More stories

  • in

    Trees outside of forests as natural climate solutions

    1.Chao, S. Forest Peoples: Numbers Across the World (Forest Peoples Programme, 2021).2.Zomer, R. J. et al. Sci. Rep. 6, 29987 (2016).CAS 
    Article 

    Google Scholar 
    3.Schnell, S., Altrell, D., Ståhl, G. & Kleinn, C. Environ. Monit. Assess. 187, 4197 (2015).Article 

    Google Scholar 
    4.Brandt, M. et al. Nature 587, 78–82 (2020).Article 

    Google Scholar 
    5.Baccini, A. et al. Nat. Clim. Chang. 2, 182–185 (2012).CAS 
    Article 

    Google Scholar 
    6.Miller, D. C., Muñoz-Mora, J. C. & Christiaensen, L. Forest Policy Econ. 84, 47–61 (2017).Article 

    Google Scholar 
    7.Mbow, C., Smith, P., Skole, D., Duguma, L. & Bustamante, M. Curr. Opin. Environ. Sustain. 6, 8–14 (2014).Article 

    Google Scholar 
    8.Brandt, M. et al. Nat. Geosci. 11, 328–333 (2018).CAS 
    Article 

    Google Scholar 
    9.Mbow, C. et al. in Climate Change and Agriculture (ed. Deryng, D.) Ch. 10 (Burleigh Dodds Science Publishing, 2020).10.Akinyemi, F. O., Ghazaryan, G. & Dubovyk, O. Land Degrad. Dev. 32, 158–172 (2021).Article 

    Google Scholar 
    11.Sitch, S. et al. Biogeosciences 12, 653–679 (2015).Article 

    Google Scholar 
    12.Schnell, S., Kleinn, C. & Ståhl, G. Environ. Monit. Assess. 187, 600 (2015).Article 

    Google Scholar 
    13.Hansen, M. C. et al. Science 342, 850–853 (2013).CAS 
    Article 

    Google Scholar 
    14.Kuyah, S. et al. Agrofor. Syst. 86, 267–277 (2012).Article 

    Google Scholar 
    15.Nationally Determined Contributions Under the Paris Agreement Synthesis Report by the Secretariat FCCC/PA/CMA/2021/2/Add 2 (UNFCCC, 2021); https://unfccc.int/documents/26857316.Lohbeck, M. et al. Sci. Rep. 10, 15038 (2020).CAS 
    Article 

    Google Scholar 
    17.Chomba, S., Sinclair, F., Savadogo, P., Bourne, M. & Lohbeck, M. Front. For. Glob. Chang. 3, 571679 (2020).Article 

    Google Scholar 
    18.Griscom, B. W. et al. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 
    Article 

    Google Scholar  More

  • in

    Reduced deforestation and degradation in Indigenous Lands pan-tropically

    1.Weisse, M. & Goldman, E. D. We Lost a Football Pitch of Primary Rainforest Every 6 Seconds in 2019 (World Resources Institute, 2020); https://www.wri.org/blog/2020/06/global-tree-cover-loss-data-20192.Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).CAS 

    Google Scholar 
    3.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 

    Google Scholar 
    4.State of the World’s Indigenous Peoples: Rights to Lands, Territories and Resources (UN, 2021).5.Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 

    Google Scholar 
    6.Larsen, P. B. et al. Understanding and responding to the environmental human rights defenders crisis: the case for conservation action. Conserv. Lett. 14, e12777 (2020).
    Google Scholar 
    7.Tauli-Corpuz, V., Alcorn, J., Molnar, A., Healy, C. & Barrow, E. Cornered by PAs: adopting rights-based approaches to enable cost-effective conservation and climate action. World Dev. 130, 104923 (2020).
    Google Scholar 
    8.Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).CAS 

    Google Scholar 
    9.Dudley, N. et al. The essential role of other effective area-based conservation measures in achieving big bold conservation targets. Glob. Ecol. Conserv. 15, e00424 (2018).
    Google Scholar 
    10.Zero Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/2/3 (Convention on Biological Diversity, 2020).11.NGO Concerns Over the Proposed 30% Target for Protected Areas and Absence of Safeguards for Indigenous Peoples and Local Communities (Rainforest Foundation UK, 2021).12.Reyes-García, V. et al. Recognizing Indigenous Peoples’ and local communities’ rights and agency in the post-2020 Biodiversity Agenda. Ambio https://doi.org/10.1007/s13280-021-01561-7 (2021).13.Territories of Life: 2021 Report 52 (ICCA Consortium, 2021); https://report.territoriesoflife.org14.Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).
    Google Scholar 
    15.Fa, J. E. et al. Importance of Indigenous Peoples’ lands for the conservation of intact forest landscapes. Front. Ecol. Environ. 18, 135–140 (2020).
    Google Scholar 
    16.Vergara-Asenjo, G. & Potvin, C. Forest protection and tenure status: the key role of indigenous peoples and protected areas in Panama. Glob. Environ. Change 28, 205–215 (2014).
    Google Scholar 
    17.Blackman, A. & Veit, P. Titled Amazon indigenous communities cut forest carbon emissions. Ecol. Econ. 153, 56–67 (2018).
    Google Scholar 
    18.Walker, W. S. et al. The role of forest conversion, degradation, and disturbance in the carbon dynamics of Amazon indigenous territories and protected areas. Proc. Natl Acad. Sci. USA 117, 3015–3025 (2020).CAS 

    Google Scholar 
    19.Nolte, C., Agrawal, A., Silvius, K. M. & Soares-Filho, B. S. Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 110, 4956–4961 (2013).CAS 

    Google Scholar 
    20.Schleicher, J., Peres, C. A., Amano, T., Llactayo, W. & Leader-Williams, N. Conservation performance of different conservation governance regimes in the Peruvian Amazon. Sci. Rep. 7, 11318 (2017).
    Google Scholar 
    21.Jusys, T. Changing patterns in deforestation avoidance by different protection types in the Brazilian Amazon. PLoS ONE 13, e0195900 (2018).
    Google Scholar 
    22.State of the World’s Indigenous Peoples (UN, 2009).23.Jackson, J. E. & Warren, K. B. Indigenous movements in Latin America, 1992–2004: controversies, ironies, new directions. Annu. Rev. Anthropol. 34, 549–573 (2005).
    Google Scholar 
    24.Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).
    Google Scholar 
    25.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 

    Google Scholar 
    26.Stuart, E. A. & Rubin, D. B. in Best Practices in Quantitative Methods (ed. Osborne, J.) 155–176 (SAGE Publications, 2008).27.Pfaff, A., Robalino, J., Lima, E., Sandoval, C. & Herrera, L. D. Governance, location and avoided deforestation from protected areas: greater restrictions can have lower impact, due to differences in location. World Dev. 55, 7–20 (2014).
    Google Scholar 
    28.Leberger, R., Rosa, I. M. D., Guerra, C. A., Wolf, F. & Pereira, H. M. Global patterns of forest loss across IUCN categories of protected areas. Biol. Conserv. 241, 108299 (2020).
    Google Scholar 
    29.Borrini-Feyerabend, G. et al. Governance of Protected Areas: From Understanding to Action (IUCN, 2013).30.Who Owns the World’s Land? A Global Baseline of Formally Recognized Indigenous and Community Land Rights (Rights and Resources Initiative, 2015); https://rightsandresources.org/wp-content/uploads/GlobalBaseline_web.pdf31.Dubertret, F. & Alden Wily, L. Percent of Indigenous and Community Lands (Landmark, 2015).32.Under the Cover of COVID: New Laws in Asia Favor Business at the Cost of Indigenous Peoples’ and Local Communities’ Land and Territorial Rights (Rights and Resources Initiative, 2020).33.Domínguez, L. & Luoma, C. Decolonising conservation policy: how colonial land and conservation ideologies persist and perpetuate indigenous injustices at the expense of the environment. Land 9, 65 (2020).
    Google Scholar 
    34.Pyhälä, A., Orozco, A. O. & Counsell, S. Protected Areas in the Congo Basin: Failing both people and biodiversity? (FAO, 2016).35.Pearson, T. R. H., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).
    Google Scholar 
    36.Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).CAS 

    Google Scholar 
    37.Hansen, A. J. et al. A policy-driven framework for conserving the best of Earth’s remaining moist tropical forests. Nat. Ecol. Evol. 4, 1377–1384 (2020).
    Google Scholar 
    38.Milodowski, D. T. et al. The impact of logging on vertical canopy structure across a gradient of tropical forest degradation intensity in Borneo. J. Appl. Ecol. 58, 1764–1775 (2021).
    Google Scholar 
    39.Benítez-López, A., Santini, L., Schipper, A. M., Busana, M. & Huijbregts, M. A. J. Intact but empty forests? Patterns of hunting-induced mammal defaunation in the tropics. PLoS Biol. 17, e3000247 (2019).
    Google Scholar 
    40.Miettinen, J., Stibig, H.-J. & Achard, F. Remote sensing of forest degradation in Southeast Asia—aiming for a regional view through 5–30 m satellite data. Glob. Ecol. Conserv. 2, 24–36 (2014).
    Google Scholar 
    41.Yuliani, E. L. et al. Keeping the land: indigenous communities’ struggle over land use and sustainable forest management in Kalimantan, Indonesia. Ecol. Soc. 23, art49 (2018).
    Google Scholar 
    42.Berkes, F. Sacred Ecology (Routledge, 2017).43.Sheil, D., Boissière, M. & Beaudoin, G. Unseen sentinels: local monitoring and control in conservation’s blind spots. Ecol. Soc. 20, 39 (2015).
    Google Scholar 
    44.Sasaoka, M. & Laumonier, Y. Suitability of local resource management practices based on supernatural enforcement mechanisms in the local social-cultural context. Ecol. Soc. 17, 6 (2012).
    Google Scholar 
    45.Asante, E. A., Ababio, S. & Boadu, K. B. The use of indigenous cultural practices by the Ashantis for the conservation of forests in Ghana. SAGE Open 7, 215824401668761 (2017).
    Google Scholar 
    46.Schwartzman, S. et al. The natural and social history of the indigenous lands and protected areas corridor of the Xingu River basin. Philos. Trans. R. Soc. B 368, 20120164 (2013).
    Google Scholar 
    47.Hayes, T. M. & Murtinho, F. Are indigenous forest reserves sustainable? An analysis of present and future land-use trends in Bosawas, Nicaragua. Int. J. Sustain. Dev. World Ecol. 15, 497–511 (2008).
    Google Scholar 
    48.Tellman, B. et al. Illicit drivers of land use change: narcotrafficking and forest loss in central America. Glob. Environ. Change 63, 102092 (2020).
    Google Scholar 
    49.Bryan, J. For Nicaragua’s indigenous communities, land rights in name only: delineating boundaries among indigenous and black communities in eastern Nicaragua was supposed to guaranteed their land rights. Instead, it did the opposite. NACLA Rep. Am. 51, 55–64 (2019).
    Google Scholar 
    50.Seymour, F., La Vina, T. & Hite, K. Evidence Linking Community-level Tenure and Forest Condition: An Annotated Bibliography (Climate and Land Use Alliance, 2014).51.Tseng, T.-W. J. et al. Influence of land tenure interventions on human well-being and environmental outcomes. Nat. Sustain. 4, 242–251 (2021).
    Google Scholar 
    52.Robinson, B. E. et al. Incorporating land tenure security into conservation: conservation and land tenure security. Conserv. Lett. 11, e12383 (2018).
    Google Scholar 
    53.Smith, D. A., Holland, M. B., Michon, A., Ibáñez, A. & Herrera, F. The hidden layer of indigenous land tenure: informal forest ownership and its implications for forest use and conservation in Panama’s largest collective territory. Int. For. Rev. 19, 478–494 (2017).
    Google Scholar 
    54.Larson, A. M. & Springer, J. Recognition and Respect for Tenure Rights (IUCN, CEESP, CIFOR, 2016).55.Arizona, Y., Wicaksono, M. T. & Vel, J. The role of indigeneity NGOs in the legal recognition of adat communities and customary forests in Indonesia. Asia Pac. J. Anthropol. 20, 487–506 (2019).
    Google Scholar 
    56.Malavasi, M. The map of biodiversity mapping. Biol. Conserv. 252, 108843 (2020).
    Google Scholar 
    57.Witter, R. & Satterfield, T. The ebb and flow of indigenous rights recognitions in conservation policy: indigenous rights recognitions in conservation policy. Dev. Change 50, 1083–1108 (2019).
    Google Scholar 
    58.Dutta, A. et al. Response to a “global safety net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2021).
    Google Scholar 
    59.Herrera, D., Pfaff, A. & Robalino, J. Impacts of protected areas vary with the level of government: comparing avoided deforestation across agencies in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 116, 14916–14925 (2019).CAS 

    Google Scholar 
    60.Bebbington, A. J. et al. Resource extraction and infrastructure threaten forest cover and community rights. Proc. Natl Acad. Sci. USA 115, 13164–13173 (2018).CAS 

    Google Scholar 
    61.Johnson, C. J., Venter, O., Ray, J. C. & Watson, J. E. M. Growth‐inducing infrastructure represents transformative yet ignored keystone environmental decisions. Conserv. Lett. https://doi.org/10.1111/conl.12696 (2020).62.Davis, K. F., Yu, K., Rulli, M. C., Pichdara, L. & D’Odorico, P. Accelerated deforestation driven by large-scale land acquisitions in Cambodia. Nat. Geosci. 8, 772–775 (2015).CAS 

    Google Scholar 
    63.Conigliani, C., Cuffaro, N. & D’Agostino, G. Large-scale land investments and forests in Africa. Land Use Policy 75, 651–660 (2018).
    Google Scholar 
    64.Global Land Analysis & Discovery. Global 2010 Tree Cover (30m) (Department of Geographical Sciences, Univ. Maryland, 2013).65.Global Forest Watch. Tree Cover Loss version 1.6 (World Resources Institute, 2019).66.Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650–8655 (2010).CAS 

    Google Scholar 
    67.Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC & IUCN, accessed January 2020; www.protectedplanet.net68.Hanson, J. O. wdpar: Interface to the world database on protected areas (CRAN, 2020); https://CRAN.R-project.org/package=wdpar69.Global Forest Watch. Spatial Database of Planted Trees (World Resources Institute, data aaccessed May 2021).70.Transparent World & Global Forest Watch. Tree Plantations (World Resources Institute, date accessed May 2021).71.Nelson, A. & Chomitz, K. M. Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. PLoS ONE 6, e22722 (2011).CAS 

    Google Scholar 
    72.Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
    Google Scholar 
    73.Global Forest Watch. Tree Cover 2000 version 1.2 (World Resources Institute, 2015).74.Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).
    Google Scholar 
    75.Nelson, A. et al. A suite of global accessibility indicators. Sci. Data 6, 266 (2019).
    Google Scholar 
    76.Global Roads Open Access Data Set Version 1 (gROADSv1) (1980–2010) (NASA SEDAC, 2013).77.Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 170001 (2017).
    Google Scholar 
    78.GADM Database of Global Administrative Areas version 3.6 (FAO, 2018).79.Ho, D., Imai, K., King, G. & Stuart, E. matchIt: Nonparametric preprocessing for parametric causal inference (CRAN, 2018); https://CRAN.R-project.org/package=MatchIt80.Wood, S. mgcv: Mixed GAM computation vehicle with automatic smoothness estimation (CRAN, 2019); https://CRAN.R-project.org/package=mgcv More

  • in

    Observed increases in extreme fire weather driven by atmospheric humidity and temperature

    1.Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M. & Kolden, C. A. Global patterns of interannual climate-fire relationships (2018). Glob. Change Biol. 24, 5164–5175 (2018).
    Google Scholar 
    2.Littell, J. S., McKenzie, D., Peterson, D. L. & Westerling, A. L. Climate and wildfire area burned in western US ecoprovinces, 1916-2003. Ecol. Appl. 19, 1003–1021 (2009).
    Google Scholar 
    3.Abatzoglou, J. T. & Kolden, C. A. Relationships between climate and macroscale area burned in the western United States. Int. J. Wildland Fire 22, 1003–1020 (2013).
    Google Scholar 
    4.Wang, X. et al. Projected changes in daily fire spread across Canada over the next century. Environ. Res. Lett. 12, 025005 (2017).
    Google Scholar 
    5.Hanes, C. C. et al. Fire-regime changes in Canada over the last half century. Can. J. Res. 49, 256–269 (2019).
    Google Scholar 
    6.Amiro, B. D. et al. Fire weather index system components of large fires in the Canadian boreal forest. Int. J. Wildland Fire 13, 391–400 (2004).
    Google Scholar 
    7.Flannigan, M. D., Krawchuck, M. A., de Groot, W. J., Wotton, B. M. & Gowman, L. M. Implications of changing climate for global wildland fire. Int. J. Wildland Fire 18, 483–507 (2009).
    Google Scholar 
    8.Bowman, D. M. J. S. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058 (2017).
    Google Scholar 
    9.Coogan, S. C. P., Robinne, F.-N., Jain, P. & Flannigan, M. D. Scientists’ warning on wildfire—a Canadian perspective. Can. J. Res. 49, 1015–1023 (2019).
    Google Scholar 
    10.Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).
    Google Scholar 
    11.Van Wagner, C. E. et al. Development and Structure of the Canadian Forest Fire Weather Index System (Canadian Forestry Service Headquarters, 1987); https://www.eea.europa.eu/data-and-maps/indicators/forest-fire-danger-3/camia-et-al.-2008-past12.Flannigan, M. D. & Harrington, J. B. A study of the relation of meteorological variables to monthly provincial area burned by wildfire in Canada (1953-80). J. Appl. Meteorol. 27, 441–452 (1988).
    Google Scholar 
    13.Flannigan, M. D. et al. Fuel moisture sensitivity to temperature and precipitation: climate change implications. Clim. Change 134, 59–71 (2016).CAS 

    Google Scholar 
    14.Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).CAS 

    Google Scholar 
    15.Touma, D., Stevenson, S., Lehner, F. & Coats, S. Human-driven greenhouse gas and aerosol emissions cause distinct regional impacts on extreme fire weather. Nat. Commun. 12, 212 (2021).16.Clarke, H. G., Smith, P. L. & Pitman, A. J. Regional signatures of future fire weather over eastern Australia from global climate models. Int. J. Wildland Fire 20, 550–562 (2011).
    Google Scholar 
    17.Bedia, J. et al. Sensitivity of fire weather index to different reanalysis products in the Iberian Peninsula. Nat. Hazards Earth Syst. Sci. 12, 699–708 (2012).
    Google Scholar 
    18.Jain, P., Wang, X. & Flannigan, M. D. Trend analysis of fire season length and extreme fire weather in North America between 1979 and 2015. Int. J. Wildland Fire 26, 1009–1020 (2017).
    Google Scholar 
    19.Dowdy, A. J. Climatological variability of fire weather in Australia. J. Appl. Meteorol. Climatol. 57, 221–234 (2018).
    Google Scholar 
    20.Zhao, F., Liu, Y. & Shu, L. Change in the fire season pattern from bimodal to unimodal under climate change: the case of Daxing’anling in Northeast China. Agric. Meteorol. 291, 108075 (2020).
    Google Scholar 
    21.Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
    Google Scholar 
    22.Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).CAS 

    Google Scholar 
    23.Kirchmeier-Young, M. C., Gillet, N. P., Zwiers, F. W., Cannon, A. J. & Anslow, F. S. Attribution of the influence of human-induced climate change on an extreme fire season. Earths Future 7, 2–10 (2019).
    Google Scholar 
    24.Pausas, J. G. & Ribeiro, E. The global-fire productivity relationship. Glob. Ecol. Biogeogr. 22, 728–736 (2013).
    Google Scholar 
    25.Cochrane, M. A. Fire science for rainforests. Nature 421, 913–919 (2003).CAS 

    Google Scholar 
    26.Ziel, R. H. et al. A comparison of fire weather indices with MODIS fire days for the natural regions of Alaska. Forests 11, 516 (2020).
    Google Scholar 
    27.Giannaros, T. M., Kotroni, V. & Lagouvardos, K. Climatology and trend analysis (1987–2016) of fire weather in the Euro-Mediterranean. Int. J. Climatol. 41, E491–E508 (2021).
    Google Scholar 
    28.Harris, S. & Lucas, C. Understanding the variability of Australian fire weather between 1973 and 2017. PLoS ONE 14, e0222328 (2019).CAS 

    Google Scholar 
    29.Climate at a Glance (NOAA, 2021); https://www.ncdc.noaa.gov/cag/30.van Oldenborgh, G. J. et al. Attribution of the Australian bushfire risk to anthropogenic climate change. Nat. Hazards Earth Syst. Sci. 21, 941–960 (2021).
    Google Scholar 
    31.Barbero, R., Abatzoglou, J. T., Pimont, F., Ruffault, J. & Curt, T. Attributing increases in fire weather to anthropogenic climate change over France. Front. Earth Sci. https://doi.org/10.3389/feart.2020.00104 (2020).32.Byrne, M. P. & O’Gorman, P. A. Understanding decreases in land relative humidity with global warming: conceptual model and GCM simulations. J. Clim. 29, 9045–9061 (2016).
    Google Scholar 
    33.Willett, K. M., Jones, P. D., Gillett, N. P. & Thorne, P. W. Recent changes in surface humidity: development of the HadCRUH dataset. J. Clim. 21, 5364–5383 (2008).
    Google Scholar 
    34.Matsoukas, C. et al. Potential evaporation trends over land between 1983-2008: driven by radiative fluxes or vapour-pressure deficit? Atmos. Chem. Phys. 11, 7601–7616 (2011).CAS 

    Google Scholar 
    35.Grotjahn, R. & Huynh, J. Contiguous US summer maximum temperature and heat stress trends in CRU and NOAA climate division data plus comparisons to reanalyses. Sci. Rep. 8, 11146 (2018).CAS 

    Google Scholar 
    36.Denson, E., Wasko, C. & Peel, M. C. Decreases in relative humidity across Australia. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ac0aca (2021).37.Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9, 15331 (2019).
    Google Scholar 
    38.Findell, K. L. et al. The impact of anthropogenic land use and land cover change on regional climate extremes. Nat. Commun. 8, 989 (2017).39.McKinnon, K. A., Poppick, A. & Simpson, I. R. Hot extremes have become drier in the United States Southwest. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01076-9 (2021).40.Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).
    Google Scholar 
    41.Mishra, V. et al. Moist heat stress extremes in India enhanced by irrigation. Nat. Geosci. 13, 722–728 (2020).CAS 

    Google Scholar 
    42.Dong, B. & Dai, A. The influence of the interdecadal Pacific oscillation on temperature and precipitation over the globe. Clim. Dyn. 45, 2667–2681 (2015).
    Google Scholar 
    43.Fischer, E. M. & Knutti, R. Robust projections of combined humidity and temperature extremes. Nat. Clim. Change 3, 126–130 (2013).
    Google Scholar 
    44.Tymstra C., Flannigan M. D., Stocks B. J., Cai X. & Morrison K. Wildfire management in Canada: review, challenges and opportunities. Prog. Disaster Sci. https://doi.org/10.1016/j.pdisas.2019.100045 (2020).45.Flannigan, M. D., Stocks, B., Turetsky, M. & Wotton, M. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob. Change Biol. 15, 549–560 (2009).
    Google Scholar 
    46.Chen, Y. et al. Future increases in Arctic lightning and fire risk for permafrost carbon. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01011-y (2021).47.Hope, E. S., McKenney, D. W., Pedlar, J. H., Stocks, B. J. & Gauthier, S. Wildfire suppression costs for Canada under a changing climate. PLoS ONE 11, e0157425 (2016).
    Google Scholar 
    48.Podur, J. & Wotton, B. M. Will climate change overwhelm fire management capacity? Ecol. Modell. 221, 1301–1309 (2010).
    Google Scholar 
    49.Abatzoglou, J. T., Juang, C. S., Williams, A. P., Kolden, C. A. & Westerling, A. L. Increasing synchronous fire danger in forests of the western United States. Geophys. Res. Lett. 48, e2020GL091377 (2021).
    Google Scholar 
    50.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
    Google Scholar 
    51.Copernicus Climate Change Service Data Store (Copernicus Climate Change Service, accessed 4 March 2020); https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation52.Ramon, J., Lledo, L., Torralba, V., Soret, A. & Doblas-Reyes, F. J. What global reanalysis best represents near-surface winds? Q. J. R. Meteorol. Soc. 145, 3236–3251 (2019).
    Google Scholar 
    53.Beck, H. E. et al. Daily evaluation of 26 precipitation datasets using stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci. 23, 207–224 (2019).
    Google Scholar 
    54.Tarek, M., Brissette, F. P. & Arsenault, R. Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrol. Earth Syst. Sci. 24, 2527–2544 (2020).
    Google Scholar 
    55.Torralba, V., Doblas-Reyes, F. J. & Gonzalez-Reviriego, N. Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison. Environ. Res. Lett. 12, 114019 (2017).
    Google Scholar 
    56.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
    Google Scholar 
    57.Andela, N. et al. The global fire atlas of individual fire size, duration, speed and direction. Earth Syst. Sci. Data 11, 529–552 (2019).
    Google Scholar 
    58.Wotton, B. M. Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications. Environ. Ecol. Stat. 16, 107–131 (2009).CAS 

    Google Scholar 
    59.Field, R. D. et al. Development of a global fire weather database. Nat. Hazards Earth Syst. Sci. 15, 1407–1423 (2015).
    Google Scholar 
    60.Bedia, J. et al. Global patterns in the sensitivity of burned area to fire weather: implications for climate change. Agric. Meteorol. 214–215, 369–379 (2015).
    Google Scholar 
    61.McElhinny, M., Beckers, J. F., Hanes, C., Flannigan, M. & Jain, P. A high-resolution reanalysis of global fire weather from 1979 to 2018 – overwintering the Drought Code. Earth Syst. Sci. Data 12, 1823–1833 (2020).
    Google Scholar 
    62.Wotton, B. M. & Flannigan, M. D. Length of the fire season in a changing climate. Forestry Chron. 69, 187–192 (1993).
    Google Scholar 
    63.Sedano, F. & Randerson, J. T. Vapor pressure deficit controls on fire ignition and fire spread in boreal forest ecosystems. Biogeosciences 11, 1309–1353 (2014).
    Google Scholar 
    64.Williams, P. A. et al. Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States. Int. J. Wildland Fire 24, 14–26 (2014).
    Google Scholar 
    65.Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earths Future 7, 892–910 (2019).
    Google Scholar 
    66.Mueller, S. E. et al. Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015. For. Ecol. Manage. 460, 117861 (2020).
    Google Scholar 
    67.Alduchov, O. A. & Eskridge, R. E. Improved Magnus form approximation of saturation vapor pressure. J. Appl. Meteorol. 35, 601–609 (1996).
    Google Scholar 
    68.Knauer, J., El-Madany, T. S., Zaehle, S. & Migliavacca, M. Bigleaf—an R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLoS ONE 13, e0201114 (2018).
    Google Scholar 
    69.Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
    Google Scholar 
    70.Loveland, T. R. & Belward, A. S. The IGBP-DIS global 1 km land cover data set, DISCover: first results. Int. J. Remote Sens. 18, 3291–3295 (1997).
    Google Scholar 
    71.Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).
    Google Scholar 
    72.Kendall, M. G. Rank Correlation Methods (Griffin, 1975).
    Google Scholar 
    73.Theil, H. A rank-invariant method of linear and polynomial regression analysis. I, II, III. Nederl. Akad. Wetensch. Proc. 53, part I: 386–392; part II: 521–525; part III: 1397–1412 (1950).74.Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).
    Google Scholar 
    75.Yue, S., Pilon, P. & Phinney, B. Canadian streamflow trend detection: impacts of serial and cross-correlation. Hydrol. Sci. J. 48, 51–63 (2003).
    Google Scholar 
    76.Wilks, D. S. On ‘field significance’ and the false discovery rate. J. Appl. Meteorol. Climatol. 45, 1181–1189 (2006).
    Google Scholar 
    77.Wilks, D. ‘The stippling shows statistically significant grid points’: how research results are routinely overstated and overinterpreted, and what to do about it. Bull. Am. Meteorol. Soc. 97, 2263–2273 (2016).
    Google Scholar 
    78.Libiseller, C. & Grimvall, A. Performance of partial Mann–Kendall tests for trend detection in the presence of covariates. Environmetrics 13, 71–84 (2002).CAS 

    Google Scholar 
    79.Mediero, L., Santillán, D., Garrote, L. & Granados, A. Detection and attribution of trends in magnitude, frequency and timing of floods in Spain. J. Hydrol. 517, 1072–1088 (2014).
    Google Scholar 
    80.Dowdy, A. J., Mills, G. A., Finkele, K. & de Groot, W. Index sensitivity analysis applied to the Canadian Forest Fire Weather Index and the McArthur Forest Fire Danger Index. Meteorol. Appl. 17, 298–312 (2010).
    Google Scholar 
    81.Millard, S. P. EnvStats: An R Package for Environmental Statistics (Springer, 2013).82.Pohlert, T. trend: Non-Parametric Trend Tests and Change-Point Detection. R package v.1.1.4. https://CRAN.R-project.org/package=trend (2020). More

  • in

    Transcriptional responses of Trichodesmium to natural inverse gradients of Fe and P availability

    1.Falkowski PG. Evolution of the nitrogen cycle and its influence on the biological sequestration of CO2 in the ocean. Nature. 1997;387:272–5.CAS 

    Google Scholar 
    2.Zehr JP. Nitrogen fixation by marine cyanobacteria. Trends Microbiol. 2011;19:162–73.CAS 
    PubMed 

    Google Scholar 
    3.Capone DG, Zehr JP, Paerl HW, Bergman B, Carpenter EJ. Trichodesmium a globally significant marine cyanobacterium. Science (80-). 1997;276:1221–9.CAS 

    Google Scholar 
    4.Bergman B, Sandh G, Lin S, Larsson J, Carpenter EJ. Trichodesmium – a widespread marine cyanobacterium with unusual nitrogen fixation properties. FEMS Microbiol Rev. 2013;37:286–302. 37(3):286–302CAS 
    PubMed 

    Google Scholar 
    5.Capone DG, Burns JA, Montoya JP, Subramaniam A, Mahaffey C, Gunderson T, et al. Nitrogen fixation by Trichodesmium spp.: An important source of new nitrogen to the tropical and subtropical North Atlantic Ocean. Glob Biogeochem Cycles. 2005;19:1–17.
    Google Scholar 
    6.Mahaffey C, Michaels AF, Capone DG. The conundrum of marine N2 fixation. Am J Sci. 2005;305:546–95.CAS 

    Google Scholar 
    7.Moore C, Mills MM, Achterberg EP, Geider RJ, Laroche J, Lucas MI, et al. Large-scale distribution of Atlantic nitrogen fixation controlled by iron availability. Nat Geosci. 2009;2:867–71.CAS 

    Google Scholar 
    8.Dyhrman ST, Webb EA, Anderson DM, Moffett JW, Waterbury JB. Cell-specific detection of phosphorus stress in Trichodesmium from the Western North Atlantic. Limnol Oceanogr. 2002;47:1832–6.
    Google Scholar 
    9.Snow JT, Schlosser C, Woodward EMS, Mills MM, Achterberg EP, Mahaffey C, et al. Environmental controls on the biogeography of diazotrophy and Trichodesmium in the Atlantic Ocean. Glob Biogeochem Cycles. 2015;29:865–84.CAS 

    Google Scholar 
    10.Jickells TD, An ZS, Andersen KK, Baker AR, Bergametti C, Brooks N, et al. Global iron connections between desert dust, ocean biogeochemistry, and climate. Science. 2005;308:67–71.CAS 
    PubMed 

    Google Scholar 
    11.Schlosser CA, Strzepek K, Gao X, Fant C, Blanc É, Paltsev S, et al. The future of global water stress: an integrated assessment. Earth’s Future. 2014;2:341–61.
    Google Scholar 
    12.Wu J, Sunda W, Boyle EA, Karl DM. Phosphate depletion in the Western North Atlantic. Ocean Sci. 2000;289:759–62.CAS 

    Google Scholar 
    13.Mather RL, Reynolds SE, Wolff GA, Williams RG, Torres-Valdes S, Woodward EMS, et al. Phosphorus cycling in the North and South Atlantic Ocean subtropical gyres. Nat Geosci. 2008;1:439–43.CAS 

    Google Scholar 
    14.Ward BA, Dutkiewicz S, Moore CM, Follows MJ. Iron, phosphorus, and nitrogen supply ratios define the biogeography of nitrogen fixation. Limnol Oceanogr. 2013;58:2059–75.CAS 

    Google Scholar 
    15.Mills MM, Moore CM, Langlois R, Milne A, Achterberg E, Nachtigall K, et al. Nitrogen and phosphorus co-limitation of bacterial productivity and growth in the oligotrophic subtropical North Atlantic. Limnol Oceanogr. 2008;53:824–34.CAS 

    Google Scholar 
    16.Garcia NS, Fu F, Sedwick PN, Hutchins DA. Iron deficiency increases growth and nitrogen-fixation rates of phosphorus-deficient marine cyanobacteria. ISME J. 2015;9:238–45.CAS 
    PubMed 

    Google Scholar 
    17.Walworth NG, Fu FX, Webb EA, Saito MA, Moran D, McLlvin MR, et al. Mechanisms of increased Trichodesmium fitness under iron and phosphorus co-limitation in the present and future ocean. Nat Commun. 2016;7:1–11.
    Google Scholar 
    18.Walworth NG, Fu FX, Lee MD, Cai X, Saito MA, Webb EA, et al. Nutrient-colimited Trichodesmium as a nitrogen source or sink in a future ocean. Appl Environ Microbiol. 2018;84:1–14.CAS 

    Google Scholar 
    19.Held NA, Webb EA, McIlvin MM, Hutchins DA, Cohen NR, Moran DM, et al. Co-occurrence of Fe and P stress in natural populations of the marine diazotroph Trichodesmium. Biogeosciences 2020;17:2537–51.
    Google Scholar 
    20.Polyviou D, Baylay AJ, Hitchcock A, Robidart J, Moore CM, Bibby TS. Desert dust as a source of iron to the globally important diazotroph Trichodesmium. Front Microbiol. 2018;8:2683.PubMed 
    PubMed Central 

    Google Scholar 
    21.Snow JT, Polyviou D, Skipp P, Chrismas NA, Hitchcock A, Geider R, et al. Quantifying Integrated Proteomic Responses to Iron Stress in the Globally Important Marine Diazotroph Trichodesmium. PLOS ONE 2015;10:e0142626.22.Frischkorn KR, Haley ST, Dyhrman ST. Transcriptional and proteomic choreography under phosphorus deficiency and re-supply in the N2 fixing cyanobacterium Trichodesmium erythraeum. Front Microbiol. 2019;10:330. 2012;6:1728–39PubMed 
    PubMed Central 

    Google Scholar 
    23.Rouco M, Frischkorn KR, Haley ST, Alexander H, Dyhrman ST. Transcriptional patterns identify resource controls on the diazotroph Trichodesmium in the Atlantic and Pacific oceans. ISME J. 2018;12:1486–95.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Shi T, Sun Y, Falkowski PG. Effects of iron limitation on the expression of metabolic genes in the marine cyanobacterium Trichodesmium erythraeum IMS101. Environ Microbiol. 2007;9:2945–56.CAS 
    PubMed 

    Google Scholar 
    25.Saito MA, Bertrand EM, Dutkiewicz S, Bulygin VV, Moran DM, Monteiro FM, et al. Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc Natl Acad Sci USA 2011;108:2184–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.La Roche J, Boyd PW, McKay RML, Geider RJ. Flavodoxin as an in situ marker for iron stress in phytoplankton. Nature. 1996;382:802–5.
    Google Scholar 
    27.De la Cerda B, Castielli O, Durán RV, Navarro JA, Hervás M, De la Rosa MA. A proteomic approach to iron and copper homeostasis in cyanobacteria. Brief Funct Genom Proteom. 2007;6:322–9.
    Google Scholar 
    28.Chappell PD, Webb EA. A molecular assessment of the iron stress response in the two phylogenetic clades of Trichodesmium. Environ Microbiol. 2010;12:13–27.CAS 
    PubMed 

    Google Scholar 
    29.Polyviou D, Machelett MM, Hitchcock A, Baylay AJ, MacMillan F, Mark Moore C, et al. Structural and functional characterization of IdiA/FutA (Tery_3377), an iron-binding protein from the ocean diazotroph Trichodesmium erythraeum. J Biol Chem. 2018;293:18099–109.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Berman-Frank I, Lundgren P, Chen YB, Küpper H, Kolber Z, Bergman B, et al. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium Trichodesmium. Science. 2001;294:1534–7.CAS 
    PubMed 

    Google Scholar 
    31.Berman-Frank I, Lundgren P, Falkowski P. Nitrogen fixation and photosynthetic oxygen evolution in cyanobacteria. Res Microbiol. 2003;154:157–64.CAS 
    PubMed 

    Google Scholar 
    32.Sandh G, Ran L, Xu L, Sundqvist G, Bulone V, Bergman B. Comparative proteomic profiles of the marine cyanobacterium Trichodesmium erythraeum IMS101 under different nitrogen regimes. Proteomics. 2011;11:406–19.CAS 
    PubMed 

    Google Scholar 
    33.Orchard ED, Webb EA, Dyhrman ST. Molecular analysis of the phosphorus starvation response in Trichodesmium spp. Environ Microbiol. 2009;11:2400–11.CAS 
    PubMed 

    Google Scholar 
    34.Dyhrman ST, Ruttenberg KC. Presence and regulation of alkaline phosphatase activity in eukaryotic phytoplankton from the coastal ocean: Implications for dissolved organic phosphorus remineralization. Limnol Oceanogr. 2006;51:1381–90.CAS 

    Google Scholar 
    35.Karl DM. Nutrient dynamics in the deep blue sea. Trends Microbiol. 2002;10:410–8.CAS 
    PubMed 

    Google Scholar 
    36.Polyviou D, Hitchcock A, Baylay AJ, Moore CM, Bibby TS. Phosphite utilization by the globally important marine diazotroph Trichodesmium. Environ Microbiol Rep. 2015;7:824–30.CAS 
    PubMed 

    Google Scholar 
    37.Obata H, Karatani H, Matsui M, Nakayama E. Fundamental studies for chemical speciation of iron in seawater with an improved analytical method. Marine Chemistry. 1997;56:97–106.38.Kunde K, Wyatt NJ, González-Santana D, Tagliabue A, Mahaffey C, Lohan MC. Iron Distribution in the Subtropical North Atlantic: The Pivotal Role of Colloidal Iron. Glob Biogeochem Cycles. 2019;33:1532–47.CAS 

    Google Scholar 
    39.Woodward EMS, Rees AP. Nutrient distributions in an anticyclonic eddy in the northeast Atlantic Ocean, with reference to nanomolar ammonium concentrations. Deep Res Part II Top Stud Oceanogr. 2001;48:775–93.CAS 

    Google Scholar 
    40.Davis CE, Blackbird S, Wolff G, Woodward M, Mahaffey C. Seasonal organic matter dynamics in a temperate shelf sea. Prog Oceanogr. 2019;177:101925.
    Google Scholar 
    41.Lomas MW, Burke AL, Lomas DA, Bell DW, Shen C, Dyhrman ST, et al. Sargasso Sea phosphorus biogeochemistry: an important role for dissolved organic phosphorus (DOP). Biogeosci Discuss. 2009;6:10137–75.
    Google Scholar 
    42.Klawonn I, Lavik G, Böning P, et al. Simple approach for the preparation of 15−15N2-enriched water for nitrogen fixation assessments: evaluation, application and recommendations. Front Microbiol. 2015;6:769.PubMed 
    PubMed Central 

    Google Scholar 
    43.Frischkorn KR, Haley ST, Dyhrman ST. Coordinated gene expression between Trichodesmium and its microbiome over day-night cycles in the North Pacific Subtropical Gyre. ISME J. 2018;12:997–1007.PubMed 
    PubMed Central 

    Google Scholar 
    44.Tang W, Cerdán-García E, Berthelot H, Polyviou D, Wang S, Baylay A, et al. New insights into the distributions of nitrogen fixation and diazotrophs revealed by high-resolution sensing and sampling methods. ISME J. 2020;14:2514–26.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Zehr JP, McReynolds LA. Use of degenerate oligonucleotides for amplification of the nifH gene from the marine cyanobacterium Trichodesmium thiebautii. Appl Environ Microbiol. 1989;55:2522–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Zani S, Mellon MT, Collier JL, Zehr JP. Expression of nifH genes in natural microbial assemblages in Lake George, New York, detected by reverse transcriptase PCR. Appl Environ Microbiol. 2000;66:3119–24.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Turk KA, Rees AP, Zehr JP, Pereira N, Swift P, Shelley R, et al. Nitrogen fixation and nitrogenase (nifH) expression in tropical waters of the eastern North Atlantic. ISME J. 2011;5:1201–12.CAS 
    PubMed 

    Google Scholar 
    48.Hitchen J, Sooknanan R, Khanna A. ScriptSeq V2 Library Preparation Method: A Rapid and Efficient Method for Preparing Directional RNA-Seq Libraries. J Biomol Tech. 2012;23:S33–S34.PubMed Central 

    Google Scholar 
    49.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Embnet J. 2011;17:10–2.
    Google Scholar 
    50.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. MetaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using. Microbiome. 2014;2:4904–9.
    Google Scholar 
    52.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Hyatt D, Chen GL, LoCascio PF, Land ML, Frank W, Larimer LJH. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:1–11.
    Google Scholar 
    55.Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002;30:1575–84.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42:D206–14.CAS 
    PubMed 

    Google Scholar 
    57.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
    PubMed 

    Google Scholar 
    58.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. correspondence QIIME allows analysis of high- throughput community sequencing data Intensity normalization improves color calling in SOLiD sequencing. Nat Publ Gr. 2010;7:335–6.CAS 

    Google Scholar 
    59.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
    PubMed 

    Google Scholar 
    60.Edgar RC. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Westreich ST, Treiber ML, Mills DA, Korf I, Lemay DG. SAMSA2: a standalone metatranscriptome analysis pipeline. BMC Bioinforma. 2018;19:1–11.
    Google Scholar 
    62.Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Kopylova E, Noé L, Touzet H. SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012;28:3211–7.CAS 

    Google Scholar 
    64.Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20.CAS 
    PubMed 

    Google Scholar 
    65.Tatusova T, Ciufo S, Fedorov B, O’Neill K, Tolstoy I. RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 2014;42:D553–9.66.Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907–15.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics 2014;30:923–30.CAS 

    Google Scholar 
    68.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.
    Google Scholar 
    69.Wu S, Mi T, Zhen Y, Yu K, Wang F, Yu Z. A Rise in ROS and EPS Production: New Insights into the Trichodesmium erythraeum Response to Ocean Acidification. J Phycol. 2021;57:172–82.CAS 
    PubMed 

    Google Scholar 
    70.Sedwick PN, Church TM, Bowie AR, Marsay CM, Ussher SJ, Achilles KM, et al. Iron in the Sargasso Sea (Bermuda Atlantic Time-series Study region) during summer: Eolian imprint, spatiotemporal variability, and ecological implications. Global Biogeochem Cycles. 2005;19:GB4006.71.Hatta M, Measures CI, Wu J, Roshan S, Fitzsimmons JN, Sedwick P, et al. An overview of dissolved Fe and Mn distributions during the 2010-2011 U.S. GEOTRACES north Atlantic cruises: GEOTRACES GA03. Deep Res Part II Top Stud Oceanogr. 2015;116:117–29.CAS 

    Google Scholar 
    72.Mahaffey C, Reynolds S, Davis CE, Lohan MC. Alkaline phosphatase activity in the subtropical ocean: insights from nutrient, dust and trace metal addition experiments. Front Mar Sci. 2014;1:73.
    Google Scholar 
    73.Church MJ, Mahaffey C, Letelier RM, Lukas R, Zehr JP, Karl DM. Physical forcing of nitrogen fixation and diazotroph community structure in the North Pacific subtropical gyre. Global Biogeochem Cycles. 2009;23:GB2020.74.Zehr JP, Capone DG. Changing perspectives in marine nitrogen fixation. Science. 2020;368:eaay9514.75.Benavides M, Moisander PH, Daley MC, Bode A, Arístegui J. Longitudinal variability of diazotroph abundances in the subtropical North Atlantic Ocean. J Plankton Res. 2016;38:662–72.CAS 

    Google Scholar 
    76.Luo YW, Doney SC, Anderson LA, Benavides M, Berman-Frank I, Bode A, et al. Database of diazotrophs in global ocean: Abundance, biomass and nitrogen fixation rates. Earth Syst Sci Data. 2012;4:47–73.
    Google Scholar 
    77.Moisander PH, Beinart RA, Voss M, Zehr JP. Diversity and abundance of diazotrophic microorganisms in the South China Sea during intermonsoon. ISME J. 2008;2:954–67.CAS 
    PubMed 

    Google Scholar 
    78.Moisander PH, Serros T, Paerl RW, Beinart RA, Zehr JP. Gammaproteobacterial diazotrophs and nifH gene expression in surface waters of the South Pacific Ocean. ISME J 2014;8:1962–73.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Robidart JC, Church MJ, Ryan JP, et al. Ecogenomic sensor reveals controls on N2-fixing microorganisms in the North Pacific Ocean. ISME J. 2014;8:1175–85.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Stenegren M, Caputo A, Berg C, Bonnet S, Foster R. Distribution and drivers of symbiotic and free-living diazotrophic cyanobacteria in the western tropical South Pacific. Biogeosciences 2018;15:1559–78.CAS 

    Google Scholar 
    81.Langlois R, Großkopf T, Mills M, Takeda S, LaRoche J. Widespread Distribution and Expression of Gamma A (UMB), an Uncultured, Diazotrophic, γ-Proteobacterial nifH Phylotype. PLoS ONE. 2015;10:e0128912.PubMed 
    PubMed Central 

    Google Scholar 
    82.Ratten J-M, LaRoche J, Desai DK, et al. Sources of iron and phosphate affect the distribution of diazotrophs in the North Atlantic. Deep Sea Res Part II: Topical Stud Oceanogr. 2015;116:332–41.CAS 

    Google Scholar 
    83.Voss, M, Croot, P, Lochte, K, Mills, M, Peeken, I. Patterns of nitrogen fixation along 10°N in the tropical Atlantic. Geophys Res Lett. 2004;31:L23S09.84.Bibby TS, Nield J, Barber J. Iron deficiency induces the formation of an antenna ring around trimeric photosystem I in cyanobacteria. Nature. 2001;412:743–5.85.Richier S, Macey AI, Pratt NJ, Honey DJ, Moore CM, Bibby TS. Abundances of iron-binding photosynthetic and nitrogen-fixing proteins of Trichodesmium both in culture and in situ from the North Atlantic. PLoS ONE. 2012;7:e35571.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    86.Keren N, Aurora R, Pakrasi HB. Critical roles of bacterioferritins in iron storage and proliferation of cyanobacteria. Plant Physiol. 2004;135:1666–73.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    87.González A, Bes MT, Valladares A, Peleato ML, Fillat MF. FurA is the master regulator of iron homeostasis and modulates the expression of tetrapyrrole biosynthesis genes in Anabaena sp. PCC 7120. Environ Microbiol. 2012;14:3175–87.PubMed 

    Google Scholar 
    88.Sebastian M, Ammerman JW. The alkaline phosphatase PhoX is more widely distributed in marine bacteria than the classical PhoA. ISME J. 2009;3:563–72.CAS 
    PubMed 

    Google Scholar 
    89.Browning TJ, Achterberg EP, Yong JC, Rapp I, Utermann C, Engel A, et al. Iron limitation of microbial phosphorus acquisition in the tropical North Atlantic. Nat Commun. 2017;8:1–7.
    Google Scholar 
    90.Proudfoot M, Kuznetsova E, Brown G, Rao NN, Kitagawa M, Mori H, et al. General enzymatic screens identify three new nucleotidases in Escherichia coli: Biochemical characterization of SurE, YfbR, and YjjG. J Biol Chem. 2004;279:54687–94.CAS 
    PubMed 

    Google Scholar 
    91.Orchard ED, Benitez-Nelson CR, Pellechia PJ, Lomas MW, Dyhrman ST. Polyphosphate in Trichodesmium from the low-phosphorus Sargasso Sea. Limnol Oceanogr. 2010;55:2161–9.CAS 

    Google Scholar 
    92.Berman-Frank I, Cullen JT, Shaked Y, Sherrell RM, Falkowski PG. Iron availability, cellular iron quotas, and nitrogen fixation in Trichodesmium. Limnol Oceanogr. 2001;46:1249–60.CAS 

    Google Scholar 
    93.Schoffman H, Keren N. Function of the IsiA pigment–protein complex in vivo. Photosynth Res. 2019;141:343–53.CAS 
    PubMed 

    Google Scholar 
    94.Küpper H, Ferimazova N, Šetlík I, Berman-Frank I. Traffic lights in Trichodesmium. Regulation of photosynthesis for nitrogen fixation studied by chlorophyll fluorescence kinetic microscopy. Plant Physiol. 2004;135:2120–33.PubMed 
    PubMed Central 

    Google Scholar 
    95.Behrenfeld MJ, Milligan AJ. Photophysiological expressions of iron stress in phytoplankton. Ann Rev Mar Sci. 2013;5:217–46.PubMed 

    Google Scholar 
    96.Ho TY. Nickel limitation of nitrogen fixation in Trichodesmium. Limnol Oceanogr. 2013;58:112–20.CAS 

    Google Scholar 
    97.Tilman D. Resources: a graphical‐mechanistic approach to competition and predation. Am Nat. 1980;116:362–3.
    Google Scholar 
    98.Mills MM, Ridame C, Davey M, La Roche J, Geider RJ. Iron and phosphorus co-limit nitrogen fixation in the eastern tropical North Atlantic. Nature 2004;429:292–4.CAS 
    PubMed 

    Google Scholar 
    99.Saito MA, McIlvin MR, Moran DM, Goepfert TJ, DiTullio GR, Post AF, et al. Multiple nutrient stresses at intersecting Pacific Ocean biomes detected by protein biomarkers. Science 2014;345:1173–7.CAS 
    PubMed 

    Google Scholar 
    100.NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data. MODIS-Aqua Level 3 Mapped Chlorophyll Data Version R2018.0. NASA OB.DAAC, Greenbelt, MD, USA. Published online 2017. More

  • in

    The science of the host–virus network

    1.Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Woolhouse, M. E. et al. Temporal trends in the discovery of human viruses. Proc. R. Soc. B 275, 2111–2115 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    3.Smith, K. F. et al. Global rise in human infectious disease outbreaks. J. R. Soc. Interface 11, 20140950 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    4.Carlson, C. J. et al. Climate change will drive novel cross-species viral transmission. Preprint at bioRxiv https://doi.org/10.1101/2020.01.24.918755 (2020).5.Swei, A., Couper, L. I., Coffey, L. L., Kapan, D. & Bennett, S. Patterns, drivers, and challenges of vector-borne disease emergence. Vector Borne Zoonotic Dis. 20, 159–170 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    6.Belay, E. D. et al. Zoonotic disease programs for enhancing global health security. Emerg. Infect. Dis. 23, S65 (2017).PubMed Central 

    Google Scholar 
    7.Morse, S. S. et al. Prediction and prevention of the next pandemic zoonosis. Lancet 380, 1956–1965 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    8.Carroll, D. et al. The global virome project. Science 359, 872–874 (2018).CAS 
    PubMed 

    Google Scholar 
    9.Carlson, C. J., Zipfel, C. M., Garnier, R. & Bansal, S. Global estimates of mammalian viral diversity accounting for host sharing. Nat. Ecol. Evol. 3, 1070–1075 (2019).PubMed 

    Google Scholar 
    10.Babayan, S. A., Orton, R. J. & Streicker, D. G. Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science 362, 577–580 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Han, B. A. et al. Undiscovered bat hosts of filoviruses. PLoS Negl. Trop. Dis. 10, e0004815 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    12.Schmidt, J. P. et al. Spatiotemporal fluctuations and triggers of Ebola virus spillover. Emerg. Infect. Dis. 23, 415 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    13.Guth, S., Visher, E., Boots, M. & Brook, C. E. Host phylogenetic distance drives trends in virus virulence and transmissibility across the animal–human interface. Phil. Trans. R. Soc. Biol. Sci. 374, 20190296 (2019).
    Google Scholar 
    14.Glennon, E. E. et al. Syndromic detectability of haemorrhagic fever outbreaks. Preprint at medRxiv https://doi.org/10.1101/2020.03.28.20019463 (2020).15.Pigott, D. M. et al. Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis. Lancet 390, 2662–2672 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    16.Palmer, S., Brown, D. & Morgan, D. Early qualitative risk assessment of the emerging zoonotic potential of animal diseases. BMJ 331, 1256–1260 (2005).PubMed 
    PubMed Central 

    Google Scholar 
    17.Grange, Z. L. et al. Ranking the risk of animal-to-human spillover for newly discovered viruses. Proc. Natl Acad. Sci. USA 118, e2002324118 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Carlson, C. J. From PREDICT to prevention, one pandemic later. Lancet Microbe 1, e6–e7 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    19.Holmes, E., Rambaut, A. & Andersen, K. Pandemics: spend on surveillance, not prediction. Nature 558, 180–182 (2018).CAS 
    PubMed 

    Google Scholar 
    20.Breiman, L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199–231 (2001).
    Google Scholar 
    21.Mouquet, N. et al. Predictive ecology in a changing world. J. Appl. Ecol. 52, 1293–1310 (2015).
    Google Scholar 
    22.Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Stephens, P. R. et al. Global mammal parasite database version 2.0. Ecology 98, 1476 (2017).PubMed 

    Google Scholar 
    24.Wardeh, M., Risley, C., McIntyre, M. K., Setzkorn, C. & Baylis, M. Database of host–pathogen and related species interactions, and their global distribution. Sci. Data 2, 150049 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Shaw, L. P. et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol. Ecol. 29, 3361–3379 (2020).PubMed 

    Google Scholar 
    26.Gibb, R. et al. Data proliferation, reconciliation, and synthesis in viral ecology. BioScience https://doi.org/10.1093/biosci/biab080 (2021).27.Dallas, T., Park, A. W. & Drake, J. M. Predicting cryptic links in host–parasite networks. PLoS Comput. Biol. 13, e1005557 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    28.Poisot, T. et al. Imputing the mammalian virome with linear filtering and singular value decomposition. Preprint at https://arxiv.org/abs/2105.14973 (2021).29.Carlson, C. J. et al. The Global Virome in One Network (VIRION): an atlas of vertebrate–virus associations. Preprint at bioRxiv https://doi.org/10.1101/2021.08.06.455442 (2021).30.Albery, G. F., Eskew, E. A., Ross, N. & Olival, K. J. Predicting the global mammalian viral sharing network using phylogeography. Nat. Commun. 11, 2260 (2020).31.Davies, T. J. & Pedersen, A. B. Phylogeny and geography predict pathogen community similarity in wild primates and humans. Proc. R. Soc. B Biol. Sci. 275, 1695–1701 (2008).
    Google Scholar 
    32.Guy, C., Thiagavel, J., Mideo, N. & Ratcliffe, J. M. Phylogeny matters: revisiting ‘a comparison of bats and rodents as reservoirs of zoonotic viruses’. R. Soc. Open Sci. 6, 181182 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    33.Washburne, A. D. et al. Taxonomic patterns in the zoonotic potential of mammalian viruses. PeerJ 6, e5979 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    34.Plowright, R. K. et al. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Stephens, P. R. et al. The macroecology of infectious diseases: a new perspective on global-scale drivers of pathogen distributions and impacts. Ecol. Lett. 19, 1159–1171 (2016).PubMed 

    Google Scholar 
    36.Longdon, B., Brockhurst, M. A., Russell, C. A., Welch, J. J. & Jiggins, F. M. The evolution and genetics of virus host shifts. PLoS Pathog. 10, e1004395 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    37.Farrell, M. J., Elmasri, M., Stephens, D. A. & Davies, T. J. Predicting missing links in global host–parasite networks. bioRxiv https://doi.org/10.1101/2020.02.25.965046 (2020).38.Gilbert, A. T. et al. Deciphering serology to understand the ecology of infectious diseases in wildlife. EcoHealth 10, 298–313 (2013).PubMed 

    Google Scholar 
    39.Becker, D. J., Seifert, S. N. & Carlson, C. J. Beyond infection: integrating competence into reservoir host prediction. Trends Ecol. Evol. 35, 1062–1065 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    40.Walsh, M. G., Mor, S. M., Maity, H. & Hossain, S. A preliminary ecological profile of Kyasanur Forest disease virus hosts among the mammalian wildlife of the Western Ghats, India. Ticks Tick Borne Dis. 11, 101419 (2020).PubMed 

    Google Scholar 
    41.Plowright, R. K. et al. Prioritizing surveillance of Nipah virus in India. PLoS Negl. Trop. Dis. 13, e0007393 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    42.Schmidt, J. P. et al. Ecological indicators of mammal exposure to Ebolavirus. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180337 (2019).
    Google Scholar 
    43.Worsley-Tonks, K. E. et al. Using host traits to predict reservoir host species of rabies virus. PLoS Negl. Trop. Dis. 14, e0008940 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    44.Woolhouse, M. E. & Gowtage-Sequeria, S. Host range and emerging and reemerging pathogens. Emerg. Infect. Dis. 11, 1842 (2005).PubMed 
    PubMed Central 

    Google Scholar 
    45.Johnson, C. K. et al. Spillover and pandemic properties of zoonotic viruses with high host plasticity. Sci. Rep. 5, 14830 (2015).
    Google Scholar 
    46.Elena, S. F. & Sanjuán, R. Adaptive value of high mutation rates of RNA viruses: separating causes from consequences. J. Virol. 79, 11555–11558 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Duffy, S. Why are RNA virus mutation rates so damn high? PLoS Biol. 16, e3000003 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    48.Grewelle, R. E. Larger viral genome size facilitates emergence of zoonotic diseases. Preprint at bioRxiv https://doi.org/10.1101/2020.03.10.986109 (2020).49.Mollentze, N. & Streicker, D. G. Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts. Proc. Natl Acad. Sci. USA 117, 9423–9430 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Walker, J. W., Han, B. A., Ott, I. M. & Drake, J. M. Transmissibility of emerging viral zoonoses. PLoS ONE 13, e0206926 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    51.Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2010146117 (2020).52.Zhang, Z. et al. Rapid identification of human-infecting viruses. Transbound. Emerg. Dis. 66, 2517–2522 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Eng, C. L., Tong, J. C. & Tan, T. W. Predicting zoonotic risk of influenza A viruses from host tropism protein signature using random forest. Int. J. Mol. Sci. 18, 1135 (2017).PubMed Central 

    Google Scholar 
    54.Li, J. et al. Machine learning methods for predicting human-adaptive influenza A viruses based on viral nucleotide compositions. Mol. Biol. Evol. 37, 1224–1236 (2020).CAS 
    PubMed 

    Google Scholar 
    55.Kim, B., Niu, X., Hunter, D. R. & Cao, X. A dynamic additive and multiplicative effects model with application to the United Nations voting behaviors. Preprint at https://arxiv.org/abs/1803.06711 (2018).56.Becker, D. et al. Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs. Lancet Microbe (in the press).57.Han, B. A., Schmidt, J. P., Bowden, S. E. & Drake, J. M. Rodent reservoirs of future zoonotic diseases. Proc. Natl Acad. Sci. USA 112, 7039–7044 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Plourde, B. T. et al. Are disease reservoirs special? Taxonomic and life history characteristics. PLoS ONE 12, e0180716 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    59.Keesing, F. et al. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468, 647–652 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Albery, G. F. & Becker, D. J. Fast-lived hosts and zoonotic risk. Trends Parasitol. 37, 117–129 (2021).CAS 
    PubMed 

    Google Scholar 
    61.Young, C. C. & Olival, K. J. Optimizing viral discovery in bats. PLoS ONE 11, e0149237 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    62.Albery, G. F. et al. Urban-adapted mammal species have more known pathogens. Preprint at bioRxiv https://doi.org/10.1101/2021.01.02.425084 (2021).63.Wille, M., Geoghegan, J. L. & Holmes, E. C. How accurately can we assess zoonotic risk? PLoS Biol. 19, e3001135 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Gibb, R. et al. Mammal virus diversity estimates are unstable due to accelerating discovery effort. Preprint at bioRxiv https://doi.org/10.1101/2021.08.10.455791 (2021).65.Xu, G. J. et al. Comprehensive serological profiling of human populations using a synthetic human virome. Science 348, aaa0698 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    66.Geoghegan, J. L. & Holmes, E. C. Predicting virus emergence amid evolutionary noise. Open Biol. 7, 170189 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    67.Fischhoff, I. R., Castellanos, A. A., Rodrigues, J. P., Varsani, A. & Han, B. A. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2021.1651 (2021).68.Hou, Y. et al. Angiotensin-converting enzyme 2 (ACE2) proteins of different bat species confer variable susceptibility to SARS-CoV entry. Arch. Virol. 155, 1563–1569 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Thompson, A. J., de Vries, R. P. & Paulson, J. C. Virus recognition of glycan receptors. Curr. Opin. Virol. 34, 117–129 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Kocher, J. F. et al. Bat caliciviruses and human noroviruses are antigenically similar and have overlapping histo-blood group antigen binding profiles. Mbio 9, e00869-18 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    71.Chiramel, A. I. et al. TRIM5α restricts flavivirus replication by targeting the viral protease for proteasomal degradation. Cell Rep. 27, 3269–3283 (2019).CAS 
    PubMed 

    Google Scholar 
    72.Young, F., Rogers, S. & Robertson, D. L. Predicting host taxonomic information from viral genomes: a comparison of feature representations. PLoS Comput. Biol. 16, e1007894 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).CAS 
    PubMed 

    Google Scholar 
    74.Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Truong, P., Garcia-Vallve, S. & Puigbo, P. An unsupervised algorithm for host identification in flaviviruses. Life https://doi.org/10.3390/life11050442 (2021).76.Mollentze, N., Babayan, S. & Streicker, D. Identifying and prioritizing potential human-infecting viruses from their genome sequences. PLoS Biol. 19, e3001390 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    77.Wang, W. et al. A network-based integrated framework for predicting virus–prokaryote interactions. NAR Genom. Bioinform. 2, lqaa044 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    78.Bartoszewicz, J. M., Seidel, A. & Renard, B. Y. Interpretable detection of novel human viruses from genome sequencing data. NAR Genom. Bioinform. 3, lqab004 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    79.He, X. et al. Neural collaborative filtering. In Proc. 26th International Conference on World Wide Web 26, 173–182 (Republic and Canton of Geneva, Switzerland, 2017).80.Fout, A., Byrd, J., Shariat, B. & Ben-Hur, A. Protein interface prediction using graph convolutional networks. NIPS’17: Proc. 31st International Conference on Neural Information Processing Systems 31, 6533–6542 (2017).
    Google Scholar 
    81.Hamilton, W. L., Ying, R. & Leskovec, J. Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40, 52–74 (2017).
    Google Scholar 
    82.Bergner, L. M. et al. Characterizing and evaluating the zoonotic potential of novel viruses discovered in vampire bats. Viruses 13, 252 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.Dietze, M. C. et al. Iterative near-term ecological forecasting: needs, opportunities, and challenges. Proc. Natl Acad. Sci. USA 115, 1424–1432 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    84.Schulz, J. E. et al. Serological evidence for henipa-like and filo-like viruses in Trinidad bats. J. Infect. Dis. 221, S375–S382 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    85.Brook, C. E. et al. Disentangling serology to elucidate henipa- and filovirus transmission in Madagascar fruit bats. J. Anim. Ecol. 88, 1001–1016 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    86.Seifert, S. N. et al. Rousettus aegyptiacus bats do not support productive Nipah virus replication. J. Infect. Dis. 221, S407–S413 (2020).CAS 
    PubMed 

    Google Scholar 
    87.Carlson, C. J. et al. The future of zoonotic risk prediction. Phil. Trans. R. Soc. B Biol. Sci. 376, 20200358 (2021).
    Google Scholar 
    88.Ge, X.-Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    89.Menachery, V. D. et al. A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence. Nat. Med. 21, 1508–1513 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Guan, Y. et al. Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China. Science 302, 276–278 (2003).CAS 
    PubMed 

    Google Scholar 
    91.Woo, P. C. Y. et al. Characterization and complete genome sequence of a novel coronavirus, coronavirus HKU1, from patients with pneumonia. J. Virol. 79, 884–895 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.Li, W. et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 310, 676–679 (2005).CAS 
    PubMed 

    Google Scholar 
    93.Wang, M. et al. SARS-CoV infection in a restaurant from palm civet. Emerg. Infect. Dis. 11, 1860–1865 (2005).PubMed 
    PubMed Central 

    Google Scholar 
    94.Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog. 13, e1006698 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    95.Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.Xiao, K. et al. Isolation of SARS-CoV-2-related coronavirus from Malayan pangolins. Nature 583, 286–289 (2020).CAS 
    PubMed 

    Google Scholar 
    97.Lam, T.-Y. et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature 583, 282–285 (2020).CAS 
    PubMed 

    Google Scholar 
    98.Wacharapluesadee, S. et al. Evidence for SARS-CoV-2 related coronaviruses circulating in bats and pangolins in Southeast Asia. Nat. Commun. 12, 972 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Holmes, E. C. et al. The origins of SARS-CoV-2: a critical review. Cell 184, 4848–4856 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Oude Munnink, B. B. et al. Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans. Science 371, 172–177 (2021).CAS 
    PubMed 

    Google Scholar 
    101.Chandler, J. C. et al. SARS-CoV-2 exposure in wild white-tailed deer (Odocoileus virginianus). Proc. Natl Acad. Sci. USA 118, e2114828118 (2021).PubMed 

    Google Scholar 
    102.Jia, P., Dai, S., Wu, T. & Yang, S. New approaches to anticipate the risk of reverse zoonosis. Trends Ecol. Evol. 36, 580–590 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    103.Lednicky, J. A. et al. Isolation of a novel recombinant canine coronavirus from a visitor to Haiti: further evidence of transmission of coronaviruses of zoonotic origin to humans. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciab924 (2021).104.Vlasova, A. N. et al. Novel canine coronavirus isolated from a hospitalized pneumonia patient, East Malaysia. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciab456 (2021).105.Lednicky, J. A. et al. Emergence of porcine delta-coronavirus pathogenic infections among children in Haiti through independent zoonoses and convergent evolution. Preprint at medRxiv https://doi.org/10.1101/2021.03.19.21253391 (2021).106.Hay, A. J. & McCauley, J. W. The WHO global influenza surveillance and response system (GISRS)—a future perspective. Influenza Other Respir. Viruses 12, 551–557 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    107.Subbarao, K. et al. Characterization of an avian influenza A (H5N1) virus isolated from a child with a fatal respiratory illness. Science 279, 393–396 (1998).CAS 
    PubMed 

    Google Scholar 
    108.Kandeel, A. et al. Zoonotic transmission of avian influenza virus (H5N1), Egypt, 2006–2009. Emerg. Infect. Dis. 16, 1101 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    109.Ke, C. et al. Human infection with highly pathogenic avian influenza A (H7N9) virus, China. Emerg. Infect. Dis. 23, 1332 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    110.Gaidet, N. et al. Evidence of infection by H5N2 highly pathogenic avian influenza viruses in healthy wild waterfowl. PLoS Pathog. 4, e1000127 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    111.Webster, R. G., Bean, W. J., Gorman, O. T., Chambers, T. M. & Kawaoka, Y. Evolution and ecology of influenza A viruses. Microbiol. Mol. Biol. Rev. 56, 152–179 (1992).CAS 

    Google Scholar 
    112.Pawar, S. D. et al. Avian influenza surveillance reveals presence of low pathogenic avian influenza viruses in poultry during 2009–2011 in the West Bengal State, India. Virol. J. 9, 151 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    113.Parry, R., Wille, M., Turnbull, O. M., Geoghegan, J. L. & Holmes, E. C. Divergent influenza-like viruses of amphibians and fish support an ancient evolutionary association. Viruses 12, 1042 (2020).CAS 
    PubMed Central 

    Google Scholar 
    114.Campbell, P. J. et al. The M segment of the 2009 pandemic influenza virus confers increased neuraminidase activity, filamentous morphology, and efficient contact transmissibility to A/Puerto Rico/8/1934-based reassortant viruses. J. Virol. 88, 3802–3814 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    115.Carlson, C. Evolutionary surprise, artificial intelligence, and H5N8. The Verena Blog https://www.viralemergence.org/blog/evolutionary-surprise-artificial-intelligence-and-h5n8 (2021).116.Wardeh, M., Baylis, M. & Blagrove, M. S. Predicting mammalian hosts in which novel coronaviruses can be generated. Nat. Commun. 12, 780 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    117.Crossman, L. C. Leveraging deep learning to simulate coronavirus spike proteins has the potential to predict future zoonotic sequences. Preprint at bioRxiv https://doi.org/10.1101/2020.04.20.046920 (2020). More

  • in

    Reply to: Spatial scale and the synchrony of ecological disruption

    1.Colwell, R. K. Spatial scale and the synchrony of ecological disruption. Nature https://doi.org/10.1038/s41586-021-03760-4 (2021).2.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    3.Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evol. 4, 109–114 (2020).Article 

    Google Scholar 
    4.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    5.Ricklefs, R. E. Disintegration of the ecological community. Am. Nat. 172, 741–750 (2008).Article 

    Google Scholar 
    6.Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Nadeau, C. P., Urban, M. C. & Bridle, J. R. Coarse climate change projections for species living in a fine-scaled world. Glob. Change Biol. 23, 12–24 (2017).ADS 
    Article 

    Google Scholar 
    8.Stewart, S. B. et al. Climate extreme variables generated using monthly time‐series data improve predicted distributions of plant species. Ecography 44, 626–639 (2021).Article 

    Google Scholar 
    9.Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).ADS 
    Article 

    Google Scholar 
    10.McKechnie, A. E. & Wolf, B. O. The Physiology of Heat Tolerance in Small Endotherms. Physiology 34, 302–313 (2019).CAS 
    Article 

    Google Scholar 
    11.Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).Article 

    Google Scholar 
    12.Mahony, C. R. & Cannon, A. J. Wetter summers can intensify departures from natural variability in a warming climate. Nat. Commun. 9, 783 (2018).ADS 
    Article 

    Google Scholar 
    13.Molnár, P. K., Derocher, A. E., Thiemann, G. W. & Lewis, M. A. Predicting survival, reproduction and abundance of polar bears under climate change. Biol. Conserv. 143, 1612–1622 (2010).Article 

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

    Google Scholar 
    15.Vergés, A. et al. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc. Natl Acad. Sci. USA 113, 13791–13796 (2016).Article 

    Google Scholar 
    16.Genin, A., Levy, L., Sharon, G., Raitsos, D. E. & Diamant, A. Rapid onsets of warming events trigger mass mortality of coral reef fish. Proc. Natl Acad. Sci. USA 117, 25378–25385 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    17.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).ADS 
    Article 

    Google Scholar 
    18.Ruthrof, K. X. et al. Subcontinental heat wave triggers terrestrial and marine, multi-taxa responses. Sci. Rep. 8, 13094 (2018).ADS 
    Article 

    Google Scholar 
    19.Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    20.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).ADS 
    CAS 
    Article 

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

    Google Scholar 
    22.Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).Article 

    Google Scholar 
    23.Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).ADS 
    CAS 
    Article 

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

    Google Scholar 
    25.Williams, J. W., Ordonez, A. & Svenning, J. C. A unifying framework for studying and managing climate-driven rates of ecological change. Nat. Ecol. Evol. 5, 17–26 (2021).Article 

    Google Scholar 
    26.NOAA National Geophysical Data Center. 2009: ETOPO1 1 Arc-Minute Global Relief Model. NOAA National Centers for Environmental Information. Accessed 10.05.2021. More

  • in

    Spatial scale and the synchrony of ecological disruption

    1.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    2.Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).Article 

    Google Scholar 
    3.Sih, T. L., Cappo, M. & Kingsford, M. Deep-reef fish assemblages of the Great Barrier Reef shelf-break (Australia). Sci. Rep. 7, 10886 (2017).ADS 
    Article 

    Google Scholar 
    4.Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365, 1108–1113 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    5.Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. Proc. Natl Acad. Sci. USA 107, 9707–9711 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    8.Stroud, J. T. et al. Is a community still a community? Reviewing definitions of key terms in community ecology. Ecol. Evol. 5, 4757–4765 (2015).Article 

    Google Scholar 
    9.Lima, F. P. et al. Loss of thermal refugia near equatorial range limits. Glob. Change Biol. 22, 254–263 (2016).ADS 
    Article 

    Google Scholar 
    10.Lenoir, J. & Svenning, J. C. Climate‐related range shifts–a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2014).Article 

    Google Scholar 
    11.Bell, G. Evolutionary rescue. Annu. Rev. Ecol. Evol. Syst. 48, 605–627 (2017).Article 

    Google Scholar 
    12.Ackerly, D. D. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci. 164, S165–S184 (2003).Article 

    Google Scholar 
    13.Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).Article 

    Google Scholar 
    14.Rangel, T. F. et al. Modeling the ecology and evolution of biodiversity: biogeographical cradles, museums, and graves. Science 361, eaar5452 (2018). CAS 
    Article 

    Google Scholar 
    15.Desbruyères, D., McDonagh, E. L., King, B. A. & Thierry, V. Global and full-depth ocean temperature trends during the early twenty-first century from Argo and repeat hydrography. J. Clim. 30, 1985–1997 (2017).ADS 
    Article 

    Google Scholar  More

  • in

    Hard times tear coupled seabirds apart

    .readcube-buybox { display: none !important;}

    Many seabirds form long-term pairings, but do not necessarily mate for life — and are more likely to ‘break up’ in years when environmental conditions are unfavourable, researchers reveal.

    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 .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 .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:block;padding-right:20px;padding-left:20px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254: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{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{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:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label{color:#069}
    /* style specs end */Subscribe 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.Rent or Buy articleGet time limited or full article access on ReadCube.from$8.99Rent or BuyAll prices are NET prices.

    Additional access options:

    Log in

    Learn about institutional subscriptions

    doi: https://doi.org/10.1038/d41586-021-03509-z

    References1.Ventura, F., Granadeiro, J. P., Lukacs, P. M., Kuepfer, A. & Catry, P. Proc. R. Soc. B https://doi.org/10.1098/rspb.2021.2112 (2021).Article 

    Google Scholar 
    Download references

    Subjects

    Ecology

    Latest on:

    Ecology

    Link knowledge and action networks to tackle disasters
    Correspondence 16 NOV 21

    Whales’ gigantic appetites, climate fears — the week in infographics
    News 05 NOV 21

    COP26 climate pledges: What scientists think so far
    News 05 NOV 21

    Jobs

    Faculty Position in Faculty of Synthetic Biology at SIAT

    Shenzhen Institutes of Advanced Technology (SIAT), CAS
    Shenzhen, China

    Research Associate (m/f/x)

    Technische Universität Dresden (TU Dresden)
    01069 Dresden, Germany

    Research Associate / PhD Position (m/f/x)

    Technische Universität Dresden (TU Dresden)
    01069 Dresden, Germany

    Principal Scientist (f/m/d) – High Sensitivity Proteomics

    Evotec AG
    Munich, Germany More