Philippa Kaur

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

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

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

1.Jenkins, P. T. Free trade and exotic species introductions. Conserv. Biol. 10, 300–302 (1996).Article 

Google Scholar 
2.Sharov, A. A. Bioeconomics of managing the spread of exotic pest species with barrier zones. Risk Anal. 24, 879–892 (2004).Article 

Google Scholar 
3.Lodge, D. M. et al. Biological invasions: Recommendations for U.S. policy and management. Ecol. Appl. 16, 2035–2054 (2006).Article 

Google Scholar 
4.Yemshanov, D. et al. Optimizing surveillance strategies for early detection of invasive alien species. Ecol. Econ. 162, 87–99 (2019).Article 

Google Scholar 
5.Hauser, C. E. & Mccarthy, M. A. Streamlining “search and destroy”: Cost-effective surveillance for invasive species management. Ecol. Lett. 12, 683–692 (2009).Article 

Google Scholar 
6.Gottwald, T. R., da Graça, J. V. & Bassanezi, R. B. Citrus Huanglongbing: The pathogen and its impact. Plant Health Prog. https://doi.org/10.1094/PHP-2007-0906-01-RV (2007).Article 

Google Scholar 
7.Anderson, D. P. et al. Bio-economic optimisation of surveillance to confirm broadscale eradications of invasive pests and diseases. Biol. Invasions 19, 2869–2884 (2017).Article 

Google Scholar 
8.Russell, J. C., Binnie, H. R., Oh, J., Anderson, D. P. & Samaniego-Herrera, A. Optimizing confirmation of invasive species eradication with rapid eradication assessment. J. Appl. Ecol. 54, 160–169 (2017).Article 

Google Scholar 
9.Moffitt, L. J., Stranlund, J. K. & Osteen, C. D. Robust detection protocols for uncertain introductions of invasive species. J. Environ. Manag. 89, 293–299 (2008).Article 

Google Scholar 
10.Knight, F. H. Risk, Uncertainty, and Profit (Houghton Mifflin Company, 1921).11.Ben-Haim, Y. Uncertainty, probability and information-gaps. Reliab. Eng. Syst. Saf. 85, 249–266 (2004).Article 

Google Scholar 
12.Johnson, D. R. & Geldner, N. B. Contemporary decision methods for agricultural, environmental, and resource management and policy. Annu. Rev. Resour. Econ. 11, 19–41 (2019).Article 

Google Scholar 
13.Baker, C. M. & Bode, M. Recent advances of quantitative modeling to support invasive species eradication on islands. Conserv. Sci. Pract. 3, e246. https://doi.org/10.1111/csp2.246 (2021).Article 

Google Scholar 
14.Bertsimas, D. & Sim, M. The price of robustness. Oper. Res. 52, 35–53 (2004).MathSciNet 
Article 

Google Scholar 
15.Ben-Haim, Y. & Demertzis, M. Decision making in times of knightian uncertainty: An info-gap perspective. Economics 10, 1–29 (2016).Article 

Google Scholar 
16.Ben-Haim, Y. Management of invasive species: Info-gap perspectives. in Invasive Species: Risk Assessment and Management (eds. Robinson, A., Walshe, T., Burgman, M. A., Nunn, M.) 266–286 (Cambridge University Press, 2017).17.Davidovitch, L. et al. Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island. J. Environ. Manag. 90, 2785–2793 (2009).Article 

Google Scholar 
18.Rout, T. M., Thompson, C. J. & McCarthy, M. A. Robust decisions for declaring eradication of invasive species. J. Appl. Ecol. 46, 782–786 (2009).Article 

Google Scholar 
19.Foxcroft, L. C. Developing thresholds of potential concern for invasive alien species: Hypotheses and concepts. Koedoe. https://doi.org/10.4102/koedoe.v51i1.157 (2009).Article 

Google Scholar 
20.Pitt, J. P. W. Modelling the Spread of Invasive Species Across Heterogeneous Landscapes. (Lincoln University, 2008).21.Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S. & Venette, R. C. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61, 237–245 (2007).Article 

Google Scholar 
22.Mcdonald-madden, E., Peter, W. J. B. & Possingham, H. P. Making robust decisions for conservation with restricted money and knowledge. J. Appl. Ecol. 45, 1630–1638 (2008).Article 

Google Scholar 
23.Rout, T. M., Moore, J. L. & Mccarthy, M. A. Prevent, search or destroy? A partially observable model for invasive species management. J. Appl. Ecol. 51, 804–813 (2014).Article 

Google Scholar 
24.Yemshanov, D. et al. Robust surveillance and control of invasive species using a scenario optimization approach. Ecol. Econ. 133, 86–98 (2017).Article 

Google Scholar 
25.Rödder, D., Solé, M. & Böhme, W. Predicting the potential distributions of two alien invasive Housegeckos (Gekkonidae: Hemidactylus frenatus, Hemidactylus mabouia). North-West. J. Zool. 4, 236–246 (2008).
Google Scholar 
26.Hoskin, C. J. The invasion and potential impact of the Asian House Gecko (Hemidactylus frenatus) in Australia. Austral. Ecol. 36, 240–251 (2011).Article 

Google Scholar 
27.García-Díaz, P., Ross, J. V., Vall-llosera, M. & Cassey, P. Low detectability of alien reptiles can lead to biosecurity management failure: A case study from Christmas Island (Australia). NeoBiota 45, 75–92 (2019).Article 

Google Scholar 
28.Scott, J. K. et al. Zero-tolerance biosecurity protects high-conservation-value island nature reserve. Sci. Rep. 7, 772–779 (2017).ADS 
Article 

Google Scholar 
29.Commonwealth Government of Australia. Approval—Gorgon Gas Development (EPBC Reference: 2008/4178). (2009).30.Jarrad, F. C. et al. Improved design method for biosecurity surveillance and early detection of non-indigenous rats. N. Z. J. Ecol. 35, 132–144 (2011).
Google Scholar 
31.Metlay, D. From tin roof to torn wet blanket: Predicting and observing ground water movement at a proposed nuclear waste site. in Prediction: Science, Decision Making, and the Future of Nature (eds. Sarewitz, D. R., Byerly, R., Pielke, R. A.). 276–319. (Island Press, 2000).32.Wintle, B. & Burgman, M. Expert Elicitation for Barrow Island Surveillance System Revision, Project Report. (2015).33.Vanderduys, E. & Kutt, A. Is the Asian house gecko, Hemidactylus frenatus, really a threat to Australia’s biodiversity?. Aust. J. Zool. 60, 361–367 (2013).Article 

Google Scholar 
34.McGinnis, S. M. & Stebbins, R. C. A Field Guide to Western Reptiles and Amphibians. 4th edn. (Houghton Mifflin Harcourt, 2018).35.Whittle, P., Jarrad, F., Edwards, K. & Mengersen, K. Design of the quarantine surveillance for non-indigenous species of invertebrates on Barrow Island. Rec. West. Aust. Mus. Suppl. 83, 113–130 (2013).Article 

Google Scholar 
36.Ben-Haim, Y. Info-gap Decision Theory: Decisions Under Severe Uncertainty. 2nd edn. (Academic Press, 2006).37.MathWorks. MATLAB R2018b. (MathWorks, 2018).38.Bogich, T. L., Liebhold, A. M. & Shea, K. To sample or eradicate? A cost minimization model for monitoring and managing an invasive species. J. Appl. Ecol. 45, 1134–1142 (2008).Article 

Google Scholar 
39.Epanchin-Niell, R. S., Haight, R. G., Berec, L., Kean, J. M. & Liebhold, A. M. Optimal surveillance and eradication of invasive species in heterogeneous landscapes. Ecol. Lett. 15, 803–812 (2012).Article 

Google Scholar 
40.Trebitz, A. S. et al. Early detection monitoring for aquatic non-indigenous species: Optimizing surveillance, incorporating advanced technologies, and identifying research needs. J. Environ. Manag. 202, 299–310 (2017).CAS 
Article 

Google Scholar 
41.Molina, R., Horton, T., Trappe, J. & Marcot, B. Addressing uncertainty: How to conserve and manage rare or little-known fungi. Fungal Ecol. 4, 134–146 (2011).Article 

Google Scholar  More

1.Paillard, D. The timing of Pleistocene glaciations from a simple multiple-state climate model. Nature 391, 378–381 (1998).ADS 

Google Scholar 
2.Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 359, 183–195 (2004).CAS 

Google Scholar 
3.Hewitt, G. The genetic legacy of the quaternary ice ages. Nature 405, 907–913 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Taberlet, P., Fumagalli, L., Wust-Saucy, A.-G. & Cosson, J.-F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464 (1998).CAS 
PubMed 

Google Scholar 
5.Incagnone, G., Marrone, F., Barone, R., Robba, L. & Naselli-Flores, L. How do freshwater organisms cross the ‘dry ocean’? A review on passive dispersal and colonization processes with a special focus on temporary ponds. Hydrobiologia 750, 103–123 (2015).
Google Scholar 
6.Schmitt, T. & Varga, Z. Extra-Mediterranean refugia: The rule and not the exception?. Front Zool 9, 22 (2012).PubMed 
PubMed Central 

Google Scholar 
7.Hewitt, G. M. Speciation, hybrid zones and phylogeography—Or seeing genes in space and time. Mol. Ecol. 10, 537–549 (2001).CAS 
PubMed 

Google Scholar 
8.Habel, J. C., Drees, C., Schmitt, T. & Assmann, T. Review refugial areas and postglacial colonizations in the Western Palearctic. In Relict Species (eds Habel, J. C. & Assmann, T.) 189–197 (Springer, 2010).
Google Scholar 
9.Hewitt, G. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Lin. Soc. 58, 247–276 (1996).
Google Scholar 
10.Marrone, F., Lo Brutto, S. & Arculeo, M. Molecular evidence for the presence of cryptic evolutionary lineages in the freshwater copepod genus Hemidiaptomus G.O. Sars, 1903 (Calanoida, Diaptomidae). Hydrobiologia 644, 115–125 (2010).CAS 

Google Scholar 
11.Husemann, M., Schmitt, T., Zachos, F. E., Ulrich, W. & Habel, J. C. Palaearctic biogeography revisited: Evidence for the existence of a North African refugium for Western Palaearctic biota. J. Biogeogr. 41, 81–94 (2014).
Google Scholar 
12.García-Vázquez, D., Bilton, D. T., Foster, G. N. & Ribera, I. Pleistocene range shifts, refugia and the origin of widespread species in western Palaearctic water beetles. Mol. Phylogenet. Evol. 114, 122–136 (2017).PubMed 

Google Scholar 
13.Perktas, U., Barrowclough, G. F. & Groth, J. G. Phylogeography and species limits in the green woodpecker complex (Aves: Picidae): Multiple Pleistocene refugia and range expansion across Europe and the Near East. Biol. J. Lin. Soc. 104, 710–723 (2011).
Google Scholar 
14.Stewart, J. R. & Lister, A. M. Cryptic northern refugia and the origins of the modern biota. Trends Ecol. Evol. 16, 608–613 (2001).
Google Scholar 
15.Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: Individualistic responses of species in space and time. Proc. R. Soc. B Biol. Sci. 277, 661–671 (2010).
Google Scholar 
16.Sworobowicz, L., Mamos, T., Grabowski, M. & Wysocka, A. Lasting through the ice age: The role of the proglacial refugia in the maintenance of genetic diversity, population growth, and high dispersal rate in a widespread freshwater crustacean. Freshw. Biol. 65, 1028–1046 (2020).CAS 

Google Scholar 
17.Provan, J. & Bennett, K. D. Phylogeographic insights into cryptic glacial refugia. Trends Ecol. Evol. 23, 564–571 (2008).PubMed 

Google Scholar 
18.Antal, L. et al. Phylogenetic evidence for a new species of Barbus in the Danube River basin. Mol. Phylogenet. Evol. 96, 187–194 (2016).CAS 
PubMed 

Google Scholar 
19.Copilaş-Ciocianu, D., Fišer, C., Borza, P. & Petrusek, A. Is subterranean lifestyle reversible? Independent and recent large-scale dispersal into surface waters by two species of the groundwater amphipod genus Niphargus. Mol. Phylogenet. Evol. 119, 37–49 (2018).PubMed 

Google Scholar 
20.Říčanová, Š et al. Multilocus phylogeography of the European ground squirrel: Cryptic interglacial refugia of continental climate in Europe. Mol. Ecol. 22, 4256–4269 (2013).PubMed 

Google Scholar 
21.Vörös, J., Mikulíček, P., Major, Á., Recuero, E. & Arntzen, J. W. Phylogeographic analysis reveals northerly refugia for the riverine amphibian Triturus dobrogicus (Caudata: Salamandridae). Biol. J. Linn. Soc. 119, 974–991 (2016).
Google Scholar 
22.Wielstra, B. et al. Tracing glacial refugia of Triturus newts based on mitochondrial DNA phylogeography and species distribution modeling. Front. Zool. 10, 13 (2013).PubMed 
PubMed Central 

Google Scholar 
23.Hutchison, D. W. & Templeton, A. R. Correlation of pairwise genetic and geographic distance measures: Inferring the relative influences of gene flow and drift on the distribution of genetic variability. Evolution 53, 1898–1914 (1999).PubMed 

Google Scholar 
24.Schmitt, T. Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Front. Zool. 4, 11 (2007).PubMed 
PubMed Central 

Google Scholar 
25.Ewart, K. M. et al. Phylogeography of the iconic Australian red-tailed black-cockatoo (Calyptorhynchus banksii) and implications for its conservation. Heredity 125, 85–100 (2020).PubMed 
PubMed Central 

Google Scholar 
26.Hutama, A. et al. Identifying spatially concordant evolutionary significant units across multiple species through DNA barcodes: Application to the conservation genetics of the freshwater fishes of Java and Bali. Glob. Ecol. Conserv. 12, 170–187 (2017).
Google Scholar 
27.Médail, F. & Baumel, A. Using phylogeography to define conservation priorities: The case of narrow endemic plants in the Mediterranean Basin hotspot. Biol. Cons. 224, 258–266 (2018).
Google Scholar 
28.Previšić, A., Walton, C., Kučinić, M., Mitrikeski, P. T. & Kerovec, M. Pleistocene divergence of Dinaric Drusus endemics (Trichoptera, Limnephilidae) in multiple microrefugia within the Balkan Peninsula. Mol. Ecol. 18, 634–647 (2009).PubMed 

Google Scholar 
29.Brendonck, L. & Riddoch, B. J. Wind-borne short-range egg dispersal in anostracans (Crustacea: Branchiopoda). Biol. J. Linn. Soc. 67, 87–95 (1999).
Google Scholar 
30.Horváth, Z., Vad, C. F. & Ptacnik, R. Wind dispersal results in a gradient of dispersal limitation and environmental match among discrete aquatic habitats. Ecography 39, 726–732 (2016).PubMed 

Google Scholar 
31.Brochet, A. L. et al. Field evidence of dispersal of branchiopods, ostracods and bryozoans by teal (Anas crecca) in the Camargue (southern France). Hydrobiologia 637, 255 (2009).
Google Scholar 
32.Figuerola, J. & Green, A. J. Dispersal of aquatic organisms by waterbirds: A review of past research and priorities for future studies. Freshw. Biol. 47, 483–494 (2002).
Google Scholar 
33.Vanschoenwinkel, B. et al. Dispersal of freshwater invertebrates by large terrestrial mammals: A case study with wild boar (Sus scrofa) in Mediterranean wetlands. Freshw. Biol. 53, 2264–2273 (2008).
Google Scholar 
34.Brendonck, L., Rogers, D. C., Olesen, J., Weeks, S. & Hoeh, W. R. Global diversity of large branchiopods (Crustacea : Branchiopoda) in freshwater. Hydrobiologia 595, 167–176 (2008).
Google Scholar 
35.Dumont, H. J. & Negrea, S. V. Introduction to the Class Branchiopoda. (Backhuys Publishers, 2002).36.Belk, D. Global status and trends in ephemeral pool invertebrate conservation: Implications for Californian fairy shrimp. In Ecology, Conservation, and Management of Vernal Pool Ecosystems—Proceedings from a 1996 conference 147–150 (California Native Plant Society, 1998).37.Jocque, M., Vanschoenwinkel, B. & Brendonck, L. Anostracan monopolisation of early successional phases in temporary waters?. Fundam. Appl. Limnol. 176, 127–132 (2010).
Google Scholar 
38.Lukić, D., Horváth, Z., Vad, C. F. & Ptacnik, R. Food spectrum of Branchinecta orientalis—Are anostracans omnivorous top consumers of plankton in temporary waters?. J. Plankton Res. 40, 436–445 (2018).
Google Scholar 
39.Lukić, D., Ptacnik, R., Vad, C. F., Pόda, C. & Horváth, Z. Environmental constraint of intraguild predation: Inorganic turbidity modulates omnivory in fairy shrimps. Freshw. Biol. 65, 226–239 (2020).
Google Scholar 
40.Waterkeyn, A., Grillas, P., Anton-Pardo, M., Vanschoenwinkel, B. & Brendonck, L. Can large branchiopods shape microcrustacean communities in Mediterranean temporary wetlands?. Mar. Freshw. Res. 62, 46–53 (2011).CAS 

Google Scholar 
41.Brendonck, L. & De Meester, L. Egg banks in freshwater zooplankton: Evolutionary and ecological archives in the sediment. Hydrobiologia 491, 65–84 (2003).
Google Scholar 
42.Hairston, N. G., Brunt, R. A. V., Kearns, C. M. & Engstrom, D. R. Age and survivorship of diapausing eggs in a sediment egg bank. Ecology 76, 1706–1711 (1995).
Google Scholar 
43.Lukić, D. et al. High genetic variation and phylogeographic relations among Palearctic fairy shrimp populations reflect persistence in multiple southern refugia during Pleistocene ice ages and postglacial colonisation. Freshw. Biol. 64, 1896–1907 (2019).
Google Scholar 
44.Marrone, F., Alfonso, G., Naselli-Flores, L. & Stoch, F. Diversity patterns and biogeography of Diaptomidae (Copepoda, Calanoida) in the Western Palearctic. Hydrobiologia 800, 45–60 (2017).CAS 

Google Scholar 
45.Vanschoenwinkel, B. et al. Toward a global phylogeny of the “living fossil’’ crustacean order of the Notostraca. PLos ONE 7, e34998 (2012).46.Boileau, M. & Hebert, P. Genetic consequences of passive dispersal in pond-dwelling Copepods. Evolution 45, 721–733 (1991).PubMed 

Google Scholar 
47.Deng, Z., Chen, Y., Ma, X., Hu, W. & Yin, M. Dancing on the top: Phylogeography and genetic diversity of high-altitude freshwater fairy shrimps (Branchiopoda, Anostraca) with a focus on the Tibetan Plateau. Hydrobiologia 848, 2611–2626 (2021).CAS 

Google Scholar 
48.Ketmaier, V. et al. Mitochondrial DNA regionalism and historical demography in the extant populations of Chirocephalus kerkyrensis (Branchiopoda: Anostraca). PLoS ONE 7, e30082 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Korn, M. et al. Phylogeny, molecular ecology and taxonomy of southern Iberian lineages of Triops mauritanicus (Crustacea: Notostraca). Org. Divers. Evol. 10, 409–440 (2010).
Google Scholar 
50.Stoch, F., Korn, M., Turki, S., Naselli-Flores, L. & Marrone, F. The role of spatial environmental factors as determinants of large branchiopod distribution in Tunisian temporary ponds. Hydrobiologia 782, 37–51 (2016).
Google Scholar 
51.Lindholm, M., d’Auriac, M. A., Thaulow, J. & Hobaek, A. Dancing around the pole: Holarctic phylogeography of the Arctic fairy shrimp Branchinecta paludosa (Anostraca, Branchiopoda). Hydrobiologia 772, 189–205 (2016).CAS 

Google Scholar 
52.Vörös, J., Alcobendas, M., Martínez-Solano, I. & García-París, M. Evolution of Bombina bombina and Bombina variegata (Anura: Discoglossidae) in the Carpathian Basin: A history of repeated mt-DNA introgression across species. Mol. Phylogenet. Evol. 38, 705–718 (2006).PubMed 

Google Scholar 
53.Zharov, A. A. et al. Pleistocene branchiopods (Cladocera, Anostraca) from Transbaikalian Siberia demonstrate morphological and ecological stasis. Water 12, 3063 (2020).
Google Scholar 
54.Velonà, A., Luchetti, A., Scanabissi, F. & Mantovani, B. Genetic variability and reproductive modalities in European populations of Triops cancriformis (Crustacea, Branchiopoda, Notostraca). Ital. J. Zool. 76, 366–375 (2009).
Google Scholar 
55.Vanschoenwinkel, B., Gielen, S., Vandewaerde, H., Seaman, M. & Brendonck, L. Relative importance of different dispersal vectors for small aquatic invertebrates in a rock pool metacommunity. Ecography 31, 567–577 (2008).
Google Scholar 
56.Hulsmans, A., Moreau, K., Meester, L. D., Riddoch, B. J. & Brendonck, L. Direct and indirect measures of dispersal in the fairy shrimp Branchipodopsis wolfi indicate a small scale isolation-by-distance pattern. Limnol. Oceanogr. 52, 676–684 (2007).ADS 

Google Scholar 
57.Vanschoenwinkel, B., Vries, C. D., Seaman, M. & Brendonck, L. The role of metacommunity processes in shaping invertebrate rock pool communities along a dispersal gradient. Oikos 116, 1255–1266 (2007).
Google Scholar 
58.Sánchez, M. I., Green, A. J., Amat, F. & Castellanos, E. M. Transport of brine shrimps via the digestive system of migratory waders: Dispersal probabilities depend on diet and season. Mar. Biol. 151, 1407–1415 (2007).
Google Scholar 
59.Horváth, Z. et al. Eastern spread of the invasive Artemia franciscana in the Mediterranean Basin, with the first record from the Balkan Peninsula. Hydrobiologia 822, 229–235 (2018).
Google Scholar 
60.Muñoz, J., Amat, F., Green, A. J., Figuerola, J. & Gómez, A. Bird migratory flyways influence the phylogeography of the invasive brine shrimp Artemia franciscana in its native American range. PeerJ 1, e200 (2013).PubMed 
PubMed Central 

Google Scholar 
61.Muñoz, J. et al. Phylogeography and local endemism of the native Mediterranean brine shrimp Artemia salina (Branchiopoda: Anostraca). Mol. Ecol. 17, 3160–3177 (2008).PubMed 

Google Scholar 
62.Sánchez, M. I., Hortas, F., Figuerola, J. & Green, A. J. Comparing the potential for dispersal via waterbirds of a native and an invasive brine shrimp. Freshw. Biol. 57, 1896–1903 (2012).
Google Scholar 
63.Viana, D. S., Santamaría, L., Michot, T. C. & Figuerola, J. Migratory strategies of waterbirds shape the continental-scale dispersal of aquatic organisms. Ecography 36, 430–438 (2013).
Google Scholar 
64.Green, A. J. et al. Dispersal of invasive and native brine shrimps Artemia (Anostraca) via waterbirds. Limnol. Oceanogr. 50, 737–742 (2005).ADS 

Google Scholar 
65.Kappas, I. et al. Molecular and morphological data suggest weak phylogeographic structure in the fairy shrimp Streptocephalus torvicornis (Branchiopoda, Anostraca). Hydrobiologia 801, 21–32 (2017).CAS 

Google Scholar 
66.Rogers, D. C. Larger hatching fractions in avian dispersed anostracan eggs (Branchiopoda). J. Crustac. Biol. 34, 135–143 (2014).
Google Scholar 
67.Angeler, D. G., Viedma, O., Sánchez-Carrillo, S. & Alvarez-Cobelas, M. Conservation issues of temporary wetland Branchiopoda (Anostraca, Notostraca: Crustacea) in a semiarid agricultural landscape: What spatial scales are relevant?. Biol. Cons. 141, 1224–1234 (2008).
Google Scholar 
68.Horváth, Z., Vad, C. F., Vörös, L. & Boros, E. Distribution and conservation status of fairy shrimps (Crustacea: Anostraca) in the astatic soda pans of the Carpathian basin: the role of local and spatial factors. J. Limnol. 72, 103–116 (2013).
Google Scholar 
69.Svensson, L., Mullarney, K. & Zetterström, D. Collins Bird Guide 2nd edn. (HarperCollins Publishers Ltd., 2009).
Google Scholar 
70.Horváth, Z., Vad, C. F., Vörös, L. & Boros, E. The keystone role of anostracans and copepods in European soda pans during the spring migration of waterbirds. Freshw. Biol. 58, 430–440 (2013).
Google Scholar 
71.Gill, J. L. Ecological impacts of the late Quaternary megaherbivore extinctions. New Phytol. 201, 1163–1169 (2014).PubMed 

Google Scholar 
72.Neretina, A. N. et al. Crustacean remains from the Yuka mammoth raise questions about non-analogue freshwater communities in the Beringian region during the Pleistocene. Sci. Rep. 10, 859 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Chang, D. et al. The evolutionary and phylogeographic history of woolly mammoths: A comprehensive mitogenomic analysis. Sci. Rep. 7, 44585 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Lister, A. M., Sher, A. V., van Essen, H. & Wei, G. The pattern and process of mammoth evolution in Eurasia. Quatern. Int. 126–128, 49–64 (2005).
Google Scholar 
75.Vanschoenwinkel, B. et al. Passive external transport of freshwater invertebrates by elephant and other mud-wallowing mammals in an African savannah habitat. Freshw. Biol. 56, 1606–1619 (2011).
Google Scholar 
76.Waterkeyn, A., Pineau, O., Grillas, P. & Brendonck, L. Invertebrate dispersal by aquatic mammals: A case study with nutria Myocastor coypus (Rodentia, Mammalia) in Southern France. Hydrobiologia 654, 267–271 (2010).
Google Scholar 
77.Belk, D. & Brtek, J. Checklist of the Anostraca. Hydrobiologia 298, 315–353 (1995).
Google Scholar 
78.Marrone, F., Korn, M., Stoch, F., Naselli Flores, L. & Turki, S. Updated checklist and distribution of large branchiopods (Branchiopoda: Anostraca, Notostraca, Spinicaudata) in Tunisia. Biogeogr. J. Integr. Biogeogr. 31, 27–53 (2016).79.Mura, G. & Brtek, J. Revised key to families and genera of the Anostraca with notes on their geographical distribution. Crustaceana 73, 1037–1088 (2000).
Google Scholar 
80.Atashbar, B., Agh, N., Van Stappen, G., Mertens, J. & Beladjal, L. Combined effect of temperature and salinity on hatching characteristics of three fairy shrimp species (Crustacea: Anostraca). J. Limnol. 73, 574–583 (2014).
Google Scholar 
81.Eder, E., Hödl, W. & Gottwald, R. Distribution and phenology of large branchiopods in Austria. Hydrobiologia 359, 13–22 (1997).
Google Scholar 
82.Šćiban, M., Marković, A., Lukić, D. & Miličić, D. Autumn populations of Branchinecta orientalis G. O. Sars, 1903 and Chirocephalus diaphanus Prevost, 1803 (Crustacea, Branchiopoda) in the Central European Lowlands (Pannonian Plain, Serbia). North-West. J. Zool. 10, 435–437 (2014).
Google Scholar 
83.Alonso, M. A survey of the Spanish Euphyllopoda. Miscelania Zool. 9, 179–208 (1985).
Google Scholar 
84.Petkovski, S. On the presence of the genus Branchinecta Verrill, 1869 (Crustacea, Anostraca) in Yugoslavia. Hydrobiologia 226, 17–27 (1991).
Google Scholar 
85.Dimentman, C. The rainpool ecosystems of Israel: Geographical distribution of freshwater Anostraca (Crustacea). Israel J. Ecol. Evol. 30, 1–15 (1981).
Google Scholar 
86.Eid, E. K. New records of large branchiopods from northern Jordan (Crustacea: Branchiopoda). Zool. Middle East 46, 116–117 (2009).
Google Scholar 
87.Mura, G., Ozkutuk, S. R., Aygen, C. & Cottarelli, V. New data on the taxonomy and distribution of anostracan fauna from Turkey. J. Biol. Res. 15, 17–23 (2011).
Google Scholar 
88.Rogers, D. C., Quinney, D. L., Weaver, J. & Olesen, J. A new giant species of predatory fairy shrimp from Idaho, USA (Branchiopoda: Anostraca). J. Crustac. Biol. 26, 1–12 (2006).
Google Scholar 
89.Rodríguez-Flores, P. C., Jiménez-Ruiz, Y., Forró, L., Vörös, J. & García-París, M. Non-congruent geographic patterns of genetic divergence across European species of Branchinecta (Anostraca: Branchinectidae). Hydrobiologia 801, 47–57 (2017).
Google Scholar 
90.Atashbar, B., Agh, N., Van Stappen, G. & Beladjal, L. Diversity and distribution patterns of large branchiopods (Crustacea: Branchiopoda) in temporary pools (Iran). J. Arid. Environ. 111, 27–34 (2014).ADS 

Google Scholar 
91.Belk, D. & Esparza, C. E. Anostraca of the Indian Subcontinent. Hydrobiologia 298, 287–293 (1995).
Google Scholar 
92.Brtek, J. & Thiéry, A. The geographic distribution of the European Branchiopods (Anostraca, Notostraca, Spinicaudata, Laevicaudata). Hydrobiologia 298, 263–280 (1995).
Google Scholar 
93.Horn, W. & Paul, M. Occurrence and distribution of the Eurasian Branchinecta orientalis (Anostraca) in Central Asia (Northwest Mongolia, Uvs Nuur Basin) and in other holarctic areas. Lauterbornia 49, 81–91 (2004).
Google Scholar 
94.Marrone, F., Alonso, M., Pieri, V., Augugliaro, C. & Stoch, F. The crustacean fauna of Bayan Onjuul area (Tov Province, Mongolia) (Crustacea: Branchiopoda, Copepoda, Ostracoda). North West. J. Zool. 11, 288–295 (2015).
Google Scholar 
95.Mura, G. & Takami, G. A. A contribution to the knowledge of the anostracan fauna of Iran. Hydrobiologia 441, 117–121 (2000).
Google Scholar 
96.Naganawa, H. et al. Does the dispersal of fairy shrimps (Branchiopoda, Anostraca) reflect the shifting geographical distribution of freshwaters since the late Mesozoic?. Limnology https://doi.org/10.1007/s10201-019-00589-9 (2019).Article 

Google Scholar 
97.Padhye, S. M., Kulkarni, M. R. & Dumont, H. J. Diversity and zoogeography of the fairy shrimps (Branchiopoda: Anostraca) on the Indian subcontinent. Hydrobiologia 801, 117–128 (2017).
Google Scholar 
98.Petkovski, S. Taksonomsko-morfološka i zoogeografsko-ekološka studija Anostraca (Crustacea: Branchiopoda) jugoslovenskih zemalja. (Prirodno-matematički fakultet, Novi Sad, 1993).99.Pretus, J. L. A commented check-list of the Balearic Branchiopoda (Crustacea). Limnetica 6, 157–164 (1990).
Google Scholar 
100.van den Broeck, M., Waterkeyn, A., Rhazi, L. & Brendonck, L. Distribution, coexistence, and decline of Moroccan large branchiopods. J. Crustacean Biol. 35, 355–365 (2015).
Google Scholar 
101.Hijmans, R. J., Philips, S., Leathwick, J. & Elith, J. Package ‘dismo’. 9, 1–68 (2017).102.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. (2014).103.Hijmans, R. J., Cameron, S. E. & Parra, J. L. Climate Date from Worldclim (2004).104.Alfonso, G. & Marrone, F. Branchiopoda Anostraca, Notostraca, Spinicaudata. In Checklist of the Italian fauna (in press).105.Defaye, D., Rabet, N. & Thiéry, A. Atlas et bibliographie des crustaces branchiopodes (Anostraca, Notostraca, Spinicaudata) de France metropolitaine. Collection patrimoines naturels (1998).106.Song, H., Buhay, J. E., Whiting, M. F. & Crandall, K. A. Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. PNAS 105, 13486–13491 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
107.Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acids Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
108.Aguilar, A. et al. High intraspecific genetic divergence in the versatile fairy shrimp Branchinecta lindahli with a comment on cryptic species in the genus Branchinecta (Crustacea: Anostraca). Hydrobiologia 801, 59–69 (2017).
Google Scholar 
109.Jeffery, N. W., Elías-Gutiérrez, M. & Adamowicz, S. J. Species diversity and phylogeographical affinities of the Branchiopoda (Crustacea) of Churchill, Manitoba, Canada. PLoS ONE 6, e18364 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
110.Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).CAS 
PubMed 

Google Scholar 
111.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
112.Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).113.Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
PubMed 

Google Scholar 
114.Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
115.Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).116.Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
117.Leigh, J. W. & Bryant, D. popart: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Google Scholar 
118.Xia, X. & Kumar, S. DAMBE7: New and improved tools for data analysis in molecular biology and evolution. Mol. Biol. Evol. 35, 1550–1552 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
119.Xia, X. & Lemey, P. Assessing substitution saturation with DAMBE. In The phylogenetic Handbook 615–630 (Cambridge University Press, 2009).120.Xia, X., Xie, Z., Salemi, M., Chen, L. & Wang, Y. An index of substitution saturation and its application. Mol. Phylogenet. Evol. 26, 1–7 (2003).CAS 
PubMed 

Google Scholar 
121.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
PubMed 

Google Scholar 
122.Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).CAS 

Google Scholar 
123.Nychka, D. et al. fields: Tools for Spatial Data (2020).124.Oksanen, J. et al. vegan: Community ecology package. – R package ver. 2.0-4. http://CRAN.R-project.org/package=vegan. (2012). More

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

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

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