More stories

  • in

    Argentina: wildfires jeopardize rewilding

    CORRESPONDENCE
    12 April 2022

    Argentina: wildfires jeopardize rewilding

    Mario S. Di Bitetti

     ORCID: http://orcid.org/0000-0001-9704-8649

    0
    ,

    Carlos De Angelo

     ORCID: http://orcid.org/0000-0002-7759-3321

    1
    ,

    Agustín Paviolo

     ORCID: http://orcid.org/0000-0001-7855-4298

    2
    ,

    Adrián S. Di Giacomo

     ORCID: http://orcid.org/0000-0002-7976-0197

    3
    ,

    Diego Varela

     ORCID: http://orcid.org/0000-0003-3123-6756

    4
    &

    Alejandro R. Giraudo

     ORCID: http://orcid.org/0000-0003-0708-4481

    5

    Mario S. Di Bitetti

    Universidad Nacional de Misiones – CONICET, Puerto Iguazú, Argentina.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Carlos De Angelo

    Universidad Nacional de Río Cuarto – CONICET, Río Cuarto, Argentina.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Agustín Paviolo

    Universidad Nacional de Misiones – CONICET, Puerto Iguazú, Argentina.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Adrián S. Di Giacomo

    Universidad Nacional del Nordeste – CONICET, Corrientes, Argentina.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Diego Varela

    Universidad Nacional de Misiones – CONICET, Puerto Iguazú, Argentina.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Alejandro R. Giraudo

    Universidad Nacional del Litoral-CONICET, Santa Fé, Argentina.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Twitter

    Facebook

    Email

    Ferocious wildfires have already destroyed more than one million hectares this year in the Corrientes province of Argentina — including more than half of Iberá National Park, where a crucial rewilding project is under way (see E. Donadio et al. Nature 603, 225–227; 2022). We call for greater wildfire awareness and improved alarm systems to prevent such large-scale devastation in the future.

    Access options

    Access through your institution

    Change institution

    Buy or subscribe

    /* style specs start */
    style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
    /* style specs end */Subscribe to Nature+Get immediate online access to the entire Nature family of 50+ journals$29.99monthlySubscribeSubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00BuyAll prices are NET prices.

    Additional access options:

    Log in

    Learn about institutional subscriptions

    Nature 604, 246 (2022)
    doi: https://doi.org/10.1038/d41586-022-01006-5

    Competing Interests
    The authors declare no competing interests.

    Related Articles

    See more letters to the editor

    Subjects

    Conservation biology

    Jobs

    Postdoctoral Fellow (PhD)

    Baylor College of Medicine (BCM)
    Houston, TX, United States

    Postdoctoral Research Scientist

    UK Research and Innovation (UKRI)
    London, United Kingdom

    Associate or Senior Editor, Nature Human Behavior

    Springer Nature
    London, United Kingdom

    Multiple Faculty Positions in Neuroscience and Neuroengineering

    IDG/McGovern Institute for Brain Research, TH
    Beijin, China More

  • in

    Deforestation-induced climate change reduces carbon storage in remaining tropical forests

    Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Chang 2, 182–185 (2012).ADS 
    CAS 

    Google Scholar 
    Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 13, 3927–3950 (2021).Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Davidson, E. A. et al. The Amazon basin in transition. Nature 481, 321–328 (2012).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: croplands from 1700 to 1992. Glob. Biogeochem. Cy. 13, 997–1027 (1999).ADS 
    CAS 

    Google Scholar 
    Pongratz, J., Reick, C., Raddatz, T. & Claussen, M. A reconstruction of global agricultural areas and land cover for the last millennium. Glob. Biogeochem. Cy. 22, GB3018 (2008).ADS 

    Google Scholar 
    Kaplan, J. O. et al. Holocene carbon emissions as a result of anthropogenic land cover change. Holocene 21, 775–791 (2011).ADS 

    Google Scholar 
    Fearnside, P. M. Deforestation in Brazilian Amazonia: history, rates, and consequences. Conserv Biol. 19, 680–688 (2005).
    Google Scholar 
    van Marle, M. J. et al. Fire and deforestation dynamics in Amazonia (1973–2014). Glob. Biogeochem. Cy 31, 24–38 (2017).
    Google Scholar 
    Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850–2015. Glob. Biogeochem. Cy 31, 456–472 (2017).ADS 
    CAS 

    Google Scholar 
    Houghton, R. A. Aboveground forest biomass and the global carbon balance. Glob. Change Biol. 11, 945–958 (2005).ADS 

    Google Scholar 
    Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 

    Google Scholar 
    Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7, eabe9829 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Brando, P. M. et al. The gathering firestorm in southern Amazonia. Sci. Adv. 6, eaay1632 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Chang. 11, 442–448 (2021).ADS 

    Google Scholar 
    Erb, K. H. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davin, E. L. & de Noblet-Ducoudré, N. Climatic impact of global-scale deforestation: radiative versus nonradiative processes. J. Clim. 23, 97–112 (2010).ADS 

    Google Scholar 
    Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 1–8 (2015).ADS 

    Google Scholar 
    Silvério, D. V. et al. Agricultural expansion dominates climate changes in southeastern Amazonia: the overlooked non-GHG forcing. Environ. Res. Lett. 10, 104015 (2015).
    Google Scholar 
    Betts, R. Implications of land ecosystem-atmosphere interactions for strategies for climate change adaptation and mitigation. Tellus Ser. B-Chem. Phys. Meteorol. 59, 602–615 (2007).ADS 

    Google Scholar 
    Gibbard, S., Caldeira, K., Bala, G., Phillips, T. J. & Wickett, M. Climate effects of global land cover change. Geophys. Res. Lett. 32, L23705 (2005).ADS 

    Google Scholar 
    Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl Acad. Sci. USA 104, 6550–6555 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bathiany, S., Claussen, M., Brovkin, V., Raddatz, T. & Gayler, V. Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model. Biogeosciences 7, 1383–1399 (2010).ADS 
    CAS 

    Google Scholar 
    Devaraju, N., Bala, G. & Modak, A. Effects of large-scale deforestation on precipitation in the monsoon regions: Remote versus local effects. Proc. Natl Acad. Sci. USA 112, 3257–3262 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Devaraju, N., Bala, G. & Nemani, R. Modelling the influence of land‐use changes on biophysical and biochemical interactions at regional and global scales. Plant Cell Environ. 38, 1931–1946 (2015).CAS 
    PubMed 

    Google Scholar 
    Henderson-Sellers, A. & Gornitz, V. Possible climatic impacts of land cover transformations, with particular emphasis on tropical deforestation. Clim. Change 6, 231–257 (1984).ADS 

    Google Scholar 
    Dickinson, R. E. & Henderson‐Sellers, A. Modelling tropical deforestation: a study of GCM land‐surface parametrizations. Q. J. R. Meteorol. Soc. 114, 439–462 (1988).ADS 

    Google Scholar 
    Zhang, H., Henderson-Sellers, A. & McGuffie, K. Impacts of tropical deforestation. Part I: process analysis of local climatic change. J. Clim. 9, 1497–1517 (1996).ADS 

    Google Scholar 
    Costa, M. H. & Foley, J. A. Combined effects of deforestation and doubled atmospheric CO2 concentrations on the climate of Amazonia. J. Clim. 13, 18–34 (2000).ADS 

    Google Scholar 
    Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Chang. 5, 27–36 (2015).ADS 

    Google Scholar 
    Nobre, C. A., Sellers, P. J. & Shukla, J. Amazonian deforestation and regional climate change. J. Clim. 4, 957–988 (1991).ADS 

    Google Scholar 
    Gedney, N. & Valdes, P. J. The effect of Amazonian deforestation on the northern hemisphere circulation and climate. Geophys. Res. Lett. 27, 3053–3056 (2000).ADS 

    Google Scholar 
    Nobre, P., Malagutti, M., Urbano, D. F., de Almeida, R. A. & Giarolla, E. Amazon deforestation and climate change in a coupled model simulation. J. Clim. 22, 5686–5697 (2009).ADS 

    Google Scholar 
    Snyder, P. K. The influence of tropical deforestation on the Northern Hemisphere climate by atmospheric teleconnections. Earth Interact. 14, 1–34 (2010).
    Google Scholar 
    Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L. & Marsham, J. H. The effects of tropical vegetation on rainfall. Annu. Rev. Environ. Resour. 43, 193–218 (2018).
    Google Scholar 
    Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M. & Börner, J. Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nat. Commun. 12, 1–7 (2021).
    Google Scholar 
    Baidya Roy, S. & Avissar, R. Impact of land use/land cover change on regional hydrometeorology in Amazonia. J. Geophys. Res. Atmos. 107, LBA-4 (2002).
    Google Scholar 
    Khanna, J., Medvigy, D., Fisch, G. & de Araújo Tiburtino Neves, T. T. Regional hydroclimatic variability due to contemporary deforestation in southern Amazonia and associated boundary layer characteristics. J. Geophys. Res. Atmos. 123, 3993–4014 (2018).ADS 

    Google Scholar 
    McGuffie, K., Henderson-Sellers, A., Zhang, H., Durbidge, T. B. & Pitman, A. J. Global climate sensitivity to tropical deforestation. Glob. Planet. Change 10, 97–128 (1995).ADS 

    Google Scholar 
    Zhang, H., Henderson-Sellers, A. & McGuffie, K. The compounding effects of tropical deforestation and greenhouse warming on climate. Clim. Change 49, 309–338 (2001).CAS 

    Google Scholar 
    Voldoire, A. & Royer, J. F. Climate sensitivity to tropical land surface changes with coupled versus prescribed SSTs. Clim. Dyn. 24, 843–862 (2005).
    Google Scholar 
    Mahmood, R. et al. Land cover changes and their biogeophysical effects on climate. Int. J. Climatol. 34, 929–953 (2014).
    Google Scholar 
    Kooperman, G. J. et al. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Chang. 8, 434–440 (2018).ADS 

    Google Scholar 
    Doughty, C. E. & Goulden, M. L. Are tropical forests near a high temperature threshold? J. Geophys. Res. Biogeosci. 113, G00B07 (2008).ADS 

    Google Scholar 
    Sullivan, M. J. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Wu, C. et al. Historical and future global burned area with changing climate and human demography. One Earth 4, 517–530 (2021).ADS 

    Google Scholar 
    Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nobre, C. A. et al. Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proc. Natl Acad. Sci. USA 113, 10759–10768 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Green, J. K., Berry, J., Ciais, P., Zhang, Y. & Gentine, P. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6, eabb7232 (2020).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Numata, I. et al. Biomass collapse and carbon emissions from forest fragmentation in the Brazilian Amazon. J. Geophys. Res. Biogeosci. 115, G03027 (2010).ADS 

    Google Scholar 
    Junior, C. H. S. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).ADS 

    Google Scholar 
    Lawrence, D. M. et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. Model Dev. 9, 2973–2998 (2016).ADS 

    Google Scholar 
    Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).ADS 

    Google Scholar 
    Wu, T. et al. The Beijing Climate Center climate system model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geosci. Model Dev. 12, 1573–1600 (2019).ADS 

    Google Scholar 
    Swart, N. C. et al. The Canadian Earth System Model version 5 (CanESM5. 0.3). Geosci. Model Dev. 12, 4823–4873 (2019).ADS 
    CAS 

    Google Scholar 
    Danabasoglu, G. et al. The Community Earth System Model version 2 (CESM2). J. Adv. Model Earth Syst. 12, e2019MS001916 (2020).ADS 

    Google Scholar 
    Séférian, R. et al. Evaluation of CNRM Earth System Model, CNRM‐ESM2‐1: role of earth system processes in present‐day and future climate. J. Adv. Model Earth Syst. 11, 4182–4227 (2019).ADS 

    Google Scholar 
    Boucher, O. et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Model Earth Syst. 12, 1–52 (2020).
    Google Scholar 
    Kelley, M. et al. GISS‐E2. 1: configurations and climatology. J. Adv. Model Earth Syst. 12, e2019MS002025 (2020).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sellar, A. A. et al. Implementation of UK Earth system models for CMIP6. J. Adv. Model Earth Syst. 12, e2019MS001946 (2020).ADS 

    Google Scholar 
    Mauritsen, T. et al. Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1. 2) and its response to increasing CO2. J. Adv. Model Earth Syst. 11, 998–1038 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boysen, L. et al. Global climate response to idealized deforestation in CMIP6 models. Biogeosciences 17, 5615–5638 (2020).ADS 
    CAS 

    Google Scholar 
    Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Chang. 6, 1023–1027 (2016).ADS 
    CAS 

    Google Scholar 
    Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).ADS 
    CAS 

    Google Scholar 
    Ciais, P. et al. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (eds Stocker et al.) 465–570 (Cambridge Univ Press, UK and USA, 2013).Arora, V. K. et al. Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models. Biogeosciences 17, 4173–4222 (2020).ADS 
    CAS 

    Google Scholar 
    Jones, C. D. et al. C4MIP–The coupled climate–carbon cycle model intercomparison project: experimental protocol for CMIP6. Geosci. Model Dev. 9, 2853–2880 (2016).ADS 
    CAS 

    Google Scholar 
    UNFCCC. Background paper for the Workshop on Reducing Emissions from Deforestation in Developing Countries, Part 1: Scientific, Socio-economic, Technical, and Methodological Issues Related to Deforestation in Developing Countries 30 August to 1 September, Rome, Italy. Working paper No. 1(a) (2006).Asner, G. P. Tropical forest carbon assessment: integrating satellite and airborne mapping approaches. Environ. Res. Lett. 4, 034009 (2009).ADS 

    Google Scholar 
    Mahowald, N. M. et al. Interactions between land use change and carbon cycle feedbacks. Glob. Biogeochem. Cy 31, 96–113 (2017).CAS 

    Google Scholar 
    Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gibbs, H. K., Brown, S., Niles, J. O. & Foley, J. A. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett. 2, 045023 (2007).ADS 

    Google Scholar 
    Zhao, Z. et al. Fire enhances forest degradation within forest edge zones in Africa. Nat. Geosci. 14, 479–483 (2021).ADS 
    CAS 

    Google Scholar 
    Ordway, E. M. & Asner, G. P. Carbon declines along tropical forest edges correspond to heterogeneous effects on canopy structure and function. Proc. Natl Acad. Sci. USA117, 7863–7870 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fischer, R. et al. Accelerated forest fragmentation leads to critical increase in tropical forest edge area. Sci. Adv. 7, eabg7012 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McDowell, N. et al. Drivers and mechanisms of tree mortality in moist tropical forests. N. Phytol. 219, 851–869 (2018).
    Google Scholar 
    Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).ADS 
    CAS 
    MATH 

    Google Scholar 
    Fu, R. et al. Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proc. Natl Acad. Sci. USA 110, 18110–18115 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bagley, J. E., Desai, A. R., Harding, K. J., Snyder, P. K. & Foley, J. A. Drought and deforestation: has land cover change influenced recent precipitation extremes in the Amazon? J. Clim. 27, 345–361 (2014).ADS 

    Google Scholar 
    Arora, V. K. et al. Carbon–concentration and carbon–climate feedbacks in CMIP5 Earth system models. J. Clim. 26, 5289–5314 (2013).ADS 

    Google Scholar 
    Duveiller, G. et al. Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations. Earth Syst. Sci. Data 10, 1265–1279 (2018).ADS 

    Google Scholar 
    Schulzweida, U. Climate data operators (CDO) user guide (Version 1.9.8). https://doi.org/10.5281/zenodo.3539275 (2019).Tropical Rainfall Measuring Mission (TRMM) TRMM (TMPA/3B43) Rainfall Estimate L3 1 month 0.25 degree x 0.25 degree V7, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/TRMM/TMPA/MONTH/7 (2011).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, 1–18 (2020).
    Google Scholar 
    Yang, H. et al. Comparison of forest above‐ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation‐based estimates. Glob. Chang. Biol. 26, 3997–4012 (2020).ADS 
    PubMed 

    Google Scholar 
    Schneider, U. et al. GPCC Full Data Reanalysis Version 6.0 at 1.0o: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. https://doi.org/10.5676/DWD_GPCC/FD_M_V7_100 (2011).Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Chang 5, 470–474 (2015).ADS 

    Google Scholar 
    Spracklen, D. V. & Garcia‐Carreras, L. The impact of Amazonian deforestation on Amazon basin rainfall. Geophys. Res. Lett. 42, 9546–9552 (2015).ADS 

    Google Scholar  More

  • in

    High genomic diversity in the endangered East Greenland Svalbard Barents Sea stock of bowhead whales (Balaena mysticetus)

    Kovacs, K. M. et al. The endangered Spitsbergen bowhead whales’ secrets revealed after hundreds of years in hiding. Biol. Lett. https://doi.org/10.1098/rsbl.2020.0148 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cooke, J. & Reeves, R. Balaena mysticetus (East Greenland-Svalbard-Barents Sea subpopulation). The IUCN Red List of Threatened Species 2018, e.T2472A50348144 (2018). https://doi.org/10.2305/IUCN.UK.2018-1.RLTS.T2472A50348144.enAllen, R. C. & Keay, I. Bowhead whales in the eastern Arctic, 1611–1911: Population reconstruction with historical whaling records. Environ. Hist. 12, 89–113 (2006).Article 

    Google Scholar 
    Reeves, R. R. Spitsbergen bowhead stock: A short review. Mar. Fish. Rev. 42, 65–69 (1980).
    Google Scholar 
    Shelden, K. E. W. & Rugh, D. J. The Bowhead Whale, Balaena mysticetus: Its Historic and Current Status. Mar. Fish. Rev. 57, 1–20 (1995).
    Google Scholar 
    Gilg, O. & Born, E. W. Recent sightings of the bowhead whale (Balaena mysticetus) in Northeast Greenland and the Greenland Sea. Polar Biol. 28, 796–801. https://doi.org/10.1007/s00300-005-0001-9 (2005).Article 

    Google Scholar 
    Boertmann, D., Kyhn, L. A., Witting, L. & Heide-Jørgensen, M. P. A hidden getaway for bowhead whales in the Greenland Sea. Polar Biol. 38, 1315–1319. https://doi.org/10.1007/s00300-015-1695-y (2015).Article 

    Google Scholar 
    Wiig, Ø., Bachmann, L., Janik, V., Kovac, K. & Lydersen, C. Spitsbergen bowhead whales revisited. Mar. Mamm. Sci. 23, 688–693. https://doi.org/10.1111/j.1748-7692.2007.02373.x (2007).Article 

    Google Scholar 
    Wiig, Ø., Bachmann, L., Øien, N., Kovacs, K. & Lydersen, C. Observations of bowhead whales (Balaena mysticetus) in the Svalbard area 1940–2009. Polar Biol. 33, 979–984. https://doi.org/10.1007/s00300-010-0776-1 (2010).Article 

    Google Scholar 
    Lydersen, C. et al. Lost highway not forgotten: Satellite tracking of a bowhead whale (Balaena mysticetus) from the critically endangered Spitsbergen stock. Arctic 65, 76–86. https://doi.org/10.14430/arctic4167 (2012).Article 

    Google Scholar 
    Vacquié-Garcia, J. et al. Late summer distribution and abundance of ice-associated whales in the Norwegian High Arctic. Endang. Spec. Res. 32, 59–70. https://doi.org/10.3354/esr00791 (2017).Article 

    Google Scholar 
    Givens, G. H. & Heide-Jørgensen, M. P. Abundance. In The Bowhead Whale: Balaena Mysticetus: Biology and Human Interactions (eds George, J. C. & Thewissen, J. G. M.) 77–86 (Academic Press, 2020).
    Google Scholar 
    Rooney, A. P., Honeycutt, R. L. & Derr, J. N. Historical population size change of bowhead whales inferred from DNA sequence polymorphism data. Evolution 55, 1678–1685. https://doi.org/10.1111/j.0014-3820.2001.tb00687.x (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    Borge, T., Bachmann, L., Bjørnstad, G. & Wiig, Ø. Genetic variation in Holocene bowhead whales from Svalbard. Mol. Ecol. 16, 2223–2235. https://doi.org/10.1111/j.1365-294X.2007.03287.x (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    LeDuc, R. G. et al. Genetic analyses (mtDNA and microsatellites) of Okhotsk and Bering/Chukchi/Beaufort Seas populations of bowhead whales. J. Cetacean Res. Manag. 7, 107–111 (2005).
    Google Scholar 
    Meschersky, I. G., Chichkina, A. N., Shpak, O. V. & Rozhnov, V. V. Molecular genetic analysis of the Shantar Summer Group of bowhead whales (Balaena mysticetus L.) in the Okhotsk Sea. Russ. J. Genet. 50, 395–405. https://doi.org/10.1134/S1022795414040097 (2014).CAS 
    Article 

    Google Scholar 
    Bachmann, L. et al. Mitogenomics and the genetic differentiation of contemporary Balaena mysticetus (Cetacea) from Svalbard. Zool. J. Linn. Soc. 191, 1192–1203. https://doi.org/10.1093/zoolinnean/zlaa082 (2021).Article 

    Google Scholar 
    Grond, J., Płecha, M., Hahn, C., Wiig, Ø. & Bachmann, L. Mitochondrial genomes of ancient bowhead whales (Balaena mysticetus) from Svalbard. Mitochondrial DNA Part B 4, 4152–4154. https://doi.org/10.1080/23802359.2019.1693284 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nyhus, E. S. et al. Mitogenomes of contemporary Spitsbergen stock bowhead whales (Balaena mysticetus). Mitochondrial DNA Part B 1, 898–900. https://doi.org/10.1080/23802359.2016.1258345 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Keane, M. et al. Insights into the evolution of longevity from the bowhead whale genome. Cell Rep. 10, 112–122. https://doi.org/10.1016/j.celrep.2014.12.008) (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993. https://doi.org/10.1093/bioinformatics/btr509 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ortiz, E. M. vcf2phylip v2.0: Convert a VCF matrix into several matrix formats for phylogenetic analysis. zenodo.org, https://zenodo.org/record/2540861#.YDUOKy1Q0f0 (2019).Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267. https://doi.org/10.1093/molbev/msj030 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    Purcell, S. et al. PLINK: A tool set for whole-genome and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–576. https://doi.org/10.1086/519795 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (2020).Knaus, B. J. & Grunwald, N. J. VcfR: An R package to manipulate and visualize VCF format data. bioRxiv, 041277 (2016). https://doi.org/10.1101/041277Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hanghøj, K., Moltke, I., Alstrup Andersen, P., Manica, A. & Korneliussen, T. S. Fast and accurate relatedness estimation from high-throughput sequencing data in the presence of inbreeding. GigaScience 8, giz034. https://doi.org/10.1093/gigascience/giz034 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: Analysis of next generation sequencing data. BMC Bioinform. 15, 356. https://doi.org/10.1186/s12859-014-0356-4 (2014).Article 

    Google Scholar 
    Renaud, G., Hanghøj, K., Korneliussen, T. S., Willerslev, E. & Orlando, L. Joint estimates of heterozygosity and runs of homozygosity for modern and ancient samples. Genetics 212, 587–614. https://doi.org/10.1534/genetics.119.302057 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grabherr, M. G. et al. Genome-wide synteny through highly sensitive sequence alignment: Satsuma. Bioinformatics 26, 1145–1151. https://doi.org/10.1093/bioinformatics/btq102 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079. https://doi.org/10.1093/bioinformatics/btp352 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Westbury, M. V. et al. Extended and continuous decline in effective population size results in low genomic diversity in the world’s rarest hyena species, the brown hyena. Mol. Biol. Evol. 35, 1225–1237. https://doi.org/10.1093/molbev/msy037 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. & Durbin, R. Inference of human population history from whole genome sequence of a single individual. Nature 475, 493–496. https://doi.org/10.1038/nature10231 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Westbury, M. V., Petersen, B., Garde, E., Heide-Jørgensen, M. P. & Lorenzen, E. D. Narwhal genome reveals long-term low genetic diversity despite current large abundance size. iScience 15, 592–599. https://doi.org/10.1016/j.isci.2019.03.023 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Taylor, B. et al. Synthesis of lines of evidence for population structure for bowhead whales in the Bering-Chukchi-Beaufort region. Paper SC/59/BRG35 presented to the IWC Scientific Committee, Anchorage, Alaska (2007).Phillips, C. D. et al. Molecular insights into the historic demography of bowhead whales: Understanding the evolutionary basis of contemporary management practices. Ecol. Evol. 3, 18–37. https://doi.org/10.1002/ece3.374 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Liu, X. & Fu, Y. X. Stairway Plot 2: Demographic history inference with folded SNP frequency spectra. Genome Biol. 21, 280. https://doi.org/10.1186/s13059-020-02196-9 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Westbury, M. V. et al. Speciation in the face of gene flow within the toothed whale superfamily Delphinoidea. bioRxiv, https://doi.org/10.1101/2020.10.23.352286 (2020).Westbury, M. V. et al. Ecological specialisation and evolutionary reticulation in extant Hyaenidae. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msab055 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    IWC. Report of the Scientific Committee Virtual Meeting, 11–26 May 2020. J. Cetacean Res. Manag. (Supplement) 22, 1–122 (2021).Jonsgård, Å. A right whale (Balaena sp.), in all probability a Greenland right whale (Balaena mysticetus) observed in the Barents Sea. Norsk Hvalfangst-Tidende 53, 311–313 (1964).
    Google Scholar 
    De Jong, C. The hunt of the Greenland whale: A short history and statistical sources. Rep. Int. Whaling Comm. Spec. Issue 5, 83–106 (1983).
    Google Scholar 
    Weslawski, J. M., Hacquebord, L., Stempniewicz, L. & Malinga, M. Greenland whales and walruses in the Svalbard food web before and after exploitation. Oceanologia 2, 37–56 (2000).
    Google Scholar 
    George, J. C. et al. Age and growth estimates of bowhead whales (Balaena mysticetus) via aspartic acid racemization. Can. J. Zool. 77, 571–580. https://doi.org/10.1139/z99-015 (1999).Article 

    Google Scholar 
    de Jager, D. et al. High diversity, inbreeding and a dynamic Pleistocene demographic history revealed by African buffalo genomes. Sci. Rep. 11, 4540. https://doi.org/10.1038/s41598-021-83823-8 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Belikov, S. E., Gorbunov, Y. A. & Shil’nikov, V. I. Distribution of pinnipedia and cetacea in Soviet arctic seas and the Bering Sea in winter. Sov. J. Marine Biology 15, 251–257 (1989).
    Google Scholar 
    Gavrilo, M. V. Status of the bowhead whale Balaena mysticetus in the waters of Franz Josef Land Archipelago. Paper SC/66a/BRG20 Presented to the IWC Scientific Committee, May 2015, San Diego, USA (2015).Heide-Jorgensen, M. P., Hansen, R. G. & Shpak, O. V. Distribution, migrations, and ecology of the Atlantic and the Okhotsk Sea Populations. In The Bowhead Whale: Balaena Mysticetus: Biology and Human Interactions (eds George, J. C. & Thewissen, J. G. M.) 57–75 (Academic Press, 2020).
    Google Scholar 
    Petrov, S. A. et al. The results of marine mammal countins during the four expeditions in the Arctic in 2014 and 2015. Collection of scientific papers 9th International Conference ‘Marine mammals of the Holarctic’, Astrakhan, Russia, 2016. 91–102 (2018).Gavrilo, M. V. & Tretiakov V. Y. Observation of bowhead whales (Balaena mysticetus) in the East-Siberian Sea during 2007 season with record-low ice cover – Marine mammals of the Holarctic. In: Collection of Scientific Papers. Odessa, 191–194 (2008).Citta, J. J., Quakenbush, L. & George, J. C. Distribution and behavior of Bering-Chukchi-Beaufort bowhead whales as inferred by telemetry. In The Bowhead Whale: Balaena Mysticetus: Biology and Human Interactions (eds George, J. C. & Thewissen, J. G. M.) 31–56 (Academic Press, 2021). https://doi.org/10.1016/B978-0-12-818969-6.00004-2.Chapter 

    Google Scholar 
    Arnason, Ú., Lammers, F., Kumar, V., Nilsson, M. A. & Janke, A. Whole-genome sequencing of the blue whale and other rorquals finds signatures for introgressive gene flow. Sci. Adv. 4, eaap9873. https://doi.org/10.1126/sciadv.aap9873 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bazin, E., Glémin, S. & Galtier, N. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570–572. https://doi.org/10.1126/science.1122033 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    Corbett-Detig, R., Hartl, D. L. & Sackton, T. B. Natural selection constrains neutral diversity across a wide range of species. PLoS Biol. 13, e1002112. https://doi.org/10.1371/journal.pbio.1002112 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vachon, F., Whitehead, H. & Frasier, T. R. What factors shape genetic diversity in cetaceans?. Ecol. Evol. 8, 1554–1572. https://doi.org/10.1002/ece3.3727 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kumar, S. & Subramanian, S. Mutation rates in mammalian genomes. Proc. Natl. Acad. Sci. U.S.A. 99, 803–808. https://doi.org/10.1073/pnas.022629899 (2002).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bininda-Emonds, O. R. P. Fast genes and slow clades: Comparative rates of molecular evolution in mammals. Evol. Bioinf. 3, 59–85. https://doi.org/10.1177/117693430700300008 (2007).CAS 
    Article 

    Google Scholar 
    Jackson, J. A. et al. Big and slow: Phylogenetic estimates of molecular evolution in baleen whales (Suborder Mysticeti). Mol. Biol. Evol. 26, 2427–2440. https://doi.org/10.1093/molbev/msp169 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Foote, A. D. et al. Ancient DNA reveals that bowhead whale lineages survived Late Pleistocene climate change and habitat shifts. Nat. Commun. 4, 1667. https://doi.org/10.1038/ncomms2714 (2013).CAS 
    Article 

    Google Scholar 
    Wiig, Ø., Bachmann, L. & Hufthammer, A. K. Late Pleistocene and Holocene occurrence of bowhead whales (Balaena mysticetus) along the coasts of Norway. Polar Biol. 42, 645–656. https://doi.org/10.1007/s00300-019-02460-0 (2018).Article 

    Google Scholar 
    Alter, S. E. et al. Gene flow on ice: The role of sea ice and whaling in shaping Holarctic genetic diversity and population differentiation in bowhead whales (Balaena mysticetus). Ecol. Evol. 2, 2895–2911. https://doi.org/10.1093/zoolinnean/zlaa082 (2012).Article 

    Google Scholar  More

  • in

    Glycoside hydrolase from the GH76 family indicates that marine Salegentibacter sp. Hel_I_6 consumes alpha-mannan from fungi

    Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
    PubMed 
    Article 

    Google Scholar 
    Falkowski PG, Barber RT, Smetacek V. Biogeochemical controls and feedbacks on ocean primary production. Science. 1998;281:200–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Schnepf E, Kühn S. Food uptake and fine structure of Cryothecomonas longipes sp. nov., a marine nanoflagellate incertae sedis feeding phagotrophically on large diatoms. Helgol Mar Res. 2000;54:18–32.Article 

    Google Scholar 
    Garvetto A, Nézan E, Badis Y, Bilien G, Arce P, Bresnan E, et al. Novel widespread marine oomycetes parasitising diatoms, including the toxic genus pseudo-nitzschia: genetic, morphological, and ecological characterisation. Front Microbiol. 2018;9:2918.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gutiérrez MH, Jara AM, Pantoja S. Fungal parasites infect marine diatoms in the upwelling ecosystem of the Humboldt current system off central Chile. Environ Microbiol. 2016;18:1646–53.PubMed 
    Article 

    Google Scholar 
    Scholz B, Guillou L, Marano AV, Neuhauser S, Sullivan BK, Karsten U, et al. Zoosporic parasites infecting marine diatoms – A black box that needs to be opened. Fungal Ecol. 2016;19:59–76.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hedges J, Baldock J, Gélinas Y, Lee C, Peterson M, Wakeham S. The biochemical and elemental compositions of marine plankton: A NMR perspective. Mar Chem. 2002;78:47–63.CAS 
    Article 

    Google Scholar 
    Hedges JI, Baldock JA, Gelinas Y, Lee C, Peterson M, Wakeham SG. Evidence for non-selective preservation of organic matter in sinking marine particles. Nature. 2001;409:801–4.CAS 
    PubMed 
    Article 

    Google Scholar 
    Laine RA. A calculation of all possible oligosaccharide isomers both branched and linear yields 1.05 x 10 (12) structures for a reducing hexasaccharide: the Isomer Barrier to development of single-method saccharide sequencing or synthesis systems. Glycobiology. 1994;4:759–67.CAS 
    PubMed 
    Article 

    Google Scholar 
    Chin W-C, Orellana MV, Verdugo P. Spontaneous assembly of marine dissolved organic matter into polymer gels. Nature. 1998;391:568–72.CAS 
    Article 

    Google Scholar 
    Passow U. Transparent exopolymer particles (TEP) in aquatic environments. Prog Oceanogr. 2002;55:287–333.Article 

    Google Scholar 
    Fangel JU, Pedersen HL, Vidal-Melgosa S, Ahl LI, Salmean AA, Egelund J, et al. Carbohydrate microarrays in plant science. Methods Mol Biol. 2012;918:351–62.CAS 
    PubMed 
    Article 

    Google Scholar 
    Vidal-Melgosa S, Pedersen HL, Schuckel J, Arnal G, Dumon C, Amby DB, et al. A new versatile microarray-based method for high throughput screening of carbohydrate-active enzymes. J Biol Chem. 2015;290:9020–36.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vidal-Melgosa S, Sichert A, Francis TB, Bartosik D, Niggemann J, Wichels A, et al. Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat Commun. 2021;12:1150.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Becker S, Scheffel A, Polz MF, Hehemann JH. Accurate quantification of laminarin in marine organic matter with enzymes from marine microbes. Appl Environ Microbiol. 2017;83:e03389-16.Krüger K, Chafee M, Francis TB, del Rio TG, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. eLife. 2016;5:e11888.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kabisch A, Otto A, König S, Becher D, Albrecht D, Schüler M, et al. Functional characterization of polysaccharide utilization loci in the marine Bacteroidetes ‘Gramella forsetii’ KT0803. ISME J. 2014;8:1492–502.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kappelmann L, Krüger K, Hehemann JH, Harder J, Markert S, Unfried F, et al. Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J. 2019;13:76–91.CAS 
    PubMed 
    Article 

    Google Scholar 
    Unfried F, Becker S, Robb CS, Hehemann J-H, Markert S, Heiden SE, et al. Adaptive mechanisms that provide competitive advantages to marine bacteroidetes during microalgal blooms. ISME J. 2018;12:2894–906.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xing P, Hahnke RL, Unfried F, Markert S, Huang S, Barbeyron T, et al. Niches of two polysaccharide-degrading Polaribacter isolates from the North Sea during a spring diatom bloom. ISME J. 2015;9:1410–22.CAS 
    PubMed 
    Article 

    Google Scholar 
    Bjursell MK, Martens EC, Gordon JI. Functional genomic and metabolic studies of the adaptations of a prominent adult human gut symbiont, Bacteroides thetaiotaomicron, to the suckling period. J Biol Chem. 2006;281:36269–79.CAS 
    PubMed 
    Article 

    Google Scholar 
    Martens EC, Chiang HC, Gordon JI. Mucosal glycan foraging enhances fitness and transmission of a saccharolytic human gut bacterial symbiont. Cell Host Microbe. 2008;4:447–57.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hehemann JH, Correc G, Barbeyron T, Helbert W, Czjzek M, Michel G. Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota. Nature. 2010;464:908–12.CAS 
    PubMed 
    Article 

    Google Scholar 
    Larsbrink J, Rogers TE, Hemsworth GR, McKee LS, Tauzin AS, Spadiut O. et al. A discrete genetic locus confers xyloglucan metabolism in select human gut Bacteroidetes. Nature. 2014;506:498–502.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Larsbrink J, Thompson AJ, Lundqvist M, Gardner JG, Davies GJ, Brumer H. A complex gene locus enables xyloglucan utilization in the model saprophyte Cellvibrio japonicus. Mol Microbiol. 2014;94:418–33.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cuskin F, Lowe EC, Temple MJ, Zhu Y, Cameron E, Pudlo NA, et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature. 2015;517:165–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ndeh D, Rogowski A, Cartmell A, Luis AS, Basle A, Gray J, et al. Complex pectin metabolism by gut bacteria reveals novel catalytic functions. Nature. 2017;544:65–70.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reisky L, Préchoux A, Zühlke MK, Bäumgen M, Robb CS, Gerlach N, et al. A marine bacterial enzymatic cascade degrades the algal polysaccharide ulvan. Nat Chem Biol. 2019;15:803–12.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hahnke RL, Harder J. Phylogenetic diversity of Flavobacteria isolated from the North Sea on solid media. Syst Appl Microbiol. 2013;36:497–504.CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen J, Robb CS, Unfried F, Kappelmann L, Markert S, Song T, et al. Alpha- and beta-mannan utilization by marine Bacteroidetes. Environ Microbiol. 2018;20:4127–40.CAS 
    PubMed 
    Article 

    Google Scholar 
    Bågenholm V, Reddy SK, Bouraoui H, Morrill J, Kulcinskaja E, Bahr CM, et al. Galactomannan catabolism conferred by a polysaccharide utilization locus of Bacteroides ovatus: enzyme synergy and crystal structure of a β-mannanase. J Biol Chem. 2017;292:229–43.PubMed 
    Article 
    CAS 

    Google Scholar 
    Le Costaouëc T, Unamunzaga C, Mantecon L, Helbert W. New structural insights into the cell-wall polysaccharide of the diatom Phaeodactylum tricornutum. Algal Res. 2017;26:172–9.Article 

    Google Scholar 
    Matulewicz M, Cerezo A. Water-soluble sulfated polysaccharides from the red seaweed Chaetangium fastigiatum. Analysis of the system and the structures of the α-D-(1→ 3)-linked mannans. Carbohydr Polym. 1987;7:121–32.CAS 
    Article 

    Google Scholar 
    Tabarsa M, Karnjanapratum S, Cho M, Kim JK, You S. Molecular characteristics and biological activities of anionic macromolecules from Codium fragile. Int J Biol Macromol. 2013;59:1–12.CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen Y, Mao WJ, Yan MX, Liu X, Wang SY, Xia Z, et al. Purification, chemical characterization, and bioactivity of an extracellular polysaccharide produced by the marine sponge endogenous fungus Alternaria sp. SP-32. Mar Biotechnol. 2016;18:301–13.CAS 
    Article 

    Google Scholar 
    Gimenez-Abian MI, Bernabe M, Leal JA, Jimenez-Barbero J, Prieto A. Structure of a galactomannan isolated from the cell wall of the fungus Lineolata rhizophorae. Carbohydr Res. 2007;342:2599–603.CAS 
    PubMed 
    Article 

    Google Scholar 
    Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.CAS 
    PubMed 
    Article 

    Google Scholar 
    Bennke CM, Krüger K, Kappelmann L, Huang S, Gobet A, Schüler M, et al. Polysaccharide utilisation loci of Bacteroidetes from two contrasting open ocean sites in the North Atlantic. Environ Microbiol. 2016;18:4456–70.CAS 
    PubMed 
    Article 

    Google Scholar 
    Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 
    CAS 

    Google Scholar 
    Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2012;40(W1):W445–51.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42(D1):D490–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gilchrist CLM, Chooi YH. Clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics. 2021;37:2473–75.CAS 
    Article 

    Google Scholar 
    Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stecher G, Tamura K, Kumar S. Molecular evolutionary genetics analysis (MEGA) for macOS. Mol Biol Evol. 2020;37:1237–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47(W1):W256–W9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liu H, Naismith JH. An efficient one-step site-directed deletion, insertion, single and multiple-site plasmid mutagenesis protocol. BMC Biotechnol. 2008;8:91.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hehemann JH, Smyth L, Yadav A, Vocadlo DJ, Boraston AB. Analysis of keystone enzyme in agar hydrolysis provides insight into the degradation (of a polysaccharide from) red seaweeds. J Biol Chem. 2012;287:13985–95.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, et al. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol. 1999;112:531–52.CAS 
    PubMed 

    Google Scholar 
    Plante OJ, Palmacci ER, Seeberger PH. Automated solid-phase synthesis of oligosaccharides. Science. 2001;291:1523–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kabsch W. Xds. Acta Crystallogr D Biol Crystallogr. 2010;66:125–32.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kabsch W. Integration, scaling, space-group assignment and post-refinement. Acta Crystallogr D Biol Crystallogr. 2010;66:133–44.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McCoy AJ, Grosse-Kunstleve RW, Adams PD, Winn MD, Storoni LC, Read RJ. Phaser crystallographic software. J Appl Crystallogr. 2007;40:658–74.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cohen SX, Ben Jelloul M, Long F, Vagin A, Knipscheer P, Lebbink J. et al. ARP/wARP and molecular replacement: the next generation. Acta Crystallogr D Biol Crystallogr. 2008;64:49–60.CAS 
    PubMed 
    Article 

    Google Scholar 
    Afonine PV, Grosse-Kunstleve RW, Echols N, Headd JJ, Moriarty NW, Mustyakimov M. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr D Biol Crystallogr. 2012;68:352–67.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Emsley P, Lohkamp B, Scott WG, Cowtan K. Features and development of Coot. Acta Crystallogr D Biol Crystallogr. 2010;66:486–501.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Battye TG, Kontogiannis L, Johnson O, Powell HR, Leslie AG. iMOSFLM: a new graphical interface for diffraction-image processing with MOSFLM. Acta Crystallogr D Biol Crystallogr. 2011;67(Pt 4):271–81.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Terwilliger TC, Grosse-Kunstleve RW, Afonine PV, Moriarty NW, Zwart PH, Hung LW, et al. Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard. Acta crystallogr D Biol Crystallogr. 2008;64:61–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Murshudov GN, Skubak P, Lebedev AA, Pannu NS, Steiner RA, Nicholls RA. et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr D Biol Crystallogr. 2011;67:355–67.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Murshudov GN, Vagin AA, Dodson EJ. Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr D Biol Crystallogr. 1997;53:240–55.CAS 
    PubMed 
    Article 

    Google Scholar 
    Mystkowska AA, Robb C, Vidal-Melgosa S, Vanni C, Fernandez-Guerra A, Hohne M, et al. Molecular recognition of the beta-glucans laminarin and pustulan by a SusD-like glycan-binding protein of a marine. Bacteroidetes FEBS J. 2018;285:4465–81.CAS 
    PubMed 
    Article 

    Google Scholar 
    Jones DR, Xing X, Tingley JP, Klassen L, King ML, Alexander TW, et al. Analysis of active site architecture and reaction product linkage chemistry reveals a conserved cleavage substrate for an endo-alpha-mannanase within diverse yeast mannans. J Mol Biol. 2020;432:1083–97.CAS 
    PubMed 
    Article 

    Google Scholar 
    Starr CM, Masada RI, Hague C, Skop E, Klock JC. Fluorophore-assisted carbohydrate electrophoresis in the separation, analysis, and sequencing of carbohydrates. J Chromatogr A. 1996;720:295–321.CAS 
    PubMed 
    Article 

    Google Scholar 
    Ivanova EP, Bowman JP, Christen R, Zhukova NV, Lysenko AM, Gorshkova NM, et al. Salegentibacter flavus sp. nov. Int J Syst Evol Microbiol. 2006;56:583–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Liang QY, Xu ZX, Zhang J, Chen GJ, Du ZJ. Salegentibacter sediminis sp. nov., a marine bacterium of the family Flavobacteriaceae isolated from coastal sediment. Int J Syst Evol Microbiol. 2018;68:2375–80.CAS 
    PubMed 
    Article 

    Google Scholar 
    Nedashkovskaya OI, Kim SB, Lysenko AM, Mikhailov VV, Bae KS, Kim IS. Salegentibacter mishustinae sp. nov., isolated from the sea urchin Strongylocentrotus intermedius. Int J Syst Evol Microbiol. 2005;55:235–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Nedashkovskaya OI, Kim SB, Vancanneyt M, Shin DS, Lysenko AM, Shevchenko LS, et al. Salegentibacter agarivorans sp. nov., a novel marine bacterium of the family Flavobacteriaceae isolated from the sponge Artemisina sp. Int J Syst Evol Microbiol. 2006;56:883–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Nedashkovskaya OI, Suzuki M, Vancanneyt M, Cleenwerck I, Zhukova NV, Vysotskii MV, et al. Salegentibacter holothuriorum sp. nov., isolated from the edible holothurian Apostichopus japonicus. Int J Syst Evol Microbiol. 2004;54:1107–10.CAS 
    PubMed 
    Article 

    Google Scholar 
    Xia HF, Li XL, Liu QQ, Miao TT, Du ZJ, Chen GJ. Salegentibacter echinorum sp. nov., isolated from the sea urchin Hemicentrotus pulcherrimus. Antonie Van Leeuwenhoek. 2013;104:315–20.CAS 
    PubMed 
    Article 

    Google Scholar 
    Yoon JH, Jung SY, Kang SJ, Jung YT, Oh TK. Salegentibacter salarius sp. nov., isolated from a marine solar saltern. Int J Syst Evol Microbiol. 2007;57:2738–42.CAS 
    PubMed 
    Article 

    Google Scholar 
    Regmi A, Boyd EF. Carbohydrate metabolic systems present on genomic islands are lost and gained in Vibrio parahaemolyticus. BMC Microbiol. 2019;19:112-.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shi H, Zhang Y, Xu B, Tu M, Wang F. Characterization of a novel GH2 family alpha-L-arabinofuranosidase from hyperthermophilic bacterium Thermotoga thermarum. Biotechnol Lett. 2014;36:1321–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhu Y, Suits MD, Thompson AJ, Chavan S, Dinev Z, Dumon C, et al. Mechanistic insights into a Ca2+-dependent family of alpha-mannosidases in a human gut symbiont. Nat Chem Biol. 2010;6:125–32.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gregg KJ, Zandberg WF, Hehemann JH, Whitworth GE, Deng L, Vocadlo DJ, et al. Analysis of a new family of widely distributed metal-independent alpha-mannosidases provides unique insight into the processing of N-linked glycans. J Biol Chem. 2011;286:15586–96.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Thompson AJ, Speciale G, Iglesias-Fernandez J, Hakki Z, Belz T, Cartmell A, et al. Evidence for a boat conformation at the transition state of GH76 alpha-1,6-mannanases-key enzymes in bacterial and fungal mannoprotein metabolism. Angew Chem. 2015;54:5378–82.CAS 
    Article 

    Google Scholar 
    Thompson AJ, Cuskin F, Spears RJ, Dabin J, Turkenburg JP, Gilbert HJ, et al. Structure of the GH76 α-mannanase homolog, BT2949, from the gut symbiont Bacteroides thetaiotaomicron. Acta Crystallogr D Biol Crystallogr. 2015;71:408–15.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Eklöf JM, Shojania S, Okon M, McIntosh LP, Brumer H. Structure-function analysis of a broad specificity Populus trichocarpa endo-β-glucanase reveals an evolutionary link between bacterial licheninases and plant XTH gene products. J Biol Chem. 2013;288:15786–99.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Venugopal V. Marine polysaccharides: food applications. Boca Raton: CRC Press; 2016.Ferrer-González FX, Widner B, Holderman NR, Glushka J, Edison AS, Kujawinski EB, et al. Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME J. 2021;15:762–73.PubMed 
    Article 
    CAS 

    Google Scholar 
    Comeau AM, Vincent WF, Bernier L, Lovejoy C. Novel chytrid lineages dominate fungal sequences in diverse marine and freshwater habitats. Sci Rep. 2016;6:30120.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hassett BT, Gradinger R. Chytrids dominate arctic marine fungal communities. Environ Microbiol. 2016;18:2001–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Duan Y, Xie N, Song Z, Ward CS, Yung C-M, Hunt DE, et al. A high-resolution time series reveals distinct seasonal patterns of planktonic fungi at a temperate coastal ocean site (Beaufort, North Carolina, USA). Appl Environ Microbiol. 2018;84:e00967–18.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Priest T, Fuchs B, Amann R, Reich M. Diversity and biomass dynamics of unicellular marine fungi during a spring phytoplankton bloom. Environ Microbiol. 2021;23:448–63.CAS 
    PubMed 
    Article 

    Google Scholar 
    Picard KT. Coastal marine habitats harbor novel early-diverging fungal diversity. Fungal Ecol. 2017;25:1–13.Article 

    Google Scholar 
    Taylor JD, Cunliffe M. Multi-year assessment of coastal planktonic fungi reveals environmental drivers of diversity and abundance. ISME J. 2016;10:2118–28.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Banos S, Gysi DM, Richter-Heitmann T, Glöckner FO, Boersma M, Wiltshire KH, et al. Seasonal dynamics of pelagic mycoplanktonic communities: interplay of taxon abundance, temporal occurrence, and biotic interactions. Front Microbiol. 2020;11:1305.Tisthammer KH, Cobian GM, Amend AS. Global biogeography of marine fungi is shaped by the environment. Fungal Ecol. 2016;19:39–46.Article 

    Google Scholar 
    Tian T, Merico A, Su J, Staneva J, Wiltshire K, Wirtz K. Importance of resuspended sediment dynamics for the phytoplankton spring bloom in a coastal marine ecosystem. J Sea Res. 2009;62:214–28.Article 

    Google Scholar 
    Gutiérrez MH, Pantoja S, Tejos E, Quiñones RA. The role of fungi in processing marine organic matter in the upwelling ecosystem off Chile. Mar Biol. 2011;158:205–19.Article 

    Google Scholar 
    Cunliffe M, Hollingsworth A, Bain C, Sharma V, Taylor JD. Algal polysaccharide utilisation by saprotrophic planktonic marine fungi. Fungal Ecol. 2017;30:135–8.Article 

    Google Scholar 
    Chambouvet A, Monier A, Maguire F, Itoïz S, del Campo J, Elies P, et al. Intracellular infection of diverse diatoms by an evolutionary distinct relative of the fungi. Curr Biol. 2019;29:4093–101.e4.CAS 
    PubMed 
    Article 

    Google Scholar 
    Buaya AT, Ploch S, Hanic L, Nam B, Nigrelli L, Kraberg A, et al. Phylogeny of Miracula helgolandica gen. et sp. nov. and Olpidiopsis drebesii sp. nov., two basal oomycete parasitoids of marine diatoms, with notes on the taxonomy of Ectrogella-like species. Mycol Prog. 2017;16:1041–50.Article 

    Google Scholar 
    Meyers SP, Ahearn DG, Gunkel W, Roth FJ. Yeasts from the North Sea. Mar Biol. 1967;1:118–23.Article 

    Google Scholar 
    Grossart H-P, Van den Wyngaert S, Kagami M, Wurzbacher C, Cunliffe M, Rojas-Jimenez K. Fungi in aquatic ecosystems. Nat Rev Microbiol. 2019;17:339–54.CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Trade-off between tree planting and wetland conservation in China

    Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    MacDicken, K. G. Global forest resources assessment 2015: what, why and how? For. Ecol. Manag. 352, 3–8 (2015).
    Google Scholar 
    Li, M.-M. et al. An overview of the “Three-North” Shelterbelt project in China. Forestry Stud. China 14, 70–79 (2012).ADS 

    Google Scholar 
    Zhang, P. et al. China’s forest policy for the 21st century. Science 288, 2135–2136 (2000).CAS 
    PubMed 

    Google Scholar 
    Chen, Y. et al. Balancing green and grain trade. Nat. Geosci. 8, 739–741 (2015).ADS 

    Google Scholar 
    Xu, J., Yin, R., Li, Z. & Liu, C. China’s ecological rehabilitation: unprecedented efforts, dramatic impacts, and requisite policies. Ecol. Econ. 57, 595–607 (2006).
    Google Scholar 
    Piao, S., Fang, J., Liu, H. & Zhu, B. NDVI-indicated decline in desertification in China in the past two decades. Geophys. Res. Lett. 32, L06402 (2005).ADS 

    Google Scholar 
    Wang, X., Chen, F., Hasi, E. & Li, J. Desertification in China: an assessment. Earth Sci. Rev. 88, 188–206 (2008).ADS 

    Google Scholar 
    Ouyang, Z. et al. Improvements in ecosystem services from investments in natural capital. Science 352, 1455–1459 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bryan, B. A. et al. China’s response to a national land-system sustainability emergency. Nature 559, 193–204 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 6, 1019–1022 (2016).ADS 

    Google Scholar 
    Cao, S., Zhang, J., Chen, L. & Zhao, T. Ecosystem water imbalances created during ecological restoration by afforestation in China, and lessons for other developing countries. J. Environ. Manag. 183, 843–849 (2016).
    Google Scholar 
    Liu, Y. et al. Recent trends in vegetation greenness in China significantly altered annual evapotranspiration and water yield. Environ. Res. Lett. 11, 094010 (2016).ADS 

    Google Scholar 
    Yao, Y. et al. The effect of afforestation on soil moisture content in Northeastern China. PLoS ONE 11, e0160776 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    An, W. et al. Exploring the effects of the “Grain for Green” program on the differences in soil water in the semi-arid Loess Plateau of China. Ecol. Eng. 107, 144–151 (2017).
    Google Scholar 
    Li, Y. et al. Divergent hydrological response to large-scale afforestation and vegetation greening in China. Sci. Adv. 4, eaar4182 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Global Wetland Outlook: State of the World’s Wetlands and their Services to People (Ramsar Convention Secretariat, 2018).Baumgartner, R. J. Sustainable development goals and the forest sector—a complex relationship. Forests 10, 152 (2019).
    Google Scholar 
    15-year Comprehensive Plan for Ecological System Protection and Recovery Work (National Development and Reform Commission, 2020).Prigent, C., Jimenez, C. & Bousquet, P. Satellite-derived global surface water extent and dynamics over the last 25 years (GIEMS-2). J. Geophys. Res. Atmos. 125, e2019JD030711 (2020).ADS 

    Google Scholar 
    Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cy. 19, GB1015 (2005).ADS 

    Google Scholar 
    Tootchi, A. Development of a global wetland map and application to describe hillslope hydrology in the ORCHIDEE land surface model. Sorbonne Université, https://www.metis.upmc.fr/~ducharne/documents/These_Tootchi_revised_11Sep2019.pdf (2019).Beven, K. J. & Kirkby, M. J. A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol. Sci. B. 24, 43–69 (1979).
    Google Scholar 
    Stocker, B. D., Spahni, R. & Joos, F. DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands. Geosci. Model Dev. 7, 3089–3110 (2014).ADS 

    Google Scholar 
    Xi, Y., Peng, S., Ciais, P. & Chen, Y. Future impacts of climate change on inland Ramsar wetlands. Nat. Clim. Chang. 11, 45–51 (2021).ADS 

    Google Scholar 
    Kim, H. Global soil wetness project phase 3 atmospheric boundary conditions (Experiment 1). Data Integration and Analysis System (DIAS). (2017).Cucchi, M. et al. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12, 2097–2120 (2020).ADS 

    Google Scholar 
    Donchyts, G. et al. Earth’s surface water change over the past 30 years. Nat. Clim. Chang. 6, 810–813 (2016).ADS 

    Google Scholar 
    Zhu, Q. et al. Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades. Sci. Rep. 6, 38020 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mao, D. et al. Remote observations in China’s Ramsar Sites: wetland dynamics, anthropogenic threats, and implications for sustainable development goals. J. Remote Sens. 2021, 9849343 (2021).ADS 

    Google Scholar 
    Budyko, M. I. Climate and Life (Academic Press, 1974).Zhang, L., Dawes, W. R. & Walker, G. R. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res. 37, 701–708 (2001).ADS 

    Google Scholar 
    Woodward, C., Shulmeister, J., Larsen, J., Jacobsen, G. E. & Zawadzki, A. The hydrological legacy of deforestation on global wetlands. Science 346, 844–847 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Zhang, Z., Zimmermann, N. E., Kaplan, J. O. & Poulter, B. Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties. Biogeosciences 13, 1387–1408 (2016).ADS 

    Google Scholar 
    Ringeval, B. et al. Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data. Geosci. Model Dev. 5, 941 (2012).ADS 

    Google Scholar 
    Tootchi, A., Jost, A. & Ducharne, A. Multi-source global wetland maps combining surface water imagery and groundwater constraints. Earth Syst. Sci. Data 11, 189–220 (2019).ADS 

    Google Scholar 
    List of Protected Wetlands in China. http://www.zrbhq.cn/web/confirm.html (National Forestry and Grassland Administration, 2011).Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).ADS 

    Google Scholar 
    Lu, F. et al. Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. Proc. Natl Acad. Sci. USA 115, 4039–4044 (2018).CAS 
    PubMed 
    PubMed Central 

    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).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Levia, D. F. et al. Homogenization of the terrestrial water cycle. Nat. Geosci. 13, 656–658 (2020).ADS 
    CAS 

    Google Scholar 
    Zhang, J., Fu, B., Stafford-Smith, M., Wang, S. & Zhao, W. Improve forest restoration initiatives to meet sustainable development goal 15. Nat. Ecol. Evol. 5, 10–13 (2020).
    Google Scholar 
    Zeng, Z. et al. Impact of earth greening on the terrestrial water cycle. J. Clim. 31, 2633–2650 (2018).ADS 

    Google Scholar 
    Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Meier, R. et al. Empirical estimate of forestation-induced precipitation changes in Europe. Nat. Geosci. 14, 473–478 (2021).ADS 
    CAS 

    Google Scholar 
    Bosch, J. M. & Hewlett, J. D. A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. J. Hydrol. 55, 3–23 (1982).ADS 

    Google Scholar 
    Teuling, A. J. & Hoek van Dijke, A. J. Forest age and water yield. Nature 578, E16–E18 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Doelman, J. C. et al. Afforestation for climate change mitigation: Potentials, risks and trade-offs. Glob. Change Biol. 26, 1576–1591 (2020).ADS 

    Google Scholar 
    Peng, S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Chang. 9, 84–87 (2019).ADS 

    Google Scholar 
    Brown, I. Challenges in delivering climate change policy through land use targets for afforestation and peatland restoration. Environ. Sci. Policy 107, 36–45 (2020).
    Google Scholar 
    The 2nd – 9th National Forest Resource Inventory Report (State Forestry Administration of the People’s Republic of China, 1973–2018).Fang, J. et al. Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth. Glob. Change Biol. 20, 2019–2030 (2014).ADS 

    Google Scholar 
    Hou, X. Vegetation atlas of China. Chinese Academy of Science, the editorial board of vegetation map of China (2001).Xi, Y. et al. Contributions of climate change, CO2, land-use change, and human activities to changes in river flow across 10 Chinese Basins. J. Hydrometeorol. 19, 1899–1914 (2018).ADS 

    Google Scholar 
    Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).ADS 

    Google Scholar 
    Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).ADS 

    Google Scholar 
    Herold, M., Van Groenestijn, A., Kooistra, L., Kalogirou, V. & Arino, O. Land cover CCI, product user guide version 2.0. https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (2015).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).ADS 
    CAS 

    Google Scholar 
    Zhou, G. et al. Global pattern for the effect of climate and land cover on water yield. Nat. Commun. 6, 5918 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Yang, H. et al. Changing retention properties of catchments and their influence on runoff under climate change. Environ. Res. Lett. 13, 094019 (2018).ADS 

    Google Scholar 
    Berghuijs, W. R., Larsen, J. R., van Emmerik, T. H. M. & Woods, R. A. A global assessment of runoff sensitivity to changes in precipitation, potential evaporation, and other factors. Water Resour. Res. 53, 8475–8486 (2017).ADS 

    Google Scholar 
    Piao, S. et al. Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proc. Natl Acad. Sci. USA 104, 15242 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Guimberteau, M. et al. Testing conceptual and physically based soil hydrology schemes against observations for the Amazon Basin. Geosci. Model Dev. 7, 1115–1136 (2014).ADS 

    Google Scholar 
    Traore, A. K. et al. Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite-based water and carbon measurements. J. Geophys. Res. Biogeosci. 119, 1554–1575 (2014).
    Google Scholar 
    de Rosnay, P. & Polcher, J. Impact of a physically based soil water flow and soil‐plant interaction representation for modeling large‐scale land surface processes. J. Geophys. Res. Atmos. 107, ACL 3-1–ACL 3-19 (2002).
    Google Scholar 
    Campoy, A. et al. Influence of soil bottom hydrological conditions on land surface fluxes and climate in a general circulation model. J. Geophys. Res. Atmos. 118, 10725–10739 (2013).ADS 

    Google Scholar 
    Guimberteau, M. et al. Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets. Hydrol. Earth Syst. Sci. 16, 11171–11232 (2012).
    Google Scholar 
    Boucher, O. et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Model. Earth Sy. 12, e2019MS002010 (2020).ADS 

    Google Scholar 
    Fan, Y. et al. Hillslope hydrology in global change research and earth system modeling. Water Resour. Res. 55, 1737–1772 (2019).ADS 

    Google Scholar 
    Rayner, P. J. et al. Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS). Glob. Biogeochem. Cy. 19, GB2026 (2005).ADS 

    Google Scholar 
    Ducharne, A. Reducing scale dependence in TOPMODEL using a dimensionless topographic index. Hydrol. Earth Syst. Sci. 13, 2399–2412 (2009).ADS 

    Google Scholar 
    Niu, G., Yang, Z., Dickinson, R. E. & Gulden, L. E. A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models. J. Geophys. Res. 110, D21106 (2005).ADS 

    Google Scholar 
    Xi, Y. et al. Monthly inundated fraction over China for 2000-2015 from GIEMS-2 (Version v1.0). Zenodo https://doi.org/10.5281/zenodo.5750962 (2021).Xi, Y. et al. Code of wetland simulation for trade-off between tree planting and wetland conservation in China (Version v1.0). Zenodo https://doi.org/10.5281/zenodo.4435082 (2021). More

  • in

    Glasgow forest declaration needs new modes of data ownership

    Glasgow Leaders’ Declaration on Forests and Land Use (UNFCCC, 2021); https://go.nature.com/3FmrE2iIPCC: Summary for Policymakers. In Special Report on Climate Change and Land (eds Shukla, P. R. et al.) (WMO, 2019); https://go.nature.com/3itqkRWTomppo, E. et al. National Forest Inventories: Pathways for Common Reporting (Springer, 2010).Jeanjean, H. & Achard, F. Int. J. Remote Sens. 18, 2455–2461 (1997).Article 

    Google Scholar 
    Ceccherini, G. et al. Nature 583, 72–77 (2020).CAS 
    Article 

    Google Scholar 
    Palahí, M. et al. Nature 592, E15–E17 (2021).Article 

    Google Scholar 
    Breidenbach, J. et al. Ann. For. Sci. 79, 2 (2022).Article 

    Google Scholar 
    ForestPlots.net Forest. et al. Biol. Conserv. 260, 108849 (2021).Article 

    Google Scholar 
    A Fresh Perspective: Global Forest Resources Assessment 2020 (FAO, 2020); https://go.nature.com/3uhpfBZCurtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Science 361, 1108–1111 (2018).CAS 
    Article 

    Google Scholar 
    Chazdon, R. L. et al. Ambio 45, 538–550 (2016).Article 

    Google Scholar 
    Sasaki, N. & Putz, F. E. Conserv. Lett. 2, 226–232 (2009).Article 

    Google Scholar 
    Wulder, M. A. & Coops, N. C. Nature 513, 30–31 (2014).CAS 
    Article 

    Google Scholar 
    Reiche, J. et al. Nat. Clim. Change 6, 120–122 (2016).Article 

    Google Scholar 
    Gorelick, N. et al. Remote Sens. Environ. 202, 18–27 (2017).Article 

    Google Scholar 
    Valbuena, R. et al. Trends Ecol. Evol. 35, 656–667 (2020).CAS 
    Article 

    Google Scholar 
    Porter-Bolland, L. et al. For. Ecol. Manage. 268, 6–17 (2012).Article 

    Google Scholar 
    Boissière, M. et al. PLoS ONE 12, e0176897 (2017).Article 

    Google Scholar 
    Armenteras, D. Nat. Ecol. Evol. 5, 1193–1194 (2021).Article 

    Google Scholar 
    Forest Information System for Europe (FISE) (EEA, 2022); https://go.nature.com/3D1CcUw More

  • in

    The travelling particles: community dynamics of biofilms on microplastics transferred along a salinity gradient

    Rochman CM. Microplastics research—from sink to source. Science. 2018;360:28–9.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Galloway TS, Cole M, Lewis C. Interactions of microplastic debris throughout the marine ecosystem. Nat Ecol Evol. 2017;1:116.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Hale RC, Seeley ME, La Guardia MJ, Mai L, Zeng EY. A global perspective on microplastics. J Geophys Res Oceans. 2020;125:1–40.Article 

    Google Scholar 
    Harrison JP, Hoellein TJ, Sapp M, Tagg AS, Ju-Nam Y, Ojeda JJ. Microplastic-associated biofilms: a comparison of freshwater and marine environments. In: Freshwater microplastics. Cham: Springer; 2018. p. 181–201.Dunne WM Jr. Bacterial adhesion: seen any good biofilms lately? Clin Microbiol Rev. 2002;15:155–66.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dang H, Lovell CR. Microbial surface colonization and biofilm development in marine environments. Microbiol Mol Biol Rev. 2016;80:91–138.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    McCormick AR, Hoellein TJ, London MG, Hittie J, Scott JW, Kelly JJ. Microplastic in surface waters of urban rivers: concentration, sources, and associated bacterial assemblages. Ecosphere. 2016;7:e01556.Article 

    Google Scholar 
    Kesy K, Oberbeckmann S, Kreikemeyer B, Labrenz M. Spatial environmental heterogeneity determines young biofilm assemblages on microplastics in Baltic Sea mesocosms. Front Microbiol. 2019;10:1665.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oberbeckmann S, Loeder MG, Gerdts G, Osborn AM. Spatial and seasonal variation in diversity and structure of microbial biofilms on marine plastics in Northern European waters. FEMS Microbiol Ecol. 2014;90:478–92.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Masó M, Garcés E, Pagès F, Camp J. Drifting plastic debris as a potential vector for dispersing Harmful Algal Bloom (HAB) species. Sci Mar. 2003;67:107–11.Article 

    Google Scholar 
    Kirstein IV, Kirmizi S, Wichels A, Garin-Fernandez A, Erler R, Loder M, et al. Dangerous hitchhikers? Evidence for potentially pathogenic Vibrio spp. on microplastic particles. Mar Environ Res. 2016;120:1–8.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Zettler ER, Mincer TJ, Amaral-Zettler LA. Life in the “plastisphere”: microbial communities on plastic marine debris. Environ Sci Technol. 2013;47:7137–46.CAS 
    Article 

    Google Scholar 
    Oberbeckmann S, Kreikemeyer B, Labrenz M. Environmental factors support the formation of specific bacterial assemblages on microplastics. Front Microbiol. 2018;8:2709.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dussud C, Meistertzheim AL, Conan P, Pujo-Pay M, George M, Fabre P, et al. Evidence of niche partitioning among bacteria living on plastics, organic particles and surrounding seawaters. Environ Pollut. 2018;236:807–16.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Frère L, Maignien L, Chalopin M, Huvet A, Rinnert E, Morrison H, et al. Microplastic bacterial communities in the Bay of Brest: Influence of polymer type and size. Environ Pollut. 2018;242:614–25.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    Amaral-Zettler LA, Zettler ER, Slikas B, Boyd GD, Melvin DW, Morrall CE, et al. The biogeography of the Plastisphere: implications for policy. Front Ecol Environ. 2015;13:541–6.Article 

    Google Scholar 
    Amaral-Zettler LA, Ballerini T, Zettler ER, Asbun AA, Adame A, Casotti R, et al. Diversity and predicted inter- and intra-domain interactions in the Mediterranean Plastisphere. Environ Pollut. 2021;286.Li W, Zhang Y, Wu N, Zhao Z, Xu W, Ma Y, et al. Colonization characteristics of bacterial communities on plastic debris influenced by environmental factors and polymer types in the Haihe Estuary of Bohai Bay, China. Environ Sci Technol. 2019;53:10763–73.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Oberbeckmann S, Labrenz M. Marine microbial assemblages on microplastics: diversity, adaptation, and role in degradation. Ann Rev Mar Sci. 2020;12:209–32.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Yang Y, Liu W, Zhang Z, Grossart HP, Gadd GM. Microplastics provide new microbial niches in aquatic environments. Appl Microbiol Biotechnol. 2020;104:6501–11.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lebreton LCM, van der Zwet J, Damsteeg JW, Slat B, Andrady A, Reisser J. River plastic emissions to the world’s oceans. Nat Commun. 2017;8:15611.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Song J, Jongmans-Hochschulz E, Mauder N, Imirzalioglu C, Wichels A, Gerdts G. The Travelling Particles: Investigating microplastics as possible transport vectors for multidrug resistant E. coli in the Weser estuary (Germany). Sci Total Environ. 2020;720:137603.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1–e.CAS 
    PubMed 
    Article 
    PubMed Central 

    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 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
    PubMed 
    PubMed Central 
    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.PubMed 
    PubMed Central 
    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:D590–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Janssen S, McDonald D, Gonzalez A, Navas-Molina JA, Jiang L, Xu ZZ, et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems. 2018;3:e00021-18.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Team RC. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.Beule L, Karlovsky P. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ. 2020;8:e9593.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara R, et al. Package ‘vegan’. Community ecology package, version. 2013;2:1–295.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Dinno A. dunn. test: Dunn’s test of multiple comparisons using rank sums. R package version. Vienna, Austria: R Foundation for Statistical Computing. 2017;1:1.Foster ZS, Sharpton TJ, Grunwald NJ. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput Biol. 2017;13:e1005404.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Clarke K, Gorley R. Getting started with PRIMER v7. PRIMER-E, 20. Plymouth: Plymouth Marine Laboratory; 2015.Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. UpSet: visualization of intersecting sets. IEEE Trans Vis Comput Graph. 2014;20:1983–92.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). Wiley Statsref: Statistics Reference Online; 2014. p. 1–15.Baselga A, Orme CDL. betapart: an R package for the study of beta diversity. Methods Ecol Evol. 2012;3:808–12.Article 

    Google Scholar 
    Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:370.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Richter-Heitmann T, Hofner B, Krah FS, Sikorski J, Wust PK, Bunk B, et al. Stochastic dispersal rather than deterministic selection explains the spatio-temporal distribution of soil bacteria in a temperate grassland. Front Microbiol. 2020;11:1391.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chase JM, Kraft NJ, Smith KG, Vellend M, Inouye BD. Using null models to disentangle variation in community dissimilarity from variation in α‐diversity. Ecosphere. 2011;2:1–11.Article 

    Google Scholar 
    Miao L, Wang P, Hou J, Yao Y, Liu Z, Liu S, et al. Distinct community structure and microbial functions of biofilms colonizing microplastics. Sci Total Environ. 2019;650:2395–402.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Cai L, Wu D, Xia J, Shi H, Kim H. Influence of physicochemical surface properties on the adhesion of bacteria onto four types of plastics. Sci Total Environ. 2019;671:1101–7.CAS 
    Article 

    Google Scholar 
    Wang L, Luo Z, Zhen Z, Yan Y, Yan C, Ma X, et al. Bacterial community colonization on tire microplastics in typical urban water environments and associated impacting factors. Environ Pollut. 2020;265:114922.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Vukanti R, Crissman M, Leff LG, Leff AA. Bacterial communities of tyre monofill sites: growth on tyre shreds and leachate. J Appl Microbiol. 2009;106:1957–66.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Wagner S, Huffer T, Klockner P, Wehrhahn M, Hofmann T, Reemtsma T. Tire wear particles in the aquatic environment – a review on generation, analysis, occurrence, fate and effects. Water Res. 2018;139:83–100.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Degaffe FS, Turner A. Leaching of zinc from tire wear particles under simulated estuarine conditions. Chemosphere. 2011;85:738–43.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Halsband C, Sørensen L, Booth AM, Herzke D. Car tire crumb rubber: does leaching produce a toxic chemical cocktail in coastal marine systems? Front Environ Sci. 2020;8:1–15.Article 

    Google Scholar 
    Thavamani P, Malik S, Beer M, Megharaj M, Naidu R. Microbial activity and diversity in long-term mixed contaminated soils with respect to polyaromatic hydrocarbons and heavy metals. J Environ Manage. 2012;99:10–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Toshchakov SV, Korzhenkov AA, Chernikova TN, Ferrer M, Golyshina OV, Yakimov MM, et al. The genome analysis of Oleiphilus messinensis ME102 (DSM 13489(T)) reveals backgrounds of its obligate alkane-devouring marine lifestyle. Mar. Genomics. 2017;36:41–7.Article 

    Google Scholar 
    Love CR, Arrington EC, Gosselin KM, Reddy CM, Van Mooy BAS, Nelson RK, et al. Microbial production and consumption of hydrocarbons in the global ocean. Nat Microbiol. 2021;6:489–98.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Ribicic D, McFarlin KM, Netzer R, Brakstad OG, Winkler A, Throne-Holst M, et al. Oil type and temperature dependent biodegradation dynamics – combining chemical and microbial community data through multivariate analysis. BMC Microbiol. 2018;18:83.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ribicic D, Netzer R, Hazen TC, Techtmann SM, Drablos F, Brakstad OG. Microbial community and metagenome dynamics during biodegradation of dispersed oil reveals potential key-players in cold Norwegian seawater. Mar Pollut Bull. 2018;129:370–8.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Rezaei Somee M, Dastgheib SMM, Shavandi M, Ghanbari Maman L, Kavousi K, Amoozegar MA, et al. Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents. Sci Rep. 2021;11:11316.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ren X, Tang J, Wang L, Sun H. Combined effects of microplastics and biochar on the removal of polycyclic aromatic hydrocarbons and phthalate esters and its potential microbial ecological mechanism. Front Microbiol. 2021;12:647766.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dussud C, Hudec C, George M, Fabre P, Higgs P, Bruzaud S, et al. Colonization of non-biodegradable and biodegradable plastics by marine microorganisms. Front Microbiol. 2018;9:1571.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vaksmaa A, Knittel K, Abdala Asbun A, Goudriaan M, Ellrott A, Witte HJ, et al. Microbial communities on plastic polymers in the Mediterranean Sea. Front Microbiol. 2021;12:673553.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pinto M, Langer TM, Huffer T, Hofmann T, Herndl GJ. The composition of bacterial communities associated with plastic biofilms differs between different polymers and stages of biofilm succession. PLoS ONE. 2019;14:e0217165.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Erni-Cassola G, Wright RJ, Gibson MI, Christie-Oleza JA. Early colonization of weathered polyethylene by distinct bacteria in Marine Coastal Seawater. Microb Ecol. 2020;79:517–26.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Berry D, Gutierrez T. Evaluating the detection of hydrocarbon-degrading bacteria in 16S rRNA gene sequencing surveys. Front Microbiol. 2017;8:896.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jiang P, Zhao S, Zhu L, Li D. Microplastic-associated bacterial assemblages in the intertidal zone of the Yangtze Estuary. Sci Total Environ. 2018;624:48–54.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Dang H, Li T, Chen M, Huang G. Cross-ocean distribution of Rhodobacterales bacteria as primary surface colonizers in temperate coastal marine waters. Appl Environ Microbiol. 2008;74:52–60.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tobias-Hunefeldt S. Community assembly drivers shift from bottom-up to top-down in a maturing in situ marine biofilm model. University of Otago; 2020.Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lozupone CA, Knight R. Global patterns in bacterial diversity. Proc Natl Acad Sci USA. 2007;104:11436–40.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Rocca JD, Simonin M, Bernhardt ES, Washburne AD, Wright JP. Rare microbial taxa emerge when communities collide: freshwater and marine microbiome responses to experimental mixing. Ecology. 2020;101:e02956.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Crump BC, Hopkinson CS, Sogin ML, Hobbie JE. Microbial biogeography along an estuarine salinity gradient: combined influences of bacterial growth and residence time. Appl Environ Microbiol. 2004;70:1494–505.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stewart PS. Diffusion in biofilms. J Bacteriol. 2003;185:1485–91.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Palomo A, Dechesne A, Smets BF. Genomic profiling of Nitrospira species reveals ecological success of comammox Nitrospira. 2019. https://www.biorxiv.org/content/10.1101/612226v1.Kielak AM, van Veen JA, Kowalchuk GA. Comparative analysis of acidobacterial genomic fragments from terrestrial and aquatic metagenomic libraries, with emphasis on acidobacteria subdivision 6. Appl Environ Microbiol. 2010;76:6769–77.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McCormick A, Hoellein TJ, Mason SA, Schluep J, Kelly JJ. Microplastic is an abundant and distinct microbial habitat in an urban river. Environ Sci Technol. 2014;48:11863–71.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Teixeira L, Merquior V. The family moraxellaceae. The prokaryotes: Gammaproteobacteria. Berlin: Springer. 2014. p. 443–76.Stalder T, Press MO, Sullivan S, Liachko I, Top EM. Linking the resistome and plasmidome to the microbiome. ISME J. 2019;13:2437–46.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lu SY, Zhang YL, Geng SN, Li TY, Ye ZM, Zhang DS, et al. High diversity of extended-spectrum beta-lactamase-producing bacteria in an urban river sediment habitat. Appl Environ Microbiol. 2010;76:5972–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Making forest data fair and open

    The risks of open forest data exploitation are magnified by features of how forests are measured and who does the measuring. Generating long-term data on forest health and change involves physically measuring and identifying millions of trees. This means establishing, maintaining and revisiting plots, and curating records indefinitely. Trees are long-lived organisms so forests require decades of monitoring to properly infer change. Sustaining local observations for decades needs deep, long-term commitment to the unique but shifting combinations of people, institutions, regulations, interests and relationships that characterize each forest site. The challenge is enhanced by the great biodiversity of tropical forests. Measuring a single hectare of Amazon forest involves collecting and identifying up to ten times the number of tree species that are present in the UK’s entire 24 million hectares. There are very few people with the skills to do this.Long-term tropical-forest data measurements not only require effort and skill but also often carry risk and depend on some of the most disadvantaged actors in the global science community. Many forest workers (researchers, technicians, students, field assistants and local communities) lack basic job security, much less a career path, despite the long-term dedication that monitoring forests requires. In addition, many tropical forest workers may endure dangerous field conditions, with threats including kidnapping, armed insurgents, narcotraffickers, land-grabbers, infectious disease, snakebite, floods, fire, dangerous transport and gender-based violence. Besides these personal dangers, tropical scientists often lack the basic resources to measure and maintain their forest plots, let alone develop their research groups8.In contrast to the experiences of those monitoring forests on the ground, consider the context for satellite and aircraft-based measurements, which require ground-based data for validation. Space-based forest missions are expensive but are funded by public or private capital. Once in orbit, they stream data to analysts ‘for free’. This requires relatively few people to sustain, and although the analysts’ work is highly skilled, it carries little professional and physical risk and lacks commitment to place. Forest fieldwork is less capital-intensive, but needs sustained investment, is intensely human and carries substantial costs and risks. There are no automated collecting stations to help to identify and measure trees, so without the long-term dedication of many forest workers data collection simply stops.The risks and costs involved in acquiring and sustaining ground forest data are persistently overlooked, ignored or regarded as externalities to be picked up by the forest workers themselves. This is especially problematic because countries that hold the most tropical forests are among those least able to invest in science and development (Fig. 1, Supplementary Fig. 1). For example, monitoring the carbon balance of intact tropical moist forests has been estimated to cost US $7 million a year12, easily exceeding present support. By contrast, the USA alone spends over $90 million annually on its national forest inventory13. So, many tropical forest data are collected by skilled people working with minimal funding, in challenging conditions and facing other constraints, including complex layers of rules, agreements and research permits. Given such huge disparities, it is hardly reasonable to expect this output to be served on an open plate to the world.Fig. 1: Global distributions of per capita gross domestic product and tropical forest area.a,b, The 2008–2018 national average gross domestic product per capita (a) and tropical forest area per capita (b). Countries are coloured according to position from lowest (dark red) to highest (dark blue) within each global distribution.Full size imageIt is perhaps unsurprising that the most vocal proponents of making tropical and subtropical forest data open are often not those who actually measure and monitor them. Meanwhile, key beneficiaries include powerful publishers (usually with commercial interests), agencies and technology companies (often with commercial or political interests), and highly educated computer-savvy analysts wishing to integrate earth observation data with forest data (naturally with a career interest). Relatively few of these institutions and people are based in the tropics and subtropics. Fewer still are also data originators.And so, for many data originators the present meaning of making tropical forest data ‘open’ is to transfer the hard-won output of their labours to more privileged individuals and institutions, and lose more of the limited control they have over their professional lives. Power flows from the originators to public agencies, private companies and data scientists, mainly in the Global North. More