Philippa Kaur

CORRESPONDENCE
11 January 2022

EU Nature Restoration Law needs ambitious and binding targets

Kris Decleer

 ORCID: http://orcid.org/0000-0001-9621-8925

0
,

Jordi Cortina-Segarra

 ORCID: http://orcid.org/0000-0002-8231-3793

1
&

Aveliina Helm

 ORCID: http://orcid.org/0000-0003-2338-4564

2

Kris Decleer

Research Institute for Nature and Forest, Brussels, Belgium.

View author publications

You can also search for this author in PubMed
 Google Scholar

Jordi Cortina-Segarra

University of Alicante, Alicante, Spain.

View author publications

You can also search for this author in PubMed
 Google Scholar

Aveliina Helm

University of Tartu, Tartu, Estonia.

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Initiatives by the European Commission to restore the continent’s degraded areas (J. Cortina-Segarra et al. Nature 535, 231; 2016) have proved disappointing. As the United Nations Decade on Ecosystem Restoration gathers momentum, the commission is preparing a law that has legally binding targets. To underscore the urgency, some 1,400 European scientists and 30 expert networks and institutions have signed a declaration by the Society for Ecological Restoration Europe (see go.nature.com/3st6k88).

Access options

Access through your institution

Change institution

Buy or subscribe

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

Additional access options:

Log in

Learn about institutional subscriptions

Nature 601, 191 (2022)
doi: https://doi.org/10.1038/d41586-022-00011-y

Competing Interests
The authors declare no competing interests.

Related Articles

See more letters to the editor

Subjects

Law

Ecology

Environmental sciences

Latest on:

Law

Elizabeth Holmes verdict: researchers share lessons for science
News 04 JAN 22

What Sci-Hub’s latest court battle means for research
News 13 DEC 21

Science agency on trial following deadly White Island volcano eruption
News 06 OCT 21

Ecology

Wind power versus wildlife: root mitigation in evidence
Correspondence 11 JAN 22

Two million species catalogued by 500 experts
Correspondence 11 JAN 22

From the archive
News & Views 11 JAN 22

Environmental sciences

Rapid microbial methanogenesis during CO2 storage in hydrocarbon reservoirs
Article 22 DEC 21

Half measures in One Health fail people and the environment
Correspondence 21 DEC 21

Uncovering global-scale risks from commercial chemicals in air
Article 15 DEC 21

Jobs

W1 Professor (Tenure Track) of Molecular Plant Biologyr

University of Tübingen (Uni Tübingen)
Tübingen, Baden-Württemberg, Germany

Early Career Fellowship Programme 2021

Human Technopole
Milano, Italy

A tenure-track position in the field of ecology/evolutionary biology/conservation biology

Ben-Gurion University of the Negev (BGU)
Midrashet Ben-Gurion, Israel

Leaders of Independent Junior Research Group (JRG) 2022

Asia Pacific Center for Theoretical Physics (APCTP)
Pohang, Korea, South More

1.Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).PubMed 
PubMed Central 

Google Scholar 
2.Robinson, L. M. et al. Pushing the limits in marine species distribution modelling: Lessons from the land present challenges and opportunities. Glob. Ecol. Biogeogr. 20, 789–802 (2011).
Google Scholar 
3.Yates, K. L. et al. Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol. 33, 790–802 (2018).PubMed 

Google Scholar 
4.Mayor, S. J., Schneider, D. C., Schaefer, J. A. & Mahoney, S. P. Habitat selection at multiple scales. Ecoscience 16, 238–247 (2009).
Google Scholar 
5.Mannocci, L. et al. Temporal resolutions in species distribution models of highly mobile marine animals: Recommendations for ecologists and managers. Divers. Distrib. 23, 1098–1109 (2017).
Google Scholar 
6.Sequeira, A. M. M., Bouchet, P. J., Yates, K. L., Mengersen, K. & Caley, M. J. Transferring biodiversity models for conservation: Opportunities and challenges. Methods Ecol. Evol. 9, 1250–1264 (2018).
Google Scholar 
7.Cleguer, C., Grech, A., Garrigue, C. & Marsh, H. Spatial mismatch between marine protected areas and dugongs in New Caledonia. Biol. Conserv. 184, 154–162 (2015).
Google Scholar 
8.Hays, G. C. et al. Translating marine animal tracking data into conservation policy and management. Trends Ecol. Evol. 34, 459–473 (2019).PubMed 

Google Scholar 
9.Hays, G. C. et al. Key questions in marine megafauna movement ecology. Trends Ecol. Evol. 31, 463–475 (2016).PubMed 

Google Scholar 
10.Hazen, E. L. et al. WhaleWatch: A dynamic management tool for predicting blue whale density in the California Current. J. Appl. Ecol. 54, 1415–1428 (2017).
Google Scholar 
11.Sequeira, A. M. M. et al. Overhauling ocean spatial planning to improve marine megafauna conservation. Front. Mar. Sci. 6, 639 (2019).
Google Scholar 
12.Marsh, H. & Sobtzick, S. Dugong dugon. In The IUCN RedList of Threatened Species (2019:e.T6909A160756767). https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T6909A160756767.en. Accessed November 2020 (2019).13.Marsh, H., O’Shea, T. J. & Reynolds, J. E. I. Ecology and Conservation of the Sirenia: Dugongs and Manatees Vol. 18 (Cambridge University Press, 2011).
Google Scholar 
14.Pimiento, C. et al. Functional diversity of marine megafauna in the Anthropocene. Sci. Adv. 6, eaay7650 (2020).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
15.Nowicki, R. J., Thomson, J. A., Fourqurean, J. W., Wirsing, A. J. & Heithaus, M. R. Loss of predation risk from apex predators can exacerbate marine tropicalization caused by extreme climatic events. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13424 (2021).Article 
PubMed 

Google Scholar 
16.Wirsing, A. J., Heithaus, M. R. & Dill, L. M. Living on the edge: Dugongs prefer to forage in microhabitats that allow escape from rather than avoidance of predators. Anim. Behav. 74, 93–101 (2007).
Google Scholar 
17.Aragones, L. V., Lawler, I. R., Foley, W. J. & Marsh, H. Dugong grazing and turtle cropping: Grazing optimization in tropical seagrass systems?. Oecologia 149, 635–647 (2006).PubMed 
ADS 

Google Scholar 
18.Preen, A. Impacts of dugong foraging on seagrass habitats: Observational and experimental evidence for cultivation grazing. Mar. Ecol. Prog. Ser. 124, 201–213 (1995).ADS 

Google Scholar 
19.Unsworth, R. K. F., Collier, C. J., Waycott, M., Mckenzie, L. J. & Cullen-Unsworth, L. C. A framework for the resilience of seagrass ecosystems. Mar. Pollut. Bull. 100, 34–46 (2015).CAS 
PubMed 

Google Scholar 
20.Tol, S. J. et al. Long distance biotic dispersal of tropical seagrass seeds by marine mega-herbivores. Sci. Rep. 7, 1–8 (2017).CAS 
ADS 

Google Scholar 
21.Ponnampalam, L. S., Fairul Izmal, J. H., Adulyanukosol, K., Ooi, J. L. S. & Reynolds, J. E. Aligning conservation and research priorities for proactive species and habitat management: The case of dugongs Dugong dugon in Johor, Malaysia. Oryx 49, 743–749 (2015).
Google Scholar 
22.Preen, A. The Status and Conservation of Dugongs in the Arabian Region. Saudi Arabia, Meteorological and Environmental Protection Administration (MEPA), Coastal and Marine Management Series. Report, No 10, (1989).23.Preen, A. Distribution, abundance and conservation status of dugongs and dolphins in the southern and western Arabian Gulf. Biol. Conserv. 118, 205–218 (2004).
Google Scholar 
24.Findlay, K. P., Cockcroft, V. G. & Guissamulo, A. T. Dugong abundance and distribution in the Bazaruto Archipelago, Mozambique. Afr. J. Mar. Sci. 33, 441–452 (2011).
Google Scholar 
25.Pilcher, N. J. et al. A low-cost solution for documenting distribution and abundance of endangered marine fauna and impacts from fisheries. PLoS ONE 12, e0190021 (2017).PubMed 
PubMed Central 

Google Scholar 
26.Hashim, M. et al. Using fisher knowledge, mapping population, habitat suitability and risk for the conservation of dugongs in Johor Straits of Malaysia. Mar. Policy 78, 18–25 (2017).
Google Scholar 
27.Bayliss, P. & Hutton, M. Integrating Indigenous Knowledge and Survey Techniques to Develop a Baseline for Dugong (Dugong dugon) Management in the Kimberley. Final Report of project 1.2.5 of the Kimberley Marine Research Program Node of the Western Australian Marine Science Institution, WAMSI (2017).28.Campbell, R., Holley, D. & Bardi-Jawi Ranger Group. Movement Behaviours and Habitat Usage of West Kimberley Dugongs : A Community Based Approach Final Report to the National Marine Mammal Centre November 2010. Final Report to the National Marine Mammal Centre (2010).29.Cleguer, C. et al. Working with the Community to Understand Use of Space by Dugongs and Green Turtles in Torres Strait. Final Report to the Mura Badulgal Representative NativeTitle Body Corporate and the Department of the Environment, National Environment Science Program TropicalWater Quality Hub (James Cook University, Townsville, 2016).30.Gredzens, C. et al. Satellite tracking of sympatric marine megafauna can inform the biological basis for species co-management. PLoS ONE 9, e98944 (2014).PubMed 
PubMed Central 
ADS 

Google Scholar 
31.Holley, D. Movement Patterns and Habitat Usage of Shark Bay Dugongs. MSc thesis, Edith Cowan University, Perth. https://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1070&context=theses (2006).32.Sheppard, J. et al. Movement heterogeneity of dugongs, Dugong dugon (Müller), over large spatial scales. J. Exp. Mar. Bio. Ecol. 334, 64–83 (2006).
Google Scholar 
33.Hagihara, R. et al. Improving the Estimates of Abundance of Dugongs and Large Immature and Adult-Sized Green Turtles in Western and Central Torres Strait. Report to the National Environmental Science Programme (Reef and Rainforest Research Centre Limited, Cairns 2016).34.De Iongh, H. H., Langeveld, P. & Van Der Wal, M. Movement and home ranges of dugongs around the Lease Islands, East Indonesia. Mar. Ecol. 19, 179–193 (1998).ADS 

Google Scholar 
35.Cleguer, C., Garrigue, C. & Marsh, H. Dugong (Dugong dugon) movements and habitat use in a coral reef lagoonal ecosystem. Endanger. Species Res. 43, 167–181 (2020).
Google Scholar 
36.Sheppard, J., Jones, R. E., Marsh, H. & Lawler, I. R. Effects of tidal and diel cycles on dugong habitat use. J. Wildl. Manag. 73, 45–59 (2009).
Google Scholar 
37.Sheppard, J., Marsh, H., Jones, R. E. & Lawler, I. R. Dugong habitat use in relation to seagrass nutrients, tides, and diel cycles. Mar. Mammal Sci. 26, 855–879 (2010).
Google Scholar 
38.Zeh, D. R. et al. Evidence of behavioural thermoregulation by dugongs at the high latitude limit to their range in eastern Australia. J. Exp. Mar. Bio. Ecol. 508, 27–34 (2018).
Google Scholar 
39.UNESCO. Lagoons of New Caledonia: Reef Diversity and Associated Ecosystems (U.W.H. Centre, 2009).40.Payri, C. New Caledonia: World of Corals (IRD Editions/Solaris, Marseille/Nouméa, 2018).41.Oremus, M., Garrigue, C. & Cleguer, C. Isolement et diversité génétique des dugongs de Nouvelle-Calédonie (Unpublished Report, 2011).42.Oremus, M., Garrigue, C. & Cleguer, C. Etude génétique complémentaire sur le statut de la population de dugong de Nouvelle-Calédonie (Unpublished Report, 2015).43.Garrigue, C., Patenaude, N. & Marsh, H. Distribution and abundance of the dugong in New Caledonia, southwest Pacific. Mar. Mammal Sci. 24, 81–90 (2008).
Google Scholar 
44.Cleguer, C. et al. Drivers of change in the relative abundance of dugongs in New Caledonia. Wildl. Res. 44, 365–376 (2017).
Google Scholar 
45.Gonson, C. et al. Decadal increase in the number of recreational users is concentrated in no-take marine reserves. Mar. Pollut. Bull. 107, 144–154 (2016).CAS 
PubMed 

Google Scholar 
46.Fraser, K. C. et al. Tracking the conservation promise of movement ecology. Front. Ecol. Evol. 6, 150 (2018).
Google Scholar 
47.Hussey, N. E. et al. Aquatic animal telemetry: A panoramic window into the underwater world. Science (80-.) 348, 1255642 (2015).
Google Scholar 
48.Hagihara, R. et al. Compensating for geographic variation in detection probability with water depth improves abundance estimates of coastal marine megafauna. PLoS ONE 13, e0191476 (2018).PubMed 
PubMed Central 

Google Scholar 
49.Sequeira, A. M. M. et al. The importance of sample size in marine megafauna tagging studies. Ecol. Appl. 29, e01947 (2019).CAS 
PubMed 

Google Scholar 
50.Derville, S., Constantine, R., Baker, C. S., Oremus, M. & Torres, L. G. Environmental correlates of nearshore habitat distribution by the Critically Endangered Maui dolphin. Mar. Ecol. Prog. Ser. 551, 261–275 (2016).CAS 
ADS 

Google Scholar 
51.Derville, S., Torres, L. G., Iovan, C. & Garrigue, C. Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Divers. Distrib. 24, 1657–1673 (2018).
Google Scholar 
52.Pinto, C. et al. Using individual tracking data to validate the predictions of species distribution models. Divers. Distrib. 22, 682–693 (2016).
Google Scholar 
53.Tingley, M. W., Wilkerson, R. L., Howell, C. A. & Siegel, R. B. An integrated occupancy and space-use model to predict abundance of imperfectly detected, territorial vertebrates. Methods Ecol. Evol. 7, 508–517 (2016).
Google Scholar 
54.Roberts, J. J. et al. Habitat-based cetacean density models for the U.S. Atlantic and Gulf of Mexico. Sci. Rep. 6, 22615 (2016).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
55.Mannocci, L., Roberts, J. J., Pedersen, E. J. & Halpin, P. N. Geographical differences in habitat relationships of cetaceans across an ocean basin. Ecography (Cop.) 43, 1250–1259 (2020).
Google Scholar 
56.Wirsing, A. J., Heithaus, M. R. & Dill, L. M. Fear factor: Do dugongs (Dugong dugon) trade food for safety from tiger sharks (Galeocerdo cuvier)?. Oecologia 153, 1031–1040 (2007).PubMed 
ADS 

Google Scholar 
57.Jollit, I. Spatialisation des activités humaines et aide à la décision pour une gestion durable des écosystèmes coralliens: la pêche plaisancière dans le lagon sud-ouest de la Nouvelle-Calédonie. PhD dissertation, Université de la Nouvelle-Calédonie (2010).58.Maitland, R. N., Lawler, I. R. & Sheppard, J. K. Assessing the risk of boat strike on Dugongs Dugong dugon at Burrum Heads, Queensland, Australia. Pac. Conserv. Biol. 12, 321–326 (2006).
Google Scholar 
59.Preen, A. Interactions Between Dugongs and Seagrasses in a Subtropical Environment. PhD dissertation, James Cook University, Townsville, Australia (1992).60.Hodgson, A. Dugong Behaviour and Responses to Human Influences. PhD dissertation, James Cook University (2004).61.Edwards, H. H. et al. Influence of manatees’ diving on their risk of collision with watercraft. PLoS ONE 11, e0151450 (2016).PubMed 
PubMed Central 

Google Scholar 
62.Rycyk, A. M. et al. Manatee behavioral response to boats. Mar. Mamm. Sci. 34, 924–962 (2018).
Google Scholar 
63.Garrigue, C. Macrophyte associations on the soft bottoms of the South-West Lagoon of New Caledonia: Description, structure and biomass. Bot. Mar. 38, 481–492 (1995).
Google Scholar 
64.Andréfouët, S. et al. Nation-wide hierarchical and spatially-explicit framework to characterize seagrass meadows in the Indo-Pacific: Example application to New Caledonia. Mar. Pollut. Bull. 173, 113036 (2021).PubMed 

Google Scholar 
65.Cleguer, C. Informing Dugong Conservation at Several Spatial and Temporal Scales in New Caledonia. PhD dissertation, James Cook University (2015).66.Anderson, P. K. Dugongs of Shark Bay, Australia–Seasonal migration, water temperature and forage. Natl. Geogr. Res. 2, 473–490 (1986).
Google Scholar 
67.Heithaus, M. R. & Dill, L. M. Food availability and tiger shark predation risk influence bottlenose dolphin habitat use. Ecology 83, 480–491 (2002).
Google Scholar 
68.Roger, J. Données bathymétriques et topographiques de Nouvelle-Calédonie : Réalisation d’un MNT terre-mer pour l’étude de l’aléa tsunami (projet TSUCAL). (Institut de Recherche pour le Développement, 2020).69.Andréfouët, S. et al. Global assessment of modern coral reef extent and diversity for regional science and management applications: A view from space. In Opening Talk, 10th International Coral Reef Symposium (eds Suzuki, Y. et al.) 1732–1745 (Japanese Coral Reef Society, 2006).
Google Scholar 
70.Andréfouët, S., Cabioch, G., Flamand, B. & Pelletier, B. A reappraisal of the diversity of geomorphological and genetic processes of New Caledonian coral reefs: A synthesis from optical remote sensing, coring and acoustic multibeam observations. Coral Reefs 28, 691–707 (2009).ADS 

Google Scholar 
71.Rousseeuw, P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).MATH 

Google Scholar 
72.Marsh, H. & Rathbun, G. B. Development and application of conventional and satellite radio tracking techniques for studying dugong movements and habitat use. Aust. Wildl. Res. 17, 83–100 (1990).
Google Scholar 
73.Lanyon, J. M. et al. A method for capturing dugongs (Dugong dugong) in open water. Aquat. Mamm. 32, 196–201 (2006).
Google Scholar 
74.Cleguer, C., Derville, S., Kelly, N., Lambourne, R. & Garrigue, C. Programme SIREN : Suivi à fine échelle de la fréquentation et du déplacement des dugongs dans la zone Voh-Koné- Pouembout , pour une gestion améliorée de l’espèce Rapport final (Technical report prepared for Koniambo Nickel SAS, 2020).75.Johnson, D., London, J., Lea, M. A. & Durban, J. Continuous-time correlated random walk model for animal telemetry data. Ecology 89, 1208–1215 (2008).PubMed 
PubMed Central 

Google Scholar 
76.Barraquand, F. & Benhamou, S. Animal movements in heterogeneous landscapes: Identifying profitable places and homogeneous movements bouts. Ecology 89, 3336–3348 (2008).PubMed 

Google Scholar 
77.Hyndman, R. et al. Forecast: Forecasting Functions for Time Series and Linear Models. https://pkg.robjhyndman.com/forecast/ (R package version 8.15, 2021).78.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. nlme: Linear and Nonlinear Mixed Effects Models. https://CRAN.R-project.org/package=nlme (R package version 3.1–152, 2021).79.Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models, volume 43 of Monographs on Statistics and Applied Probability (Chapman and Hall/CRC, 1990).
Google Scholar 
80.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. 73, 3–36 (2011).MathSciNet 
MATH 

Google Scholar 
81.Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press, 2017).MATH 

Google Scholar 
82.Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).MathSciNet 
MATH 

Google Scholar 
83.Cox, T. & Schepers, L. Tides: Quasi-periodic Time Series Characteristics. https://CRAN.R-project.org/package=Tides (R package version 2.1., 2018).84.Boldina, I. & Beninger, P. G. Strengthening statistical usage in marine ecology: Linear regression. J. Exp. Mar. Bio. Ecol. 474, 81–91 (2016).
Google Scholar 
85.Russell, L. emmeans: Estimated Marginal Means, aka Least-Squares Means. https://CRAN.R-project.org/package=emmeans (R package version 1.4.7., 2020).86.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020).
Google Scholar  More

Research model and softwareCLIMEX modelFAW growth and development are primarily related to climate conditions, especially temperature patterns17. The current study used CLIMEX (version 4)42, a semi-mechanistic niche modeling platform, to project FAW distribution in relation to climate. The model parameters that describe the species’ response to climate were overlaid onto FAW occurrence data and climate data to project the species’ potential global distribution. Briefly, the annual growth index (GI) was used to describe the potential for FAW population growth during favorable climatic conditions, while stress indices (SI: cold, wet, hot, and dry) and interaction stresses (SX: hot-dry, hot-wet, cold-dry, and cold-wet) (Table 1) were applied to describe the probability that FAW populations could survive unfavorable conditions. The Ecoclimatic index (EI) was derived from a combination of GI, SI, and SX indices to provide an overall annual index of climatic suitability on a scale of 0–10042. An EI value of 0 indicates that the location is not suitable for the long-term survival of the species, whereas an EI value of 100 indicates maximum climatic suitability comparable to conditions in incubators. EI values of more than 30 indicate the optimal climate for a species. In this study, the climatic suitability was classified into four arbitrary categories; unsuitable for EI = 0, marginal for 0  More

1.Stein, E. D., Cohen, Y. & Winer, A. M. Environmental distribution and transformation of mercury compounds. Crit. Rev. Environ. Sci. Technol. 26, 1–43 (1996).CAS 
Article 

Google Scholar 
2.Ciccarelli, C. et al. Assessment of sampling methods about level of mercury in fish. Ital. J. Food Saf. 8, 153–157 (2019).
Google Scholar 
3.Ditri, F. M. Mercury contamination: What we have learned since Minamata. Environ. Monit. Assess. 19, 165–182 (1991).CAS 
Article 

Google Scholar 
4.Monteiro, L. R. & Furness, R. W. Seabirds as monitors of mercury in the marine environment. Water Air Soil Pollut. 80, 851–870 (1995).CAS 
Article 
ADS 

Google Scholar 
5.Pitter, P. In Hydrochemie 5th edn (ed. Pitter, P.) (VSCHT Praha, 2015).
Google Scholar 
6.Hylander, L. D. & Meili, M. 500 years of mercury production: Global annual inventory by region until 2000 and associated emissions. Sci. Total. Environ. 304, 13–27 (2003).CAS 
Article 
ADS 

Google Scholar 
7.Pacyna, E. G. et al. Global emission of mercury to the atmosphere from anthropogenic sources in 2005 and projections to 2020. Atmos. Environ. 44, 2487–2499 (2010).CAS 
Article 
ADS 

Google Scholar 
8.Pai, P., Niemi, D. & Powers, B. A North American inventory of anthropogenic mercury emissions. Fuel Process. Technol. 65, 101–115 (2000).Article 

Google Scholar 
9.Wang, Q. R., Kim, D., Dionysiou, D. D., Sorial, G. A. & Timberlake, D. Sources and remediation for mercury contamination in aquatic systems: A literature review. Environ. Pollut. 131, 323–336 (2004).Article 

Google Scholar 
10.Buck, D. G. et al. A global-scale assessment of fish mercury concentrations and the identification of biological hotspots. Sci. Total Environ. 687, 956–966 (2019).CAS 
Article 
ADS 

Google Scholar 
11.Gentes, S. et al. Application of European water framework directive: Identification reference sites and bioindicator fish species for mercury in tropical freshwater ecosystems (French Guiana). Ecol. Indic. 106, 105468. https://doi.org/10.1016/j.ecolind.2019.105468 (2019).CAS 
Article 

Google Scholar 
12.Thomas, S. M. et al. Climate and landscape conditions indirectly affect fish mercury levels by altering lake water chemistry and fish size. Environ. Res. 188, 109750. https://doi.org/10.1016/j.envres.2020.109750 (2020).CAS 
Article 
PubMed 

Google Scholar 
13.Zupo, V. et al. Mercury accumulation in freshwater and marine fish from the wild and from aquaculture ponds. Environ. Pollut. 255, 112975. https://doi.org/10.1016/j.envpol.2019.112975 (2019).CAS 
Article 
PubMed 

Google Scholar 
14.Zhang, J. L. et al. Health risk assessment of heavy metals in Cyprinus carpio (Cyprinidae) from the upper Mekong river. Environ. Sci. Pollut. Res. 26, 9490–9499 (2019).CAS 
Article 

Google Scholar 
15.Cerna, M. Opatreni mezinarodnich instituci a Ceske republiky k omezovani rizika znecistovani zivotniho prostredi rtuti. Chem. Listy. 98, 916–921 (2004) ((Article in Czech)).CAS 

Google Scholar 
16.Janouskova, D. & Svehla, J. Mercury concentrations in fish tissues in the water reservoir Rimov, South Bohemia. Crop Sci. 19, 43–48 (2002).
Google Scholar 
17.Purba, J. S., Silalahi, J. & Haro, G. Analysis of mercury in fish, North Sumatera, Indonesia by atomic absorption spectrophotometer. Asian J. Pharm. 8, 21–25 (2020).CAS 
Article 

Google Scholar 
18.Willacker, J. J., Eagles-Smith, C. A. & Blazer, V. S. Mercury bioaccumulation in freshwater fishes of the Chesapeake Bay watershed. Ecotoxicology 29, 459484 (2020).Article 

Google Scholar 
19.Regulation (EU) 2017/852 of European Parliament and of the council of 17 May 2017 on mercury, and repealing Regulation (EC) No 1102/2008. Official Journal of the European Union.20.European Commission. The EU Fish Market. https://www.eumofa.eu/documents/20178/415635/EN_The+EU+fish+market_2020.pdf (2020).21.Nebesky, V., Policar, T., Blecha, M., Kristan, J. & Svacina, P. Trends in import and export of fishery products in the Czech Republic during 2010–2015. Aquacult. Int. 24, 1657–1668 (2016).Article 

Google Scholar 
22.FAO. Fisheries & Aquaculture—National Aquaculture Sector Overview—Czech Republic. http://www.fao.org/fishery/countrysector/naso_czechrepublic/en (accessed April 24 April 2021) (2021).23.Rakmanikhah, Z., Esmaili-Sari, A. & Bahramifar, N. Total mercury and methylmercury concentrations in native and invasive fish species in Shadegan International Wetland, Iran, and health risk assessment. Environ. Sci. Pollut. Res. 27, 6765–6773 (2020).Article 

Google Scholar 
24.Celechovska, O., Svobodova, Z., Zlabek, V. & Macharackova, B. Distribution of metals in tissues of the common carp (Cyprinus carpio L.). Acta Vet. Brno 76, 93–100 (2007).Article 

Google Scholar 
25.Cerveny, D. et al. Fish fin-clips as non-lethal approach for biomonitoring of mercury contamination in aquatic environments and human health risk assessment. Chemosphere 163, 290–295 (2016).CAS 
Article 
ADS 

Google Scholar 
26.WHO. Evaluations of the Joint FAO/WHO Expert Committee on Food Additives (JECFA). https://apps.who.int/food-additives-contaminants-jecfa-database/search.aspx.27.Kannan, K. et al. Distribution of total mercury and methyl mercury in water, sediment, and fish from south Florida estuaries. Arch. Environ. Con. Tox. 34, 109–118 (1998).CAS 
Article 

Google Scholar 
28.US EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories Documents. Volume 2: Risk Assessment and Fish Consumption Limits, Third Edition. https://www.epa.gov/fish-tech/guidance-assessing-chemical-contaminant-data-use-fish-advisories-documents (accessed 8 May 2021) (2000).29.Ministry of Agriculture of the Czech Republic. Situacni a vyhledova zprava—Ryby. http://eagri.cz/public/web/file/666957/Ryby_2020_web.pdf (accessed 8 May 2021, in Czech) (2020).30.Novotna, K., Svobodova, Z., Harustiakova, D. & Mikula, P. Spatial and temporal trends in contamination of the Czech part of the Elbe River by mercury between 1991 and 2016. Bull. Environ. Contam. Toxicol. 105, 750–757 (2020).CAS 
Article 

Google Scholar 
31.Raldua, D., Diez, S., Bayona, J. M. & Barcelo, D. Mercury levels and liver pathology in feral fish living in the vicinity of a mercury cell chlor-alkali factory. Chemosphere 66, 1217–1225 (2007).CAS 
Article 
ADS 

Google Scholar 
32.Squadrone, S. et al. Heavy metals distribution in muscle, liver, kidney and gill of European catfish (Silurus glanis) from Italian rivers. Chemosphere 90, 358–365 (2013).CAS 
Article 
ADS 

Google Scholar 
33.Cerveny, D. et al. Contamination of fish in important fishing grounds of the Czech Republic. Ecotoxicol. Environ. Saf. 109, 101–109 (2014).CAS 
Article 

Google Scholar 
34.Marsalek, P., Svobodova, Z. & Randak, T. The content of total mercury and methylmercury in common carp from selected Czech ponds. Aquac. Int. 15, 299–304 (2007).CAS 
Article 

Google Scholar 
35.Vicarova, P., Docekalova, H., Ridoskova, A. & Pelcova, P. Heavy metals in the common carp (Cyprinus carpio L.) from three reservoirs in the Czech Republic. Czech J. Food Sci. 34, 422–428 (2016).CAS 
Article 

Google Scholar 
36.Akerblom, S., Bignert, A., Meili, M., Sonesten, L. & Sundbom, M. Half a century of changing mercury levels in Swedish freshwater fish. Ambio 43, 91–103 (2014).Article 

Google Scholar 
37.Dvorak, P., Andreji, J., Mraz, J. & Dvorakova Liskova, Z. Concentration of heavy and toxic metals in fish and sediments from the Morava river basin, Czech Republic. Neuroendocrinol. Lett. 36, 126–132 (2015).CAS 
PubMed 

Google Scholar 
38.Dusek, L. et al. Bioaccumulation of mercury in muscle tissue of fish in the Elbe River (Czech Republic): Maultispecies monitoring study 1991–1996. Ecotoxicol. Environ. Saf. 61, 256–267 (2005).CAS 
Article 

Google Scholar 
39.Marsalek, P., Svobodova, Z. & Randak, T. Total mercury and methylmercury contamination in fish from various sites along the Elbe River. Acta Vet. Brno. 75, 579–585 (2006).CAS 
Article 

Google Scholar 
40.Wang, X. & Wang, W. X. The three ‘B’ of mercury in China: Bioaccumulation, biodynamics and biotransformation. Environ. Pollut. 250, 216–232 (2019).CAS 
Article 

Google Scholar 
41.Jankovska, I. et al. Importance of fish gender as a factor in environmental monitoring of mercury. Environ. Sci. Pollut. Res. 21, 6239–6242 (2014).CAS 
Article 

Google Scholar 
42.Carrasco, L. et al. Patterns of mercury and methylmercury bioaccumulation in fish species downstream of a long-term mercury-contaminated site in the lower Ebro River (NE Spain). Chemosphere 84, 1642–1649 (2011).CAS 
Article 
ADS 

Google Scholar 
43.Havelkova, M., Dusek, L., Nemethova, D., Poleszczuk, G. & Svobodova, Z. Comparison of mercury distribution between liver and muscle: A biomonitoring of fish from lightly and heavily contaminated localities. Sensors. 8, 4095–4109 (2008).CAS 
Article 
ADS 

Google Scholar 
44.Kruzikova, K. et al. The correlation between fish mercury liver/muscle ratio and high and low levels of mercury contamination in Czech localities. Int. J. Electrochem. Sc. 8, 45–56 (2013).CAS 

Google Scholar 
45.Kensova, R., Kruzikova, K. & Svobodova, Z. Mercury speciation and safety of fish from important fishing locations in the Czech Republic. Czech J. Food Sci. 30, 276–284 (2012).CAS 
Article 

Google Scholar 
46.European Commission. Commission Regulation 1881/2006 Setting Maximum Levels of Certain Contaminants in Foodstuffs. https://eur-lex.europa.eu/ (accessed 2 May 2021) (2006). More

Deer capture and samplingWe captured 100 mule deer (54 females, 46 males) during November 2018–February 2019, avoiding capture and sampling of juveniles. We attempted to distribute captures throughout the ~23 km2 study area described by Miller et al.5 to minimize spatial disparities in comparing contemporary and past data, and to assure marks were widely distributed for December ground counts to estimate deer abundance5,35,36. Field and sampling methods generally followed those used elsewhere5,31,37. Field procedures were reviewed and approved by the CPW Animal Care and Use Committee (file 14–2018).We pursued deer on foot and darted them opportunistically, delivering sedative combinations intramuscularly via projectile syringe. Premixed immobilization drug combinations included either nalbuphine (N; 0.9 mg/kg) or butorphanol (B; 0.5 mg/kg) combined with azaperone (A; 0.2 mg/kg) and medetomidine (M; 0.2 mg/kg)38, with standard total doses for respective combinations based on an estimated mass of 70 kg (average drug volume per animal was 1.3 ml NMA, 1.4 ml BAM). We collected rectal mucosa biopsies to determine CWD infection status37. We also collected whole blood and marked all deer with individually identifiable ear tags and some with telemetry (n = 51) or visual identification (n = 12) collars. Ages were estimated to the nearest year via tooth replacement and wear patterns39; observers used a pocket reference guide in the field to help assure consistency. To antagonize sedation upon completion of handling and sampling, each deer received 5 mg atipamezole/mg M administered, injected intramuscularly.Prion diagnosticsFormalin-fixed tissue biopsies were processed and analyzed by immunohistochemistry (IHC) at the Colorado State University Veterinary Diagnostic Laboratory (Fort Collins, Colorado USA; CSUVDL) for evidence of CWD-associated prion (PrPCWD) accumulations using monoclonal antibody F99/97.6.1 (VMRD Inc., Pullman, Washington, USA)40 and standard IHC methods24,37,41, except that the CSUVDL’s IHC staining machine (Leica Microsystems Inc., Buffalo Grove, Illinois, USA) was different from that used in earlier studies (Ventana Medical Systems, Oro Valley, Arizona, USA). Biopsies were evaluated microscopically and classified as positive (infected) or not detected (negative) based on PrPCWD presence or absence; the same pathologist (T. R. Spraker) read biopsies for both the current and prior5 studies.We included only data from deer with biopsies providing ≥3 lymphoid follicles in analyses involving infection status in order to maintain a relatively high (≥90%) probability of detecting infected individuals24. Two animals with low follicle counts that died shortly after capture were excepted by substituting postmortem IHC results. Limiting the acceptable follicle count excluded seven females (two 225SS, five 225SF) and two males (one 225SS, one 225SF) from some analyses. One male deer was 225FF and one female deer was missing a blood sample and thus not assigned to a PRNP gene group; these two individuals also were excluded from some analyses (e.g., Table 1).
PRNP genotypingWe used DNA extracted from whole blood buffy coat aliquots (n = 99) to screen for the presence of sequences at PRNP gene codon 225 that encode for serine (S) and/or phenylalanine (F) in the mature prion polypeptide, classifying individuals as 225SS, 225SF, or 225FF16,36,42. Methods generally followed those described by Jewell et al.16. Briefly, we extracted DNA using the DNeasy® blood and tissue kit (Qiagen, Valenica, California, USA). We amplified the complete open reading frame (ORF) plus 25 bp of 5′ flanking sequences and 53 bp of 3′ flanking sequences in the PRNP coding region using polymerase chain reaction (PCR). Purified DNA was combined in a 0.2 ml PCR tube containing a puReTag Ready-To-Go PCR bead (illustra™, GE Healthcare Bio-Sciences Corp, Piscataway, New Jersey, USA). Each PCR bead contained 2.5 units puReTag DNA polymerase, 10 mM Tris-HCI, 50 mM KCl, 1.5 mM MgCl2, 200 µM of each deoxynucleoside triphosphate, and stabilizers, including bovine serum albumin. For each PCR assay, 1 μL of each primer at 200 nM, 22 μL of RNase-free water and 1 μL of approximately 100 ng total genomic DNA was added for a final volume of 25 μL. Primers used for amplification were forward (MD582F, 5′-ACATGGGCATATGATGCTGACACC-3′) and reverse (MD1479RC, 5′-ACTACAGGGCTGCAGGTAGATACT-3′) described by Jewell et al.16. Reactions were thermal-cycled in a PTC 100 (MJ Research) at 94 C for 5 min and then 32 cycles of 94 C for 7 s, 62 C for 15 s, 72 C for 30 s and a final cycle of 72 C for 5 min, and kept at 4 C until inspected for successful amplification by agarose gel electrophoresis. As confirmed by LaCava et al.19, the MD582F and MD1479RC primers developed by Jewell et al.16 specifically amplify the functional PRNP gene ORF, thereby excluding confounding effects that could arise from the presence of a processed pseudogene that occurs in a majority of deer (Odocoileus spp.)42.We used EcoRI restriction digestion of the PCR-amplified PRNP region16—a validated assay targeting the singular polymorphism at codon 225 in mule deer—to screen all 99 samples for presence of S or F codons. Aliquots (10 μl) of completed PCR reactions were incubated with 10 U EcoRI (New England Biolabs) in a total volume of 12 μl containing 50 mM NaCl, 100 mM Tris/HCl, 10 mM MgCl2, 0.025% Triton X-100 (pH 7.5) at 37 C for 2–16 h followed by the addition of 2.5 μl 6× concentrate gel loading solution (Sigma- Aldrich) per sample, and the inspection of products by agarose gel electrophoresis for the presence of one 897bp-sized band for 225SS, two bands—one 897 bp and one 719 bp—for 225SF, or one 719 bp-sized band for 225FF. As noted by Jewell et al.16, occurrence of TTC (the F codon) at position 225 creates an EcoRI recognition DNA sequence and cleavage site GAATTC from codons 224–225, whereas TCC (the S codon) creates the sequence GAATCC, which is not cut by EcoRI. When incubated with EcoRI, PCR products with a TTC codon at position 225 yielded cleavage fragments of the predictable sizes listed16. Because no other sites within the PRNP ORF DNA sequence are potentially transformable to GAATTC with one base change, this represents a specific genotyping method for assessing the S225F polymorphism in mule deer16.To confirm findings from EcoRI screening, we examined sequences of the complete PRNP ORF from 20 samples that showed evidence of cleavage indicating 225*F and 6 samples without cleavage identified as 225SS. For DNA sequencing, we used primers 245 (5′-GGTGGTGACTGACTGTGTGTTGCTTGA-3′), 12 (5′-TGGTGGTGACTGTGTGTTGCTTGA-3′) and 3FL1 (5′-GATTAAGAAGATAATGAAAACAGGAAGG-3′; Integrated DNA Technologies). Sanger sequencing was done on purified PCR product by Eurofins Genomics (Louisville, Kentucky, USA). Sequence chromatograms were viewed and DNA sequence alignments and comparisons were made using the MAFFT multiple sequence alignment program v7.450 module, software platform v2020.2.3 of Geneious Prime. Sequencing confirmed the presence of coding for F in all samples identified as 225*F by EcoRI digestion, as well as the absence of such coding in samples identified by EcoRI digestion as 225SS. Moreover, presence of AGC at codon 138 in all sequenced samples reconfirmed that the primers we used had amplified the functional PRNP gene42.Statistics and reproducibilityFor analyses, we tabulated IHC-positive and -negative results to estimate apparent prevalence of prion infection. We also tabulated the number of individuals assigned to PRNP genotypes and to age groupings as described. Age groupings were selected based on relevance to CWD epidemiology in mule deer1,5,8,12,16,17,18,20,24,31,37. Assuming a ~2-year disease course5,8,17 and relative scarcity of end-stage disease in 225SS deer More

NEWS
11 January 2022

Landmark Colombian bird study repeated to right colonial-era wrongs

A re-run of a 100-year-old, US-led bird survey will inform future conservation efforts — but be helmed by local researchers.

Luke Taylor

Luke Taylor

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Download PDF

Ornithologist Andrés Cuervo takes a selfie of a team of Colombia Resurvey Project researchers during an expedition in Caquetá.Credit: Andrés M. Cuervo

Colombia has more animal and plant species per square kilometre than anywhere else in the world. Pioneering US bird scientist Frank Chapman once said that the country was so rich in biodiversity that when his research team explored the area in the early twentieth century, it could have studied a single mountain range for five years and still not have mapped all of its fauna.More than 100 years later, Colombian researchers are redoing his legendary bird survey, which is a reference for ornithologists the world over. They are surveying the areas that Chapman catalogued between 1911 and 1915, to investigate how a century of war, global warming and industrialization has affected the landscape and its biodiversity.
The world’s species are playing musical chairs: how will it end?
But this project will not snatch birds and whisk them to a museum abroad — as Chapman’s team did. Instead, local scientists will keep specimens in Colombia and engage with local communities during their expeditions, to include them in the momentous endeavour, improve the quality of the research and set an ethical standard for future fieldwork.Chapman and at least 5 other collectors shot many of the nearly 16,000 birds that they hauled back to the American Museum of Natural History in New York City, offering local residents little explanation — or credit. “You wouldn’t like it if I came to your house, surveyed it without permission, took photos and then went back to Colombia without telling you what I had found,” says Nelsy Niño-Rodríguez, the Colombia Resurvey Project’s community-relations coordinator, who is an ornithologist at the Alexander von Humboldt Biological Resources Research Institute in Bogotá. Without local guides knowledgeable about Colombia and its birds, Chapman couldn’t possibly have located and collected so many specimens, says Natalia Ocampo-Peñuela, a research partner on the resurvey project and a conservation ecologist at the University of California, Santa Cruz. Yet Chapman’s logs hardly mention guides; when they are discussed, it’s usually in racist or pejorative terms, she says.“His interest was to feed his curiosity, his scientific intellect and the museum,” she adds, but not to inform the wider population — and definitely not the local populations.A changed landscape Colombian researchers have dreamt of re-running Chapman’s expeditions for decades. But it wasn’t possible until the past few years, because many areas were inaccessible owing to armed conflict. Following a landmark peace deal in 2016, remote regions that had been under the control of the Revolutionary Armed Forces of Colombia (FARC), a left-wing guerrilla group, once again opened to exploration. That, and an infusion of funding from the Colombian government and international donors, meant researchers could attempt a resurvey.

Birds of Colombia: top, many-banded araçari (Pteroglossus pluricinctus); left, pileated finch (Coryphospingus pileatus); right, white-fringed antwren (Formicivora grisea).Credit: Andrés M. Cuervo

Chapman visited Colombia because he thought that its geography made it one of the most biodiverse places in the world. He theorized that the presence of the Andes Mountains, combined with the country’s position bridging South and Central America, made it an evolutionary melting pot.
FARC and the forest: Peace is destroying Colombia’s jungle — and opening it to science
Although Colombia is still home to around 10% of the world’s biodiversity, the forests once explored by Chapman have changed immensely. Pristine jungles have been cleared to create uniform pastures resembling golf courses, says Andrés Cuervo, an ornithologist at the National University of Colombia in Bogotá who is one of the directors of the resurvey project. The dirt tracks that Chapman and his team traversed on mules are now roads. And climate change has pushed birds to higher elevations and altered their migratory patterns.Seeking to understand the effects of these changes on biodiversity, researchers launched the Colombia Resurvey Project in 2019. The main objective is to gather bird specimens, including DNA and tissue samples, to compare the modern population with Chapman’s collection. The team, which includes US researchers as well as local ones, has so far conducted 6 expeditions, visiting 14 of Chapman’s original sites — leaving 60 to go. A useful catalogue The researchers are finding that they have to venture deep into the forest to find birds that were once a stone’s throw from Chapman’s campsites, Cuervo says. And some species are nowhere to be found, including the red-ruffed fruitcrow (Pyroderus scutatus) — almost certainly lost when the trees in its territory were cut down to grow avocados, he adds.

Resurvey project researchers Jessica Diaz (right) and Andrés Sierra (left) record data from a mist net, used to collect birds during expeditions.Credit: Andrés M. Cuervo

The team has also confirmed that birds dependent on unique ecosystems are being replaced by generalist species — which are more adaptable to fragmented forest and a disrupted diet — reducing the country’s biodiversity1. Larger species and fruit eaters seem to have been hit particularly hard over the past century, because they require vast expanses of forest to thrive.
Illegal mining in the Amazon hits record high amid Indigenous protests
The effects of climate and landscape changes on bird populations in the tropics are not well understood, so the project will inform future conservation efforts, researchers say. “It’s almost impossible to imagine all the ways in which this data can potentially be used down the road,” says John Bates, curator and head of life sciences at the Field Museum of Natural History in Chicago, Illinois. Members of the resurvey project hope their catalogue will have as much impact as Chapman’s. It will include resources such as a genomic map illustrating birds’ evolution, generated from DNA samples. “We are collecting the most complete set of specimens that one can imagine so that scientists from now and the future can answer questions that we haven’t thought of,” Ocampo says.Taking chargeThe Colombia Resurvey Project team especially hopes that its anti-colonial approach will resonate with the scientific community. The researchers run workshops before each excursion to inform local communities about why they are planning to kill some birds, and how this is important for conservation and science. They are storing the specimens at the National University of Colombia, where the birds will be digitally catalogued, so that people can view them online, listen to audio of their song and scroll through interactive maps of the expeditions. And the team is creating birdwatching tours at the expedition sites to boost tourism.
Brazilian road proposal threatens famed biodiversity hotspot
Involving communities leads to better results, Niño-Rodríguez says. For instance, even if some Indigenous people do not know the scientific names for birds, they might be able to identify them on sight and know where they are most likely to be found. And community knowledge of how the forests have changed has passed from generation to generation, so local residents are able to fill gaps when satellite data and research logs aren’t available.It’s equally important to the researchers that those leading the project are from Colombia. They say it’s common for local experts to help visiting foreign researchers to find new species and make discoveries, but be excluded from the scientific process and the credit. “We don’t want to be the guys with the permits or the guys who facilitate the logistics of someone else’s research,” Cuervo says. “We want to do our own high-quality research, and we want it to be available for people to use.” This time around, the American Museum of Natural History is a partner on the project, rather than its lead. “Although the Chapman expedition was conducted with help and permissions from the Colombian government, today’s expeditions appropriately look much different than they did in Chapman’s time,” says a museum spokesperson, adding that the museum “is proud of the very active relationship it maintains with Colombia’s scientific institutions through education and research”.
Colombia: after the violence
Meanwhile, project researchers are training curious members of local communities in how to identify birds scientifically, so they can continue to log species with their cameras and mobile phones once the researchers leave the forest. Areas previously ruled by FARC guerrillas are now falling under the control of other armed groups, which might not let outsiders in, so local residents could soon be the only people who have access to some of Colombia’s most biodiverse jungles and the birds that inhabit them.“Hopefully we won’t have to wait another hundred years for scientists to return to these sites and assess their bird fauna,” Cuervo says. “Communities can do it with empowerment and interest in their biodiversity and surroundings.”

Nature 601, 178-179 (2022)
doi: https://doi.org/10.1038/d41586-021-03527-x

References1.Gómez, C., Tenorio, E. A. & Cadena, C. D. Conserv. Biol. 35, 1552–1563 (2021).PubMed 
Article 

Google Scholar 
Download references

Related Articles

The world’s species are playing musical chairs: how will it end?

FARC and the forest: Peace is destroying Colombia’s jungle — and opening it to science

Colombia: after the violence

Brazilian road proposal threatens famed biodiversity hotspot

Illegal mining in the Amazon hits record high amid Indigenous protests

Colombia creates its first science ministry

Subjects

Conservation biology

Biodiversity

Climate change

Animal behaviour

Latest on:

Biodiversity

Wind power versus wildlife: root mitigation in evidence
Correspondence 11 JAN 22

Two million species catalogued by 500 experts
Correspondence 11 JAN 22

The UN must get on with appointing its new science board
Editorial 08 DEC 21

Climate change

How researchers can help fight climate change in 2022 and beyond
Editorial 05 JAN 22

Sustainability at the crossroads
Editorial 21 DEC 21

The science events to watch for in 2022
News 17 DEC 21

Jobs

Postdoctoral Fellowship Positions in Cancer Nanomedicine and Neuro-Engineering

University of Maryland School of Medicine
Baltimore, MD, United States

2022 Call for IBS Directors and Chief Investigators(CI)

Institute for Basic Science (IBS)
Multiple locations

Post-doctoral Fellow – Neurodegeneration

Oklahoma Medical Research Foundation (OMRF)
Oklahoma City, United States

Post-doctoral Fellow – Neurodegeneration

Oklahoma Medical Research Foundation (OMRF)
Oklahoma City, United States More

1.Olefeldt, D. et al. Circumpolar distribution and carbon storage of thermokarst landscapes. Nat. Commun. 7, 13043 (2016).
Google Scholar 
2.Lindgren, A., Hugelius, G. & Kuhry, P. Extensive loss of past permafrost carbon but a net accumulation into present-day soils. Nature 560, 219–222 (2018).
Google Scholar 
3.Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).
Google Scholar 
4.Walter Anthony, K. et al. Methane emissions proportional to permafrost carbon thawed in Arctic lakes since the 1950s. Nat. Geosci. 9, 679–682 (2016).
Google Scholar 
5.Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).
Google Scholar 
6.Gasser, T. et al. Path-dependent reductions in CO2 emission budgets caused by permafrost carbon release. Nat. Geosci. 11, 830–835 (2018).
Google Scholar 
7.McGuire, A. D. et al. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl Acad. Sci. USA 115, 3882–3887 (2018).
Google Scholar 
8.Heffernan, L., Estop-Aragonés, C., Knorr, K. H., Talbot, J. & Olefeldt, D. Long-term impacts of permafrost thaw on carbon storage in peatlands: deep losses offset by surficial accumulation. J. Geophys. Res. Biogeosci. 125, e2019JG005501 (2020).
Google Scholar 
9.Chadburn, S. E. et al. An observation-based constraint on permafrost loss as a function of global warming. Nat. Clim. Chang. 7, 340–344 (2017).
Google Scholar 
10.Bartsch, A. et al. Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra? Biogeosciences 13, 5453–5470 (2016).
Google Scholar 
11.Obu, J. et al. Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth Sci. Rev. 193, 299–316 (2019).
Google Scholar 
12.Commane, R. et al. Carbon dioxide sources from Alaska driven by increasing early winter respiration from Arctic tundra. Proc. Natl Acad. Sci. USA 114, 5361–5366 (2017).
Google Scholar 
13.Natali, S. M. et al. Permafrost carbon feedbacks threaten global climate goals. Proc. Natl. Acad. Sci. USA 118, e2100163118 (2021).
Google Scholar 
14.Zona, D. et al. Cold season emissions dominate the Arctic tundra methane budget. Proc. Natl Acad. Sci. USA 113, 40–45 (2016).
Google Scholar 
15.Comyn-Platt, E. et al. Carbon budgets for 1.5 and 2°C targets lowered by natural wetland and permafrost feedbacks. Nat. Geosci. 11, 568–573 (2018).
Google Scholar 
16.Heslop, J. K. K. et al. A synthesis of methane dynamics in thermokarst lake environments. Earth Sci. Rev. 210, 103365 (2020).
Google Scholar 
17.Keuper, F. et al. Carbon loss from northern circumpolar permafrost soils amplified by rhizosphere priming. Nat. Geosci. 13, 560–565 (2020).
Google Scholar 
18.Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E. & Boike, J. Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun. 9, 5423 (2018).
Google Scholar 
19.Lara, M. J. et al. Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics. Nat. Commun. 11, 4925 (2020).
Google Scholar 
20.Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).
Google Scholar 
21.Rey, D. M. et al. Wildfire-initiated talik development exceeds current thaw projections: observations and models from Alaska’s continuous permafrost zone. Geophys. Res. Lett. 47, e2020GL087565 (2020).
Google Scholar 
22.Kim, J. S., Kug, J. S., Jeong, S. J., Park, H. & Schaepman-Strub, G. Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation. Sci. Adv. 6, eaax3308 (2020).
Google Scholar 
23.Vonk, J. E., Tank, S. E. & Walvoord, M. A. Integrating hydrology and biogeochemistry across frozen landscapes. Nat. Commun. 10, 5377 (2019).
Google Scholar 
24.Saunois, M. et al. The global methane budget 2000–2017. Earth Syst. Sci. Data 12, 1561–1623 (2020).
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).
Google Scholar 
26.Schwab, M. S. et al. An abrupt aging of dissolved organic carbon in large Arctic rivers. Geophys. Res. Lett. 47, e2020GL088823 (2020).
Google Scholar 
27.Walter Anthony, K. M. et al. Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. Environ. Res. Lett. 16, 35010 (2021).
Google Scholar 
28.Goldstein, A. et al. Protecting irrecoverable carbon in Earth’s ecosystems. Nat. Clim. Chang. 10, 287–295 (2020).
Google Scholar 
29.Turner, M. G. et al. Climate change, ecosystems and abrupt change: science priorities. Phil. Trans. R. Soc. B 375, 20190105 (2020).
Google Scholar 
30.Fountain, A. G. et al. The disappearing cryosphere: impacts and ecosystem responses to rapid cryosphere loss. Bioscience 62, 405–415 (2012).
Google Scholar 
31.Gruber, S. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6, 221–233 (2012).
Google Scholar 
32.Zou, D. et al. A new map of permafrost distribution on the Tibetan Plateau. Cryosphere 11, 2527–2542 (2012).
Google Scholar 
33.Sayedi, S. S. et al. Subsea permafrost carbon stocks and climate change sensitivity estimated by expert assessment. Environ. Res. Lett. 15, 124075 (2020).
Google Scholar 
34.Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00240-1 (2022).Article 

Google Scholar 
35.Strauss, J. et al. Deep Yedoma permafrost: a synthesis of depositional characteristics and carbon vulnerability. Earth Sci. Rev. 172, 75–86 (2017).
Google Scholar 
36.Strauss, J. et al. The deep permafrost carbon pool of the Yedoma region in Siberia and Alaska. Geophys. Res. Lett. 40, 6165–6170 (2013).
Google Scholar 
37.Elder, C. D. et al. Greenhouse gas emissions from diverse Arctic Alaskan lakes are dominated by young carbon. Nat. Clim. Chang. 8, 166–171 (2018).
Google Scholar 
38.Martens, J. et al. Remobilization of old permafrost carbon to Chukchi Sea sediments during the end of the last deglaciation. Glob. Biogeochem. Cycles 33, 2–14 (2019).
Google Scholar 
39.Vonk, J. E. et al. Reviews and syntheses: effects of permafrost thaw on Arctic aquatic ecosystems. Biogeosciences 12, 7129–7167 (2015).
Google Scholar 
40.Turetsky, M. R. et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–24 (2019).
Google Scholar 
41.Wild, B. et al. Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proc. Natl Acad. Sci. USA 116, 10280–10285 (2019).
Google Scholar 
42.Mishra, U. et al. Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks. Sci. Adv. 7, 5236–5260 (2021).
Google Scholar 
43.Treat, C. C. et al. Tundra landscape heterogeneity, not interannual variability, controls the decadal regional carbon balance in the Western Russian Arctic. Global Chang. Biol. 24, 5188–5204 (2018).
Google Scholar 
44.Siewert, M. B., Lantuit, H., Richter, A. & Hugelius, G. Permafrost causes unique fine-scale spatial variability across tundra soils. Glob. Biogeochem. Cycles 35, e2020GB006659 (2021).
Google Scholar 
45.Niittynen, P. et al. Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions. Nat. Clim. Chang. 10, 1143–1148 (2020).
Google Scholar 
46.Hope, C. & Schaefer, K. Economic impacts of carbon dioxide and methane released from thawing permafrost. Nat. Clim. Chang. 6, 56–59 (2016).
Google Scholar 
47.Farquharson, L. M. et al. Climate change drives widespread and rapid thermokarst development in very cold permafrost in the Canadian High Arctic. Geophys. Res. Lett. 46, 6681–6689 (2019).
Google Scholar 
48.Biskaborn, B. K. et al. Permafrost is warming at a global scale. Nat. Commun. 10, 264 (2019).
Google Scholar 
49.Hood, E., Battin, T. J., Fellman, J., O’Neel, S. & Spencer, R. G. M. Storage and release of organic carbon from glaciers and ice sheets. Nat. Geosci. 8, 91–96 (2015).
Google Scholar 
50.Tanski, G. et al. Rapid CO2 release from eroding permafrost in seawater. Geophys. Res. Lett. 46, 11244–11252 (2019).
Google Scholar 
51.Liljedahl, A. K., Gädeke, A., O’Neel, S., Gatesman, T. A. & Douglas, T. A. Glacierized headwater streams as aquifer recharge corridors, subarctic Alaska. Geophys. Res. Lett. 44, 6876–6885 (2017).
Google Scholar 
52.Yumashev, D. et al. Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements. Nat. Commun. 10, 1900 (2019).
Google Scholar 
53.Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).
Google Scholar 
54.Nauta, A. L. et al. Permafrost collapse after shrub removal shifts tundra ecosystem to a methane source. Nat. Clim. Chang. 5, 67–70 (2015).
Google Scholar 
55.Anthony, K. W. et al. 21st-century modeled permafrost carbon emissions accelerated by abrupt thaw beneath lakes. Nat. Commun. 9, 3262 (2018).
Google Scholar 
56.Hugelius, G. et al. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl Acad. Sci. USA 117, 201916387 (2020).
Google Scholar 
57.Christensen, T. R., Arora, V. K., Gauss, M., Höglund-Isaksson, L. & Parmentier, F. J. W. Tracing the climate signal: mitigation of anthropogenic methane emissions can outweigh a large Arctic natural emission increase. Sci. Rep. 9, 1146 (2019).
Google Scholar 
58.United Nations Framework Convention on Climate Change. Total aggregate greenhouse gas emissions of individual nations, annex 1. World Resources Institute https://www.wri.org/resources/data-sets/climate-watch-cait-unfccc-annex-i-ghg-emissions-data (2008).59.Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment. Nat. Commun. 10, 1329 (2019).
Google Scholar 
60.Knoblauch, C., Beer, C., Liebner, S., Grigoriev, M. N. & Pfeiffer, E. M. Methane production as key to the greenhouse gas budget of thawing permafrost. Nat. Clim. Chang. 8, 309–312 (2018).
Google Scholar 
61.Jones, B. M. et al. Lake and drained lake basin systems in lowland permafrost regions. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00238-9 (2022).62.Matthews, E., Johnson, M. S., Genovese, V., Du, J. & Bastviken, D. Methane emission from high latitude lakes: methane-centric lake classification and satellite-driven annual cycle of emissions. Sci. Rep. 10, 12465 (2020).
Google Scholar 
63.Lamontagne-Hallé, P., McKenzie, J. M., Kurylyk, B. L. & Zipper, S. C. Changing groundwater discharge dynamics in permafrost regions. Environ. Res. Lett. 13, 084017 (2018).
Google Scholar 
64.Nitzbon, J. et al. Fast response of cold ice-rich permafrost in northeast Siberia to a warming climate. Nat. Commun. 11, 2201 (2020).
Google Scholar 
65.Jeong, S. J. et al. Accelerating rates of Arctic carbon cycling revealed by long-term atmospheric CO2 measurements. Sci. Adv. 4, eaao1167 (2018).
Google Scholar 
66.Disher, B. S., Connon, R. F., Haynes, K. M., Hopkinson, C. & Quinton, W. L. The hydrology of treed wetlands in thawing discontinuous permafrost regions. Ecohydrology 14, e2296 (2021).
Google Scholar 
67.Parazoo, N. C. et al. Detecting regional patterns of changing CO2 flux in Alaska. Proc. Natl Acad. Sci. USA 113, 7733–7738 (2016).
Google Scholar 
68.Silva, J. L. A., Souza, A. F., Caliman, A., Voigt, E. L. & Lichston, J. E. Weak whole-plant trait coordination in a seasonally dry South American stressful environment. Ecol. Evol. 8, 4–12 (2018).
Google Scholar 
69.Ward, C. P. & Cory, R. M. Chemical composition of dissolved organic matter draining permafrost soils. Geochim. Cosmochim. Acta 167, 63–79 (2015).
Google Scholar 
70.Johnston, E. R. et al. Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths. Proc. Natl Acad. Sci. USA 116, 15096–15105 (2019).
Google Scholar 
71.Stein, L. Y. The long-term relationship between microbial metabolism and greenhouse gases. Trends Microbiol. 28, 500–511 (2020).
Google Scholar 
72.Feng, J. et al. Warming-induced permafrost thaw exacerbates tundra soil carbon decomposition mediated by microbial community. Microbiome 8, 3 (2020).
Google Scholar 
73.Estop-Aragonés, C. et al. Assessing the potential for mobilization of old soil carbon after permafrost thaw: a synthesis of 14C measurements from the northern permafrost region. Glob. Biogeochem. Cycles 34, e2020GB006672 (2020).
Google Scholar 
74.Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S. & Bastviken, D. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9, 99–105 (2016).
Google Scholar 
75.Schaefer, K., Lantuit, H., Romanovsky, V. E., Schuur, E. A. G. & Witt, R. The impact of the permafrost carbon feedback on global climate. Environ. Res. Lett. 9, 085003 (2014).
Google Scholar 
76.Xue, K. et al. Tundra soil carbon is vulnerable to rapid microbial decomposition under climate warming. Nat. Clim. Chang. 6, 595–600 (2016).
Google Scholar 
77.Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nat. Microbiol. 6, 246–256 (2021).
Google Scholar 
78.Singleton, C. M. et al. Methanotrophy across a natural permafrost thaw environment. ISME J. 12, 2544–2558 (2018).
Google Scholar 
79.Kwon, M. J. et al. Plants, microorganisms, and soil temperatures contribute to a decrease in methane fluxes on a drained Arctic floodplain. Glob. Chang. Biol. 23, 2396–2412 (2017).
Google Scholar 
80.Jin, X.-Y. et al. Impacts of climate-induced permafrost degradation on vegetation: a review. Adv. Clim. Chang. Res. 12, 29–47 (2020).
Google Scholar 
81.Song, X. et al. Soil moisture as a key factor in carbon release from thawing permafrost in a boreal forest. Geoderma 357, 113975 (2020).
Google Scholar 
82.Zhu, Y. et al. Disproportionate increase in freshwater methane emissions induced by experimental warming. Nat. Clim. Chang. 10, 685–690 (2020).
Google Scholar 
83.Watts, J. D., Kimball, J. S., Bartsch, A. & McDonald, K. C. Surface water inundation in the boreal-Arctic: potential impacts on regional methane emissions. Environ. Res. Lett. 9, 075001 (2014).
Google Scholar 
84.Thompson, R. L. et al. Methane fluxes in the high northern latitudes for 2005–2013 estimated using a Bayesian atmospheric inversion. Atmos. Chem. Phys. 17, 3553–3572 (2017).
Google Scholar 
85.Oh, Y. et al. Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic. Nat. Clim. Chang. 10, 317–321 (2020).
Google Scholar 
86.Street, L. E. et al. Plant carbon allocation drives turnover of old soil organic matter in permafrost tundra soils. Glob. Chang. Biol. 26, 4559–4571 (2020).
Google Scholar 
87.Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Chang. 9, 852–857 (2019).
Google Scholar 
88.Hu, Y., Fernandez-Anez, N., Smith, T. E. L. & Rein, G. Review of emissions from smouldering peat fires and their contribution to regional haze episodes. Int. J. Wildland Fire 27, 293–312 (2018).
Google Scholar 
89.Abbott, B. W. et al. Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment. Environ. Res. Lett. 11, 034014 (2016).
Google Scholar 
90.Mack, M. C. et al. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science 372, 280–283 (2021).
Google Scholar 
91.Holloway, J. E. et al. Impact of wildfire on permafrost landscapes: a review of recent advances and future prospects. Permafr. Periglac. Process. 31, 371–382 (2020).
Google Scholar 
92.McCarty, J. L., Smith, T. E. L. & Turetsky, M. R. Arctic fires re-emerging. Nat. Geosci. 13, 658–660 (2020).
Google Scholar 
93.Scholten, R. C., Jandt, R., Miller, E. A., Rogers, B. M. & Veraverbeke, S. Overwintering fires in boreal forests. Nature 593, 399–404 (2021).
Google Scholar 
94.Koven, C. D. et al. A simplified, data-constrained approach to estimate the permafrost carbon-climate feedback. Phil. Trans. R. Soc. A 373, 20140423 (2015).
Google Scholar 
95.MacDougall, A. H. & Knutti, R. Projecting the release of carbon from permafrost soils using a perturbed parameter ensemble modelling approach. Biogeosciences 13, 2123–2136 (2016).
Google Scholar 
96.Cooper, M. D. A. et al. Limited contribution of permafrost carbon to methane release from thawing peatlands. Nat. Clim. Chang. 7, 507–511 (2017).
Google Scholar 
97.Andresen, C. G. et al. Soil moisture and hydrology projections of the permafrost region–a model intercomparison. Cryosphere 14, 445–459 (2020).
Google Scholar 
98.Bartsch, A., Pointner, G., Ingeman-Nielsen, T. & Lu, W. Towards circumpolar mapping of Arctic settlements and infrastructure based on Sentinel-1 and Sentinel-2. Remote Sens. 12, 2368 (2020).
Google Scholar 
99.Swingedouw, D. et al. Early warning from space for a few key tipping points in physical, biological, and social-ecological systems. Surv. Geophys. 41, 1237–1284 (2020).
Google Scholar 
100.Elder, C. D. et al. Airborne mapping reveals emergent power law of Arctic methane emissions. Geophys. Res. Lett. 47, e2019GL085707 (2020).
Google Scholar 
101.Byrne, B. et al. Improved constraints on northern extratropical CO2 fluxes obtained by combining surface-based and space-based atmospheric CO2 measurements. J. Geophys. Res. Atmos. 125, e2019JD032029 (2020).
Google Scholar 
102.Karlson, M. et al. Delineating northern peatlands using Sentinel-1 time series and terrain indices from local and regional digital elevation models. Remote Sens. Environ. 231, 111252 (2019).
Google Scholar 
103.Cusworth, D. H. et al. Synthesis of methane observations across scales: strategies for deploying a multitiered observing network. Geophys. Res. Lett. 47, e2020GL087869 (2020).
Google Scholar 
104.Bale, N. J. et al. Fatty acid and hopanoid adaption to cold in the methanotroph methylovulum psychrotolerans. Front. Microbiol. 10, 589 (2019).
Google Scholar 
105.Mackelprang, R. et al. Microbial survival strategies in ancient permafrost: insights from metagenomics. ISME J. 11, 2305–2318 (2017).
Google Scholar 
106.Siliakus, M. F., van der Oost, J. & Kengen, S. W. M. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure. Extremophiles 21, 651–670 (2017).
Google Scholar 
107.Johnson, S. S. et al. Ancient bacteria show evidence of DNA repair. Proc. Natl Acad. Sci. USA 104, 14401–14405 (2007).
Google Scholar 
108.Hueffer, K., Drown, D., Romanovsky, V. & Hennessy, T. Factors contributing to anthrax outbreaks in the circumpolar north. Ecohealth 17, 174–180 (2020).
Google Scholar 
109.Miner, K. R. et al. Emergent biogeochemical risks from Arctic permafrost degradation. Nat. Clim. Chang. 11, 809–819 (2021).
Google Scholar 
110.Perron, G. G. et al. Functional characterization of bacteria isolated from ancient Arctic soil exposes diverse resistance mechanisms to modern antibiotics. PLoS ONE 10, e0069533 (2015).
Google Scholar 
111.MacKelprang, R. et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368–371 (2011).
Google Scholar 
112.Burkert, A., Douglas, T. A., Waldrop, M. P. & Mackelprang, R. Changes in the active, dead, and dormant microbial community structure across a pleistocene permafrost chronosequence. Appl. Environ. Microbiol. 85, e02646-18 (2019).
Google Scholar 
113.Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).
Google Scholar 
114.Hultman, J. et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208–212 (2015).
Google Scholar 
115.Schadel, C. et al. Potential carbon emissions dominated by carbon dioxide from thawed permafrost soils. Nat. Clim. Chang. 6, 950–953 (2016).
Google Scholar 
116.Lee, H. et al. A spatially explicit analysis to extrapolate carbon fluxes in upland tundra where permafrost is thawing. Glob. Chang. Biol. 17, 1379–1393 (2011).
Google Scholar 
117.Euskirchen, E. S., Edgar, C. W., Turetsky, M. R., Waldrop, M. P. & Harden, J. W. Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost. J. Geophys. Res. Biogeosci. 119, 1576–1595 (2014).
Google Scholar 
118.Euskirchen, E. S., Bret-Harte, M. S., Shaver, G. R., Edgar, C. W. & Romanovsky, V. E. Long-term release of carbon dioxide from arctic tundra ecosystems in Alaska. Ecosystems 20, 960–974 (2017).
Google Scholar 
119.Karlsson, J. et al. Carbon emission from Western Siberian inland waters. Nat. Commun. 12, 825 (2021).
Google Scholar 
120.Schuur, E. A. G. et al. Tundra underlain by thawing permafrost persistently emits carbon to the atmosphere over 15 years of measurements. J. Geophys. Res. Biogeosci. 126, e2020JG006044 (2021).
Google Scholar 
121.Oechel, W. C. et al. Acclimation of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming. Nature 406, 978–981 (2000).
Google Scholar 
122.Heijmans, M. M. P. D. et al. Tundra vegetation change and impacts on permafrost. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00233-0 (2022).Article 

Google Scholar 
123.Kanevskiy, M. et al. Patterns and rates of riverbank erosion involving ice-rich permafrost (yedoma) in northern Alaska. Geomorphology 253, 370–384 (2016).
Google Scholar 
124.Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).
Google Scholar 
125.Schimel, D. & Schneider, F. D. Flux towers in the sky: global ecology from space. New Phytol. 224, 570–584 (2019).
Google Scholar 
126.Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).
Google Scholar 
127.Jammet, M. et al. Year-round CH4 and CO2 flux dynamics in two contrasting freshwater ecosystems of the subarctic. Biogeosciences 14, 5189–5216 (2017).
Google Scholar 
128.Kohnert, K., Serafimovich, A., Metzger, S., Hartmann, J. & Sachs, T. Strong geologic methane emissions from discontinuous terrestrial permafrost in the Mackenzie Delta, Canada. Sci. Rep. 7, 5828 (2017).
Google Scholar 
129.Sayres, D. S. et al. Arctic regional methane fluxes by ecotope as derived using eddy covariance from a low-flying aircraft. Atmos. Chem. Phys. 17, 8619–8633 (2017).
Google Scholar 
130.Ueyama, M. et al. Upscaling terrestrial carbon dioxide fluxes in Alaska with satellite remote sensing and support vector regression. J. Geophys. Res. Biogeosci. 118, 1266–1281 (2013).
Google Scholar 
131.Davidson, S. J. et al. Upscaling CH4 fluxes using high-resolution imagery in Arctic tundra ecosystems. Remote Sens. 9, 1227 (2017).
Google Scholar 
132.Peltola, O. et al. Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations. Earth Syst. Sci. Data 11, 1263–1289 (2019).
Google Scholar 
133.Chang, R. Y. W. et al. Methane emissions from Alaska in 2012 from CARVE airborne observations. Proc. Natl Acad. Sci. USA 111, 16694–16699 (2014).
Google Scholar 
134.Saeki, T. et al. Carbon flux estimation for Siberia by inverse modeling constrained by aircraft and tower CO2 measurements. J. Geophys. Res. Atmos. 118, 1100–1122 (2013).
Google Scholar 
135.Kim, J. et al. Impact of Siberian observations on the optimization of surface CO2 flux. Atmos. Chem. Phys. 17, 2881–2899 (2017).
Google Scholar 
136.O’Shea, S. J. et al. Methane and carbon dioxide fluxes and their regional scalability for the European Arctic wetlands during the MAMM project in summer 2012. Atmos. Chem. Phys. 14, 13159–13174 (2014).
Google Scholar 
137.Gottwald, M. & Bovensmann, H. SCIAMACHY — Exploring the Changing Earth’s Atmosphere (Springer, 2011).138.Siewert, M. B. High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment. Biogeosciences 15, 1663–1682 (2018).
Google Scholar 
139.Arndt, K. A. et al. Arctic greening associated with lengthening growing seasons in Northern Alaska. Environ. Res. Lett. 14, 125018 (2019).
Google Scholar 
140.Widhalm, B., Bartsch, A. & Heim, B. A novel approach for the characterization of tundra wetland regions with C-band SAR satellite data. Int. J. Remote Sens. 36, 5537–5556 (2015).
Google Scholar 
141.Varon, D. J. et al. High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations. Atmos. Meas. Tech. 14, 2771–2785 (2021).
Google Scholar 
142.Bartsch, A., Hofler, A., Kroisleitner, C. & Trofaier, A. M. Land cover mapping in northern high latitude permafrost regions with satellite data: achievements and remaining challenges. Remote Sens. 8, 979 (2016).
Google Scholar 
143.Flato, G. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Ch. 9 (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).144.Kivimäki, E. et al. Evaluation and analysis of the seasonal cycle and variability of the trend from GOSAT methane retrievals. Remote Sens. 11, 882 (2019).
Google Scholar 
145.Lindqvist, H. et al. Does GOSAT capture the true seasonal cycle of carbon dioxide? Atmos. Chem. Phys. 15, 13023–13040 (2015).
Google Scholar 
146.Chadburn, S. et al. Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models. Biogeosciences 14, 5143–5169 (2017).
Google Scholar 
147.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).
Google Scholar 
148.Aas, K. S. et al. Thaw processes in ice-rich permafrost landscapes represented with laterally coupled tiles in a land surface model. Cryosphere 13, 591–609 (2019).
Google Scholar 
149.Westermann, S. et al. Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia. Cryosphere 11, 1441–1463 (2017).
Google Scholar 
150.Houweling, S. et al. An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements. J. Geophys. Res. 120, 5253–5266 (2015).
Google Scholar 
151.Houweling, S. et al. Global inverse modeling of CH4 sources and sinks: an overview of methods. Atmos. Chem. Phys. 17, 235–256 (2017).
Google Scholar 
152.Tsuruta, A. et al. Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0. Geosci. Model Dev. 10, 1261–1289 (2017).
Google Scholar 
153.Virkkala, A. M., Abdi, A. M., Luoto, M. & Metcalfe, D. B. Identifying multidisciplinary research gaps across Arctic terrestrial gradients. Environ. Res. Lett. 14, 124061 (2019).
Google Scholar 
154.Hakkarainen, J., Ialongo, I., Maksyutov, S. & Crisp, D. Analysis of four years of global XCO2 anomalies as seen by Orbiting Carbon Observatory-2. Remote Sens. 11, 850 (2019).
Google Scholar 
155.Fisher, J. B. et al. Missing pieces to modeling the Arctic-Boreal puzzle. Environ. Res. Lett. 13, 020202 (2018).
Google Scholar 
156.McGuire, A. D. et al. An assessment of the carbon balance of Arctic tundra: comparisons among observations, process models, and atmospheric inversions. Biogeosciences 9, 3185–3204 (2012).
Google Scholar 
157.Lenton, T. M. & Williams, H. T. P. On the origin of planetary-scale tipping points. Trends Ecol. Evol. 28, 380–382 (2013).
Google Scholar 
158.Lenton, T. M. Arctic climate tipping points. Ambio 41, 10–22 (2012).
Google Scholar 
159.Lenton, T. M. et al. Climate tipping points — too risky to bet against. Nature 575, 592–595 (2019).
Google Scholar 
160.Fleisher, A. J., Long, D. A., Liu, Q., Gameson, L. & Hodges, J. T. Optical measurement of radiocarbon below unity fraction modern by linear absorption spectroscopy. J. Phys. Chem. Lett. 8, 4550–4556 (2017).
Google Scholar 
161.Genoud, G. et al. Laser spectroscopy for monitoring of radiocarbon in atmospheric samples. Anal. Chem. 91, 12315–12320 (2019).
Google Scholar 
162.Levin, I. et al. Observations and modelling of the global distribution and long-term trend of atmospheric 14CO2. Tellus B Chem. Phys. Meteorol. 62, 26–46 (2010).
Google Scholar 
163.Voigt, C. et al. Warming of subarctic tundra increases emissions of all three important greenhouse gases — carbon dioxide, methane, and nitrous oxide. Glob. Chang. Biol. 23, 3121–3138 (2017).
Google Scholar 
164.Mu, C. C. et al. Permafrost collapse shifts alpine tundra to a carbon source but reduces N2O and CH4 release on the northern Qinghai-Tibetan Plateau. Geophys. Res. Lett. 44, 8945–8952 (2017).
Google Scholar 
165.Krogh, S. A., Pomeroy, J. W. & Marsh, P. Diagnosis of the hydrology of a small Arctic basin at the tundra-taiga transition using a physically based hydrological model. J. Hydrol. 550, 685–703 (2017).
Google Scholar 
166.Burke, E. J., Zhang, Y. & Krinner, G. Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change. Cryosphere 14, 3155–3174 (2020).
Google Scholar 
167.Treat, C. C., Bloom, A. A. & Marushchak, M. E. Nongrowing season methane emissions — a significant component of annual emissions across northern ecosystems. Glob. Chang. Biol. 24, 3331–3343 (2018).
Google Scholar 
168.Kelley, J. J., Weaver, D. F. & Smith, B. P. The variation of carbon dioxide under the snow in the Arctic. Ecology 49, 358–361 (1968).
Google Scholar 
169.Du, J. et al. Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat. Remote Sens. Environ. 213, 1–17 (2018).
Google Scholar 
170.Webb, E. E. et al. Increased wintertime CO2 loss as a result of sustained tundra warming. J. Geophys. Res. Biogeosci. 121, 249–265 (2016).
Google Scholar 
171.Grosse, G., Goetz, S., McGuire, A. D., Romanovsky, V. E. & Schuur, E. A. G. Changing permafrost in a warming world and feedbacks to the Earth system. Environ. Res. Lett. 11, 040201 (2016).
Google Scholar 
172.Kleinen, T. & Brovkin, V. Pathway-dependent fate of permafrost region carbon. Environ. Res. Lett. 13, 094001 (2018).
Google Scholar 
173.Anthony, K. M. W. et al. A shift of thermokarst lakes from carbon sources to sinks during the Holocene epoch. Nature 511, 452–456 (2014).
Google Scholar 
174.Crichton, K. A., Bouttes, N., Roche, D. M., Chappellaz, J. & Krinner, G. Permafrost carbon as a missing link to explain CO2 changes during the last deglaciation. Nat. Geosci. 9, 683–686 (2016).
Google Scholar 
175.Tesi, T. et al. Massive remobilization of permafrost carbon during post-glacial warming. Nat. Commun. 7, 13653 (2016).
Google Scholar 
176.McClain, M. E. et al. Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems 6, 301–312 (2003).
Google Scholar 
177.Bernhardt, E. S. et al. Control points in ecosystems: moving beyond the hot spot hot moment concept. Ecosystems 20, 665–682 (2016).
Google Scholar 
178.Kuze, A. et al. Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space. Atmos. Meas. Tech. 9, 2445–2461 (2016).
Google Scholar 
179.Eldering, A. et al. The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes. Science 358, eaam5745 (2017).
Google Scholar 
180.Yang, D. et al. First global carbon dioxide maps produced from TanSat measurements. Adv. Atmos. Sci. 35, 621–623 (2018).
Google Scholar 
181.Glumb, R., Davis, G. & Lietzke, C. in IEEE International Geoscience and Remote Sensing Symposium 1238–1240 (IEEE, 2014).182.Lorente, A. et al. Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements. Atmos. Meas. Tech. 14, 665–684 (2021).
Google Scholar 
183.Ehret, G. et al. MERLIN: a French–German space lidar mission dedicated to atmospheric methane. Remote Sens. 9, 1052 (2017).
Google Scholar 
184.Bousquet, P. et al. Error budget of the MEthane Remote LIdar missioN and its impact on the uncertainties of the global methane budget. J. Geophys. Res. Atmos. 123, 11,766–11,785 (2018).
Google Scholar 
185.Bezy, J.-L. et al. in IEEE International Geoscience and Remote Sensing Symposium 8400–8403 (IEEE, 2019).186.Ingmann, P. et al. Requirements for the GMES atmosphere service and ESA’s implementation concept: Sentinels-4/-5 and -5p. Remote Sens. Environ. 120, 58–69 (2012).
Google Scholar 
187.Nassar, R. et al. The atmospheric imaging mission for northern regions: AIM-North. Can. J. Remote Sens. 45, 423–442 (2019).
Google Scholar 
188.Polonsky, I. N., O’Brien, D. M., Kumer, J. B., O’Dell, C. W. & the geoCARB Team. Performance of a geostationary mission, geoCARB, to measure CO2, CH4 and CO column-averaged concentrations. Atmos. Meas. Tech. 7, 959–981 (2014).
Google Scholar 
189.Chahine, M. T. et al. Improving weather forecasting and providing new data on greenhouse gases. Bull. Am. Meteorol. Soc. 87, 911–926 (2006).
Google Scholar 
190.Clerbaux, C. et al. Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder. Atmos. Chem. Phys. 9, 6041–6054 (2009).
Google Scholar 
191.Han, Y. et al. Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality. J. Geophys. Res. Atmos. 118, 734–12,748 (2013).
Google Scholar 
192.Zou, C. Z. et al. The reprocessed Suomi NPP satellite observations. Remote Sens. 12, 2891 (2020).
Google Scholar 
193.Obu, J. et al. ESA Permafrost Climate Change Initiative (Permafrost_CCI): permafrost climate research data package v1 (CEDA, 2020).194.Voigt, C. et al. Nitrous oxide emissions from permafrost-affected soils. Nat. Rev. Earth Environ. 1, 420–434 (2020).
Google Scholar 
195.Arctic Climate Impact Assessment. Impacts of a Warming Arctic: Arctic Climate Impact Assessment (Cambridge Univ. Press, 2004). More

Abdel-Ghani AH, Parzies HK, Omary A, Geiger HH (2004) Estimating the outcrossing rate of barley landraces and wild barley populations collected from ecologically different regions of Jordan Theor Appl Genet 109(3):588–595PubMed 

Google Scholar 
Akerman A, Bürger R (2014) The consequences of gene flow for local adaptation and differentiation: a two-locus two-deme model J Math Biol 68(5):1135–1198PubMed 

Google Scholar 
Al-Asadi H, Petkova D, Stephens M, Novembre J (2019) Estimating recent migration and population-size surfaces PLoS Genet 15(1):e1007908PubMed 
PubMed Central 

Google Scholar 
Baker HG (1967) Support for Baker’s law-as a rule Evolution 21(4):853–856PubMed 

Google Scholar 
Baker K, Baker K, Bayer M, Cook N, Dreißig S, Dhillon T, Russell J, Hedley PE, Morris J, Ramsay L, Colas I et al. (2014) The low-recombining pericentromeric region of barley restricts gene diversity and evolution but not gene expression Plant J 79(6):981–992CAS 
PubMed 
PubMed Central 

Google Scholar 
Battey C, Ralph PL, Kern AD (2020) Space is the place: effects of continuous spatial structure on analysis of population genetic data Genetics 215(1):193–214CAS 
PubMed 
PubMed Central 

Google Scholar 
Baum M, Grando S, Backes G, Jahoor A, Sabbagh A, Ceccarelli S (2003) QTLs for agronomic traits in the mediterranean environment identified in recombinant inbred lines of the cross’ Arta’ × H. spontaneum 41-1 Theor Appl Genet 107(7):1215–1225CAS 
PubMed 

Google Scholar 
Bedada G, Westerbergh A, Nevo E, Korol A, Schmid KJ (2014) DNA sequence variation of wild barley Hordeum spontaneum (L.) across environmental gradients in Israel Heredity 112(6):646–655CAS 
PubMed 
PubMed Central 

Google Scholar 
Berner D, Roesti M (2017) Genomics of adaptive divergence with chromosome-scale heterogeneity in crossover rate Mol Ecol 26(22):6351–6369CAS 
PubMed 

Google Scholar 
Bhatia G, Patterson N, Sankararaman S, Price AL (2013) Estimating and interpreting FST: the impact of rare variants Genome Res 23(9):1514–1521CAS 
PubMed 
PubMed Central 

Google Scholar 
Bohra A, Kilian B, Kilian B, Sivasankar S, Caccamo M, Mba, C, McCouch SR, Varshney RK (2021) Reap the crop wild relatives for breeding future crops. Trends Biotechnol. https://doi.org/10.1016/j.tibtech.2021.08.009Bradburd GS, Coop GM, Ralph PL (2018) Inferring continuous and discrete population genetic structure across space. Genetics 210(1):33–52PubMed 
PubMed Central 

Google Scholar 
Bürger R, Akerman A (2011) The effects of linkage and gene flow on local adaptation: a two-locus continent–island model. Theor Popul Biol 80(4):272–288PubMed 
PubMed Central 

Google Scholar 
Cabreros I, Storey JD (2019) A likelihood-free estimator of population structure bridging admixture models and principal components analysis. Genetics 212(4):1009–1029PubMed 
PubMed Central 

Google Scholar 
Caldwell KS, Russell J, Langridge P, Powell W (2006) Extreme population-dependent linkage disequilibrium detected in an inbreeding plant species, Hordeum vulgare. Genetics 172(1):557–567CAS 
PubMed 
PubMed Central 

Google Scholar 
Capblancq T, Luu K, Blum MG, Bazin E (2018) Evaluation of redundancy analysis to identify signatures of local adaptation. Mol Ecol Resour 18(6):1223–1233CAS 
PubMed 

Google Scholar 
Caye K, Jumentier B, Lepeule J, François O (2019) LFMM 2: fast and accurate inference of gene-environment associations in genome-wide studies. Mol Biol Evol 36(4):852–860CAS 
PubMed 
PubMed Central 

Google Scholar 
Contreras-Moreira B, Serrano-Notivoli R, Mohammed NE, Cantalapiedra CP, Beguería S, Casas AM, Igartua E (2019) Genetic association with high-resolution climate data reveals selection footprints in the genomes of barley landraces across the Iberian Peninsula. Mol Ecol 28(8):1994–2012PubMed 
PubMed Central 

Google Scholar 
Dawson IK, Russell J, Powell W, Steffenson B, Thomas WTB, Waugh R (2015) Barley: a translational model for adaptation to climate change. New Phytol 206(3):913–931PubMed 

Google Scholar 
Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (pcnm). Ecol Model 196(3-4):483–493
Google Scholar 
Dray S, Bauman D, Blanchet G, Borcard D, Clappe S, Guenard G, Jombart T, Larocque G, Legendre P, Madi N, Wagner HH (2019) adespatial: multivariate multiscale spatial analysis. R package version 0.3-7. https://CRAN.R-project.org/package=adespatialElshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6(5):e19379CAS 
PubMed 
PubMed Central 

Google Scholar 
Excoffier L, Hofer T, Foll M (2009) Detecting loci under selection in a hierarchically structured population. Heredity 103(4):285–298CAS 
PubMed 

Google Scholar 
Fang Z, Gonzales AM, Clegg MT, Smith KP, Muehlbauer GJ, Steffenson BJ, Morrell PL (2014) Two genomic regions contribute disproportionately to geographic differentiation in wild barley. G34(7):1193–1203PubMed 
PubMed Central 

Google Scholar 
Fick SE, Hijmans RJ (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315
Google Scholar 
Forester BR, Lasky JR, Wagner HH, Urban DL (2018) Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations. Mol Ecol 27(9):2215–2233CAS 
PubMed 

Google Scholar 
Forester BR, Jones MR, Joost S, Landguth EL, Lasky JR (2016) Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol Ecol 25(1):104–120CAS 
PubMed 

Google Scholar 
Galkin E, Dalal A, Evenko A, Fridman E, Kan I, Wallach R, Moshelion M (2018) Risk-management strategies and transpiration rates of wild barley in uncertain environments. Physiol Plant 164(4):412–428CAS 
PubMed 

Google Scholar 
Gautier M (2015) Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics 201(4):1555–1579CAS 
PubMed 
PubMed Central 

Google Scholar 
Gibson MJ, Moyle LC (2020) Regional differences in the abiotic environment contribute to genomic divergence within a wild tomato species. Mol Ecol 29(12):2204–2217CAS 
PubMed 

Google Scholar 
Günther T, Coop G (2013) Robust identification of local adaptation from allele frequencies. Genetics 195(1):205–220PubMed 
PubMed Central 

Google Scholar 
Gutaker RM, Groen SC, Bellis ES, Choi JY, Pires IS, Bocinsky RK, Slayton ER, Wilkins O, Castillo CC, Negrão S et al. (2020) Genomic history and ecology of the geographic spread of rice. Nat Plants 6(5):492–502PubMed 

Google Scholar 
Hämälä T, Savolainen O (2019) Genomic patterns of local adaptation under gene flow in arabidopsis lyrata. Mol Biol Evol 36(11):2557–2571
Google Scholar 
Harlan JR, Zohary D (1966) Distribution of wild wheats and barley. Science 153(3740):1074–1080CAS 
PubMed 

Google Scholar 
Hartfield M, Bataillon T, Glémin S (2017) The evolutionary interplay between adaptation and self-fertilization. Trends Genet 33(6):420–431CAS 
PubMed 
PubMed Central 

Google Scholar 
Hendrick MF, Finseth FR, Mathiasson ME, Palmer KA, Broder EM, Breigenzer P, Fishman L (2016) The genetics of extreme microgeographic adaptation: an integrated approach identifies a major gene underlying leaf trichome divergence in yellowstone mimulus guttatus. Mol Ecol 25(22):5647–5662CAS 
PubMed 

Google Scholar 
Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B et al. (2017) Soilgrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2):e0169748PubMed 
PubMed Central 

Google Scholar 
Herzig P, Herzig P, Maurer A, Draba V, Sharma R, Draicchio F, Bull H, Milne L, Thomas WTB, Flavell AJ, Pillen K (2018) Contrasting genetic regulation of plant development in wild barley grown in two European environments revealed by nested association mapping. J Exp Bot 69(7):1517–1531CAS 
PubMed 
PubMed Central 

Google Scholar 
Hill W, Weir B (1988) Variances and covariances of squared linkage disequilibria in finite populations. Theor Popul Biol 33(1):54–78CAS 
PubMed 

Google Scholar 
Hodgins KA, Yeaman S (2019) Mating system impacts the genetic architecture of adaptation to heterogeneous environments. New Phytol 224(3):1201–1214PubMed 

Google Scholar 
House GL, Hahn MW (2018) Evaluating methods to visualize patterns of genetic differentiation on a landscape. Mol Ecol Resour 18(3):448–460PubMed 

Google Scholar 
Hübner S, Korol AB, Schmid KJ (2015) Rna-seq analysis identifies genes associated with differential reproductive success under drought-stress in accessions of wild barley hordeum spontaneum. BMC Plant Biol15(1):1–14
Google Scholar 
Hübner S, Bdolach E, Ein-Gedy S, Schmid KJ, Korol A, Fridman E (2013) Phenotypic landscapes: phenological patterns in wild and cultivated barley. J Evol Biol 26(1):163–174PubMed 

Google Scholar 
Hübner S, Günther T, Flavell A, Fridman E, Graner A, Korol A, Schmid KJ (2012) Islands and streams: clusters and gene flow in wild barley populations from the Levant. Mol Ecol 21(5):1115–1129PubMed 

Google Scholar 
Hübner S, Höffken M, Oren E, Haseneyer G, Stein N, Graner A, Schmid K, Fridman E (2009) Strong correlation of wild barley (Hordeum spontaneum) population structure with temperature and precipitation variation. Mol Ecol 18(7):1523–1536PubMed 

Google Scholar 
Jakob SS, Rödder D, Engler JO, Shaaf S, Özkan H, Blattner FR, Kilian B (2014) Evolutionary history of wild barley (Hordeum vulgare subsp. spontaneum) analyzed using multilocus sequence data and paleodistribution modeling. Genome Biol Evol 6(3):685–702CAS 
PubMed 
PubMed Central 

Google Scholar 
Jayakodi M, Padmarasu S, Haberer G, Bonthala VS, Gundlach H, Monat C, Lux T, Kamal N, Lang D, Himmelbach A, Ens J, Zhang X-Q, Angessa TT, Zhou G, Tan C, Hill C, Wang P, Schreiber M, Boston LB, Plott C, Jenkins J, Guo Y, Fiebig A, Budak H, Xu D, Zhang J, Wang C, Grimwood J, Schmutz J, Guo G, Zhang G, Mochida K, Hirayama T, Sato K, Chalmers KJ, Langridge P, Waugh R, Pozniak CJ, Scholz U, Mayer KFX, Spannagl M, Li C, Mascher M, Stein N (2020) The barley pan-genome reveals the hidden legacy of mutation breeding Nature 588:284–289. https://doi.org/10.1038/s41586-020-2947-8CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation. Ecol Lett 7(12):1225–1241
Google Scholar 
Kilian B, Özkan H, Kohl J, von Haeseler A, Barale F, Deusch O, Brandolini A, Yucel C, Martin W, Salamini F (2006) Haplotype structure at seven barley genes: relevance to gene pool bottlenecks, phylogeny of ear type and site of barley domestication. Mol Genet Genom 276(3):230–241CAS 

Google Scholar 
Lasky JR, Des Marais DL, McKAY JK, Richards JH, Juenger TE, Keitt TH (2012) Characterizing genomic variation of arabidopsis thaliana: the roles of geography and climate. Mol Ecol 21(22):5512–5529PubMed 

Google Scholar 
Lasky JR, Upadhyaya HD, Ramu P, Deshpande S, Hash CT, Bonnette J, Juenger TE, Hyma K, Acharya C, Mitchell SE et al. (2015) Genome-environment associations in sorghum landraces predict adaptive traits. Sci Adv 1(6):e1400218PubMed 
PubMed Central 

Google Scholar 
Lawson DJ, Van Dorp L, Falush D (2018) A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nat Commun 9(1):1–11CAS 

Google Scholar 
Lee C-R, Mitchell-Olds T (2011) Quantifying effects of environmental and geographical factors on patterns of genetic differentiation. Mol Ecol 20(22):4631–4642PubMed 
PubMed Central 

Google Scholar 
Leek JT (2011) Asymptotic conditional singular value decomposition for high-dimensional genomic data. Biometrics 67(2):344–352PubMed 

Google Scholar 
Legendre P, Legendre L (2012) Canonical analysis. In: Numerical ecology, 3rd English edn, chap. 11. Elsevier Science BV, The Netherlands, pp 625–710López-Goldar X, Agrawal AA (2021) Ecological interactions, environmental gradients, and gene flow in local adaptation Trends Plant Sci 26(8):796–809PubMed 

Google Scholar 
Lotterhos KE, Whitlock MC (2015) The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol Ecol 24(5):1031–1046PubMed 

Google Scholar 
Lundgren E, Ralph PL (2019) Are populations like a circuit? Comparing isolation by resistance to a new coalescent-based method. Mol Ecol Resour 19(6):1388–1406PubMed 

Google Scholar 
Makowski D, Ben-Shachar M, Lüdecke D (2019) bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J Open Source Softw 4(40):1541
Google Scholar 
Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, Radchuk V, Dockter C, Hedley PE, Russell J et al. (2017) A chromosome conformation capture ordered sequence of the barley genome. Nature 544(7651):427–433CAS 
PubMed 

Google Scholar 
Mascher M (2019) Pseudomolecules and annotation of the second version of the reference genome sequence assembly of barley cv. morex [morex v2]. https://doi.ipk-gatersleben.de:443/DOI/83e8e186-dc4b-47f7-a820-28ad37cb176b/d1067eba-1d08-42e2-85ec-66bfd5112cd8/2McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5(10):e1000686Mee JA, Yeaman S (2019) Unpacking conditional neutrality: genomic signatures of selection on conditionally beneficial and conditionally deleterious mutations. Am Nat 194(4):529–540PubMed 

Google Scholar 
Milner SG, Jost M, Taketa S, Mazón ER, Himmelbach A, Oppermann M, Weise S, Knüpffer H, Basterrechea M, König P et al. (2019) Genebank genomics highlights the diversity of a global barley collection. Nat Genet 51(2):319–326CAS 
PubMed 

Google Scholar 
Morrell PL, Toleno DM, Lundy KE, Clegg MT (2005) Low levels of linkage disequilibrium in wild barley (Hordeum vulgare ssp. spontaneum) despite high rates of self-fertilization. Proc Natl Acad Sci USA 102(7):2442–2447CAS 
PubMed 
PubMed Central 

Google Scholar 
Navarro JAR, Willcox M, Burgueño J, Romay C, Swarts K, Trachsel S, Preciado E, Terron A, Delgado HV, Vidal V et al. (2017) A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat Genet 49(3):476
Google Scholar 
Nevo E, Zohary D, Brown A, Haber M (1979) Genetic diversity and environmental associations of wild barley, Hordeum spontaneum, in Israel. Evolution 33(3):815–833CAS 
PubMed 

Google Scholar 
Nevo E, Beharav A, Meyer RC, Hackett CA, Forster BP, Russell JR, Powell W (2005) Genomic microsatellite adaptive divergence of wild barley by microclimatic stress in ‘Evolution Canyon’, Israel. Biol J Linn Soc84(2):205–224
Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2019) vegan: community ecology package. R package version 2.5-6. https://CRAN.R-project.org/package=veganPankin A, Altmüller J, Becker C, von Korff M (2018) Targeted resequencing reveals genomic signatures of barley domestication. New Phytol 218(3):1247–1259CAS 
PubMed 
PubMed Central 

Google Scholar 
Pekel J-F, Cottam A, Gorelick N, Belward AS (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540(7633):418–422CAS 

Google Scholar 
Pembleton L, Cogan N, Forster J (2013) StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations Mol Ecol Res 13:946–952. https://doi.org/10.1111/1755-0998.12129CAS 
Article 

Google Scholar 
Peterman WE (2018) Resistancega: an r package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol Evol 9(6):1638–1647
Google Scholar 
Petkova D, Novembre J, Stephens M (2016) Visualizing spatial population structure with estimated effective migration surfaces. Nat Genet 48(1):94CAS 
PubMed 

Google Scholar 
Pham A-T, Maurer A, Pillen K, Brien C, Dowling K, Berger B, Eglinton JK, March TJ (2019) Genome-wide association of barley plant growth under drought stress using a nested association mapping population. BMC Plant Biol 19(1):134PubMed 
PubMed Central 

Google Scholar 
Poland JA, Brown PJ, Sorrells ME, Jannink J-L (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7(2):e32253CAS 
PubMed 
PubMed Central 

Google Scholar 
Pyhäjärvi T, Hufford MB, Mezmouk S, Ross-Ibarra J (2013) Complex patterns of local adaptation in teosinte. Genome Biol Evol 5(9):1594–1609PubMed 
PubMed Central 

Google Scholar 
Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24(17):4348–4370PubMed 

Google Scholar 
Renaut S, Grassa CJ, Yeaman S, Moyers BT, Lai Z, Kane NC, Bowers JE, Burke JM, Rieseberg LH (2013) Genomic islands of divergence are not affected by geography of speciation in sunflowers. Nat Commun 4(1):1–8
Google Scholar 
Russell J, Mascher M, Dawson IK, Kyriakidis S, Calixto C, Freund F, Bayer M, Milne I, Marshall-Griffiths T, Heinen S et al. (2016) Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation. Nat Genet 48(9):1024CAS 
PubMed 

Google Scholar 
Samuk K, Samuk K, Owens GL, Delmore KE, Miller SE, Rennison DJ, Schluter D (2017) Gene flow and selection interact to promote adaptive divergence in regions of low recombination. Mol Ecol 26(17):4378–4390PubMed 

Google Scholar 
Sato K, Mascher M, Himmelbach A, Haberer G, Spannagl M, Stein N (2021) Chromosome-scale assembly of wild barley accession ‘OUH602’. G3 11(10):jkab244PubMed 
PubMed Central 

Google Scholar 
Schmid K, Kilian B. Russell J (2018) Barley domestication, adaptation and population genomics. In: The Barley Genome, Springer International Publishing: Cham, pp 317–336Szkiba D, Kapun M, von Haeseler A, Gallach M (2014) SNP2GO: functional analysis of genome-wide association studies. Genetics 197(1):285–289PubMed 
PubMed Central 

Google Scholar 
Terrazas RA, Balbirnie-Cumming K, Morris J, Hedley PE, Russell J, Paterson E, Baggs EM, Fridman E, Bulgarelli D (2020) A footprint of plant eco-geographic adaptation on the composition of the barley rhizosphere bacterial microbiota. Sci Rep 10(1):1–13
Google Scholar 
Tiffin P, Ross-Ibarra J (2014) Advances and limits of using population genetics to understand local adaptation. Trends Ecol Evol 29(12):673–680PubMed 

Google Scholar 
Tsuda Y, Chen J, Stocks M, Källman T, Sønstebø JH, Parducci L, Semerikov V, Sperisen C, Politov D, Ronkainen T et al. (2016) The extent and meaning of hybridization and introgression between siberian spruce (picea obovata) and norway spruce (picea abies): cryptic refugia as stepping stones to the west? Mol Ecol 25(12):2773–2789CAS 
PubMed 

Google Scholar 
Turner-Hissong SD, Mabry ME, Beissinger TM, Ross-Ibarra J, Pires JC (2020) Evolutionary insights into plant breeding Curr Opin Plant Biol 54:93–100. https://doi.org/10.1016/j.pbi.2020.03.003CAS 
Article 
PubMed 

Google Scholar 
de Villemereuil P, Frichot É, Bazin É, François O, Gaggiotti OE (2014) Genome scan methods against more complex models: when and how much should we trust them? Mol Ecol 23(8):2006–2019PubMed 

Google Scholar 
Volis S (2011) Adaptive genetic differentiation in a predominantly self-pollinating species analyzed by transplanting into natural environment, crossbreeding and QST-FST test. New Phytol 192(1):237–248CAS 
PubMed 

Google Scholar 
Volis S, Mendlinger S, Ward D (2002a) Differentiation in populations of Hordeum spontaneum along a gradient of environmental productivity and predictability: life history and local adaptation. Biol J Linn Soc 77(4):479–490
Google Scholar 
Volis S, Mendlinger S, Ward D (2002b) Adaptive traits of wild barley plants of Mediterranean and desert origin. Oecologia 133(2):131–138PubMed 

Google Scholar 
Volis S, Zaretsky M, Shulgina I (2010) Fine-scale spatial genetic structure in a predominantly selfing plant: role of seed and pollen dispersal. Heredity 105(4):384–393CAS 
PubMed 

Google Scholar 
Volis S, Shulgina I, Ward D, Mendlinger S (2003) Regional subdivision in wild barley allozyme variation: adaptive or neutral? J Hered 94(4):341–351CAS 
PubMed 

Google Scholar 
Volis S, Verhoeven K, Mendlinger S, Ward D (2004) Phenotypic selection and regulation of reproduction in different environments in wild barley. J Evol Biol 17(5):1121–1131CAS 
PubMed 

Google Scholar 
Volis S, Yakubov B, Shulgina I, Ward D, Mendlinger S (2005) Distinguishing adaptive from nonadaptive genetic differentiation: comparison of q st and f st at two spatial scales. Heredity 95(6):466–475CAS 
PubMed 

Google Scholar 
Volis S, Yakubov B, Shulgina I, Ward D, Zur V, Mendlinger S (2001) Tests for adaptive RAPD variation in population genetic structure of wild barley, Hordeum spontaneum Koch. Biol J Linn Soc 74(3):289–303
Google Scholar 
Wang X, Chen Z-H, Yang C, Zhang X, Jin G, Chen G, Wang Y, Holford P, Nevo E, Zhang G et al. (2018) Genomic adaptation to drought in wild barley is driven by edaphic natural selection at the Tabigha Evolution Slope. Proc Natl Acad Sci USA 115(20):5223–5228CAS 
PubMed 
PubMed Central 

Google Scholar 
Wiegmann M, Wiegmann M, Maurer A, Pham A, March TJ, Al-Abdallat A, Thomas WTB, Bull HJ, Shahid M, Eglinton J, Baum M, Flavell AJ, Tester M, Pillen K (2019) Barley yield formation under abiotic stress depends on the interplay between flowering time genes and environmental cues Sci Rep 9(1):6397. https://doi.org/10.1038/s41598-019-42673-1CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yeaman S, Whitlock MC (2011) The genetic architecture of adaptation under migration–selection balance. Evolution 65(7):1897–1911PubMed 

Google Scholar 
Zheng X, Levine D, Shen J, Gogarten S, Laurie C, Weir B (2012) A high-performance computing toolset for relatedness and principal component analysis of snp data Bioinformatics 28(24):3326–3328. https://doi.org/10.1093/bioinformatics/bts606CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More