditions. Biogeosciences 13, 2241–2255 (2016).
    ADS  CAS  Article  Google Scholar 

    68.
    Seibel, B. A., Schneider, J. L., Kaartvedt, S., Wishner, K. F. & Daly, K. L. Hypoxia tolerance and metabolic suppression in oxygen minimum zone euphausiids: implications for ocean deoxygenation and biogeochemical cycles. Integr. Comp. Biol. 56, 510–523 (2016).
    CAS  PubMed  Article  Google Scholar 

    69.
    Christiansen, B. et al. SEAMOX: The influence of Seamounts and Oxygen Minimum Zones on Pelagic Fauna in the Eastern Tropical Atlantic. Cruise No. MSM49 (MARIA S. MERIAN-Berichte) 1–42 (2016). https://doi.org/10.2312/cr_msm49.

    70.
    Haeckel, S. The Deep-Sea Guide, (DSG) at http://dsg.mbari.org. Monterey Bay Aquarium Research Institute (MBARI). Consult. 2020-04-14. (1879)

    71.
    Lilley, M. K. S. & Lombard, F. Respiration of fragile planktonic zooplankton: extending the possibilities with a single method. J. Exp. Mar. Bio. Ecol. 471, 226–231 (2015).
    Article  Google Scholar 

    72.
    Raskoff, K. A. Foraging, prey capture, and gut contents of the mesopelagic narcomedusa Solmissus spp. (Cnidaria: Hydrozoa). Mar. Biol. 141, 1099–1107 (2002).
    Article  Google Scholar 

    73.
    Thuesen, E. V. & Childress, J. J. Metabolic rates, enzyme activities and chemical compositions of some deep-sea pelagic worms, particularly Nectonemertes mirabilis (Nemertea; Hoplonemertinea) and Poeobius meseres (Annelida; Polychaeta). Deep. Res. I(40), 937–951 (1993).
    Article  Google Scholar 

    74.
    Biard, T. et al. In situ imaging reveals the biomass of giant protists in the global ocean. Nature 532, 504–519 (2016).
    ADS  CAS  PubMed  Article  Google Scholar 

    75.
    Childress, J. J. Are there physiological and biochemical adaptations of metabolism in deep-sea animals?. Trends Ecol. Evol. 10, 1–10 (1995).
    Article  Google Scholar 

    76.
    Koslow, J. A., Goericke, R., Lara-Lopez, A. & Watson, W. Impact of declining intermediate-water oxygen on deepwater fishes in the California Current. Mar. Ecol. Prog. Ser. 436, 207–218 (2011).
    ADS  Article  Google Scholar 

    77.
    Netburn, A. N. & Koslow, J. A. Dissolved oxygen as a constraint on daytime deep scattering layer depth in the southern California current ecosystem. Deep. Res. Part I(104), 149–158 (2015).
    Article  CAS  Google Scholar 

    78.
    Klevjer, T. A., Torres, D. J. & Kaartvedt, S. Distribution and diel vertical movements of mesopelagic scattering layers in the Red Sea. Mar. Biol. 159, 1833–1841 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    79.
    Aksnes, D. L. et al. Light penetration structures the deep acoustic scattering layers in the global ocean. Sci. Adv. 3, 1–6 (2017).
    Article  Google Scholar 

    80.
    Osborn, D. A., Silver, M. W., Castro, C. G., Bros, S. M. & Chavez, F. P. The habitat of mesopelagic scyphomedusae in Monterey Bay, California. Deep. Res. Part I(54), 1241–1255 (2007).
    Article  Google Scholar 

    81.
    Roe, H. S. J., James, P. T. & Thurston, M. H. The diel migrations and distributions within a mesopelagic community in the North East Atlantic. 6. Medusae, Ctenophores, Amphipods and Euphasusiids. Prog. Oceanogr. 13, 425–460 (1984).
    ADS  Article  Google Scholar 

    82.
    Morita, H. et al. Spatio-temporal structure of the jellyfish community in the transition zone of cold and warm currents in the northwest pacific. Plankt. Benthos Res. 12, 266–284 (2017).
    Article  Google Scholar 

    83.
    Grossmann, M. M., Nishikawa, J. & Lindsay, D. J. Diversity and community structure of pelagic cnidarians in the Celebes and Sulu Seas, southeast Asian tropical marginal seas. Deep. Res. I Oceanogr. Res. Pap. 100, 54–63 (2015).
    ADS  Article  Google Scholar 

    84.
    Hidaka-Umetsu, M. & Lindsay, D. J. Comparative ROV surveys reveal jellyfish blooming in a deep-sea caldera: the first report of Earleria bruuni from the Pacific Ocean. J. Mar. Biol. Assoc. UK 98, 2075–2085 (2018).
    Article  Google Scholar 

    85.
    Haddock, S. H. D., Dunn, C. W. & Pugh, P. R. A re-examination of siphonophore terminology and morphology, applied to the description of two new prayine species with remarkable bio-optical properties. J. Mar. Biol. Assoc. UK 85, 695–707 (2005).
    Article  Google Scholar 

    86.
    Fenaux, R. & Youngbluth, M. J. A new mesopelagic Appendicularian, Mesochordaeus bahamasi gen. nov., sp. nov. J. Mar. Biol. Assoc. UK 70, 755–760 (1990).
    Article  Google Scholar 

    87.
    Hopcroft, R. R. & Robison, B. H. A new mesopelagic larvacean, Mesochordaeus erythrocephalus, sp. nov., from Monterey Bay, with a description of its filtering house. J. Plankton Res. 21, 1923–1937 (1999).
    Article  Google Scholar 

    88.
    Stramma, L., Schmidtko, S., Levin, L. A. & Johnson, G. C. Ocean oxygen minima expansions and their biological impacts. Deep. Res. I Oceanogr. Res. Pap. 57, 587–595 (2010).
    ADS  CAS  Article  Google Scholar 

    89.
    Garçon, V. et al. Multidisciplinary observing in the world ocean’s oxygen minimum zone regions: from climate to fish—the VOICE Initiative. Front. Mar. Sci. 6, 1–22 (2019).
    Article  Google Scholar 

    90.
    Christiansen, B. et al. SEAMOX: The Influence of Seamounts and Oxygen Minimum Zones on Pelagc Fauna in the Eastern Tropical Atlantic – Cruise No. MSM49 – November 28 – December 21, 2015 – Las Palmas de Gran Canaria (Spain) – Mindelo (Republic of Cape Verde). MARIA S. MERIAN-Berichte 1–42 (2016). https://doi.org/10.2312/cr_msm49.

    91.
    Picheral, M. et al. The Underwater Vision Profiler 5: an advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol. Oceanogr. Methods 8, 462–473 (2010).
    Article  Google Scholar 

    92.
    Schlining, B. M. & Jacobsen Stout, N. MBARI’s Video Annotation and Reference System. IEEE 1–5 (2006). https://doi.org/10.1109/OCEANS.2006.306879.

    93.
    Reisenbichler, K. R. et al. Automating MBARI ’s midwater time-series video surveys: the transition from ROV to AUV. Ocean. 2016 MTS/IEEE Monterey 1–9 (2016). https://doi.org/10.1109/OCEANS.2016.7761499.

    94.
    Biard, T. & Ohman, M. D. Vertical niche definition of test-bearing protists (Rhizaria) into the twilight zone revealed by in situ imaging. Limnol. Oceanogr. https://doi.org/10.1002/lno.11472 (2020).
    Article  Google Scholar 

    95.
    Nakamura, Y. et al. Optics-based surveys of large unicellular zooplankton: a case study on radiolarians and phaeodarians. Plankt. Benthos Res. 12, 95–103 (2017).
    Article  Google Scholar 

    96.
    Hunt, J. C. & Lindsay, D. J. Observations on the behavior of Atolla (Scyphozoa: Coronatae) and Nanomia (Hydrozoa: Physonectae): use of the hypertrophied tentacle in prey capture. Plankt. Biol. Ecol. 45, 239–242 (1998).
    Google Scholar 

    97.
    Kramp, P. L. Synopsis of the Medusae of the World. J. Mar. Biol. Assoc. UK 40, 7–382 (1961).
    Article  Google Scholar 

    98.
    Mills, C. E., Haddock, S. H. D., Dunn, C. W. & Pugh, P. R. Key To the Siphonophora. In Light Smith’s Man Intertidal Invertebr Cent Calif Coast (ed. Carlton, J. T.) 150–166 (University of California Press, San Francisco, 2007).
    Google Scholar 

    99.
    Sherlock, R. E., Walz, K. R., Schlining, K. L. & Robison, B. H. Morphology, ecology, and molecular biology of a new species of giant larvacean in the eastern North Pacific: Bathochordaeus mcnutti sp. nov.. Mar. Biol. 164, 1–15 (2017).
    CAS  Article  Google Scholar 

    100.
    Latasa, M., Cabello, A. M., Morán, X. A. G., Massana, R. & Scharek, R. Distribution of phytoplankton groups within the deep chlorophyll maximum. Limnol. Oceanogr. 62, 665–685 (2017).
    ADS  CAS  Article  Google Scholar  More

  • in

    Consistent effects of pesticides on community structure and ecosystem function in freshwater systems

    Experimental design and community composition
    We conducted a randomized-block experiment at the Russell E. Larsen Agricultural Research Center (Pennsylvania Furnace, PA, USA) with replicated mesocosm ponds. Mesocosms were 1100-L cattle tanks covered with 60% shade cloth. The spatial block was distance from a tree line in our mesocosm field. Three weeks before pesticide application, these mesocosms were filled with 800 L water, 300 g mixed hardwood leaves, and inoculations of zooplankton, periphyton, and phytoplankton homogenized from four local ponds. Just before pesticide application on the same day, each tank received two snail, three larval anuran, one larval dragonfly, one water bug, one water beetle, one larval salamander, and one backswimmer species (11 Helisoma (Planorbella) trivolvis, 10 Physa gyrina; 20 Hyla versicolor, 20 Lithobates palustris, 20 Lithobates clamitans; 2 Anax junius; 2 Belostoma flumineum; 5 Hydrochara sp.; 3 Ambystoma maculatum; 6 Nototeca undulata) (Fig. 1b). These community members naturally coexist and were applied at naturally occurring densities40. Initial conditions of some mesocosms varied in simulated pesticide treatments (see below).
    We randomly assigned 18 treatments (12 pesticides, 4 simulated pesticides, 2 controls) with four replicate mesocosms of each treatment, which resulted in 72 total mesocosms (Fig. 1a). The 12 pesticide treatments were nested; we included two pesticide types (insecticide, herbicide), two classes within each pesticide type (organophosphate insecticide, carbamate insecticide, chloroacetanilide herbicide, triazine herbicide), and three different pesticides in each of four classes (Fig. 1a). To represent runoff of pesticides into freshwater systems following a rainfall event, we applied single doses of technical grade pesticides at environmentally relevant concentrations at the beginning of the experiment. To ensure our exposures represented environmental relevance, we used estimated environmental concentrations of pesticides, calculated by U.S. Environmental Protection Agency’s GENEEC v2 software, Supplementary Table 2). Our design also included water and solvent (0.0001% acetone) controls (Fig. 1a). Pesticides were obtained from ChemService (West Chester, PA, USA). Nominal concentrations of pesticides (μg/L) were: 64 chlorpyrifos, 101 malathion, 171 terbufos, 91 aldicarb, 219 carbaryl, 209 carbofuran, 123 acetochlor, 127 alachlor, 105 metolachlor, 102 atrazine, 202 simazine, and 106 propazine. We collected composite water samples 1 h after application to mesocosms and shipped samples on ice to Mississippi State Chemical Laboratory to verify these nominal concentrations. Measured concentrations of pesticides (μg/L) were: 60 chlorpyrifos, 105 malathion, 174 terbufos, 84 aldicarb, 203 carbaryl, 227 carbofuran, 139 acetochlor, 113 alachlor, 114 metolachlor, 117 atrazine, 180 simazine, and 129 propazine.
    The four simulated pesticide treatments were top-down or bottom-up food web manipulations intended to mimic effects of actual herbicides and insecticides on community members. These manipulations occurred once and were concurrent with the timing of pesticide applications. Top-down and bottom-up simulated insecticide treatments were designed to reduce densities of zooplankton, simulating effects of insecticides on zooplankton survival. For top-down simulated insecticides, we doubled the densities of zooplankton predators by including six total A. maculatum larval salamanders and 12 N. undulata backswimmers per mesocosm. For bottom-up simulated insecticides (i.e., direct manipulation of a lower arthropod trophic level), we removed zooplankton with a net. Top-down and bottom-up simulated herbicides were designed to reduce algae, simulating effects of herbicides on survival and growth of algae. For top-down simulated herbicides, we doubled the densities of large herbivores to increase grazing pressure by including 22 H. trivolvis snails, 20 P. gyrina snails, 40 H. versicolor larval anurans, 40 L. palustris larval anurans, and 40 L. clamitans larval anurans per mesocosm. For bottom-up simulated herbicides, we covered mesocosms in three sheets of 60% shade cloth in an attempt to block light and reduce photosynthesis. The experiment ran for four weeks, from June to July.
    Measurements of experimental responses
    During the experiment, we sampled periphyton using clay tiles (100 cm2) oriented perpendicularly along the bottom of the mesocosm. Each mesocosm had two periphyton measurements: ‘inaccessible periphyton’ taken from caged clay tiles that excluded herbivores and ‘accessible periphyton’ taken from clay tiles that were uncaged, allowing herbivore access. We sampled phytoplankton from water samples taken 10 cm below the water surface. Periphyton was scrubbed from tiles and phytoplankton from water samples (10 mL) were filtered onto glass fiber filters (under low vacuum pressure, More