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    Tracing the path of carbon export in the ocean though DNA sequencing of individual sinking particles

    Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237.CAS 
    Article 

    Google Scholar 
    Volk T, Hoffert M. Ocean carbon pumps: analysis of relative strengths and efficiencies in ocean-driven atmospheric CO2 changes. Geophys Monogr Ser. 1985;32:99–110.
    Google Scholar 
    Boyd PW, Claustre H, Levy M, Siegel DA, Weber T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature. 2019;568:327–35.CAS 
    Article 

    Google Scholar 
    Siegel DA, Buesseler KO, Doney SC, Sailley SF, Behrenfeld MJ, Boyd PW. Global assessment of ocean carbon export by combining satellite observations and food-web models. Glob Biogeochem Cycles. 2014;28:181–196.Henson SA, Sanders R, Madsen E, Morris PJ, Le Moigne F, Quartly GD. A reduced estimate of the strength of the ocean’s biological carbon pump. Geophys Res Lett. 2011;38:L04606.Article 

    Google Scholar 
    Boyd PW, Trull TW. Understanding the export of biogenic particles in oceanic waters: is there consensus? Prog Oceanogr. 2007;72:276–312.Article 

    Google Scholar 
    Werdell PJ, Behrenfeld MJ, Bontempi PS, Boss E, Cairns B, Davis GT, et al. The plankton, aerosol, cloud, ocean ecosystem mission: status, science, advances. Bull Am Meteorol Soc. 2019;100:1775–94.Article 

    Google Scholar 
    Picheral M, Guidi L, Stemmann L, Karl DM, Iddaoud G, Gorsky G. The Underwater Vision Profiler 5: An advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol Oceanogr-Methods. 2010;8:462–73.Article 

    Google Scholar 
    Olson RJ, Sosik HM. A submersible imaging-in-flow instrument to analyze nano-and microplankton: imaging FlowCytobot. Limnol Oceanogr Methods. 2007;5:195–203.Article 

    Google Scholar 
    de Vargas C, Audic S, Henry N, Decelle J, Mahé F, Logares R, et al. Eukaryotic plankton diversity in the sunlit ocean. Science. 2015;348:1261605.Scholin C, Birch J, Jensen S, Marin R III, Massion E, Pargett D, et al. The quest to develop ecogenomic sensors: a 25-year history of the Environmental Sample Processor (ESP) as a case study. Oceanography. 2017;30:100–13.Article 

    Google Scholar 
    Cruz BN, Brozak S, Neuer S. Microscopy and DNA-based characterization of sinking particles at the Bermuda Atlantic Time-series Study station point to zooplankton mediation of particle flux. Limnol Oceanogr. 2021;66:3697–713.CAS 
    Article 

    Google Scholar 
    Amacher J, Neuer S, Lomas M. DNA-based molecular fingerprinting of eukaryotic protists and cyanobacteria contributing to sinking particle flux at the Bermuda Atlantic time-series study. Deep Sea Res Part II Top Stud Oceanogr. 2013;93:71–83.CAS 
    Article 

    Google Scholar 
    Fontanez KM, Eppley JM, Samo TJ, Karl DM, DeLong EF. Microbial community structure and function on sinking particles in the North Pacific Subtropical Gyre. Front Microbiol. 2015;6:469.Article 

    Google Scholar 
    Preston CM, Durkin CA, Yamahara KM DNA metabarcoding reveals organisms contributing to particulate matter flux to abyssal depths in the North East Pacific ocean. Deep Sea Res Part II Top Stud Oceanogr. 2019;173:104708.Gutierrez-Rodriguez A, Stukel MR, Lopes dos Santos A, Biard T, Scharek R, Vaulot D, et al. High contribution of Rhizaria (Radiolaria) to vertical export in the California Current Ecosystem revealed by DNA metabarcoding. ISME J. 2019;13:964–76.CAS 
    Article 

    Google Scholar 
    Boeuf D, Edwards BR, Eppley JM, Hu SK, Poff KE, Romano AE, et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc Natl Acad Sci. 2019;116:11824.CAS 
    Article 

    Google Scholar 
    Silver MW, Gowing MM. The “particle” flux: Origins and biological components. Prog Oceanogr. 1991;26:75–113.Article 

    Google Scholar 
    Ebersbach F, Assmy P, Martin P, Schulz I, Wolzenburg S, Nöthig E-M. Particle flux characterisation and sedimentation patterns of protistan plankton during the iron fertilisation experiment LOHAFEX in the Southern Ocean. Deep Sea Res Part Oceanogr Res Pap. 2014;89:94–103.CAS 
    Article 

    Google Scholar 
    Waite A, Bienfang PK, Harrison PJ. Spring bloom sedimentation in a subarctic ecosystem. II. Succession and sedimentation. Mar Biol. 1992;114:131–8.Article 

    Google Scholar 
    Venrick E, Lange C, Reid F, Dever EP. Temporal patterns of species composition of siliceous phytoplankton flux in the Santa Barbara Basin. J Plankton Res. 2007;30:283–97.Article 

    Google Scholar 
    Waite AM, Safi KA, Hall JA, Nodder SD. Mass sedimentation of picoplankton embedded in organic aggregates. Limnol Oceanogr. 2000;45:87–97.Article 

    Google Scholar 
    Valencia B, Stukel MR, Allen AE, McCrow JP, Rabines A, Palenik B, et al. Relating sinking and suspended microbial communities in the California Current Ecosystem: digestion resistance and the contributions of phytoplankton taxa to export. Environ Microbiol. 2021;23:6743–8.Article 

    Google Scholar 
    Scharek R, Tupas LM, Karl DM. Diatom fluxes to the deep sea in the oligotrophic North Pacific gyre at Station ALOHA. Mar Ecol Prog Ser. 1999;182:55–67.Article 

    Google Scholar 
    Beaulieu S. Accumulation and fate of phytodetritus on the sea floor. Oceanogr Mar Biol Annu Rev. 2002;40:171–232.
    Google Scholar 
    Ikenoue T, Kimoto K, Okazaki Y, Sato M, Honda MC, Takahashi K, et al. Phaeodaria: an important carrier of particulate organic carbon in the mesopelagic twilight zone of the North Pacific Ocean. Glob Biogeochem Cycles. 2019;33:1146–60.CAS 
    Article 

    Google Scholar 
    Smith KL, Ruhl HA, Huffard CL, Messié M, Kahru M. Episodic organic carbon fluxes from surface ocean to abyssal depths during long-term monitoring in NE Pacific. Proc Natl Acad Sci. 2018;115:12235.CAS 
    Article 

    Google Scholar 
    Durkin CA, Buesseler KO, Cetinić I, Estapa ML, Kelly RP, Omand M. A visual tour of carbon export by sinking particles. Glob Biogeochem Cycles. 2021;35:e2021GB006985.CAS 
    Article 

    Google Scholar 
    Estapa ML, Valdes J, Tradd K, Sugar J, Omand M, Buesseler K. The neutrally buoyant sediment trap: two decades of progress. J Atmospheric Ocean Technol. 2020;37:957–973.Rainville L, Pinkel R. Wirewalker: An autonomous wave-powered vertical profiler. J Atmos Ocean Technol. 2001;18:1048–51.Article 

    Google Scholar 
    Durkin CA, Estapa ML, Buesseler KO. Observations of carbon export by small sinking particles in the upper mesopelagic. Mar Chem. 2015;175:72–81.CAS 
    Article 

    Google Scholar 
    Malmstrom R RNAlater Recipe. protocols.io. https://doi.org/10.17504/protocols.io.c56y9d. Accessed 2 Oct 2018.Mackey MD, Mackey DJ, Higgins HW, Wright SW. CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar Ecol Prog Ser. 1996;144:265–83.CAS 
    Article 

    Google Scholar 
    Massana R, Murray AE, Preston CM, DeLong EF. Vertical distribution and phylogenetic characterization of marine planktonic Archaea in the Santa Barbara Channel. Appl Environ Microbiol. 1997;63:50.CAS 
    Article 

    Google Scholar 
    Stoeck T, Bass D, Nebel M, Christen R, Jones M, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
    Article 

    Google Scholar 
    Penna A, Casabianca S, Guerra A, Vernesi C, Scardi M. Analysis of phytoplankton assemblage structure in the Mediterranean Sea based on high-throughput sequencing of partial 18S rRNA sequences. Mar Genomics. 2017;36:49–55.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
    Article 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
    Article 

    Google Scholar 
    Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2012;41:D597–D604.Article 

    Google Scholar 
    Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.Article 

    Google Scholar 
    Tomas CR. Identifying marine phytoplankton. Elsevier; 1997.Hasle GR, Syvertsen EE. Marine diatoms. In: Tomas CR (ed). Identifying marine phytoplankton. San Diego, CA, USA: Academic Press; 1997. pp 5–385.Godhe A, Asplund ME, Härnström K, Saravanan V, Tyagi A, Karunasagar I. Quantification of diatom and dinoflagellate biomasses in coastal marine seawater samples by real-time PCR. Appl Environ Microbiol. 2008;74:7174–82.CAS 
    Article 

    Google Scholar 
    Smayda TJ. The suspension and sinking of phytoplankton in the sea. Oceanogr Mar Biol Annu Rev. 1970;8:353–414.
    Google Scholar 
    Sancetta C, Villareal T, Falkowski P. Massive fluxes of rhizosolenid diatoms: a common occurrence? Limnol Oceanogr. 1991;36:1452–7.Article 

    Google Scholar 
    Goldman JC. Potential role of large oceanic diatoms in new primary production. Deep Sea Res Part Oceanogr Res Pap. 1993;40:159–68.Article 

    Google Scholar 
    Kemp AE, Pike J, Pearce RB, Lange CB. The “Fall dump”—a new perspective on the role of a “shade flora” in the annual cycle of diatom production and export flux. Deep Sea Res Part II Top Stud Oceanogr. 2000;47:2129–54.Article 

    Google Scholar 
    Villareal TA, Woods S, Moore JK, CulverRymsza K. Vertical migration of Rhizosolenia mats and their significance to NO3− fluxes in the central North Pacific gyre. J Plankton Res. 1996;18:1103–21.Article 

    Google Scholar 
    Smayda TJ. Normal and accelerated sinking of phytoplankton in the sea. Mar Geol. 1971;11:105–22.Article 

    Google Scholar 
    Shiozaki T, Itoh F, Hirose Y, Onodera J, Kuwata A, Harada NA. DNA metabarcoding approach for recovering plankton communities from archived samples fixed in formalin. PLOS ONE. 2021;16:e0245936.CAS 
    Article 

    Google Scholar 
    Omand MM, Govindarajan R, He J, Mahadevan A. Sinking flux of particulate organic matter in the oceans: sensitivity to particle characteristics. Sci Rep. 2020;10:5582.CAS 
    Article 

    Google Scholar 
    DeVries T, Liang J-H, Deutsch C. A mechanistic particle flux model applied to the oceanic phosphorus cycle. Biogeosciences. 2014;11:5381–98.Article 

    Google Scholar 
    Siegel DA, Buesseler KO, Behrenfeld MJ, Benitez-Nelson CR, Boss E, Brzezinski MA, et al. Prediction of the export and fate of global ocean net primary production: the EXPORTS science plan. Front Mar Sci. 2016;3:22.Article 

    Google Scholar 
    NASA Ocean Biology Processing Group. MODIS-Aqua Level 3 mapped chlorophyll data version R2018.0. 2017. NASA Ocean Biology DAAC. https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018. More

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    Agriculture and climate change are reshaping insect biodiversity worldwide

    Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl Acad. Sci. USA 115, E10397–E10406 (2018).CAS 
    Article 

    Google Scholar 
    Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).CAS 
    Article 

    Google Scholar 
    Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384–392 (2020).Article 

    Google Scholar 
    Crossley, M. S. et al. No net insect abundance and diversity declines across US long term ecological research sites. Nat. Ecol. Evol. 4, 1368–1376 (2020).Article 

    Google Scholar 
    Janzen, D. H. & Hallwachs, W. To us insectometers, it is clear that insect decline in our Costa Rican tropics is real, so let’s be kind to the survivors. Proc. Natl Acad. Sci. USA 118, e2002546117 (2021).CAS 
    Article 

    Google Scholar 
    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
    Article 

    Google Scholar 
    Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).Article 

    Google Scholar 
    Powney, G. D. et al. Widespread losses of pollinating insects in Britain. Nat. Commun. 10, 1018 (2019).Article 

    Google Scholar 
    van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).Article 

    Google Scholar 
    Yang, L. H. & Gratton, C. Insects as drivers of ecosystem processes. Curr. Opin. Insect Sci. 2, 26–32 (2014).Article 

    Google Scholar 
    Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).Article 

    Google Scholar 
    Halsch, C. A. et al. Insects and recent climate change. Proc. Natl. Acad. Sci. USA 118, e2002543117 (2021).CAS 
    Article 

    Google Scholar 
    Wagner, D. L., Fox, R., Salcido, D. M. & Dyer, L. A. A window to the world of global insect declines: moth biodiversity trends are complex and heterogeneous. Proc. Natl Acad. Sci. USA 118, e2002549117 (2021).CAS 
    Article 

    Google Scholar 
    Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).CAS 
    Article 

    Google Scholar 
    Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).CAS 
    Article 

    Google Scholar 
    Uhler, J. et al. Relationship of insect biomass and richness with land use along a climate gradient. Nat. Commun. 12, 5946 (2021).CAS 
    Article 

    Google Scholar 
    Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).Article 

    Google Scholar 
    Williams, J. J. & Newbold, T. Local climatic changes affect biodiversity responses to land use: a review. Divers. Distrib. 26, 76–92 (2020).Article 

    Google Scholar 
    González del Pliego, P. et al. Thermally buffered microhabitats recovery in tropical secondary forests following land abandonment. Biol. Conserv. 201, 385–395 (2016).Article 

    Google Scholar 
    Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).Article 

    Google Scholar 
    Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
    Article 

    Google Scholar 
    Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239–1252 (2012).Article 

    Google Scholar 
    Northrup, J. M., Rivers, J. W., Yang, Z. & Betts, M. G. Synergistic effects of climate and land-use change influence broad-scale avian population declines. Glob. Change Biol. 25, 1561–1575 (2019).Article 

    Google Scholar 
    Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).CAS 
    Article 

    Google Scholar 
    Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).Article 

    Google Scholar 
    Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).CAS 
    Article 

    Google Scholar 
    Betts, M. G., Phalan, B., Frey, S. J. K., Rousseau, J. S. & Yang, Z. Old-growth forests buffer climate-sensitive bird populations from warming. Divers. Distrib. 24, 439–447 (2018).Article 

    Google Scholar 
    Hendershot, J. N. et al. Intensive farming drives long-term shifts in avian community composition. Nature 579, 393–396 (2020).CAS 
    Article 

    Google Scholar 
    Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes – eight hypotheses. Biol. Rev. 87, 661–685 (2012).Article 

    Google Scholar 
    Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).Article 

    Google Scholar 
    Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity in Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).Article 

    Google Scholar 
    Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).Article 

    Google Scholar 
    Johansson, F., Orizaola, G. & Nilsson-Örtman, V. Temperate insects with narrow seasonal activity periods can be as vulnerable to climate change as tropical insect species. Sci. Rep. 10, 8822 (2020).CAS 
    Article 

    Google Scholar 
    Hoskins, A. J. et al. Downscaling land-use data to provide global 30′′ estimates of five land-use classes. Ecol. Evol. 6, 3040–3055 (2016).Article 

    Google Scholar 
    Grab, H. et al. Agriculturally dominated landscapes reduce bee phylogenetic diversity and pollination services. Science 363, 282–284 (2019).CAS 
    Article 

    Google Scholar 
    Rusch, A. et al. Agricultural landscape simplification reduces natural pest control: a quantitative synthesis. Agric. Ecosyst. Environ. 221, 198–204 (2016).Article 

    Google Scholar 
    Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).Article 

    Google Scholar 
    Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA. 111, 5610–5615 (2014).CAS 
    Article 

    Google Scholar 
    Balmford, A. Extinction filters and current resilience: the significance of past selection pressures for conservation biology. Trends Ecol. Evol. 11, 193–196 (1996).CAS 
    Article 

    Google Scholar 
    Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072 (2011).Article 

    Google Scholar 
    Carvalheiro, L. G., Seymour, C. L., Veldtman, R. & Nicolson, S. W. Pollination services decline with distance from natural habitat even in biodiversity-rich areas. J. Appl. Ecol. 47, 810–820 (2010).Article 

    Google Scholar 
    Dainese, M., Luna, D. I., Sitzia, T. & Marini, L. Testing scale-dependent effects of seminatural habitats on farmland biodiversity. Ecol. Appl. 25, 1681–1690 (2015).Article 

    Google Scholar 
    Fourcade, Y. et al. Habitat amount and distribution modify community dynamics under climate change. Ecol. Lett. 24, 950–957 (2021).Article 

    Google Scholar 
    Alexander, L. V. et al. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. 111, D05109 (2006).
    Google Scholar 
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).CAS 
    Article 

    Google Scholar 
    De Palma, A. et al. Dimensions of biodiversity loss: spatial mismatch in land-use impacts on species, functional and phylogenetic diversity of European bees. Divers. Distrib. 23, 1435–1446 (2017).Article 

    Google Scholar 
    Collen, B., Ram, M., Zamin, T. & McRae, L. The tropical biodiversity data gap: addressing disparity in global monitoring. Trop. Conserv. Sci. 1, 75–88 (2008).Article 

    Google Scholar 
    Menéndez, R. How are insects responding to global warming? Tijdschr. Entomol. 150, 355 (2007).
    Google Scholar 
    Bale, J. S. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).Article 

    Google Scholar 
    Hudson, L. N. et al. The 2016 release of the PREDICTS database. Natural History Museum Data Portal, https://doi.org/10.5519/0066354 (2016).Hudson, L. N. et al. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evol. 4, 4701–4735 (2014).Article 

    Google Scholar 
    De Palma, A. et al. Annual changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001–2012. Sci. Rep. 11, 20249 (2021).Article 

    Google Scholar 
    Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol. Lett. 8, 148–159 (2005).Article 

    Google Scholar 
    Ndakidemi, B., Mtei, K. & Ndakidemi, P. A. Impacts of synthetic and botanical pesticides on beneficial insects. Agric. Sci. 07, 364–372 (2016).CAS 

    Google Scholar 
    Wang, X., Hua, F., Wang, L., Wilcove, D. S. & Yu, D. W. The biodiversity benefit of native forests and mixed‐species plantations over monoculture plantations. Divers. Distrib. 25, 1721–1735 (2019).Article 

    Google Scholar 
    Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

    Google Scholar 
    Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI-MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).CAS 
    Article 

    Google Scholar 
    van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).Article 

    Google Scholar 
    New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12, 829–856 (1999).Article 

    Google Scholar 
    Hijmans, R. J. Raster: Geographic data analysis and modelling. R package version 2.8-42018, https://CRAN.R-project.org/package=raster (2018).Rigby, R. A., Stasinopoulos, D. M. & Akantziliotou, C. A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution. Comput. Stat. Data Anal. 53, 381–393 (2008).MathSciNet 
    Article 

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

    Google Scholar  More

  • in

    Farm-scale differentiation of active microbial colonizers

    Blagodatskaya E, Kuzyakov Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol Biochem. 2013;67:192–211.CAS 
    Article 

    Google Scholar 
    Jones SE, Lennon JT. Dormancy contributes to the maintenance of microbial diversity. Proc Natl Acad Sci USA. 2010;107:5881.CAS 
    Article 

    Google Scholar 
    Lennon JT, Jones SE. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol. 2011;9:119–30.CAS 
    Article 

    Google Scholar 
    Couradeau E, Sasse J, Goudeau D, Nath N, Hazen TC, Bowen BP, et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat Commun. 2019;10:2770.Article 

    Google Scholar 
    Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2016;2:16242.Article 

    Google Scholar 
    Sorensen JW, Shade A. Dormancy dynamics and dispersal contribute to soil microbiome resilience. Philos Trans R Soc B Biolog Sci. 2020;375:20190255.CAS 
    Article 

    Google Scholar 
    Raina J-B, Fernandez V, Lambert B, Stocker R, Seymour JR. The role of microbial motility and chemotaxis in symbiosis. Nat Rev Microbiol. 2019;17:284–94.CAS 
    Article 

    Google Scholar 
    Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7.CAS 
    Article 

    Google Scholar 
    Lauber Christian L, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.CAS 
    Article 

    Google Scholar 
    Lennon JT, Aanderud ZT, Lehmkuhl BK, Schoolmaster DR Jr. Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology. 2012;93:1867–79.Article 

    Google Scholar 
    Albright MBN, Martiny JBH. Dispersal alters bacterial diversity and composition in a natural community. ISME J. 2018;12:296–9.Article 

    Google Scholar 
    Barberan A, Ladau J, Leff JW, Pollard KS, Menninger HL, Dunn RR, et al. Continental-scale distributions of dust-associated bacteria and fungi. Proc Natl Acad Sci USA. 2015;112:5756–61.CAS 
    Article 

    Google Scholar 
    Meyer KM, Memiaghe H, Korte L, Kenfack D, Alonso A, Bohannan BJM. Why do microbes exhibit weak biogeographic patterns? ISME J. 2018;12:1404–13.Article 

    Google Scholar 
    Eisenlord SD, Zak DR, Upchurch RA. Dispersal limitation and the assembly of soil Actinobacteria communities in a long-term chronosequence. Ecol Evol. 2012;2:538–49.Article 

    Google Scholar 
    Glassman SI, Lubetkin KC, Chung JA, Bruns TD. The theory of island biogeography applies to ectomycorrhizal fungi in subalpine tree “islands” at a fine scale. Ecosphere. 2017;8:e01677.Article 

    Google Scholar 
    Whitaker Rachel J, Grogan Dennis W, Taylor John W. Geographic barriers isolate endemic populations of hyperthermophilic archaea. Science. 2003;301:976–8.CAS 
    Article 

    Google Scholar 
    Amor DR, Ratzke C, Gore J. Transient invaders can induce shifts between alternative stable states of microbial communities. Sci Adv. 2020;6:eaay8676.CAS 
    Article 

    Google Scholar 
    Zhang C, Derrien M, Levenez F, Brazeilles R, Ballal SA, Kim J, et al. Ecological robustness of the gut microbiota in response to ingestion of transient food-borne microbes. ISME J. 2016;10:2235–45.Article 

    Google Scholar 
    Fox JE, Gulledge J, Engelhaupt E, Burow ME, McLachlan JA. Pesticides reduce symbiotic efficiency of nitrogen-fixing rhizobia and host plants. Proc Natl Acad Sci USA. 2007;104:10282.CAS 
    Article 

    Google Scholar 
    Ramirez KS, Craine JM, Fierer N. Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob Change Biol. 2012;18:1918–27.Article 

    Google Scholar 
    Leff JW, Jones SE, Prober SM, Barberán A, Borer ET, Firn JL, et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc Natl Acad Sci USA. 2015;112:10967.CAS 
    Article 

    Google Scholar 
    Chambers CA, Smith SE, Smith FA. Effects of ammonium and nitrate ions on mycorrhizal infection, nodulation and growth of Trifolium subterraneum. New Phytol. 1980;85:47–62.CAS 
    Article 

    Google Scholar 
    van Diepen LTA, Lilleskov EA, Pregitzer KS, Miller RM. Simulated nitrogen deposition causes a decline of intra- and extraradical abundance of arbuscular mycorrhizal fungi and changes in microbial community structure in northern hardwood forests. Ecosystems. 2010;13:683–95.Article 

    Google Scholar 
    Young IM, Ritz K. Tillage, habitat space and function of soil microbes. Soil Tillage Res. 2000;53:201–13.Article 

    Google Scholar 
    Kabir Z. Tillage or no-tillage: impact on mycorrhizae. Can J Plant Sci. 2005;85:23–29.Article 

    Google Scholar 
    Bell T, Tylianakis JM. Microbes in the anthropocene: spillover of agriculturally selected bacteria and their impact on natural ecosystems. Proc R Soc B Biol Sci. 2016;283;20160896.Article 

    Google Scholar 
    Dang K, Gong X, Zhao G, Wang H, Ivanistau A, Feng B, Intercropping alters the soil microbial diversity and community to facilitate nitrogen assimilation: a potential mechanism for increasing proso millet grain yield. Front Microbiol. 2020;11;601054.Peay KG, Garbelotto M, Bruns TD. Evidence of dispersal limitation in soil microorganisms: Isolation reduces species richness on mycorrhizal tree islands. Ecology. 2010;91:3631–40.Article 

    Google Scholar 
    Mummey DL, Rillig MC. Spatial characterization of arbuscular mycorrhizal fungal molecular diversity at the submetre scale in a temperate grassland. FEMS Microbiol Ecol. 2008;64:260–70.CAS 
    Article 

    Google Scholar 
    Hiscox J, Savoury M, Müller CT, Lindahl BD, Rogers HJ, Boddy L. Priority effects during fungal community establishment in beech wood. ISME J. 2015;9:2246–60.Article 

    Google Scholar 
    Song Z, Kennedy PG, Liew FJ, Schilling JS. Fungal endophytes as priority colonizers initiating wood decomposition. Funct Ecol. 2017;31:407–18.Article 

    Google Scholar 
    Schmidt SK, Nemergut DR, Darcy JL, Lynch R. Do bacterial and fungal communities assemble differently during primary succession? Mol Ecol. 2014;23:254–8.CAS 
    Article 

    Google Scholar 
    Reche I, D’Orta G, Mladenov N, Winget DM, Suttle CA. Deposition rates of viruses and bacteria above the atmospheric boundary layer. ISME J. 2018;12:1154–62.CAS 
    Article 

    Google Scholar 
    Castaño C, Bonet JA, Oliva J, Farré G, Martínez de Aragón J, Parladé J, et al. Rainfall homogenizes while fruiting increases diversity of spore deposition in Mediterranean conditions. Fungal Ecol. 2019;41:279–88.Article 

    Google Scholar 
    Garbelotto M, Smith T, Schweigkofler W. Variation in rates of spore deposition of Fusarium circinatum, the causal agent of pine pitch canker, over a 12-month-period at two locations in Northern California. Phytopathology®. 2007;98:137–43.Article 

    Google Scholar 
    Bowers RM, Clements N, Emerson JB, Wiedinmyer C, Hannigan MP, Fierer N. Seasonal variability in bacterial and fungal diversity of the near-surface atmosphere. Environ Sci Technol. 2013;47:12097–106.CAS 
    Article 

    Google Scholar 
    Trexler RV, Bell TH. Testing sustained soil-to-soil contact as an approach for limiting the abiotic influence of source soils during experimental microbiome transfer. FEMS Microbiol Lett. 2019;366:fnz228.CAS 
    Article 

    Google Scholar 
    King, WL, Kaminsky LM, Gannett M, Thompson GL, Kao-Kniffin J, Bell TH. Soil salinization accelerates microbiome stabilization in iterative selections for plant performance. New Phytol. 2021. https://doi.org/10.1111/nph.17774. Advance online publication.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
    Article 

    Google Scholar 
    Apprill A, McNally S, Parsons RJ, Weber LK. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Microb Ecol. 2015;75:129–37.Article 

    Google Scholar 
    Gardes M, Bruns TD. ITS primers with enhanced specificity for basidiomycetes – application to the identification of mycorrhizae and rusts. Mol Ecol. 1993;2:113–8.CAS 
    Article 

    Google Scholar 
    Martin KJ, Rygiewicz PT. Fungal-specific PCR primers developed for analysis of the ITS region of environmental DNA extracts. BMC Microbiol. 2005;5:28.Article 

    Google Scholar 
    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
    Article 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
    Article 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.CAS 
    Article 

    Google Scholar 
    Kõljalg U, Larsson K-H, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol. 2005;166:1063–8.Article 

    Google Scholar 
    R Core Team, R: a language and environment for statistical computing. R Foundation for Statistical Computing; 2012;Vienna;AustriaMcMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
    Article 

    Google Scholar 
    Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin P, O’Hara B, et al. Vegan: community ecology package. R Package Version 2.2-1. 2015;2:1–2. https://cran.r-project.org/web/packages/vegan/index.html.Ogle, DH, Wheeler P, Dinno A. FSA: fisheries stock analysis; 2020. https://cran.r-project.org/web/packages/FSA/index.htmlLahti L, Shetty S. Tools for microbiome analysis in R. Microbiome package. Bioconductor; 2017. https://microbiome.github.io/tutorials/Bell T. Experimental tests of the bacterial distance–decay relationship. ISME J. 2010;4:1357–65.Article 

    Google Scholar 
    Boynton PJ, Peterson CN, Pringle A. Superior dispersal ability can lead to persistent ecological dominance throughout succession. Appl Environ Microbiol. 2019;85:e02421–18.CAS 
    Article 

    Google Scholar 
    Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.Article 

    Google Scholar 
    Dickie A, N IA, Reich PB. Ectomycorrhizal fungal communities at forest edges. J Ecol. 2005;93:244–55.Article 

    Google Scholar 
    Vannette, RL, McMunn MS, Hall GW, Mueller TG, Munkres I, Perry D. Fungi are more dispersal limited than bacteria among flowers. https://www.biorxiv.org/content/10.1101/2020.05.19.104968v2. 2021.Zhang G, Wei G, Wei F, Chen Z, He M, Jiao S, et al., Dispersal limitation plays stronger role in the community assembly of fungi relative to bacteria in rhizosphere across the arable area of medicinal plant. Front Microbiol. 2021;12;713523.Svoboda P, Lindström ES, Ahmed Osman O, Langenheder S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 2018;12:644–6.Article 

    Google Scholar 
    Fukami T, Dickie IA, Paula Wilkie J, Paulus BC, Park D, Roberts A, et al. Assembly history dictates ecosystem functioning: evidence from wood decomposer communities. Ecol Lett. 2010;13:675–84.Article 

    Google Scholar 
    Rousk J, Bååth E, Brookes PC, Lauber CL, Lozupone C, Caporaso JG, et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010;4:1340–51.Article 

    Google Scholar 
    Hartmann M, Frey B, Mayer J, Mäder P, Widmer F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 2015;9:1177–94.Article 

    Google Scholar 
    Lori M, Symnaczik S, Mäder P, De Deyn G, Gattinger A. Organic farming enhances soil microbial abundance and activity—a meta-analysis and meta-regression. PLoS ONE. 2017;12:e0180442.Article 

    Google Scholar 
    Blundell R, Schmidt JE, Igwe A, Cheung AL, Vannette RL, Gaudin ACM, et al. Organic management promotes natural pest control through altered plant resistance to insects. Nat Plants. 2020;6:483–91.CAS 
    Article 

    Google Scholar 
    Riedo, J, Wettstein FE, Rösch A, Herzog C, Banerjee S, Büchi L, et al. Widespread occurrence of pesticides in organically managed agricultural soils—the ghost of a conventional agricultural past? Environ Sci Technol. 2021;55;2919–2928.Lacerda-Júnior GV, Noronha MF, Cabral L, Delforno TP, de Sousa STP, Fernandes-Júnior PI, et al., Land use and seasonal effects on the soil microbiome of a Brazilian dry forest. Front Microbiol. 2019;10;648.Article 

    Google Scholar  More

  • in

    Chemotaxis shapes the microscale organization of the ocean’s microbiome

    Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
    Article 

    Google Scholar 
    Blackburn, N., Fenchel, T. & Mitchell, J. Microscale nutrient patches in planktonic habitats shown by chemotactic bacteria. Science 282, 2254–2256 (1998).CAS 
    Article 

    Google Scholar 
    Stocker, R. Marine microbes see a sea of gradients. Science 338, 628 (2012).CAS 
    Article 

    Google Scholar 
    Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).Article 

    Google Scholar 
    Azam, F. Microbial control of oceanic carbon flux: the plot thickens. Science 280, 694–696 (1998).CAS 
    Article 

    Google Scholar 
    Strom, S. L. Microbial ecology of ocean biogeochemistry: a community perspective. Science 320, 1043–1045 (2008).CAS 
    Article 

    Google Scholar 
    Sarmento, H. & Gasol, J. M. Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton. Env. Microbiol. 14, 2348–2360 (2012).CAS 
    Article 

    Google Scholar 
    Grossart, H.-P., Riemann, L. & Azam, F. Bacterial motility in the sea and its ecological implications. Aquat. Microb. Ecol. 25, 247–258 (2001).Article 

    Google Scholar 
    Brumley, D. R. et al. Bacteria push the limits of chemotactic precision to navigate dynamic chemical gradients. Proc. Natl Acad. Sci. USA 116, 10792–10797 (2019).CAS 
    Article 

    Google Scholar 
    Fenchel, T. Eppur si muove: many water column bacteria are motile. Aquat. Microb. Ecol. 24, 197–201 (2001).Article 

    Google Scholar 
    Son, K., Menolascina, F. & Stocker, R. Speed-dependent chemotactic precision in marine bacteria. Proc. Natl Acad. Sci. USA 113, 8624–8629 (2016).CAS 
    Article 

    Google Scholar 
    Fenchel, T. Microbial behavior in a heterogeneous world. Science 296, 1068–1071 (2002).CAS 
    Article 

    Google Scholar 
    Kiørboe, T. & Jackson, G. A. Marine snow, organic solute plumes, and optimal chemosensory behavior of bacteria. Limnol. Oceanogr. 46, 1309–1318 (2001).Article 

    Google Scholar 
    Lambert, B. S., Fernandez, V. I. & Stocker, R. Motility drives bacterial encounter with particles responsible for carbon export throughout the ocean. Limnol. Oceanogr. Lett. 4, 113–118 (2019).Article 

    Google Scholar 
    Wadhams, G. H. & Armitage, J. P. Making sense of it all: bacterial chemotaxis. Nat. Rev. Mol. Cell. Biol. 5, 1024–1037 (2004).CAS 
    Article 

    Google Scholar 
    Stocker, R., Seymour, J. R., Samadani, A., Hunt, D. E. & Polz, M. F. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc. Natl Acad. Sci. USA 105, 4209–4214 (2008).CAS 
    Article 

    Google Scholar 
    Raina, J.-B., Fernandez, V., Lambert, B., Stocker, R. & Seymour, J. R. The role of microbial motility and chemotaxis in symbiosis. Nat. Rev. Microbiol. 17, 284–294 (2019).CAS 
    Article 

    Google Scholar 
    Seymour, J. R., Amin, S. A., Raina, J.-B. & Stocker, R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat. Microbiol. 2, 17065 (2017).CAS 
    Article 

    Google Scholar 
    Bell, W. & Mitchell, R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol. Bull. 143, 265–277 (1972).Article 

    Google Scholar 
    Smriga, S., Fernandez, V. I., Mitchell, J. G. & Stocker, R. Chemotaxis toward phytoplankton drives organic matter partitioning among marine bacteria. Proc. Natl Acad. Sci. USA 113, 1576–1581 (2016).CAS 
    Article 

    Google Scholar 
    Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).CAS 
    Article 

    Google Scholar 
    Lambert, B. S. et al. A microfluidics-based in situ chemotaxis assay to study the behaviour of aquatic microbial communities. Nat. Microbiol. 2, 1344–1349 (2017).CAS 
    Article 

    Google Scholar 
    Larsen, M. H., Blackburn, N., Larsen, J. L. & Olsen, J. E. Influences of temperature, salinity and starvation on the motility and chemotactic response of Vibrio anguillarum. Microbiology 150, 1283–1290 (2004).CAS 
    Article 

    Google Scholar 
    Rinke, C. et al. Validation of picogram- and femtogram-input DNA libraries for microscale metagenomics. PeerJ 4, e2486 (2016).Article 

    Google Scholar 
    Becker, J. et al. Closely related phytoplankton species produce similar suites of dissolved organic matter. Front. Microbiol. 5, 111 (2014).Article 

    Google Scholar 
    Vraspir, J. M. & Butler, A. Chemistry of marine ligands and siderophores. Annu. Rev. Mar. Sci. 1, 43–63 (2009).Article 

    Google Scholar 
    Tagliabue, A. et al. The integral role of iron in ocean biogeochemistry. Nature 543, 51–59 (2017).CAS 
    Article 

    Google Scholar 
    Hopkinson, B. M. & Morel, F. M. M. The role of siderophores in iron acquisition by photosynthetic marine microorganisms. BioMetals 22, 659–669 (2009).CAS 
    Article 

    Google Scholar 
    Amin, S. A. et al. Photolysis of iron–siderophore chelates promotes bacterial–algal mutualism. Proc. Natl Acad. Sci. USA 106, 17071–17076 (2009).CAS 
    Article 

    Google Scholar 
    Croft, M. T., Lawrence, A. D., Raux-Deery, E., Warren, M. J. & Smith, A. G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93 (2005).CAS 
    Article 

    Google Scholar 
    Helliwell, K. E. The roles of B vitamins in phytoplankton nutrition: new perspectives and prospects. New Phytol. 216, 62–68 (2017).CAS 
    Article 

    Google Scholar 
    Berg, G. Plant–microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture. Appl. Microbiol. Biotechnol. 84, 11–18 (2009).CAS 
    Article 

    Google Scholar 
    Christie, P. J., Whitaker, N. & González-Rivera, C. Mechanism and structure of the bacterial type IV secretion systems. Biochim. Biophys. Acta 1843, 1578–1591 (2014).CAS 
    Article 

    Google Scholar 
    Preston, G. M. Metropolitan microbes: type III secretion in multihost symbionts. Cell Host Microbe 2, 291–294 (2007).CAS 
    Article 

    Google Scholar 
    Deakin, W. J. & Broughton, W. J. Symbiotic use of pathogenic strategies: rhizobial protein secretion systems. Nat. Rev. Microbiol. 7, 312–320 (2009).CAS 
    Article 

    Google Scholar 
    Luo, H. & Moran, M. A. Evolutionary ecology of the marine Roseobacter clade. Microbiol. Mol. Biol. Rev. 78, 573–587 (2014).Article 

    Google Scholar 
    Rolland, J. L., Stien, D., Sanchez-Ferandin, S. & Lami, R. Quorum sensing and quorum quenching in the phycosphere of phytoplankton: a case of chemical interactions in ecology. J. Chem. Ecol. 42, 1201–1211 (2016).CAS 
    Article 

    Google Scholar 
    Fei, C. et al. Quorum sensing regulates ‘swim-or-stick’ lifestyle in the phycosphere. Environ. Microbiol. 22, 4761–4778 (2020).CAS 
    Article 

    Google Scholar 
    Landa, M., Burns, A. S., Roth, S. J. & Moran, M. A. Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom. ISME J. 11, 2677 (2017).CAS 
    Article 

    Google Scholar 
    Rinke, C. et al. A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). ISME J. 13, 663–675 (2019).CAS 
    Article 

    Google Scholar 
    Fenchel, T. & Blackburn, N. Motile chemosensory behaviour of phagotrophic protists: mechanisms for and efficiency in congregating at food patches. Protist 150, 325–336 (1999).CAS 
    Article 

    Google Scholar 
    Hughes, D. J. et al. Impact of nitrogen availability upon the electron requirement for carbon fixation in Australian coastal phytoplankton communities. 63, 1891–1910 (2018).Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).CAS 
    Article 

    Google Scholar 
    Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinformatics 68, e86 (2019).Article 

    Google Scholar 
    Xia, J. & Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 6, 743–760 (2011).CAS 
    Article 

    Google Scholar 
    Lambert, B. S. & Raina, J.-B. Fabrication and deployment of the in situ chemotaxis assay (ISCA). protocols.io https://doi.org/10.17504/protocols.io.kztcx6n (2019).Ritchie, R. J. Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynth. Res. 89, 27–41 (2006).CAS 
    Article 

    Google Scholar 
    Marie, D., Partensky, F., Jacquet, S. & Vaulot, D. Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR Green I. Appl. Environ. Microbiol. 63, 186–193 (1997).CAS 
    Article 

    Google Scholar 
    Bramucci, A. R. et al. Microvolume DNA extraction methods for microscale amplicon and metagenomic studies. ISME Commun. 1, 79 (2021).Article 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    Article 

    Google Scholar 
    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).Boyd, J. A., Woodcroft, B. J. & Tyson, G. W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 46, e59 (2018).Article 

    Google Scholar 
    Kuever, J., Rainey, F. A. & Widdel, F. In Bergey’s Manual of Systematics of Archaea and Bacteria https://doi.org/10.1002/9781118960608.obm00084 (2015).Bianchi, D., Weber, T. S., Kiko, R. & Deutsch, C. Global niche of marine anaerobic metabolisms expanded by particle microenvironments. Nat. Geosci. 11, 263–268 (2018).CAS 
    Article 

    Google Scholar 
    Liu, X. et al. Wide distribution of anaerobic ammonium-oxidizing bacteria in the water column of the South China Sea: implications for their survival strategies. Divers. Distrib. 27, 1893–19003 (2021).Article 

    Google Scholar 
    Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).CAS 
    Article 

    Google Scholar 
    Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).CAS 
    Article 

    Google Scholar 
    Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
    Article 

    Google Scholar 
    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59 (2014).Article 

    Google Scholar 
    Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200 (2013).CAS 
    Article 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLOS Comput. Biol. 10, e1003531 (2014).Article 

    Google Scholar 
    Berges, J. A., Franklin, D. J. & Harrison, P. J. Evolution of an artificial seawater medium: improvements in enriched seawater, artificial water over the last two decades. J. Phycol. 37, 1138–1145 (2001).Article 

    Google Scholar 
    Lane, D. In Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 115–175 (1991).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nature Methods 13, 581–583 (2016).CAS 
    Article 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).Article 

    Google Scholar 
    Oksanen, J. et al. Package ‘Vegan’ Community Ecology Package Version 2 (2013).Durham, B. P. et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat. Microbiol. 4, 1706–1715 (2019).CAS 
    Article 

    Google Scholar 
    Durham, B. P. et al. Recognition cascade and metabolite transfer in a marine bacteria–phytoplankton model system. Environ. Microbiol. 19, 3500–3513 (2017).CAS 
    Article 

    Google Scholar 
    Durham, B. P. et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc. Natl Acad. Sci. USA 112, 453–457 (2015).CAS 
    Article 

    Google Scholar 
    Landa, M. et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 13, 2536–2550 (2019).CAS 
    Article 

    Google Scholar  More

  • in

    Highly-resolved interannual phytoplankton community dynamics of the coastal Northwest Atlantic

    Boyce DG, Lewis MR, Worm B. Global phytoplankton decline over the past century. Nature. 2010;466:591–6.CAS 
    Article 

    Google Scholar 
    Bonachela JA, Klausmeier CA, Edwards KF, Litchman E, Levin SA. The role of phytoplankton diversity in the emergent oceanic stoichiometry. J Plankton Res. 2016;38:1021–35.CAS 
    Article 

    Google Scholar 
    Falkowski PG. The role of phytoplankton photosynthesis in global biogeochemical cycles. Photosynth Res. 1994;39:235–58.CAS 
    Article 

    Google Scholar 
    Longhurst A. Seasonal cycles of pelagic production and consumption. Prog Oceanogr. 1995;36:77–167.Article 

    Google Scholar 
    Li WKW, Glen Harrison W, Head EJH. Coherent assembly of phytoplankton communities in diverse temperate ocean ecosystems. Proc R Soc B Biol Sci. 2006;273:1953–60.Article 

    Google Scholar 
    Bolaños LM, Karp-Boss L, Choi CJ, Worden AZ, Graff JR, Haëntjens N, et al. Small phytoplankton dominate western North Atlantic biomass. ISME J. 2020;14:1–12.Article 

    Google Scholar 
    Buttigieg PL, Fadeev E, Bienhold C, Hehemann L, Offre P, Boetius A. Marine microbes in 4D—using time series observation to assess the dynamics of the ocean microbiome and its links to ocean health. Curr Opin Microbiol. 2018;43:169–85.Article 

    Google Scholar 
    Hirata T, Aiken J, Hardman-Mountford N, Smyth TJ, Barlow RG. An absorption model to determine phytoplankton size classes from satellite ocean colour. Remote Sens Environ. 2008;112:3153–9.Article 

    Google Scholar 
    Li WKW, Dickie PM. Monitoring phytoplankton, bacterioplankton, and virioplankton in a coastal inlet (Bedford Basin) by flow cytometry. Cytometry. 2001;44:236–46.CAS 
    Article 

    Google Scholar 
    Karl DM, Lukas R. The Hawaii Ocean Time-series (HOT) program: background, rationale and field implementation. Deep Sea Res Part II Top Stud Oceanogr. 1996;43:129–56.CAS 
    Article 

    Google Scholar 
    Steinberg DK, Carlson CA, Bates NR, Johnson RJ, Michaels AF, Knap AH. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res Part II Top Stud Oceanogr. 2001;48:1405–47.CAS 
    Article 

    Google Scholar 
    Harris R. The L4 time-series: the first 20 years. J Plankton Res. 2010;32:577–83.Article 

    Google Scholar 
    Hunter-Cevera KR, Neubert MG, Olson RJ, Solow AR, Shalapyonok A, Sosik HM. Physiological and ecological drivers of early spring blooms of a coastal phytoplankter. Science. 2016;354:326–9.CAS 
    Article 

    Google Scholar 
    Shi Q, Wallace D. A 3-year time series of volatile organic iodocarbons in Bedford Basin, Nova Scotia: a northwestern Atlantic fjord. Ocean Sci. 2018;14:1385–403.CAS 
    Article 

    Google Scholar 
    Crawford A, Shore J, Shan S. Measurement of tidal currents using an autonomous underwater vehicle. IEEE J Ocean Eng 2021;1–13.Kerrigan EA, Kienast M, Thomas H, Wallace DWR. Using oxygen isotopes to establish freshwater sources in Bedford Basin, Nova Scotia, a Northwestern Atlantic fjord. Estuar Coast Shelf Sci. 2017;199:96–104.CAS 
    Article 

    Google Scholar 
    Shan S, Sheng J. Examination of circulation, flushing time and dispersion in Halifax Harbour of Nova Scotia. Water Qual Res J. 2012;47:353–74.CAS 
    Article 

    Google Scholar 
    Clayton S, Dutkiewicz S, Jahn O, Follows MJ. Dispersal, eddies, and the diversity of marine phytoplankton. Limnol Oceanogr Fluids Environ. 2013;3:182–97.Article 

    Google Scholar 
    Barton AD, Dutkiewicz S, Flierl G, Bragg J, Follows MJ. Patterns of diversity in marine phytoplankton. Science. 2010;327:1509–11.CAS 
    Article 

    Google Scholar 
    Dutkiewicz S, Cermeno P, Jahn O, Follows MJ, Hickman AE, Taniguchi DAA, et al. Dimensions of marine phytoplankton diversity. Biogeosciences. 2020;17:609–34.Article 

    Google Scholar 
    Righetti D, Vogt M, Gruber N, Psomas A, Zimmermann NE. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci Adv. 2019;5:eaau6253.Article 

    Google Scholar 
    Li WKW. Annual average abundance of heterotrophic bacteria and Synechococcus in surface ocean waters. Limnol Oceanogr. 1998;43:1746–53.Article 

    Google Scholar 
    DFO Canada. AZMP Bulletin PMZA. 2006. DFO.Cullen JJ, Doolittle WF, Levin SA, Li WKW. Patterns and prediction in microbial oceanography. Oceanography. 2007;20:34–46.Article 

    Google Scholar 
    El‐Swais H, Dunn KA, Bielawski JP, Li WKW, Walsh DA. Seasonal assemblages and short-lived blooms in coastal north-west Atlantic Ocean bacterioplankton. Environ Microbiol. 2015;17:3642–61.Article 

    Google Scholar 
    Georges AA, El-Swais H, Craig SE, Li WK, Walsh DA. Metaproteomic analysis of a winter to spring succession in coastal northwest Atlantic Ocean microbial plankton. ISME J. 2014;8:1301–13.CAS 
    Article 

    Google Scholar 
    Conover SAM. Nitrogen utilization during spring blooms of marine phytoplankton in Bedford Basin, Nova Scotia, Canada. Mar Biol. 1975;32:247–61.CAS 
    Article 

    Google Scholar 
    Lehman PW. Comparison of chlorophyll a and carotenoid pigments as predictors of phytoplankton biomass. Mar Biol. 1981;65:237–44.CAS 
    Article 

    Google Scholar 
    Siddig AAH, Ellison AM, Ochs A, Villar-Leeman C, Lau MK. How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators. Ecol Indic. 2016;60:223–30.Article 

    Google Scholar 
    Zorz J, Willis C, Comeau AM, Langille MGI, Johnson CL, Li WKW, et al. Drivers of regional bacterial community structure and diversity in the Northwest Atlantic Ocean. Front Microbiol 2019;10.Comeau AM, Douglas GM, Langille MGI. Microbiome Helper: a custom and streamlined workflow for microbiome research. mSystems. 2017;2:e00127–16.CAS 
    Article 

    Google Scholar 
    Comeau AM, Li WKW, Tremblay J-É, Carmack EC, Lovejoy C. Arctic Ocean microbial community structure before and after the 2007 record sea ice minimum. PLOS ONE. 2011;6:e27492.CAS 
    Article 

    Google Scholar 
    Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
    Article 

    Google Scholar 
    Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, et al. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal Internal Transcribed Spacer marker gene primers for microbial community surveys. mSystems. 2015;1:e00009–15.Article 

    Google Scholar 
    Willis C, Desai D, LaRoche J. Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic. FEMS Microbiol Lett. 2019;366:1–9.Article 

    Google Scholar 
    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
    Article 

    Google Scholar 
    Decelle J, Romac S, Stern RF, Bendif EM, Zingone A, Audic S, et al. PhytoREF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol Ecol Resour. 2015;15:1435–45.CAS 
    Article 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–96.CAS 
    Article 

    Google Scholar 
    NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46:D8–13.Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. 2020. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/RStudio Team. RStudio: Integrated Development for R. 2020. RStudio, Inc., Boston, MA. http://www.rstudio.com/.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
    Article 

    Google Scholar 
    Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Res. 2008;36:W5–9.CAS 
    Article 

    Google Scholar 
    Cáceres MD, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.Article 

    Google Scholar 
    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
    Article 

    Google Scholar 
    Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.CAS 
    Article 

    Google Scholar 
    Oksanen J, Blanchet F, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R package version. 2019;2:5–6. https://CRAN.R-project.org/package=vegan.
    Google Scholar 
    Wickham H. ggplot2: Elegant graphics for data analysis. 2016. Springer-Verlag, New York.Sohm JA, Ahlgren NA, Thomson ZJ, Williams C, Moffett JW, Saito MA, et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 2016;10:333–45.CAS 
    Article 

    Google Scholar 
    Ahlgren NA, Rocap G. Diversity and distribution of marine Synechococcus: multiple gene phylogenies for consensus classification and development of qPCR assays for sensitive measurement of clades in the ocean. Front Microbiol. 2012;3:1–24.Article 

    Google Scholar 
    Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.Article 

    Google Scholar 
    Logares R, Sunagawa S, Salazar G, Cornejo‐Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ Microbiol. 2014;16:2659–71.CAS 
    Article 

    Google Scholar 
    Faust K, Raes J. CoNet app: inference of biological association networks using Cytoscape. F1000Research. 2016;5:1519.Article 

    Google Scholar 
    Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2:2366–82.CAS 
    Article 

    Google Scholar 
    Fuhrman JA, Cram JA, Needham DM. Marine microbial community dynamics and their ecological interpretation. Nat Rev Microbiol. 2015;13:133–46.CAS 
    Article 

    Google Scholar 
    Cram JA, Chow C-ET, Sachdeva R, Needham DM, Parada AE, Steele JA, et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 2015;9:563–80.Article 

    Google Scholar 
    Schoemann V, Becquevort S, Stefels J, Rousseau V, Lancelot C. Phaeocystis blooms in the global ocean and their controlling mechanisms: a review. J Sea Res. 2005;53:43–66.CAS 
    Article 

    Google Scholar 
    Li W, Dickie P, Spry J. Plankton monitoring programme in the Bedford Basin, 1991-1997. 1998. Canadian Data Report of Fisheries and Aquatic Sciences 1036. Ocean Sciences Division, Maritimes Region, Fisheries and Oceans Canada.Bork P, Bowler C, Vargas C, de, Gorsky G, Karsenti E, Wincker P. Tara Oceans studies plankton at planetary scale. Science. 2015;348:873–873.CAS 
    Article 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.Article 

    Google Scholar 
    McLachlan JL, Seguel MR, Fritz L. Tetreutreptia pomquetensis gen. et sp. nov. (Euglenophyceae): a quadriflagellate, phototrophic marine Euglenoid. J Phycol. 1994;30:538–44.Article 

    Google Scholar 
    Edlund MB, Stoermer EF. Resting spores of the freshwater diatoms Acanthoceras and Urosolenia. J Paleolimnol. 1993;9:55–61.Article 

    Google Scholar 
    Tomas CR. Marine Phytoplankton: a guide to naked flagellates and coccolithophorids. 2012. Academic Press.Haas S, Robicheau BM, Rakshit S, Tolman J, Algar CK, LaRoche J, et al. Physical mixing in coastal waters controls and decouples nitrification via biomass dilution. Proc Natl Acad Sci. 2021;118:e2004877118.CAS 
    Article 

    Google Scholar 
    Falkowski PG, Katz ME, Knoll AH, Quigg A, Raven JA, Schofield O, et al. The evolution of modern eukaryotic phytoplankton. Science. 2004;305:354–60.CAS 
    Article 

    Google Scholar 
    Needham DM, Sachdeva R, Fuhrman JA. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 2017;11:1614–29.Article 

    Google Scholar 
    Choi CJ, Bachy C, Jaeger GS, Poirier C, Sudek L, Sarma VVSS, et al. Newly discovered deep-branching marine plastid lineages are numerically rare but globally distributed. Curr Biol. 2017;27:R15–16.CAS 
    Article 

    Google Scholar 
    Yoo YD, Seong KA, Kim HS, Jeong HJ, Yoon EY, Park J, et al. Feeding and grazing impact by the bloom-forming euglenophyte Eutreptiella eupharyngea on marine eubacteria and cyanobacteria. Harmful Algae. 2018;73:98–109.Article 

    Google Scholar 
    Dasilva CR, Li WKW, Lovejoy C. Phylogenetic diversity of eukaryotic marine microbial plankton on the Scotian Shelf Northwestern Atlantic Ocean. J Plankton Res. 2014;36:344–63.CAS 
    Article 

    Google Scholar 
    Bolaños LM, Choi CJ, Worden AZ, Baetge N, Carlson CA, Giovannoni SJ. Seasonality of the microbial community composition in the North Atlantic. Front Mar Sci. 2021;8:23.Article 

    Google Scholar 
    Monier A, Worden AZ, Richards TA. Phylogenetic diversity and biogeography of the Mamiellophyceae lineage of eukaryotic phytoplankton across the oceans. Environ Microbiol Rep. 2016;8:461–9.CAS 
    Article 

    Google Scholar 
    Irion S, Christaki U, Berthelot H, L’Helguen S, Jardillier L. Small phytoplankton contribute greatly to CO2-fixation after the diatom bloom in the Southern Ocean. ISME J. 2021;15:2509–22.CAS 
    Article 

    Google Scholar 
    Choi CJ, Jimenez V, Needham D, Poirier C, Bachy C, Alexander H, et al. Seasonal and geographical transitions in eukaryotic phytoplankton community structure in the Atlantic and Pacific Oceans. Front Microbiol. 2020;11:2187.
    Google Scholar 
    Leblanc K, Quéguiner B, Diaz F, Cornet V, Michel-Rodriguez M, Durrieu de Madron X, et al. Nanoplanktonic diatoms are globally overlooked but play a role in spring blooms and carbon export. Nat Commun. 2018;9:953.Article 

    Google Scholar 
    Lundholm N, Hasle GR. Fragilariopsis (Bacillariophyceae) of the Northern Hemisphere – morphology, taxonomy, phylogeny and distribution, with a description of F. pacifica sp. nov. Phycologia. 2010;49:438–60.Article 

    Google Scholar 
    Martínez-pérez C, Mohr W, Löscher CR, Dekaezemacker J, Littmann S, Yilmaz P, et al. The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle. Nat Microbiol. 2016;1:16163.Article 

    Google Scholar 
    Zehr JP, Shilova IN, Farnelid HM, Muñoz-Marín M, del C, Turk-Kubo KA. Unusual marine unicellular symbiosis with the nitrogen-fixing cyanobacterium UCYN-A. Nat Microbiol. 2016;2:1–11.
    Google Scholar 
    Worden AZ, Janouskovec J, McRose D, Engman A, Welsh RM, Malfatti S, et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr Biol. 2012;22:R675–77.CAS 
    Article 

    Google Scholar 
    Altenburger A, Blossom HE, Garcia-Cuetos L, Jakobsen HH, Carstensen J, Lundholm N, et al. Dimorphism in cryptophytes—The case of Teleaulax amphioxeia/Plagioselmis prolonga and its ecological implications. Sci Adv. 2020;6:eabb1611.CAS 
    Article 

    Google Scholar 
    Kling JD, Lee MD, Fu F, Phan MD, Wang X, Qu P, et al. Transient exposure to novel high temperatures reshapes coastal phytoplankton communities. ISME J. 2020;14:413–24.CAS 
    Article 

    Google Scholar 
    Chassot E, Bonhommeau S, Dulvy NK, Mélin F, Watson R, Gascuel D, et al. Global marine primary production constrains fisheries catches. Ecol Lett. 2010;13:495–505.Article 

    Google Scholar 
    Gentry RR, Froehlich HE, Grimm D, Kareiva P, Parke M, Rust M, et al. Mapping the global potential for marine aquaculture. Nat Ecol Evol. 2017;1:1317–24.Article 

    Google Scholar 
    Benway HM, Lorenzoni L, White AE, Fiedler B, Levine NM, Nicholson DP, et al. Ocean time series observations of changing marine ecosystems: an era of integration, synthesis, and societal applications. Front Mar Sci. 2019;6:393.Article 

    Google Scholar 
    Rigosi A, Fleenor W, Rueda F. State-of-the-art and recent progress in phytoplankton succession modelling. Environ Rev. 2010;18:423–40.Article 

    Google Scholar 
    Daniels CJ, Poulton AJ, Esposito M, Paulsen ML, Bellerby R, St John M, et al. Phytoplankton dynamics in contrasting early stage North Atlantic spring blooms: composition, succession, and potential drivers. Biogeosciences. 2015;12:2395–409.Article 

    Google Scholar 
    Masuda Y, Yamanaka Y, Hirata T, Nakano H. Competition and community assemblage dynamics within a phytoplankton functional group: Simulation using an eddy-resolving model to disentangle deterministic and random effects. Ecol Model. 2017;343:1–14.Article 

    Google Scholar 
    Percopo I, Siano R, Cerino F, Sarno D, Zingone A. Phytoplankton diversity during the spring bloom in the northwestern Mediterranean Sea. Botanica Marina. 2011;54:243–67.Article 

    Google Scholar 
    Sun J, Liu D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J Plankton Res. 2003;25:1331–46.Article 

    Google Scholar 
    Agawin N, Duarte C, Agustí S, Vaqué D. Effect of N:P ratios on response of Mediterranean picophytoplankton to experimental nutrient inputs. Aquat Microb Ecol. 2004;34:57–67.Article 

    Google Scholar 
    Bertilsson S, Berglund O, Karl DM, Chisholm SW. Elemental composition of marine Prochlorococcus and Synechococcus: Implications for the ecological stoichiometry of the sea. Limnology Oceanogr. 2003;48:1721–31.CAS 
    Article 

    Google Scholar 
    Tomas CR. Identifying Marine Phytoplankton. 1997. Elsevier.Harrison PJ, Zingone A, Mickelson MJ, Lehtinen S, Ramaiah N, Kraberg AC, et al. Cell volumes of marine phytoplankton from globally distributed coastal data sets. Estuarine, Coastal Shelf Sci. 2015;162:130–42.CAS 
    Article 

    Google Scholar 
    Guillou L, Chrétiennot-Dinet M-J, Medlin LK, Claustre H, Goër SL, Vaulot D. Bolidomonas: a new genus with two species belonging to a new algal class, the Bolidophyceae (Heterokonta). J Phycol. 1999;35:368–81.Article 

    Google Scholar  More

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    Analysis: the biodiversity footprint of the University of Oxford

    To help to achieve ecological recovery worldwide, more multinational corporations are making commitments to biodiversity conservation1–3. According to the most recent assessment in 2018, 31 of the 100 largest companies by revenue worldwide (the global Fortune 100) have done so, from the retail corporation Walmart to the insurance company AXA4.To deliver real gains — in the population sizes of endangered species, say, or in the number of hectares of restored forests, grasslands or wetlands — large organizations need to determine which of their activities have the greatest impacts on biodiversity5. And they need to disclose and mitigate those impacts. Currently, methods for doing this are lacking (see ‘Promises are hard to keep’). (By large organizations, we mean formal entities composed of hundreds of people or more that act towards a certain purpose, whether in the public, private or non-profit sectors.)
    Promises are hard to keep

    A lack of consensus on methods and metrics means companies are struggling to clearly define — and deliver on — commitments relating to biodiversity.
    So far, most studies of the environmental impacts of organizations, such as multinational corporations and universities, have focused on greenhouse-gas emissions.
    The G7 group of the world’s largest economies endorsed the new Taskforce on Nature-related Financial Disclosures (TNFD) only last year. This builds on a similar approach used for climate change — the Taskforce on Climate-related Financial Disclosures. The TNFD aims to guide organizations on how to disclose environmental harms tied to their activities, but is still being developed.
    The number of corporations making commitments to achieve ‘net gain’ or ‘no net loss’ outcomes in relation to biodiversity has risen steadily in the past two decades3. But some of these promises have subsequently been retracted. In 2016, for example, the mining corporation Rio Tinto moved away from its 2006 agenda-setting ‘net positive impact’ biodiversity commitment, reportedly to focus on minimizing impacts3 (see also go.nature.com/3xtjggo).
    Many other commitments are not quantitative. As of 2018, only 5 of the 31 global Fortune 100 companies making biodiversity-related commitments had provided ones that were SMART — specific, measurable, ambitious, realistic and time-bound4 (the global Fortune 100 is an annual list of the 100 largest firms worldwide by revenue, as ranked by Fortune magazine).
    When quantitative analyses have been done, they tend to be of limited use, mainly because of inconsistencies in the biodiversity metrics used, and limitations in the scope of the assessment made. Disclosure of results is also limited.

    When quantitative analyses have been done, a variety of metrics have been used to quantify impacts. These range from the proportion of local species that would be lost as a result of an activity, to factors such as hectares of habitat affected, or the amount of sustainably sourced paper, fish or palm oil that is used4. But the choice of metric can radically alter the results of an impact assessment, so it is difficult to compare organizations. Likewise, few analyses consider the impacts of activities that are not under the direct control of the organization, such as those associated with supply chains6.As a proof of principle, we conducted a comprehensive assessment of biodiversity losses associated with activities at the University of Oxford, UK. We used data on purchasing, travel bookings, utility bills and other information from the 2018–19 and 2019–20 academic years. The 60 activities we assessed included the day-to-day running of buildings and transport services; travel (including flights) for students and researchers; construction of laboratories and other buildings; consumption of food and beverages at restaurants and cafeterias; and use of medical supplies and other materials in research labs.Our aim was to demonstrate what it would take for a large organization such as the University of Oxford to bring about a net gain in biodiversity — meaning that, thanks to its actions, the world’s biodiversity is left in a better state than it was before. As part of our analysis, we assessed how the university’s various activities and operations also affect greenhouse-gas emissions, and how those, in turn, affect biodiversity by driving climate change.We are confident that the approach we’ve developed for Oxford could be applied more broadly. Indeed, we hope that such a well-known institution disclosing a full assessment of its biodiversity footprint will offer powerful inspiration for others. (All seven of us have a current or previous affiliation with the university.)What we didThe University of Oxford launched an ambitious environmental sustainability strategy in March 2021. Its two main goals are to achieve biodiversity net gain and net-zero carbon, both by 2035. (The latter means that the university will remove as much carbon from the atmosphere as it adds.)To understand how challenging these goals might be to fulfil, we assessed the environmental impacts of the university’s various activities. This covered all those to do with research, education and operations during an academic year for staff and students (see ‘Upstream effects’). For our purposes, operations includes the university transport fleet, consumption of departmental food and utilities, waste disposal and the operational supply chain, including for paper.

    Source: J. W. Bull et al.

    As a first step, we defined a conceptual framework to systematically categorize the environmental impacts. We grouped activities in research, education and operations according to whether they involved any of five features: travel; food; the built environment (university buildings); the natural environment (any green space or land owned by the university, including managed parks and gardens); and resource use and waste (see ‘What we left out’). Each of these is associated with five general environmental impacts: greenhouse-gas emissions, the use of land and water, and pollution of water and air.
    What we left out

    Other organizations could assess different types of impact on biodiversity.
    Our biodiversity analysis of the University of Oxford, UK, included most upstream impacts — those resulting from consumption of goods and services created outside the university, such as food or medical supplies. We excluded the downstream impacts of research and education, such as those of a discovery in gene editing or chemistry, because it would be impossible to comprehensively account for all of the environmental impacts of knowledge generation. Also not included in our analysis were the university’s 39 colleges, 6 permanent private halls and more than 260 commercial buildings. These are independent legal entities that manage sustainability issues separately.
    Other analyses in different sectors might well be able to include downstream impacts. The effects of discarded plastic bottles or clothes could be included for a soft-drinks company or clothing manufacturer, for example.

    To further categorize the environmental impacts, we assigned each activity to one of two groups: those under direct university control or influence (through staff and key contractors), and those that the university can influence only indirectly (through students and supply chains). We deemed students buying tuna sandwiches from a university-owned cafe as direct control, for instance, because the university could decide to serve only vegetarian food. However, it can influence only indirectly what happens up the supply chain, before materials are used in a research lab, for example.Using this organizational framework, we worked with administrators to obtain the relevant information, such as travel bookings for staff and students, electricity and water bills, and purchasing records for goods, services and materials used in construction projects.Next, we used various tools to convert all the activities data into estimates of ‘mid-point environmental impacts’ (amount of carbon dioxide emitted, land or water used, and air or water pollutants produced). The database Exiobase 3 is one of the most extensive sources of international supply-chain impacts worldwide7. It shows, for instance, that the roughly US$3.5 million the university spent on paper and paper products in 2019–20 contributed to atmospheric acidification by releasing 2,448 kilograms of sulfur dioxide equivalent. Similarly, the UK Higher Education Supply Chain Emissions Tool uses spending data on goods and services to estimate greenhouse-gas emissions. The roughly $23 million Oxford spent on personal computers, printers and calculators in 2019–20, for example, produced an estimated 20,105 tonnes of CO2 equivalent.We then needed to estimate the extent of biodiversity loss associated with this wide range of broad environmental impacts. So we converted the mid-point environmental impacts into ‘end-point impacts’ that are specifically concerned with biodiversity. To do this, we used an established conversion methodology called ReCiPe8. The output metric ultimately linked to each activity is based on the proportion of local species that would be lost as a result of that activity, relative to the number that exists currently (see Supplementary information for all results and conversion factors).CaveatsWe refined our methods slightly when analysing data from the 2019–20 academic year. This, combined with the disruption caused by the COVID-19 pandemic, makes it difficult to compare years. So for simplicity, we report our results only from the 2019–20 academic year.The biodiversity metric we obtain using ReCiPe is based on strong evidence: the conversion tool is derived from the results of hundreds of studies of the impacts of human pressures on biodiversity8. But, in general, we weren’t able to factor in fine-level variables, such as whether the beef steaks in a university-owned restaurant are sourced from a UK or Brazilian farm. As such, our approach is best seen as a way to evaluate relative impacts, rather than as an indicator of precise absolute impacts.This difficulty aside, it is hard to compare the impact of the University of Oxford on biodiversity with that of similarly sized organizations. As yet, and as far as we know, no other organization has comprehensively evaluated and disclosed its impact on biodiversity, and then had its assessment independently validated. (Ecologists and other stakeholders at the University of Jyväskylä in Finland have begun to explore the impacts of that university’s activities on biodiversity using a similar approach to ours.)Using the greenhouse-gas metric, however, we can compare the impacts of the University of Oxford on emissions (which are related to its impacts on biodiversity) with those of comparably sized organizations.What we foundThe absolute size of the university’s greenhouse-gas footprint is astonishingly large — comparable to that of the eastern Caribbean island nation of Saint Lucia. It is two orders of magnitude smaller than Microsoft’s greenhouse-gas footprint, but one order of magnitude larger than that of the London Stock Exchange, according to estimates publicly disclosed by those organizations.Perhaps the most striking finding in our assessment of impacts specifically on biodiversity is that most of the harms are tied to university activities that are not under its direct control. In fact, the activities with the five biggest impacts on biodiversity are (from biggest to smallest): the supply chain for research activities (such as for chemicals, medical products, organic tissue and plastics); the supply chain for the day-to-day running of buildings (for paper, information technology and so on); food consumption; electricity consumption; and the supply chain for construction. All of these activities are associated with resource use and waste, food and the built environment.

    The University of Oxford’s use of laboratory materials has a large impact on biodiversity because of the upstream supply chain.Credit: Peter Nicholls/Reuters

    In short, supplies of lab equipment have much greater impacts on biodiversity overall than do international flights, the university’s consumption of electricity or its use of construction materials. (Personal protective equipment used in the lab, for example, requires the extraction and industrial processing of hydrocarbons, often from areas that are rich in biodiversity.)This observation is in line with the results of a handful of studies that suggest that supply chains, not transport or the day-to-day running of buildings, are the main contributors to greenhouse-gas emissions for universities (see, for example, ref. 9). It also aligns with the results of assessments by the fashion giant Kering since 2012, using its Environmental Profit & Loss account — a tool designed to quantify the environmental impacts of the company’s activities. These have revealed that Kering’s procurements of commodities, such as leather, wool and metals, have much more impact on greenhouse-gas emissions, particularly on those from land use, than does the day-to-day running of its factories and offices10.Yet the sustainability strategies of large organizations typically focus not on supply chains, but on recycling, reducing the number of flights people take or the amount of electricity used11–13 (see also Nature 546, 565–567; 2017).Another important finding is the scale of intervention needed. Restoring the university’s owned land (around 1,000 hectares) to native woodland or some other natural habitat would make little difference when it comes to compensating for the impacts on biodiversity that result from just one year of activity. The university colleges own much more land than the university itself — some 50,000 hectares — but we excluded them from our analysis because they are independent legal entities that manage sustainability issues separately.Biodiversity boostHow could the university reverse the biodiversity losses stemming from its activities and operations?Here we consider three options. It could pursue its current environmental sustainability strategy. This entails (among other steps) setting quantitative targets to reduce flights, limiting consumption of all single-use products, making university-catered food vegetarian by default, and achieving 20% net gain for biodiversity in new construction projects. Alternatively, it could focus more heavily on preventing harms to biodiversity. We model a scenario in which all staff flights are prevented, all use of paper and any further construction is stopped, and the purchasing of lab materials is halved. Or the university could focus on compensating for the impacts that its activities and operations have on the planet, by taking steps to increase biodiversity in other places (see ‘Oxford’s options’).

    Source: J. W. Bull et al.

    Using the 2018–19 academic year results (selected because the COVID-19 pandemic made 2019–20 so unusual), we estimated how far these mitigation strategies might take the university towards biodiversity net gain.Our analysis indicates that the set of preventive measures proposed under the university’s environmental sustainability strategy get it about one-third of the way towards net gain. The findings also indicate that focusing mainly on the prevention of impacts is operationally unfeasible. Activities that have most effect on biodiversity, such as purchasing lab consumables, are central to the university’s existence and cannot simply stop.To achieve net gain, preventive measures, such as reducing flights and paper use, will have to be accompanied by additional and extensive actions to compensate for the remaining impacts on biodiversity.Such actions could include investing in reforestation, wetland restoration, sustainable land-management programmes and prevention of habitat loss caused by independent parties. For example, those directing the Oyu Tolgoi mining project in Mongolia are seeking to achieve biodiversity net gain by spending around 0.6% of the total project cost on actions that benefit biodiversity, such as sustainable grazing practices (see go.nature.com/3tkkbjh). Similarly, the Ambatovy metals mine in Madagascar is on course to offset its impacts on biodiverse eastern rainforests by preventing deforestation of those same habitats through small-scale agriculture14.Achieving true biodiversity net gain will require substantial offsetting that does not necessarily contribute to the university’s reductions in greenhouse-gas emissions. But whatever mix of approaches the institution pursues, it should strive for win–wins on both biodiversity and climate.Many types of action can simultaneously increase biodiversity and reduce greenhouse-gas emissions. For example, restoring mangroves in Bangladesh increased populations of wintering water birds 20-fold in just three years from 2004. And these restored mangroves can absorb carbon four times faster than land-based forests can15. But in other cases, there are trade-offs. Constructing wind turbines and solar photovoltaics to produce renewable energy, for instance, requires extensive mining of metals in places that can be rich in biodiversity16.Net gain for other organizationsOur calculations are likely to be comparable to results for other universities. In our analysis, we do not include the impacts of individual colleges. But because similar kinds of activity occur in colleges as in the rest of the university, their inclusion — or of halls of residence at other universities — is unlikely to qualitatively change our main findings. In fact, because of the colleges’ unusually large land holdings, including them would arguably result in an assessment that doesn’t so easily compare with those of other universities.Crucially, however, the analytical framework we have developed can be applied to a wide range of large organizations — whether they be universities, multinational corporations or government institutions.

    Restoring mangroves in western Bangladesh increased populations of wintering water birds, such as this oriental darter (Anhinga melanogaster).Credit: Muhammad Mostafigur Rahman/Alamy

    Governments, intergovernmental organizations and multinational corporations are increasingly recognizing that it will not be enough to simply slow the loss of the world’s biodiversity. Damaged habitats and depleted natural resources must be restored to prevent the collapse of ecosystems.Last year, the United Nations called for the urgent revival of nature in farmlands, forests and other ecosystems, declaring 2021–30 to be the Decade on Ecosystem Restoration. Later this year, at a meeting in Kunming, China, it is hoped that 196 nations will agree to the Post-2020 Global Biodiversity Framework of the Convention on Biological Diversity. Among the goals listed in the draft document are a “net gain in the area, connectivity and integrity of natural systems of at least 5 per cent”17.We urge all large organizations, academic or otherwise, to commit to strategies for a net gain in biodiversity — and to adopt formalized approaches that quantify current impacts and allow transparent tracking of progress. Otherwise, the degree of worldwide recovery of natural resources increasingly recognized as crucial for resilient societies to function will not happen.A key challenge is the lack of traceability for commodities. Both our assessment of the University of Oxford and those of others have revealed that large organizations often don’t know which country their commodities (such as cotton, flour or cement) come from — let alone which supplier or what kinds of biodiversity are being affected as a result.According to its 2022 report, for example, even a sector leader such as Kering could trace the source of only about three-quarters of its cotton. Supply chains for other commodities, such as sand, are even more opaque18.Encouragingly, various initiatives are being developed to provide more transparency about environmental impacts across supply chains. These include the supply-chain mapping tool TRASE, which aims to address deforestation.A related challenge, covered extensively elsewhere19,20, is how to ensure that biodiversity offsets are effectively and appropriately implemented such that they lead to conservation outcomes that are truly additional.Currently, there are uncertainties around how long it takes for a restored forest to start delivering biodiversity gains, whether promises to offset harms to biodiversity are actually met, what level of biodiversity gain is delivered by the restoration of a particular habitat, and so on. Take the Ambatovy mine in Madagascar. Its directors began protecting areas of eastern rainforest in 2009 to offset the impacts of deforestation directly caused by the mine. Yet forest gains are not estimated to balance losses until sometime between 2018 and 203314.Despite such challenges, however, we think that a commitment to full transparency, and to improving data collection over time, will enable organizations to compare performance and drive change — both in their own operations and throughout supply chains.Time is too short to let the perfect be the enemy of the good, or to claim that biodiversity net gain is too hard to achieve because there is no universal biodiversity metric. Individual metrics are imperfect but improving, and their limitations should not be a reason to delay measuring, disclosing and tackling impacts on biodiversity. More

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    Protected areas have a mixed impact on waterbirds, but management helps

    High Ambition Coalition for Nature and People. 50 Countries Announce Bold Commitment to Protect at Least 30% of the World’s Land and Ocean by 2030 (Campaign for Nature, 2021).Waldron A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020).Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23223 (2019).CAS 
    Article 

    Google Scholar 
    Nelson, A. & Chomitz, K. M. Protected Area Effectiveness in Reducing Tropical Deforestation (The World Bank, 2009).Scharlemann, J. P. W. et al. Securing tropical forest carbon: the contribution of protected areas to REDD. Oryx 44, 352–357 (2010).Article 

    Google Scholar 
    Feng, Y. et al. Assessing the effectiveness of global protected areas based on the difference in differences model. Ecol. Indic. 130, 108078 (2021).Article 

    Google Scholar 
    Laurance, W. F. et al. The fate of Amazonian forest fragments: A 32-year investigation. Biol. Conserv. 144, 56–67 (2011).Article 

    Google Scholar 
    Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).CAS 
    Article 

    Google Scholar 
    Terraube, J., Van doninck, J., Helle, P. & Cabeza, M. Assessing the effectiveness of a national protected area network for carnivore conservation. Nat. Commun. 11, 2957 (2020).CAS 
    Article 

    Google Scholar 
    Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).CAS 
    Article 

    Google Scholar 
    Amano, T. et al. Successful conservation of global waterbird populations depends on effective governance. Nature 553, 199–202 (2018).CAS 
    Article 

    Google Scholar 
    Kleijn, D., Cherkaoui, I., Goedhart, P. W., van der Hout, J. & Lammertsma, D. Waterbirds increase more rapidly in Ramsar-designated wetlands than in unprotected wetlands. J. Appl. Ecol. 51, 289–298 (2014).Article 

    Google Scholar 
    Reyes-Arriagada, R. et al. Population trends of a mixed-species colony of Humboldt and Magellanic Penguins in Southern Chile after establishing a protected area. Avian Conserv. Ecol. 8, 13 (2013).
    Google Scholar 
    Bukart, K. Motion 101 passes at IUCN, calls for protecting 50% of Earth’s lands and seas. One Earth https://www.oneearth.org/motion-101-passes-at-iucn-calls-for-protecting-50-of-earths-lands-and-seas/ (2021).Protected Planet Report 2020 (UNEP-WCMC and IUCN, 2021).Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).Barnes, M. D., Glew, L., Wyborn, C. & Craigie, I. D. Prevent perverse outcomes from global protected area policy. Nat, Ecol. Evol. 2, 759–762 (2018).Article 

    Google Scholar 
    Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trends Ecol. Evol. 22, 583–592 (2007).Article 

    Google Scholar 
    Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

    Google Scholar 
    Rodrigues, A. S. L. & Cazalis, V. The multifaceted challenge of evaluating protected area effectiveness. Nat. Commun. 11, 5147 (2020).CAS 
    Article 

    Google Scholar 
    Redford, K. H. The empty forest. BioScience 42, 412–422 (1992).Article 

    Google Scholar 
    Ferraro, P. J. Counterfactual thinking and impact evaluation in environmental policy. N. Direct. Eval. 2009, 75–84 (2009).Article 

    Google Scholar 
    Adams, V. M., Barnes, M. & Pressey, R. L. Shortfalls in conservation evidence: moving from ecological effects of interventions to policy evaluation. One Earth 1, 62–75 (2019).Article 

    Google Scholar 
    Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).Article 

    Google Scholar 
    Kingsford, R. T., Roshier, D. A. & Porter, J. L. Australian waterbirds time and space travellers in dynamic desert landscapes. Mar. Freshw. Res. 61, 875–884 (2010).CAS 
    Article 

    Google Scholar 
    The Ramsar Convention Secretariat. Managing Ramsar Sites. ramsar.org https://www.ramsar.org/sites-countries/managing-ramsar-sites (2014).European Commission. The Birds Directive. https://ec.europa.eu/environment/nature/legislation/birdsdirective/index_en.htm (accessed 3 April 2022).Zhang, W., Sheldon, B. C., Grenyer, R. & Gaston, K. J. Habitat change and biased sampling influence estimation of diversity trends. Curr. Biol. 31, 3656–3662.e3 (2021).CAS 
    Article 

    Google Scholar 
    Bruner, A. G., Gullison, R. E., Rice, R. E. & da Fonseca, G. A. B. Effectiveness of parks in protecting tropical biodiversity. Science 291, 125–128 (2001).CAS 
    Article 

    Google Scholar 
    Carranza, T., Balmford, A., Kapos, V. & Manica, A. Protected area effectiveness in reducing conversion in a rapidly vanishing ecosystem: the Brazilian Cerrado. Conserv. Lett. 7, 216–223 (2014).Article 

    Google Scholar 
    Rabinowitz, D. In The Biological Aspects of Rare Plant Conservation (ed. Synge, H.) 205–217 (John Wiley & Sons, 1981).Daskalova, G. N., Myers-Smith, I. H. & Godlee, J. L. Rare and common vertebrates span a wide spectrum of population trends. Nat. Commun. 11, 4394 (2020).CAS 
    Article 

    Google Scholar 
    Hettiarachchi, M., Morrison, T. H. & McAlpine, C. Forty-three years of Ramsar and urban wetlands. Glob. Environ. Change 32, 57–66 (2015).Article 

    Google Scholar 
    Munishi, P., Chuwa, J., Kilungu, H., Moe, S. & Temu, R. Management effectiveness and conservation initiatives in the Kilombero Valley Flood Plains Ramsar Site, Tanzania. Tanzania J. For. Nat. Conserv. 81, 1–10 (2012).
    Google Scholar 
    Fahrig, L. Why do several small patches hold more species than few large patches? Glob. Ecol. Biogeogr. 29, 615–628 (2020).Article 

    Google Scholar 
    Newmark, W. D. Extinction of mammal populations in western North American National Parks. Conserv. Biol. 9, 512–526 (1995).Article 

    Google Scholar 
    Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).Article 

    Google Scholar 
    Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42 (2017).Article 

    Google Scholar 
    Wetlands International. Asian Waterbird Census. https://south-asia.wetlands.org/our-approach/healthy-wetland-nature/asian-waterbird-census/ (accessed 3 April 2022).Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).CAS 
    Article 

    Google Scholar 
    Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).Article 

    Google Scholar 
    Kingsford, R. T., Bino, G. & Porter, J. L. Continental impacts of water development on waterbirds, contrasting two Australian river basins: global implications for sustainable water use. Glob. Change Biol. 23, 4958–4969 (2017).Article 

    Google Scholar 
    Jia, Q., Wang, X., Zhang, Y., Cao, L. & Fox, A. D. Drivers of waterbird communities and their declines on Yangtze River floodplain lakes. Biol. Conserv. 218, 240–246 (2018).Article 

    Google Scholar 
    Lehikoinen, A., Rintala, J., Lammi, E. & Pöysä, H. Habitat-specific population trajectories in boreal waterbirds: alarming trends and bioindicators for wetlands. Animal Conserv. 19, 88–95 (2016).Article 

    Google Scholar 
    Boyd, C. et al. Spatial scale and the conservation of threatened species. Conserv. Lett. 1, 37–43 (2008).Article 

    Google Scholar 
    Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).Article 

    Google Scholar 
    Wauchope, H. et al. Quantifying the impact of protected areas on near-global waterbird population trends, a pre-analysis plan. Preprint at https://doi.org/10.7287/peerj.preprints.27741v2 (2019).Nosek, B. A., Ebersole, C. R., DeHaven, A. C. & Mellor, D. T. The preregistration revolution. Proc. Natl Acad. Sci. USA 115, 2600–2606 (2018).CAS 
    Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).QGIS Geographic Information System (QGIS, 2021).Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).The World Database on Protected Areas (WDPA)/The Global Database on Protected Areas Management Effectiveness (GD-PAME) www.protectedplanet.net (UNEP-WCMC and IUCN, 2019).Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) (NOAA, 2017).Coetzer, K. L., Witkowski, E. T. F. & Erasmus, B. F. N. Reviewing Biosphere Reserves globally: effective conservation action or bureaucratic label? Biol. Rev. 89, 82–104 (2014).Article 

    Google Scholar 
    Ament, J. M. & Cumming, G. S. Scale dependency in effectiveness, isolation, and social-ecological spillover of protected areas. Conserv. Biol. 30, 846–855 (2016).Article 

    Google Scholar 
    Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling? Ecography 37, 191–203 (2014).Article 

    Google Scholar 
    Salmerón Gómez, R., García, Pérez, J., López Martín, M. D. M. & García, C. G. Collinearity diagnostic applied in ridge estimation through the variance inflation factor. J. Appl. Stat. 43, 1831–1849 (2016).MathSciNet 
    Article 

    Google Scholar 
    Gu, X. S. & Rosenbaum, P. R. Comparison of multivariate matching methods: structures, distances, and algorithms. J. Comput. Graph. Stat. 2, 405–420 (1993).
    Google Scholar 
    Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci. 25, 1–21 (2010).MathSciNet 
    Article 

    Google Scholar 
    King, G. & Nielsen, R. Why propensity scores should not be used for matching. Pol. Anal. 27, 435–454 (2019).Article 

    Google Scholar 
    Rosenbaum, P. R. DOS: design of observational studies. https://cran.r-project.org/web/packages/DOS/index.html (2018).Linden, A. A matching framework to improve causal inference in interrupted time-series analysis. J. Eval. Clin. Pract. 24, 408–415 (2018).Article 

    Google Scholar 
    Simmons, B. I., Hoeppke, C. & Sutherland, W. J. Beware greedy algorithms. J. Anim. Ecol. 88, 804–807 (2019).Article 

    Google Scholar 
    Austin, P. C. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28, 3083–3107 (2009).MathSciNet 
    Article 

    Google Scholar 
    Rubin, D. B. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Serv. Outcomes Res. Methodol. 2, 169–188 (2001).Article 

    Google Scholar 
    Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/index.html (2021).Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
    Google Scholar 
    Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).Article 

    Google Scholar 
    Christensen, R. Ordinal–regression models for ordinal data. https://cran.r-project.org/web/packages/ordinal/index.html (2019).Lüdecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Op. Source Softw. 3, 772 (2018).Article 

    Google Scholar 
    McKay, M. D., Beckman, R. J. & Conover, W. J. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245 (1979).MathSciNet 

    Google Scholar 
    Carnell, R. lhs: latin hypercube samples. https://cran.r-project.org/web/packages/lhs/index.html (2020).Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

    Google Scholar 
    Lu, C. & Tian, H. Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: Shifted hot spots and nutrient imbalance. Earth Syst. Sci. Data 9, 181–192 (2017).Article 

    Google Scholar 
    Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).Article 

    Google Scholar 
    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).Article 

    Google Scholar 
    Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 17001 (2017).Article 

    Google Scholar 
    Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).CAS 
    Article 

    Google Scholar 
    Sandvik, B. World Borders Dataset. Thematic Mapping http://thematicmapping.org/downloads/world_borders.php (2009).BirdLife International. Species Distribution Data Download http://www.birdlife.org/datazone/info/spcdownload (accessed 25 February 2020).Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

    Google Scholar 
    WWF International. Management Effectiveness Tracking Tool https://wwfeu.awsassets.panda.org/downloads/mett2_final_version_july_2007.pdf (2007). More