Philippa Kaur

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

Ancient trees thrive where humans don’t: on the remote, rocky slopes of high mountains. So shows an analysis of tens of thousands of trees1.

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

doi: https://doi.org/10.1038/d41586-022-00832-x

ReferencesLiu, J. et al. Conserv. Biol. https://doi.org/10.1111/cobi.13907 (2022).Article 

Google Scholar 
Download references

Subjects

Conservation biology

Jobs

Research Associate in Joint Sensing and Communications for Next Generation Wireless Networks

Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg
Luxembourg, Luxembourg

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

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

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

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Research Technician

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

Xu, L., Saatchi, S.S., Yang, Y., Yu, Y., Pongratz, J., Bloom, A.A., Bowman, K., Worden, J., Liu, J., Yin, Y. & Domke, G. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7(27), p.eabe9829 (2001).Hoecker, T. J., Higuera, P. E., Kelly, R. & Hu, F. S. Arctic & boreal paleofire records reveal drivers of fire activity & departures from Holocene variability. Ecology 101(9), e03096 (2020).Article 

Google Scholar 
Bradshaw, C. J. & Warkentin, I. G. Global estimates of boreal forest carbon stocks & flux. Global Planet. Change 128, 24–30 (2015).Article 

Google Scholar 
Kuhry, P. & Turunen, J. The postglacial development of boreal and subarctic peatlands in Boreal Peatland Ecosystems, 25–46 (Springer, 2006).Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572(7770), 520–523 (2019).CAS 
Article 

Google Scholar 
Gauthier, S., Bernier, P., Kuuluvainen, T., Shvidenko, A. Z. & Schepaschenko, D. G. Boreal forest health & global change. Science 349(6250), 819–822 (2015).CAS 
Article 

Google Scholar 
Walker, X. J. et al. Cross-scale controls on carbon emissions from boreal forest megafires. Global Change Biol. 24(9), 4251–4265 (2018).Article 

Google Scholar 
Flannigan, M. D., Logan, K. A., Amiro, B. D., Skinner, W. R. & Stocks, B. J. Future area burned in Canada. Clim. Change 72(1), 1–16 (2005).CAS 
Article 

Google Scholar 
Balshi, M. S. et al. Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach. Global Change Biol. 15(3), 578–600 (2009).Article 

Google Scholar 
Johnstone, J. F. & Chapin, F. S. Fire interval effects on successional trajectory in boreal forests of northwest Canada. Ecosystems 9(2), 268–277 (2006).Article 

Google Scholar 
Viereck, L.A. & Little, E.L. Alaska trees & shrubs. US Forest Service 410, (1972).Paine, R. T., Tegner, M. J. & Johnson, E. A. Compounded perturbations yield ecological surprises. Ecosystems 1(6), 535–545 (1998).Article 

Google Scholar 
Buma, B. Disturbance interactions: characterization, prediction, & the potential for cascading effects. Ecosphere 6(4), 1–15 (2015).Article 

Google Scholar 
Burton, P. J., Jentsch, A. & Walker, L. R. The ecology of disturbance interactions. Bioscience 70(10), 854–870 (2020).Article 

Google Scholar 
Brown, C. D. & Johnstone, J. F. Once burned, twice shy: Repeat fires reduce seed availability & alter substrate constraints on Picea mariana regeneration. Forest Ecol. Manage. 266, 34–41 (2012).Article 

Google Scholar 
Buma, B., Brown, C. D., Donato, D. C., Fontaine, J. B. & Johnstone, J. F. The impacts of changing disturbance regimes on serotinous plant populations & communities. Bioscience 63(11), 866–876 (2013).Article 

Google Scholar 
Coop, J. D. et al. Wildfire-driven forest conversion in western North American landscapes. Bioscience 70(8), 659–673 (2020).Article 

Google Scholar 
Enright, N. J., Fontaine, J. B., Bowman, D. M., Bradstock, R. A. & Williams, R. J. Interval squeeze: altered fire regimes & demographic responses interact to threaten woody species persistence as climate changes. Front. Ecol. Enviro 13(5), 265–272 (2015).Article 

Google Scholar 
Burns, R.M., & Honkala B.H. Silvics of North America US Department of Agriculture, Forest Service, Ag. Handbook 654, (1990).Hayes, K. & Buma, B. Effects of short-interval disturbances continue to accumulate, overwhelming variability in local resilience. Ecosphere 12(3), 03379 (2021).Article 

Google Scholar 
Mack, M. C. et al. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science 372(6539), 280–283 (2021).CAS 
Article 

Google Scholar 
Viereck, L. A., Dyrness, C. T. & Foote, M. J. An overview of the vegetation & soils of the floodplain ecosystems of the Tanana River, interior Alaska. Can. J. For. Res. 23(5), 889–898 (1993).Article 

Google Scholar 
Hoy, E. E., Turetsky, M. R. & Kasischke, E. S. More frequent burning increases vulnerability of Alaskan boreal black spruce forests. Enviro. Res. Lett. 11(9), 095001 (2016).Article 

Google Scholar 
Whitman, E., Parisien, M. A., Thompson, D. K. & Flannigan, M. D. Short-interval wildfire & drought overwhelm boreal forest resilience. Sci. Rep. 9(1), 1–12 (2019).Article 

Google Scholar 
Greene, D. F. et al. The reduction of organic-layer depth by wildfire in the North American boreal forest & its effect on tree recruitment by seed. Can. J. For. Res. 37(6), 1012–1023 (2007).Article 

Google Scholar 
Héon, J., Arseneault, D. & Parisien, M. A. Resistance of the boreal forest to high burn rates. PNAS 111(38), 13888–13893 (2014).Article 

Google Scholar 
Buma, B., Weiss, S., Hayes, K. & Lucash, M. Wildland fire reburning trends across the US West suggest only short-term negative feedback & differing climatic effects. Enviro. Res. Lett. 15(3), 034026 (2020).Article 

Google Scholar 
Thompson, D. K. et al. Fuel accumulation in a high-frequency boreal wildfire regime: from wetland to upland. Can. J. For. Res. 47(7), 957–964 (2017).Article 

Google Scholar 
Kasischke, E. S. et al. Alaska’s changing fire regime—implications for the vulnerability of its boreal forests. Can. J. For. Res. 40(7), 1313–1324 (2010).Article 

Google Scholar 
Kelly, R. et al. Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. PNAS 110(32), 13055–13060 (2013).CAS 
Article 

Google Scholar 
Gaboriau, D. M. et al. Temperature & fuel availability control fire size/severity in the boreal forest of central Northwest Territories, Canada. Quat. Sci. Rev. 250, 106697 (2020).Article 

Google Scholar 
Johnstone, J. F., Rupp, T. S., Olson, M. & Verbyla, D. Modeling impacts of fire severity on successional trajectories & future fire behavior in Alaskan boreal forests. Landscape Ecol. 26(4), 487–500 (2011).Article 

Google Scholar 
Hess, K. A. et al. Satellite-based assessment of grassland conversion & related fire disturbance in the Kenai Peninsula, Alaska. Rem. Sens. 11(3), 283 (2019).Article 

Google Scholar 
Hollingsworth, T. N., Breen, A. L., Hewitt, R. E. & Mack, M. C. Does fire always accelerate shrub expansion in Arctic tundra? Examining a novel grass-dominated successional trajectory on the Seward Peninsula. A. A. A. Res. 53(1), 93–109 (2021).
Google Scholar 
Turner, M. G., Romme, W. H. & Tinker, D. B. Surprises & lessons from the 1988 Yellowstone fires. Frontiers Ecol. Environ. 1(7), 351–358 (2003).Article 

Google Scholar 
Shvidenko, A. Z. et al. Impact of wildfire in Russia between 1998–2010 on ecosystems & the global carbon budget. Dokl. Earth Sci. 441(2), 1678–1682 (2011).CAS 
Article 

Google Scholar 
Alaska Fire Service 2021. Alaska Interagency Coordination Center, Bureau of L& Management, Alaska Fire Service. https://fire.ak.blm.gov/arcgis/rest/services/Map&FeatureServices/FireHistory/MapServer/1French, N. H. et al. Using Landsat data to assess fire & burn severity in the North American boreal forest region: an overview and summary of results. Int. J. Wildland Fire 17(4), 443–462 (2008).Article 

Google Scholar 
Morimoto, M. & Juday, G. Perspectives on Sustainable Forest Management in Interior Alaska Boreal Forest: Recent History and Challenges. Forests 10(6), 484 (2019).Article 

Google Scholar 
NASA/METI/AIST/Japan Spacesystems, & U.S./Japan ASTER Science Team. ASTER Global Digital Elevation Model V003. distributed by NASA EOSDIS L& Processes DAAC, https://doi.org/10.5067/ASTER/ASTGTM.003 (2018)Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim 37(12), 4302–4315 (2017).Article 

Google Scholar 
US Environmental Protection Agency, Level III Ecoregions of the Continental United States, Corvallis, Oregon: U.S. EPA— National Health & Environmental Effects Research Laboratory https://epa.gov/eco-research/level-iii-&-ivecoregions-continental-united-states (2013)Wang, J.A., et al. ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014. ORNL DAAC, Oak Ridge, Tennessee, USA (2019).. https://doi.org/10.3334/ORNLDAAC/1691Debeer, D. & Strobl, C. Conditional permutation importance revisited. BMC Bioinform. 21(1), 1–30 (2020).Article 

Google Scholar 
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for r&om forests. BMC Bioinform. 9(1), 1–11 (2008).Article 

Google Scholar 
Moisen, G. & Frescino, T. Comparing five modelling techniques for predicting forest characteristics. Ecol. Mod. 157, 209–225 (2002).Article 

Google Scholar 
R Core Team R: A language & environment for statistical computing (2021).Pebesma, E.J. & Bivand, R.S. Classes and methods for spatial data in R. R News 5 (2), https://cran.r-project.org/doc/Rnews/. (2005)Hijmans, R.J. raster: Geographic Data Analysis and Modeling. R package version 3.4–5. (2020) https://CRAN.R-project.org/package=rasterStrobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for random forests. BMC Bioinform. 9, 307 (2008).Article 

Google Scholar 
Debeer, D., Hothorn, T. & Strobl, C permimp: Conditional Permutation Importance. R package version 1.0–1. https://CRAN.R-project.org/package=permimp (2021)Schneider, G., Chicken, E., & Becvarik, R. NSM3:Functions and Datasets to Accompany Hollander, Wolfe, and Chicken – Nonparametric Statistical Methods, Third Edition. R package version 1.16. https://CRAN.R-project.org/package=NSM3 (2021) More

Study area and dateIn the north-west of France, the macrotidal Bourgneuf Bay (1°-2° W, 46°-47° N; total area ~340 km2; Fig. 1) has an intertidal zone largely dominated by mudflats (exposed surface area ~100 km2). Bourgneuf Bay is situated south of the Loire estuary and is open to the sea along 12 km from the west to the north-west. C. gigas aquaculture here is of national importance and wild C. gigas reefs can account for over double the biomass of their farmed conspecifics65. Analysis of satellite observations covering 30 years of MPB biomass in the bay confirmed the co-occurrence of high MPB biomass with wild oyster reefs and cultivated stocks16 (Supplementary Methods: Wider Situation of the Reefs). Two small (each  > 750 m2) wild C. gigas reefs and their immediate surroundings (10–100 m) in the north of Bourgneuf Bay were deemed suitable for experimental manipulation (yellow and orange regions in Fig. 1). Méléder et al.18 described MPB biomass as mostly concentrating around the 2 m isobath, the Falleron river channel (closest point ~400 m NNE from the eastern reef), and oyster farms. Covering this isobath, we superimposed a 350 * 350 m grid (12.25 hectares) to cover the two wild oyster reefs, orientated so that the ‘Y’ axis runs parallel to the slope of bathymetry (Fig. 1). The grid was split regularly into 49 ‘grand-cells’ of 50 * 50 m (i.e., n = 49) and each of those split into 25 cells of 10 * 10 m (i.e., n = 1225; Fig. 1). Four cells per grand-cell were chosen randomly for the sampling of meiofauna, granulometry, OM (see Table 3 for specific methodology), and macrofauna. Of these cells, only every second cell was processed for meiofauna because of time constraints in assessing their abundance.Table 3 Summary of study variables and their sampling methodologies.Full size tableAlthough there were only two oyster reef complexes (‘reefs’, hereon) in this study, multiple sampling cells fell on, or in close proximity to, each reef, so that each reef had many potential (though not independent) distance decay transects running from it capturing natural variation in spatial structure66. Comparing the ecological change following the experimental burning of oyster reefs (described below) against ecological change occurring at these two reefs over the previous 25 years16 also allowed us greater confidence to disentangle the treatment effects from typical variation. Through the centres of five grand-cells to the south of the extent, a transect forming an ‘L’ shape (Fig. 1) was sampled every 10 m for in situ MPB pigment composition and biomass. We used these data to complete the remote sensing approach for MPB biomass estimation (see below, Microphytobenthos). The western reef was slightly larger than the eastern reef and contained a large rock, ‘Roche Bonnet’, rising 0.5–1 m from the sediment. Outside the grid, another larger (200 * 80 m) wild oyster reef lies WSW at ~260 m distance from the western reef. The grid was sampled for the variables listed in Table 3 during the winter MPB low and early autumn peak seasons (see also ground-truthing in16), on the dates 18-19th September 2013 and 17-18th March 2014, before treatment, and on 7-8th October 2014 after treatment.MicrophytobenthosWe mapped MPB biomass by satellite remote sensing, following the method described in detail in Echappé et al. (2018). We used the same long-term record of high-resolution satellite images to analyse the spatial distribution of the normalised difference vegetation index (NDVI), a proxy of MPB chlorophyll a concentration at the sediment’s surface18,67, before and after treatment (individual image details in captions of Fig. 2 and Supplementary Figs. S4–S7). After atmospheric correction (FLASH and US40 aerosol model), the satellite-derived NDVI was validated against associated field measurements (r2 = 0.85, root-mean-square deviation, RMSE = 0.04, n = 57, P 20°) was limited (Supplementary Results: Additional MPB Images). An optimal image was chosen as representative of MPB biomass patterns per season16. The study area would ideally be tidally uncovered for ~2 h before the image was taken, whereupon MPB biomass is concentrated at the sediment surface. The optimal images met this condition (i.e., Fig. 2).To complete NDVI maps, in situ MPB pigment composition and biomass were retrieved by HPLC analysis from the 25 triplicates of sediment. These had been sampled using contact-cores to freeze the top 2 mm of sediment in situ with liquid nitrogen, with a metal surface 56 mm in diameter. Biomass was expressed by Chl a concentration (mg m−2), and dominance of MPB taxa was broadly assessed by ratio of pigment sources to Chl a: Fucoxanthin (Fuco), Diadinoxanthin (DD), Diatoxanthin (DT) and Chl c for diatoms. The ratio of unknown carotenoids (interpreted as by-products due to the low resolution of their absorption spectra) to Chl a was also analysed for ecological purposes (dominant taxa), whereas grazing pressure was investigated using the ratio of pheophorbid a to Chl a (methodological discussion in28).Sediment variablesFor laser granulometry, we sampled two depths, 0–5 cm and 5–10 cm, in triplicate at each cell. Each of the triplicate samples was put in a vial with water and sonicated. The particle size distribution was determined on a Mastersizer 3000 with a reporting range 50 nm to 3 mm. We also determined sediment percentage OM at two depths by mass loss on ignition in comparison to the oven-dried original (procedure as described for Macrofauna, also Table 3).MacrofaunaWe sampled macrofauna by a single 200 * 200 mm (depth * diameter) core per cell. Contents were placed into labelled buckets and sieved onshore (1 mm mesh). Soft-bodied polychaetes were picked out with forceps and preserved in buffered formalin during sieving. All material left on the sieve was bagged and preserved in formalin at the laboratory. Individuals were counted and measured by the longest axis (accuracy 0.1 mm, calipers); the deep-burrowing polychaete Diopatra biscayensis was counted by the presence of visible tubes above the sediment. Calibration curves from length to mass per species per season were built by identifying size classes by Sturges rule. Multiple individuals per size class (ideally n = 100) were measured to estimate mean organic mass per individual of each size class. Shell matter was physically separated from tissue, before both being dried in aluminium foil cups for 48 h at 60 °C and weighed (g) for tissue dry mass using a mass balance. Dry mass was then incinerated for four hours at 450 °C and reweighed (g; decrease in mass of the aluminium cup was also accounted for), the difference giving the organic matter mass (including residue in the shell matter), or ash free dry weight (AFDW). This number was divided by number of individuals. Calibration curves per species used first order polynomial curves for bivalves, unless numbers of size classes and individuals were small (1% of the total abundance. All mapping and analyses were performed in the statistical computing environment R (v4.0.2)73.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

The Willmott similarity indices, over the first 4 months, for outside and inside temperatures, in sunny and shaded environments, with values close to 1, confirm that A. sexdens colonies were exposed to significantly different temperatures. The lowest values of this index for irradiance, differing between the sunny and shaded environments for internal temperature (in the initial chamber), indicates variation in this parameter between environments. Irradiance values close to 0 between environments represent differences between sunny and shaded areas, but the temperature of the initial chamber did not vary. The selection pressure from irradiance and, consequently, temperature, defines the ideal depth of the initial chamber for the founding queens16 to keep this parameter at values adequate for the fungus garden and the offspring8 in the field. The similar temperature, in the nests in the shaded environment, agrees with that reported for this leaf-cutting ant, of 24°C3, but this varies between species of these insects, being 25–28 °C for A. sexdens17, 27.5 °C for A. vollenweideri18 and 27 °C for A. heyeri19. This adaptation of an ideal depth to construct the initial chamber allows A. sexdens to adapt to different habitats in full sun and shade. The nesting process, habits and foraging strategies differ between A. bisphaerica and A. sexdens, with the former normally establishing their nests in full sun in areas with predominantly grass forages and the latter in shaded areas where it cuts dicot leaves14. Furthermore, differences in nest depth between these species may be related to soil temperature, which is lower in shaded areas7. For this reason, fungal chambers in exposed nests in pastures are deeper than in shaded areas in forests, as soil temperature is negatively correlated with depth7. Soil humidity and temperature act simultaneously due to the thermo preference of workers, resulting in the construction of shallower nests in cold soils and deeper nests in warmer ones7. Soil moisture also varies according to depth, affecting the nest-digging behavior of leaf-cutting ants7,15.The temperature in the shaded environment was higher than that reported for A. sexdens rubropilosa, from 24.82 ± 3.14 to 24.11 ± 1.30 °C at a depth of 5–25 cm underground, for optimal offspring development and, consequently, reduction in the lipid content of queens at high temperatures, without affecting their survival20. This is because the depth of the initial chamber excavated by the queen is adequate for colony success8,16.The different habitats occupied by the ant A. sexdens21, from dense forests to cerrado and caatinga may explain the greater depth and volume of the initial chamber of the nests in shaded than in sunny environments. However, the depth of the initial nests varies between Atta species with 7.5–12 cm, 6.5–13 cm, 15–25 cm, 10–30 cm, 10–15 cm, 11–34 cm, and 9–15 cm for A. colombica, A. cephalotes, A. texana, A. sexdens rubropilosa, A bisphaerica, A capiguara, and A. insulares, respectively1,13,15,22,23,24. The initial chamber volume is within the expected range for A. sexdens1,13 in both environments, with a chamber volume of 24.88 cm3 in a shaded area with eucalyptus plantation13. Different excavation efforts with the removal of small soil particles by the founding queen using her jaws in repeated biting motions25, subsequently discarded outside the nest8,16,26 may explain the greater volume of the initial chamber in the shaded area. The greater solar irradiation in sunny areas increases the temperature, with the higher soil temperature generating greater excavation effort and oxidative damage27,28, in addition to water loss, as described for seed-collecting ants29,30. Further, humid soils are easier to dig, which explains the greater volume of the initial chamber in the shaded area as found for the excavation behavior by Atta spp. in soils with different densities and moistures15,16,31.The higher mass of A. sexdens queens in the first month of the claustral phase than in the fourth, in both sunny and shaded environments, stems from a reduction in their body mass, due to the metabolism of the lipid content during the first 6 months following the flight, but with recovery in the subsequent months1. The mass loss is due to the use of body reserves by the queens to prepare and maintain the colony in the claustral phase, as stored lipids are important in the evolutionary history of the Attini tribe, from semi-claustral to claustral foundation32. The queen, with a claustral foundation, does not feed, remaining enclosed in the nest and rearing her initial offspring by metabolizing her own body reserves33, as reported for this ant species1. The selection pressure on the evolution of claustral foundation tends to minimize risk during foraging33 with a more viable adaptation being the storing of reserves in the body, as observed in our study. The greater biomass of the fungus garden in the fourth month is due to growth, but its values were lower than those reported for 4-month-old A. sexdens nests, from 2000 to 3000 mg1.The lower number of A. sexdens eggs in the first than in the third month is similar to that observed in laboratory colonies of this ant8. The irregularity in the egg production by the queen is due to hormone fluctuations regulated by the endocrine system, and is correlated with the activity cycle of the corpora allata during the 3 or 4 months of colony life34. This gland synthesizes the juvenile hormone, which acts in the oviposition of founding queens, as verified for females that underwent alatectomy34. This hormone acts in the fat body, initiating the synthesis of vitellogenin (glycolipophosphoproteins, with lipids and carbohydrates in its composition) to be deposited in the oocyte35. Thus, the production of offspring depends on body reserves (fatty body lipids and muscle mass protein), as the queen does not feed during the foundation period (claustral foundation). The lower production of small and medium workers in the first month than in the second, third or fourth months, agrees with that reported in A. sexdens nests1. The similar number of larvae over the 4 months is due to the duration of the larval period of A. sexdens, of around 25 days8, with new immature individuals produced monthly with overlapping generations, common in social insects. This overlap begins with larvae in the first month of nests and pupae, usually in the second month of ant nests in the laboratory8.The lower values of fungus biomass and number of eggs, larvae, pupae and small and medium workers of A. sexdens in nests in the sunny environment may be due to a higher incidence of solar irradiance increasing the variation in the internal temperature of the initial chamber. This agrees with reports that a lower incidence of solar irradiance improved the stability of the internal temperature of the initial chamber, favoring A. sexdens with narrow thermal tolerance range as it is a thermally protected underground species11. However, frequent heat peaks, with habitat-specific physiological consequences for subterranean ectothermic animals, are common in sunny areas11. The queen’s body mass, similar between environments, indicates a similar reduction of this parameter between them and their tolerance to temperature variations in this type of foundation. A reduction in the mass of A. sexdens queens is expected from the nuptial flight to the end of the claustral phase. The energy expenditure of A. sexdens queens, in carbohydrates and body lipids for the nuptial flight and nest excavation, was estimated at 0.58 J36 and during the claustral phase, they metabolize body lipids and proteins to survive and form the initial colony26,37.The higher mortality of A. sexdens nests in the sunny environment, during the claustral foundation, is due to a higher incidence of solar irradiance, increasing the variation in the internal temperature of the initial chamber and, consequently, the excavation effort and oxidative damage to the founding queens27,28, in addition to water losses as reported for seed-collecting ants29,30. This mortality may also be related to entomopathogens, unsuccessful symbiotic fungus regurgitation, excavation effort, density and soil moisture1,9,38,39.Atta sexdens founder queens were exposed to sunny and shaded environments with greater solar irradiance and, consequently, a greater variation range in the internal temperature of the initial chamber in the first environment. The shaded environment, with lower incidence of solar irradiance and greater stability of the internal temperature of the initial chamber, was more favorable for colony development, as confirmed by the biological parameters and greater survival of A. sexdens queens.
Atta sexdens female collection methods- after the nuptial flightAtta sexdens queens were collected at the Experimental Farm Lageado in Botucatu, Brazil in 2019 (22°50′37.3″S 48°25′38.3″W) on sunny days after heavy rains from late October to early November. Two hundred queens were collected using tweezers. They were stored separately in 250 ml pots with 1 cm wet plaster for 60 min prior to use. We had permission to collect Atta sexdens queen specimens.Experimental areasThe A. sexdens queens were individualized in two experimental areas: sunny—an open area exposed to Global Horizontal Irradiation with exclusive coverage of Paspalum notatum Flügge grass (N = 100) and shaded – an area exposed to Diffuse Horizontal Irradiation (50% shade screen—1.50 × 50 MT), in a plowed environment (N = 100). The soil is a superficial horizon of oxisols.The founding A. sexdens queens were individualized in the center of a square of land (50 × 50 cm) covered with a transparent bottle measuring 20 cm in diameter by 12 cm in height, delimiting the space to be drilled in the soil by the ant queens per experimental area of 25 m2 (Fig. S1).Development of early nests during the claustral phaseAtta sexdens queens were evaluated over 4 months following nest foundation, to monitor its development. A total of 25% of the successfully established nests were excavated per month by removing the colony with a gardening shovel. The number of eggs, larvae, pupae and adults was counted and the mass of the queen and the biomass of the fungus garden determined. The depth, width, length, and height of each nest were measured with the aid of a caliper. The estimated volume of each fungus chamber was based on a cylinder. A correction factor was used to calculate the volume of the chamber because they are rounded: V = πr2 (ch + r0.67), in which ‘r’ is the chamber base radius and ‘ch’ the cylinder height, measured by subtracting the maximum height of the chamber from its radius, ch = h − r40. Queen mortality was evaluated during the excavation of their nests.Temperature and radiation measurementThe temperatures of the external and internal environments (15 cm deep), in each area, were measured for 4 months, with Data loggers (Testo), after the foundation of the nest by the leaf-cutting ant. Global Horizontal Irradiation (GHI) was measured using an Eppley PSP Pyranometer and Diffuse Horizontal Irradiation by a Kipp & Zonen CM3 Pyranometer (Table 3). Solar measurements were obtained over a five-minute time scale (mean of 60 readings with scanning time every five seconds) in W/m2 by a CR300041 model data logger and stored in an ASCII file.Table 3 Instruments used to measure solar irradiance in nests of Atta sexdens (Hymenoptera: Formicidae) in sunny and shaded environments.Full size tableThe measurements were submitted to a quality control procedure to verify if their values were in accordance with pre-defined solar irradiance thresholds. This procedure consists of a series of checks on physically possible limits per component measured (Table 4). These checks were carried out according to the process created by the International Commission on Illumination (CIE) discarding erroneous measures to avoid compromising the processes of numerical integration or data processing.Table 4 Physically possible minimum and maximum values for each measurement of solar irradiance.Full size tableMeasures accepted as possible were those above 0 W/m2 and lower than the maximum stipulated limit, per component, according to the extraterrestrial solar irradiance (IE) (Eq. 1). This represents the maximum value reaching the top of the atmosphere, without attenuation by atmospheric elements (clouds, particles, among others). Values measured at the earth’s surface are lower than those at the top of the atmosphere. However, the phenomenon of multireflection when scattered clouds near the apparent location of the sun reflect part of the solar irradiance onto the sensor, increase the value measured even higher than the extraterrestrial irradiance over short periods45. For this reason, the global irradiance value can be up to 20% higher than that of the extraterrestrial one.The 1361 of Eq. (1), to calculate the extraterrestrial irradiance, represents the solar constant in W/m246, R the relation of the average dimensionless distance between the Earth and the Sun (Eq. 2) and Z the zenithal angle of the Sun (Eq. 3) in degrees47.$${text{I}}_{{text{E}}} = {1361}left( {text{1/R}} right) ,{{cos}}, left( {text{Z}} right)$$
(1)
$$begin{aligned} {text{R}} & = {1} – 0.000{9467};{text{sen}} left( {text{F}} right) – 0.0{1671};{text{cos}},left( {text{F}} right) – 0.000{1489}left( {{text{2F}}} right) \ & quad – 0.0000{2917};{text{sen}}left( {{text{3F}}} right) – 0.000{3438};{text{cos}},left( {{text{4F}}} right) \ end{aligned}$$
(2)
$${text{Z}} = {text{sen}} ,left(updelta right);{text{sen}}left(upphi right) + cos left(updelta right);cos left(upphi right);cos left(upomega right)$$
(3)
F, in Eq. (4), is the angular fraction of the date of interest in degrees, δ, at 5, the solar declination in degrees, Φ, at 6, the geographic latitude of the location in degrees (22.85) and ω, at 6, the clockwise angle in degrees.$${mathbf{F}} = {36}0^circ ;{text{D/365}}$$
(4)
$$begin{aligned} {{varvec{updelta}}} & = 0.{3964} + {3}.{631};{text{sen}}left( {text{F}} right) – {22}.{97};{text{cos}}left( {text{F}} right) + 0.0{3838};{text{sen}}left( {{text{2F}}} right) – 0.{3885};{text{cos}};left( {{text{2F}}} right) \ & quad + 0.0{7659};{text{sen}};left( {{text{3F}}} right) – 0.{1587};{text{cos}}left( {{text{3F}}} right) – 0.0{1}0{21};{text{cos}}left( {{text{4F}}} right) \ end{aligned}$$
(5)
$${{varvec{upomega}}} = left( {{12} – {text{Hd}}} right){15}$$
(6)
The d, in the previous expression, represents the day of the year, from 1 to 365 and the Hd, the hour and the tenth of an hour in degrees of the moment of interest.The values were numerically integrated, after applying the measurement quality control procedure, obtaining a solar irradiation value for the day in MJ/m2 representing the total energy received daily, on a horizontal surface of 1 m2.Statistical analysisThe null hypothesis that the mortality proportions (probabilities of success) of the founding queens of both groups are the same was submitted to the test of equal proportions.The null hypothesis that a data sample came from a normally distributed population was submitted to the Shapiro–Wilk test.Data structure fitting the ANOVA (completely randomized factorial scheme) assumptions were submitted to this analysis and to Tukey’s test for multiple comparisons of means. The Scheirer Ray Hare test is a nonparametric test used for a two-way completely randomized factorial design49. This procedure is an extension of the Kruskal–Wallis rank test allowing for calculation of the interaction effects and linear contrasts and were used for data structure that did not fit the ANOVA assumptions. Dunn’s test50 of multiple median comparisons was performed with a correction (the false discovery rate method) to control the experiment-wise error rate.The Willmott’s Index of Similarity (d) is a standardized measure of the degree of similarity between two data series ranging from 0.0 to 1.0 with the value 1.0 indicating a perfect match (two identical data sets), and 0 no agreement at all51. As an example, in the sunny environment, the indoor and outdoor temperature data were identical, which results in 1.0. The more identical, close, and concordant two data sets are, the closer to 1.0 they will be. The calculation of the index is presented with A and B representing two data sets whose agreement is to be evaluated.$$begin{aligned} A^{prime}_{i} & = A_{i} – overline{B} \ B^{prime}_{i} & = B_{i} – overline{B} \ d & = 1 – frac{{sumnolimits_{i = 1}^{N} {left( {A_{i} – B_{i} } right)^{2} } }}{{sumnolimits_{i = 1}^{N} {left[ {left| {A_{i} } right| – left| {B_{i} } right|} right]^{2} } }} \ end{aligned}$$The R companion package, ggplot252, FSA53, tidyverse54, and hydroGOF55 used is a free software environment for statistical computing and graphics R version 4.0.456. More

Concentration of TPHs in surface soilsStatistical results of TPHs concentrations at different geographic oilfields were showed in Fig. 2, and grid regional distribution of TPHs in YC Oilfield surface soils (Y6–Y25) were shown in Fig. 3. Results are given as mean value of triplicate analysis of each sample. The results of TPHs concentration in soil samples showed that the three oilfields all suffered from varying degrees of petroleum pollution, and 60.92% of the 47 sampling points was significantly higher than the soil critical value (500 mg/kg). The average concentration of the TPHs in each study areas conformed to be in the following law: SL Oilfield (average: 5.36 × 103 mg/kg) ( >) NY Oilfield (average: 1.73 × 103 mg/kg) ( >) YC Oilfield (average: 1.37 × 103 mg/kg). The highest concentration of the TPHs were found in SL Oilfield surface soils, ranging from 1.21 × 102 to 6.66 × 104 mg/kg, and NY Oilfield had the second highest TPHs concentrations in the range from 15.82 to 7.42 × 103 mg/kg. The concentrations of TPHs in YC Oilfield ranged from 12.34 to 5.38 × 103 mg/kg. The petroleum contamination mainly derived from abandoned and working oil wells. S4 and S8 soils were collected near the abandoned oil well and working oil well, respectively, and had the highest concentration of TPHs up to 5.28 × 104 and 6.66 × 104 mg/kg. Y1, N8 near the abandoned oil well also had high concentration of TPHs with 5.39 × 103 and 7.42 × 103 mg/kg, respectively. Pollution caused by grounded crude oil in exploitation process has been a serious problem in oilfield area. Our previous research reported that the TPHs content in Dagang Oilfield soils collected adjacent to working oil wells were about 20-folds higher than that in corn soils and living area soils25. Concentration contour map of TPHs in YC Oilfield by grid sampling method showed that regional pollution in the northwest and southeast area are more serious than other sites. Y6 near the gas station and Y15, Y21, Y23 adjacent to the working oil wells have higher concentration (2.12 × 103–5.34 × 103 mg/kg) of TPHs than other farmland and grass soils. Previous study reported that the concentrations of TPHs ranged 7.0 × 102–4.0 × 103 mg/kg in oil exploitation areas of the loess plateau region (34°20′N,107°10′E), showing a similar pollution level with this study26.Figure 2The concentration of TPHs in three oilfield soils.Full size imageFigure 3Grid regional distribution of TPHs in YC Oilfield.Full size imageThe percentage composition of total PAHs, SHs and polar components of petroleum hydrocarbons were shown in Table 1. In general, the dominant petroleum component was saturated hydrocarbons in all soils, accounting more than 50%. Yet, the percentage proportion of PAHs and SHs in contamination soils adjacent to working and abandon oil wells were significantly different (p  BbF (14.16–21.87%) ≫ BaA, Chr, InP, and BkF (less than 10%). This result aligned to the previous study that the contribution of individual PAHs to the TEQs of ∑PAH16 was BaP (45%)  > DBA (33%) in urban surface dust of Xi’an city, China46. Therefore, contamination control should priority focus on the individual PAHs of BaP, DBA, BbF in these areas. In addition, the ecological risk with abandoned time ranging 0–15 years has been assessed, and the descriptive statistic TEQBap of PAHs was shown in Supporting Information, Table S6. The highest TEQs of ∑PAH16 and ∑PAH7 with mean of 1422.27 μg/kg and 1400.48 μg/kg, respectively, were present in soils adjacent to abandoned oil well with abandoned time of 0—5 years. And the TEQs of ∑PAH16 and ∑PAH7 decreased with the abandoned time though the percentage proportion of PAHs increased. The TEQs of ∑PAH16 and ∑PAH7 were close between abandoned time of 5–10 years and 10—15 years while both had high content. It demonstrated that high ecological risk was persistent in abandoned oil well areas over abandoned time of 15 years, and basically stable after 5 years. Therefore, abandoned oil well areas need to be blocked to prevent PAHs entering the external environment, and combine physical–chemical technology for petroleum remediation instead of simple weathering biological processes.Table 3 Descriptive statistic TEQBap of PAHs in different sampling area.Full size tableAs referred the PAHs standard of Dutch soil, TEQs of ∑PAH7 was 32.02 μg/kg, calculated by ten individual PAHs times TEFs. In this study, the mean TEQs of ∑PAH7 were about 35- and 10-folds of Dutch soil in petro-related area soils and grassland soils, indicating a high and medium ecological risk in these soils respectively. However, the mean TEQs of ∑PAH7 in farmland soils (18.80 μg/kg) was below Dutch soil, presenting a low potential ecological risk. It should be noted that the minimum of TEQs of ∑PAH7 in grassland soil was 26.24 μg/kg less than TEQs of ∑PAH7 in Dutch soil, but it was vulnerable affected by the surrounding soils with high TEQs of ∑PAH7. In this study, except the farmland soils, TEQs of ∑PAH7 exhibited higher TEQ values than those reported soils in Santiago, Chile47 and Nepal24, and road dust in Tianjin, China48. Overall, the most threat of ecological risk in petro-related soils caused by the anthropogenic PAHs input, such like oil leakage, oil refining, and fossil energy combustion. Preventing oil spills accident and developing the remediation methods are the main significant ways to reduce the ecological risks in these areas. The medium ecological risk in grassland might result from the migration of PAHs via rainfall pathway. Therefore, establishment the oil-blocking isolation zones is the critical way for medium ecological risk areas to control petroleum inflow. Even though the low ecological risk was identified in farmland soils, PAHs source analysis indicated that the biomass combustion should be controlled in these areas. More

Jamieson, A. J., Fujii, T., Mayor, D. J., Solan, M. & Priede, I. G. Hadal trenches: the ecology of the deepest places on Earth. Trends Ecol. Evol. 25, 190–197 (2010).PubMed 

Google Scholar 
Stewart, H. A. & Jamieson, A. J. Habitat heterogeneity of hadal trenches: considerations and implications for future studies. Prog. Oceanogr. 161, 47–65 (2018).ADS 

Google Scholar 
Zhu, G. et al. Along-strike variation in slab geometry at the southern Mariana subduction zone revealed by seismicity through ocean bottom seismic experiments. Geophys. J. Int. 218, 2122–2135 (2019).ADS 

Google Scholar 
Bao, R. et al. Tectonically-triggered sediment and carbon export to the Hadal zone. Nat. Commun. 9, 121 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kioka, A. et al. Megathrust earthquake drives drastic organic carbon supply to the hadal trench. Sci. Rep. 9, 1553 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Luo, M., Gieskes, J., Chen, L. Y., Shi, X. F. & Chen, D. F. Provenances, distribution, and accumulation of organic matter in the southern Mariana Trench rim and slope: implication for carbon cycle and burial in hadal trenches. Mar. Geol. 386, 98–106 (2017).ADS 
CAS 

Google Scholar 
Glud, R. N. et al. High rates of microbial carbon turnover in sediments in the deepest oceanic trench on Earth. Nat. Geosci. 6, 284–288 (2013).ADS 
CAS 

Google Scholar 
Liu, S. & Peng, X. Organic matter diagenesis in hadal setting: insights from the pore-water geochemistry of the Mariana Trench sediments. Deep Sea Res. I 147, 22–31 (2019).CAS 

Google Scholar 
Nunoura, T. et al. Microbial diversity in sediments from the bottom of the Challenger Deep, the Mariana Trench. Microbes Environ. 33, 186–194 (2018).PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Genomics insights into ecotype formation of ammonia-oxidizing archaea in the deep ocean. Environ. Microbiol. 21, 716–729 (2019).CAS 
PubMed 

Google Scholar 
Nunoura, T. et al. Molecular biological and isotopic biogeochemical prognoses of the nitrification-driven dynamic microbial nitrogen cycle in hadopelagic sediments. Environ. Microbiol. 15, 3087–3107 (2013).CAS 
PubMed 

Google Scholar 
Mason, E. et al. Volatile metal emissions from volcanic degassing and lava–seawater interactions at Kīlauea Volcano, Hawai’i. Commun. Earth Environ. 2, 79 (2021).ADS 

Google Scholar 
Sun, R. et al. Methylmercury produced in upper oceans accumulates in deep Mariana Trench fauna. Nat. Commun. 11, 3389 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kalia, K. & Khambholja, D. B. in Handbook of Arsenic Toxicology (ed. Flora, S. J. S.) Ch. 28 (Elsevier, 2015).Welty, C. J., Sousa, M. L., Dunnivant, F. M. & Yancey, P. H. High-density element concentrations in fish from subtidal to hadal zones of the Pacific Ocean. Heliyon 4, e00840 (2018).PubMed 
PubMed Central 

Google Scholar 
Oremland, R. S. & Stolz, J. F. The ecology of arsenic. Science 300, 939–944 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Popowich, A., Zhang, Q. & Le, X. C. Arsenobetaine: the ongoing mystery. Natl Sci. Rev. 3, 451–458 (2016).CAS 

Google Scholar 
Hoffmann, T. et al. Arsenobetaine: an ecophysiologically important organoarsenical confers cytoprotection against osmotic stress and growth temperature extremes. Environ. Microbiol. 20, 305–323 (2018).CAS 
PubMed 

Google Scholar 
Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).
Google Scholar 
Hoffmann, A. A. & Hercus, M. J. Environmental stress as an evolutionary force. Bioscience 50, 217–226 (2000).
Google Scholar 
Cui, G., Li, J., Gao, Z. & Wang, Y. Spatial variations of microbial communities in abyssal and hadal sediments across the Challenger Deep. PeerJ 7, e6961 (2019).PubMed 
PubMed Central 

Google Scholar 
Hiraoka, S. et al. Microbial community and geochemical analyses of trans-trench sediments for understanding the roles of hadal environments. ISME J. 14, 740–756 (2020).CAS 
PubMed 

Google Scholar 
Morono, Y. et al. Aerobic microbial life persists in oxic marine sediment as old as 101.5 million years. Nat. Commun. 11, 3626 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, X. et al. Metagenomics reveals microbial diversity and metabolic potentials of seawater and surface sediment from a hadal biosphere at the Yap Trench. Front. Microbiol. 9, 2402 (2018).PubMed 
PubMed Central 

Google Scholar 
Logares, R. et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ. Microbiol. 16, 2659–2671 (2014).CAS 
PubMed 

Google Scholar 
Zhou, Z. et al. Genome- and community-level interaction insights into carbon utilization and element cycling functions of Hydrothermarchaeota in hydrothermal sediment. mSystems 5, e00795-00719 (2020).
Google Scholar 
Dombrowski, N., Teske, A. P. & Baker, B. J. Expansive microbial metabolic versatility and biodiversity in dynamic Guaymas Basin hydrothermal sediments. Nat. Commun. 9, 4999 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dong, X. et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nat. Commun. 10, 1816 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Laso-Pérez, R. et al. Anaerobic degradation of non-methane alkanes by “Candidatus Methanoliparia” in hydrocarbon seeps of the Gulf of Mexico. mBio 10, e01814–e01819 (2019).PubMed 
PubMed Central 

Google Scholar 
Gao, Z. M. et al. In situ meta-omic insights into the community compositions and ecological roles of hadal microbes in the Mariana Trench. Environ. Microbiol. 21, 4092–4108 (2019).CAS 
PubMed 

Google Scholar 
Varliero, G., Bienhold, C., Schmid, F., Boetius, A. & Molari, M. Microbial diversity and connectivity in deep-sea sediments of the South Atlantic polar front. Front. Microbiol. 10, 665 (2019).Su, X. et al. Identifying and predicting novelty in microbiome studies. mBio 9, e02099-02018 (2018).
Google Scholar 
Jing, G. et al. Microbiome Search Engine 2: a platform for taxonomic and functional search of global microbiomes on the whole-microbiome level. mSystems 6, e00943-00920 (2021).
Google Scholar 
Baltar, F., Zhao, Z. H. & Herndl, G. J. Potential and expression of carbohydrate untilization by marine fungi in the global ocean. Microbiome 9, 106 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Quemener, M. et al. Meta-omics highlights the diversity, activity and adaptations of fungi in deep oceanic crust. Environ. Microbiol. 22, 3950–3967 (2020).CAS 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).CAS 
PubMed 

Google Scholar 
Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).PubMed 
PubMed Central 

Google Scholar 
Bobay, L. M. & Ochman, H. The evolution of bacterial genome architecture. Front. Genet. 8, 72 (2017).PubMed 
PubMed Central 

Google Scholar 
Huang, L. et al. dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation. Nucleic Acids Res. 46, D516–D521 (2018).CAS 
PubMed 

Google Scholar 
Xu, Y., Ge, H. & Fang, J. Biogeochemistry of hadal trenches: Recent developments and future perspectives. Deep Sea Res. II Top. Stud. Oceanogr. 155, 19–26 (2018).ADS 
CAS 

Google Scholar 
Jørgensen, B. B. & Boetius, A. Feast and famine — microbial life in the deep-sea bed. Nat. Rev. Microbiol. 5, 770–781 (2007).PubMed 

Google Scholar 
Pérez Castro, S. et al. Degradation of biological macromolecules supports uncultured microbial populations in Guaymas Basin hydrothermal sediments. ISME J. 15, 3480–3497 (2021).Rastelli, E. et al. Drivers of bacterial α- and β-diversity patterns and functioning in subsurface hadal sediments. Front. Microbiol. 10, 2609 (2019).Vetter, Y. A. & Deming, J. W. Extracellular enzyme-activity in the Arctic northeast water polynya. Mar. Ecol. Prog. Ser. 114, 23–34 (1994).ADS 
CAS 

Google Scholar 
Li, J. et al. Recycling and metabolic flexibility dictate life in the lower oceanic crust. Nature 579, 250–255 (2020).ADS 
CAS 

Google Scholar 
Kikuchi, G., Motokawa, Y., Yoshida, T. & Hiraga, K. Glycine cleavage system: reaction mechanism, physiological significance, and hyperglycinemia. Proc. Jpn. Acad. 84, 246–263 (2008).CAS 

Google Scholar 
Chakraborty, A. et al. Hydrocarbon seepage in the deep seabed links subsurface and seafloor biospheres. Proc. Natl Acad. Sci. USA 117, 11029–11037 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, J. et al. Proliferation of hydrocarbon-degrading microbes at the bottom of the Mariana Trench. Microbiome 7, 47 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Xue, C.-X. et al. Insights into the vertical stratification of microbial ecological roles across the deepest seawater column on Earth. Microorganisms 8, 1309 (2020).CAS 
PubMed Central 

Google Scholar 
Thamdrup, B. et al. Anammox bacteria drive fixed nitrogen loss in hadal trench sediments. Proc. Natl Acad. Sci. USA 118, e2104529118 (2021).CAS 
PubMed 

Google Scholar 
Wu, J. et al. Unexpectedly high diversity of anammox bacteria detected in deep-sea surface sediments of the South China Sea. FEMS Microbiol. Ecol. 95, fiz013 (2019).Kartal, B. et al. Molecular mechanism of anaerobic ammonium oxidation. Nature 479, 127–130 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Maalcke, W. J. et al. Characterization of anammox hydrazine dehydrogenase, a key N2-producing enzyme in the global nitrogen cycle. J. Biol. Chem. 291, 17077–17092 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kartal, B. et al. How to make a living from anaerobic ammonium oxidation. FEMS Microbiol. Rev. 37, 428–461 (2013).CAS 
PubMed 

Google Scholar 
Oshiki, M., Ali, M., Shinyako-Hata, K., Satoh, H. & Okabe, S. Hydroxylamine-dependent anaerobic ammonium oxidation (anammox) by “Candidatus Brocadia sinica”. Environ. Microbiol. 18, 3133–3143 (2016).CAS 
PubMed 

Google Scholar 
Mateos, L. M. et al. in Advances in Applied Microbiology (eds Sariaslani, S. & Gadd, G. M.) Ch. 4 (Academic Press, 2017).Ben Fekih, I. et al. Distribution of arsenic resistance genes in prokaryotes. Front. Microbiol. 9, 2473 (2018).Wang, P. P., Sun, G. X. & Zhu, Y. G. Identification and characterization of arsenite methyltransferase from an archaeon, methanosarcina acetivorans C2A. Environ. Sci. Technol. 48, 12706–12713 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Masuda, H., Yoshinishi, H., Fuchida, S., Toki, T. & Even, E. Vertical profiles of arsenic and arsenic species transformations in deep-sea sediment, Nankai Trough, offshore Japan. Prog. Earth Planet Sci. 6, 28 (2019).ADS 

Google Scholar 
Dunivin, T. K., Yeh, S. Y. & Shade, A. A global survey of arsenic-related genes in soil microbiomes. BMC Biol. 17, 45 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Teske, A. et al. The Guaymas Basin hiking guide to hydrothermal mounds, chimneys, and microbial mats: complex seafloor expressions of subsurface hydrothermal circulation. Front. Microbiol. 7, 75 (2016).O’Day, P. A., Vlassopoulos, D., Root, R. & Rivera, N. The influence of sulfur and iron on dissolved arsenic concentrations in the shallow subsurface under changing redox conditions. Proc. Natl Acad. Sci. USA 101, 13703–13708 (2004).ADS 
PubMed 
PubMed Central 

Google Scholar 
Galinski, E. A. Osmoadaptation in bacteria. Adv. Microb. Physiol. 37, 273–328 (1995).CAS 

Google Scholar 
Papini, C. M., Pandharipande, P. P., Royer, C. A. & Makhatadze, G. I. Putting the piezolyte hypothesis under pressure. Biophys. J. 113, 974–977 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Caumette, G., Koch, I. & Reimer, K. J. Arsenobetaine formation in plankton: a review of studies at the base of the aquatic food chain. J. Environ. Monit. 14, 2841–2853 (2012).CAS 
PubMed 

Google Scholar 
Whaley-Martin, K. J., Koch, I., Moriarty, M. & Reimer, K. J. Arsenic speciation in blue mussels (Mytilus edulis) along a highly contaminated arsenic gradient. Environ. Sci. Technol. 46, 3110–3118 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Oremland, R. S. et al. Anaerobic oxidation of arsenite in Mono Lake water and by a facultative, arsenite-oxidizing chemoautotroph, strain MLHE-1. Appl. Environ. Microbiol. 68, 4795–4802 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rhine, E. D., Phelps, C. D. & Young, L. Y. Anaerobic arsenite oxidation by novel denitrifying isolates. Environ. Microbiol. 8, 899–908 (2006).CAS 
PubMed 

Google Scholar 
Rhine et al. LY. The arsenite oxidase genes (aroAB) in novel chemoautotrophic arsenite oxidizers. Biochem. Biophys. Res. Commun. 354, 662–667 (2007).CAS 
PubMed 

Google Scholar 
Saunders, J. K., Fuchsman, C. A., Mckay, C. & Rocap, G. Complete arsenic-based respiratory cycle in the marine microbial communities of pelagic oxygen-deficient zones. Proc. Natl Acad. Sci. USA 116, 9925–9930 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Couture, R. M., Sekowska, A., Fang, G. & Danchin, A. Linking selenium biogeochemistry to the sulfur‐dependent biological detoxification of arsenic. Environ. Microbiol. 14, 1612–1623 (2012).CAS 
PubMed 

Google Scholar 
Zhang, Y. & Gladyshev, V. N. Trends in selenium utilization in marine microbial world revealed through the analysis of the Global Ocean Sampling (GOS) project. PLoS Genet. 4, e1000095 (2008).PubMed 
PubMed Central 

Google Scholar 
Peng, T., Lin, J., Xu, Y.-Z. & Zhang, Y. Comparative genomics reveals new evolutionary and ecological patterns of selenium utilization in bacteria. ISME J. 10, 2048–2059 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Labunskyy, V. M., Hatfield, D. L. & Gladyshev, V. N. Selenoproteins: molecular pathways and physiological roles. Physiol. Rev. 94, 739–777 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin, K., Wang, Q., Lv, M. & Chen, L. Microorganism remediation strategies towards heavy metals. Chem. Eng. J. 360, 1553–1563 (2019).CAS 

Google Scholar 
O’Day, P. A., Vlassopoulos, D., Root, R. & Rivera, N. The influence of sulfur and iron on dissolved arsenic concentrations in the shallow subsurface under changing redox conditions. Proc. Natl Acad. Sci. USA 101, 13703–13708 (2004).ADS 
PubMed 
PubMed Central 

Google Scholar 
Chen, S. F., Zhou, Y. Q., Chen, Y. R. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, 884–890 (2018).
Google Scholar 
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).PubMed 
PubMed Central 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 
PubMed 

Google Scholar 
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang, Y., Gilna, P. & Li, W. Z. Identification of ribosomal RNA genes in metagenomic fragments. Bioinformatics 25, 1338–1340 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhou, Y. Microbiomes in the Challenger Deep slope and bottom-axis sediments. Zenodo https://doi.org/10.5281/zenodo.6061243 (2022).Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jing, G. C. et al. Parallel-META 3: comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities. Sci. Rep. 7, 40371 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).CAS 
PubMed 

Google Scholar 
Kang, D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).PubMed 
PubMed Central 

Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).CAS 
PubMed 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).PubMed 
PubMed Central 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed 
PubMed Central 

Google Scholar 
Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881–887 (2013).CAS 
PubMed 

Google Scholar 
Yamada, K. D., Tomii, K. & Katoh, K. Application of the MAFFT sequence alignment program to large data-reexamination of the usefulness of chained guide trees. Bioinformatics 32, 3246–3251 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 

Google Scholar 
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pachiadaki, M. G. et al. Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science 358, 1046–1051 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
Perry, M. heatmaps: flexible heatmaps for functional genomics and sequence features. R package version 1.14.0 (Bioconductor, 2020).Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).PubMed 
PubMed Central 

Google Scholar 
Aramaki, T. et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2019).PubMed Central 

Google Scholar 
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).CAS 
PubMed 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).CAS 
PubMed 

Google Scholar 
Zhou, Y. Microbiomes in the Challenger Deep slope and bottom-axis sediments. Figshare https://doi.org/10.6084/m6089.figshare.12979709 (2022). More

Morey, D. F. Burying key evidence: The social bond between dogs and people. J. Archaeol. Sci. 33, 158–175 (2006).
Google Scholar 
de Garcia, R. C. M., Calderón, N. & Ferreira, F. Consolidação de diretrizes internacionais de manejo de populações caninas em áreas urbanas e proposta de indicadores para seu gerenciamento. Rev. Panam. Salud Pública 32, 140–144 (2012).
Google Scholar 
Willis, C. M. et al. Olfactory detection of human bladder cancer by dogs: Proof of principle study. BMJ 329, 712 (2004).PubMed 
PubMed Central 

Google Scholar 
Beck, A. M. The Ecology of Stray Dogs: A Study of Free-Ranging Urban Animals (Purdue University Press, 2002).
Google Scholar 
Jackman, J. & Rowan, A. N. Free-roaming dogs in developing countries: The benefits of capture, neuter, and return programs (2007).Borhani, M. et al. Echinococcoses in Iran, Turkey, and Pakistan: Old diseases in the new millennium. Clin. Microbiol. Rev. 34, e00290-e320 (2021).
Google Scholar 
Otranto, D. et al. Zoonotic parasites of sheltered and stray dogs in the era of the global economic and political crisis. Trends Parasitol. 33, 813–825 (2017).PubMed 

Google Scholar 
Quinnell, R. J. & Courtenay, O. Transmission, reservoir hosts and control of zoonotic visceral leishmaniasis. Parasitology 136, 1915–1934 (2009).CAS 
PubMed 

Google Scholar 
Zain, S. N. M., Rahman, R. & Lewis, J. W. Stray animal and human defecation as sources of soil-transmitted helminth eggs in playgrounds of Peninsular Malaysia. J. Helminthol. 89, 740–747 (2015).
Google Scholar 
Garcia, R., Amaku, M., Biondo, A. W. & Ferreira, F. Dog and cat population dynamics in an urban area: Evaluation of a birth control strategy. Pesqui. Veterinária Bras. 38, 511–518 (2018).
Google Scholar 
Smith, L. M. et al. The effectiveness of dog population management: A systematic review. Animals 9, 1020 (2019).PubMed Central 

Google Scholar 
Tenzin, T. et al. Comparison of mark-resight methods to estimate abundance and rabies vaccination coverage of free-roaming dogs in two urban areas of south Bhutan. Prev. Vet. Med. 118, 436–448 (2015).PubMed 

Google Scholar 
Hiby, E. et al. Scoping review of indicators and methods of measurement used to evaluate the impact of dog population management interventions. BMC Vet. Res. 13, 1–20 (2017).
Google Scholar 
Chidumayo, N. N. System dynamics modelling approach to explore the effect of dog demography on rabies vaccination coverage in Africa. PLoS One 13, e0205884 (2018).PubMed 
PubMed Central 

Google Scholar 
Home, C., Bhatnagar, Y. V. & Vanak, A. T. Canine Conundrum: Domestic dogs as an invasive species and their impacts on wildlife in India. Anim. Conserv. 21, 275–282 (2018).
Google Scholar 
Humane Dog Population Management 2019 Update—ICAM. https://www.icam-coalition.org/download/humane-dog-population-management-guidance/http://www.icam-coalition.org/abhay-sankalp-a-sustainable-solution-to-human-dog-conflict-for-improved-human-dog-relationships/.Belo, V. S., Werneck, G. L., da Silva, E. S., Barbosa, D. S. & Struchiner, C. J. Population estimation methods for free-ranging dogs: A systematic review. PLoS One 10, e0144830 (2015).PubMed 
PubMed Central 

Google Scholar 
Font, E. Spacing and social organization: Urban stray dogs revisited. Appl. Anim. Behav. Sci. 17, 319–328 (1987).
Google Scholar 
Patronek, G. J., Beck, A. M. & Glickman, L. T. Dynamics of dog and cat populations in a community. J. Am. Vet. Med. Assoc. 210, 637–642 (1997).CAS 
PubMed 

Google Scholar 
Kato, M., Yamamoto, H., Inukai, Y. & Kira, S. Survey of the stray dog population and the health education program on the prevention of dog bites and dog-acquired infections: A comparative study in Nepal and Okayama Prefecture, Japan. Acta Med. Okayama 57, 261–266 (2003).PubMed 

Google Scholar 
Tiwari, H. K., Vanak, A. T., O’Dea, M., Gogoi-Tiwari, J. & Robertson, I. D. A comparative study of enumeration techniques for Free-roaming dogs in rural Baramati, District Pune, India. Front. Vet. Sci. 5, 104 (2018).PubMed 
PubMed Central 

Google Scholar 
Hiby, L. R. et al. A mark-resight survey method to estimate the roaming dog population in three cities in Rajasthan, India. BMC Vet. Res. 7, 1–10 (2011).
Google Scholar 
Childs, J. E. et al. Density estimates of rural dog populations and an assessment of marking methods during a rabies vaccination campaign in the Philippines. Prev. Vet. Med. 33, 207–218 (1998).CAS 
PubMed 

Google Scholar 
Tiwari, H. K., Robertson, I. D., O’Dea, M. & Vanak, A. T. Demographic characteristics of free-roaming dogs (FRD) in rural and urban India following a photographic sight-resight survey. Sci. Rep. 9, 16562 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Punjabi, G. A., Athreya, V. & Linnell, J. D. C. Using natural marks to estimate free-ranging dog Canis familiaris abundance in a MARK-RESIGHT framework in suburban Mumbai, India. Trop. Conserv. Sci. 5, 510–520 (2012).
Google Scholar 
Cleaton, J. M. et al. Use of photography to identify free-roaming dogs during sight-resight surveys: Impacts on estimates of population size and vaccination coverage, Haiti 2016. Vaccine X 2, 100025 (2019).PubMed 
PubMed Central 

Google Scholar 
Rockwood, L. L. Introduction to Population Ecology (Wiley, 2015).
Google Scholar 
Baquero, O. S., Marconcin, S., Rocha, A. & de Garcia, R. C. M. Companion animal demography and population management in Pinhais, Brazil. Prev. Vet. Med. 158, 169–177 (2018).PubMed 

Google Scholar 
Totton, S. C. et al. Stray dog population demographics in Jodhpur, India following a population control/rabies vaccination program. Prev. Vet. Med. 97, 51–57 (2010).PubMed 

Google Scholar 
Høgåsen, H. R., Er, C., Di Nardo, A. & Dalla Villa, P. Free-roaming dog populations: A cost-benefit model for different management options, applied to Abruzzo, Italy. Prev. Vet. Med. 112, 401–413 (2013).PubMed 

Google Scholar 
Kisiel, L. M. et al. Modeling the effect of surgical sterilization on owned dog population size in Villa de Tezontepec, Hidalgo, Mexico, using an individual-based computer simulation model. PLoS One 13, e0198209 (2018).PubMed 
PubMed Central 

Google Scholar 
Yoak, A. J., Reece, J. F., Gehrt, S. D. & Hamilton, I. M. Optimizing free-roaming dog control programs using agent-based models. Ecol. Model. 341, 53–61 (2016).
Google Scholar 
Belsare, A. & Vanak, A. T. Modelling the challenges of managing free-ranging dog populations. Sci. Rep. 10, 1–12 (2020).
Google Scholar 
Harandi, M. F. et al. Sonographical and serological survey of human cystic echinococcosis and analysis of risk factors associated with seroconversion in rural communities of Kerman, Iran. Zoonoses Public Health 58, 582–588 (2011).CAS 
PubMed 

Google Scholar 
Abedi, M., Doosti-Irani, A., Jahanbakhsh, F. & Sahebkar, A. Epidemiology of animal bite in Iran during a 20-year period (1993–2013): A meta-analysis. Trop. Med. Health 47, 1–13 (2019).
Google Scholar 
Kartal, T. & Rowan, A. N. Stray dog population management. F. Man. Small Anim. Med. 15–28 (2018).Amaral, A. C., Ward, M. P. & da Costa Freitas, J. Estimation of roaming dog populations in Timor Leste. Prev. Vet. Med. 113, 608–613 (2014).PubMed 

Google Scholar 
de Melo, S. N. et al. Effects of gender, sterilization, and environment on the spatial distribution of free-roaming dogs: An intervention study in an urban setting. Front. Vet. Sci. 7, 289 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Conan, A. et al. Population dynamics of owned, free-roaming dogs: Implications for rabies control. PLoS Negl. Trop. Dis. 9, e0004177 (2015).PubMed 
PubMed Central 

Google Scholar 
Belsare, A. V. & Gompper, M. E. Assessing demographic and epidemiologic parameters of rural dog populations in India during mass vaccination campaigns. Prev. Vet. Med. 111, 139–146 (2013).PubMed 

Google Scholar 
Mustiana, A. et al. Owned and unowned dog population estimation, dog management and dog bites to inform rabies prevention and response on Lombok Island, Indonesia. PLoS One 10, e0124092 (2015).PubMed 
PubMed Central 

Google Scholar 
Hossain, M. et al. A survey of the dog population in rural Bangladesh. Prev. Vet. Med. 111, 134–138 (2013).PubMed 

Google Scholar 
Acosta-Jamett, G., Cleaveland, S., Cunningham, A. A. & de Bronsvoort, B. M. C. Demography of domestic dogs in rural and urban areas of the Coquimbo region of Chile and implications for disease transmission. Prev. Vet. Med. 94, 272–281 (2010).CAS 
PubMed 

Google Scholar 
Massei, G. et al. Free-roaming dogs in Nepal: Demographics, health and public knowledge, attitudes and practices. Zoonoses Public Health 64, 29–40 (2017).CAS 
PubMed 

Google Scholar 
Rinzin, K. The epidemiology of the free-roaming dog and cat population in the Wellington Region of the New Zealand Palmerston North, New Zealand: Massey University. Master’s thesis (2007).Arief, R. A. et al. Determinants of vaccination coverage and consequences for rabies control in Bali, Indonesia. Front. Vet. Sci. 3, 123 (2017).PubMed 
PubMed Central 

Google Scholar 
Morters, M. K. et al. The demography of free-roaming dog populations and applications to disease and population control. J. Appl. Ecol. 51, 1096–1106 (2014).PubMed 
PubMed Central 

Google Scholar 
Pal, S. K. Population ecology of free-ranging urban dogs in West Bengal, India. Acta Theriol. (Warsz) 46, 69–78 (2001).
Google Scholar 
Rinzin, K., Tenzin, T. & Robertson, I. Size and demography pattern of the domestic dog population in Bhutan: Implications for dog population management and disease control. Prev. Vet. Med. 126, 39–47 (2016).PubMed 

Google Scholar 
Hoffman, J. M., O’Neill, D. G., Creevy, K. E. & Austad, S. N. Do female dogs age differently than male dogs?. J. Gerontol. Ser. A 73, 150–156 (2018).
Google Scholar 
Dürr, S., Dhand, N. K., Bombara, C., Molloy, S. & Ward, M. P. What influences the home range size of free-roaming domestic dogs?. Epidemiol. Infect. 145, 1339–1350 (2017).PubMed 

Google Scholar 
Aiyedun, J. O. & Olugasa, B. O. Use of aerial photograph to enhance dog population census in Ilorin, Nigeria. Sokoto J. Vet. Sci. 10, 22–27 (2012).
Google Scholar 
Butler, J. R. A. & Bingham, J. Demography and dog-human relationships of the dog population in Zimbabwean communal lands. Vet. Rec. 147, 442–446 (2000).CAS 
PubMed 

Google Scholar 
Raymond, T. N. et al. Do open garbage dumps play a role in canine rabies transmission in Biyem-Assi health district in Cameroon?. Infect. Ecol. Epidemiol. 5, 26055 (2015).PubMed 

Google Scholar 
Wright, N., Subedi, D., Pantha, S., Acharya, K. P. & Nel, L. H. The role of waste management in control of rabies: A neglected issue. Viruses 13, 225 (2021).PubMed 
PubMed Central 

Google Scholar 

Krystosik, A. et al. Solid wastes provide breeding sites, burrows, and food for biological disease vectors, and urban zoonotic reservoirs: A call to action for solutions-based research. Front. Public Health 7, 405 (2020).PubMed 
PubMed Central 

Google Scholar 
Tantanee, S. & Hantrakul, S. Municipal Waste Management Challenge of Urbanization: Lesson Learned From Phitsanulok, Thailand. Geogr. Tech. 14 (2019).Desa, U. World urbanization prospects 2018. United Nations Dep. Econ. Soc. Affairs (2018).
https://www.icam-coalition.org/cnvr-in-dehradun-female-dog-focused-cnvr-implemented-with-community-engagement/
Bacon, H., Vancia, V., Walters, H. & Waran, N. Canine trap-neuter-return: A critical review of potential welfare issues. Anim. Welf. 26, 281–292 (2017).
Google Scholar 
Bacon, H., Walters, H., Vancia, V., Connelly, L. & Waran, N. Development of a robust canine welfare assessment protocol for use in dog (Canis familiaris) catch-neuter-return (CNR) programmes. Animals 9, 564 (2019).PubMed Central 

Google Scholar 
ICAM Coalition. Are we making a difference? A guide to monitoring and evaluating dog population management interventions 2015 (2015).World Society for the Protection of Animals. Surveying roaming dog populations: Guidelines on methodology, 1–20 (2008).Acharya, M. & Dhakal, S. Survey on street dog population in Pokhara valley of Nepal, Bangladesh. J. Vet. Med. 13, 65–70 (2015).
Google Scholar 

http://www.icam-coalition.org/tool/dog-body-condition-scoring-training/
Tenzin, T., Ahmed, R., Debnath, N. C., Ahmed, G. & Yamage, M. Free-roaming dog population estimation and status of the dog population management and rabies control program in Dhaka City, Bangladesh. PLoS Negl. Trop. Dis. 9, e0003784 (2015).PubMed 
PubMed Central 

Google Scholar 
Toole, T. M. A project management causal loop diagram. in Arcom Conference 5–7 (2005).Amaku, M., Dias, R. A. & Ferreira, F. Dynamics and control of stray dog populations. Math. Popul. Stud. 17, 69–78 (2010).MathSciNet 
MATH 

Google Scholar 
Kitala, P. M., McDERMOTT, J. J., Coleman, P. G. & Dye, C. Comparison of vaccination strategies for the control of dog rabies in Machakos District, Kenya. Epidemiol. Infect. 129, 215–222 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dahabreh, I. J. et al. A review of validation and calibration methods for health care modeling and simulation. Model. Simul. Context Heal. Technol. Assess. Rev. Exist. Guid. Futur. Res. Needs, Validity Assess. [Internet] (2017).Knobel, D. L. et al. Re-evaluating the burden of rabies in Africa and Asia. Bull. World Health Organ. 83, 360–368 (2005).PubMed 
PubMed Central 

Google Scholar 
Özen, D., Böhning, D. & Gürcan, İS. Estimation of stray dog and cat populations in metropolitan Ankara, Turkey. Turk. J. Vet. Anim. Sci. 40, 7–12 (2016).
Google Scholar 
Cortez-Aguirre, G. R., Jiménez-Coello, M., Gutiérrez-Blanco, E. & Ortega-Pacheco, A. Stray dog population in a city of Southern Mexico and its impact on the contamination of public areas. Vet. Med. Int. 2018 (2018). More

Balami, S., Vašutová, M., Godbold, D., Kotas, P. & Cudlín, P. Soil fungal communities across land use types. Forest Biogeosci. For. 13, 548–558 (2020).
Google Scholar 
Deacon, J. Fungal Biology (Wiley, 2009).
Google Scholar 
Ruiz-Almenara, C., Gándara, E. & Gómez-Hernández, M. Comparison of diversity and composition of macrofungal species between intensive mushroom harvesting and non-harvesting areas in Oaxaca, Mexico. PeerJ 7, e8325 (2019).PubMed 
PubMed Central 

Google Scholar 
Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).ADS 

Google Scholar 
Egli, S. Mycorrhizal mushroom diversity and productivity—an indicator of forest health?. Ann. For. Sci. 68, 81–88 (2011).
Google Scholar 
Westover, K. M. & Bever, J. D. Mechanisms of plant species coexistence: Roles of rhizosphere bacteria and root fungal pathogens. Ecology 82, 3285–3294 (2001).
Google Scholar 
Deacon, J. Fungal Biology (Wiley, 2006).
Google Scholar 
Fernandez, C. W., Nguyen, N. H. U. H., Stefanski, A. & Han, Y. Ectomycorrhizal fungal response to warming is linked to poor host performance at the boreal-temperate ecotone. Glob. Chang. Biol. 23, 1598–1609 (2017).ADS 
PubMed 

Google Scholar 
Heilmann-Clausen, J. et al. A fungal perspective on conservation biology. Conserv. Biol. 29, 61–68 (2015).PubMed 

Google Scholar 
Shay, P.-E., Winder, R. S. & Trofymow, J. A. Nutrient-cycling microbes in coastal Douglas-fir forests: Regional-scale correlation between communities, in situ climate, and other factors. Front. Microbiol. 6, 5897 (2015).
Google Scholar 
van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).PubMed 

Google Scholar 
Richter, A., Schöning, I., Kahl, T., Bauhus, J. & Ruess, L. Regional environmental conditions shape microbial community structure stronger than local forest management intensity. For. Ecol. Manag. 409, 250–259 (2018).
Google Scholar 
Monkai, J., Hyde, K. D., Xu, J. & Mortimer, P. E. Diversity and ecology of soil fungal communities in rubber plantations. Fungal Biol. Rev. 31, 1–11 (2017).
Google Scholar 
White, F. Vegetation of Africa—a descriptive memoir to accompany the UNESCO/AETFAT/UNSO vegetation map of Africa, Natural Resources Research Report XX. U.N. Educational, Scientific and Cultural Organization, Paris (1983).Aynekulu, E. et al. Plant diversity and regeneration in a disturbed isolated dry Afromontane forest in northern Ethiopia. Folia Geobot. 51, 115–127 (2016).
Google Scholar 
Wassie, A., Sterck, F. J. & Bongers, F. Species and structural diversity of church forests in a fragmented Ethiopian Highland landscape. J. Veg. Sci. 21, 938–948 (2010).
Google Scholar 
Alem, D., Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Survey of macrofungal diversity and analysis of edaphic factors influencing the fungal community of church forests in Dry Afromontane areas of Northern Ethiopia. For. Ecol. Manag. 496, 119391 (2021).
Google Scholar 
Aerts, R. et al. Conservation of the Ethiopian church forests: Threats, opportunities and implications for their management. Sci. Total Environ. 551–552, 404–414 (2016).ADS 
PubMed 

Google Scholar 
Wassie, A., Teketay, D. & Powell, N. Church forests in North Gonder administrative zone, Northern Ethiopia. For. Trees Livelihoods 15, 349–373 (2005).
Google Scholar 
Wsaaie, A., Teketay, D. & Powell, N. Church forests in North Gondar Administative Zone, Northern Ethioopia. For. Trees Livelihoods 15, 349–373 (2005).
Google Scholar 
Lemenih, M. & Bongers, F. Dry forests of Ethiopia and their silviculture. In Silviculture in the Tropics, Tropical Forestry 8 (ed. S. G€unter et al.) 261–272 (Springer, Heidelberg, 2011). https://doi.org/10.1007/978-3-642-19986-8_17.Fernández, A., Sánchez, S., García, P. & Sánchez, J. Macrofungal diversity in an isolated and fragmented Mediterranean Forest ecosystem. Plant Biosyst. Int. J. Deal. Asp. Plant Biol. 154, 139–148 (2020).
Google Scholar 
Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant-fungal interactions. New Phytol. 204, 180–191 (2014).PubMed 

Google Scholar 
Burgess, N. D., Hales, J. D. A., Ricketts, T. H. & Dinerstein, E. Factoring species, non-species values and threats into biodiversity prioritisation across the ecoregions of Africa and its islands. Biol. Conserv. 127, 383–401 (2006).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398, 37–47 (2017).
Google Scholar 
Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 1–14 (2020).
Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science (80-. ). 346 (2014).Hawksworth, D. L. Global species numbers of fungi: are tropical studies and molecular approaches contributing to a more robust estimate?. Biodivers. Conserv. 21, 2425–2433 (2012).
Google Scholar 
Crous, P. W. et al. How many species of fungi are there at the tip of Africa?. Stud. Mycol. 55, 13–33 (2006).PubMed 
PubMed Central 

Google Scholar 
Martínez, M. L. et al. Effects of land use change on biodiversity and ecosystem services in tropical montane cloud forests of Mexico. For. Ecol. Manag. 258, 1856–1863 (2009).
Google Scholar 
Phillips, H. et al. The effects of global change on soil faunal communities: a meta-analytic approach. Res. Ideas Outcomes 5 (2019).Riutta, T. et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol. Biochem. 49, 124–131 (2012).CAS 

Google Scholar 
Rantalainen, M., Haimi, J., Fritze, H., Pennanen, T. & Setala, T. Soil decomposer community as a model system in studying the effects of habitat fragmentation and habitat corridors. Soil Biol. Biochem. 40, 853–863 (2008).CAS 

Google Scholar 
Newsham, K. K. et al. Relationship between soil fungal diversity and temperature in the maritime Antarctic. Nat. Clim. Chang. 6, 182–186 (2016).ADS 

Google Scholar 
Bahram, M., Põlme, S., Kõljalg, U., Zarre, S. & Tedersoo, L. Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran. New Phytol. 193, 465–473 (2012).PubMed 

Google Scholar 
Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).PubMed 

Google Scholar 
Krüger, C. et al. Plant communities rather than soil properties structure arbuscular mycorrhizal fungal communities along primary succession on a mine spoil. Front. Microbiol. 8, 1–16 (2017).
Google Scholar 
Bahram, M., Peay, K. G. & Tedersoo, L. Local-scale biogeography and spatiotemporal variability in communities of mycorrhizal fungi. New Phytol. 205, 1454–1463 (2015).CAS 
PubMed 

Google Scholar 
Li, P. et al. Spatial variation in soil fungal communities across paddy fields in Subtropical China. mSystems 5 (2020).Grilli, G., Urcelay, C. & Galetto, L. Forest fragment size and nutrient availability: Complex responses of mycorrhizal fungi in native–exotic hosts. Plant Ecol. 213, 155–165 (2012).
Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Shrub Resprouting Response After Fuel Reduction Treatments: Comparison of Prescribed Burning, Clearing and Mastication (Elsevier, 2013).
Google Scholar 
Tedersoo, L., Sadam, A., Zambrano, M., Valencia, R. & Bahram, M. Low diversity and high host preference of ectomycorrhizal fungi in Western Amazonia, a neotropical biodiversity hotspot. ISME J. 4, 465–471 (2010).PubMed 

Google Scholar 
Glassman, S. I., Wang, I. J. & Bruns, T. D. Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales. Mol. Ecol. 26, 6960–6973 (2017).CAS 
PubMed 

Google Scholar 
Colwell, R. K. EstimateS: statistical estimation of species richness and shared species from samples. Version 9. User’s Guide and application published at: http://purl.oclc.org/estimates (2013).Purvis, A. & Hector, A. Getting the measure of biodiversity. Nature 405, 212–219 (2000).CAS 
PubMed 

Google Scholar 
Pan, W. et al. DNA polymerase preference determines PCR priming efficiency. BMC Biotechnol. 14, 10 (2014).PubMed 
PubMed Central 

Google Scholar 
Kirk, P. M., Cannon, P. F., Minter, D. W. & J.A, S. Dictionary of the Fungi (The Centre for Agriculture and Bioscience International (CABI), 2008).Rossman, A., Samuel, G., Rogerson, C. & Lowen, R. Genera of bionectriaceae, hypocreaceae and nectriaceae (hypocreales, ascomycetes). Stud. Mycol. 42, 1–260 (1999).
Google Scholar 
Samuels, G. Trichoderma: A review of biology and systematics of the genus. Mycol. Res. 923–935 (1996).Alem, D. et al. Soil fungal communities and succession following wildfire in Ethiopian dry Afromontane forests, a highly diverse underexplored ecosystem. For. Ecol. Manag. 474, 118328 (2020).
Google Scholar 
Muleta, D., Woyessa, D. & Teferi, Y. Mushroom consumption habits of Wacha Kebele residents, southwestern Ethiopia. Glob. Res. J. Agric. Biol. Sci. 4, 6–16 (2013).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Edible wild mushrooms of Ethiopia: Neglected non-timber forest products. Rev. Fitotec. Mex. 40, 391–397 (2017).
Google Scholar 
Tedersoo, L. et al. Disentangling global soil fungal diversity. Science (80-) 346, 1052–1053 (2014).
Google Scholar 
Dejene, T., Oria-de-Rueda, J. A. & Martín-Pinto, P. Fungal community succession and sporocarp production following fire occurrence in Dry Afromontane forests of Ethiopia. For. Ecol. Manag. 398 (2017).Dang, P. et al. Changes in soil fungal communities and vegetation following afforestation with Pinus tabulaeformis on the Loess Plateau. Ecosphere 9 (2018).Gilbert, G. S., Ferrer, A. & Carranza, J. Polypore fungal diversity and host density in a moist tropical forest. Biodivers. Conserv. 11, 947–957 (2002).
Google Scholar 
Kottke, I., Beck, A., Oberwinkler, F., Homeier, J. & Neill, D. Arbuscular endomycorrhizas are dominant in the organic soil of a neotropical montane cloud forest. J. Trop. Ecol. 20, 125–129 (2004).
Google Scholar 
Barnes, C. J., Van der Gast, C. J., Burns, C. A., McNamara, N. P. & Bending, G. D. Temporally variable geographical distance effects contribute to the assembly of root-associated fungal communities. Front. Microbiol. 7, 1–13 (2016).
Google Scholar 
Tian, J. et al. Environmental factors driving fungal distribution in freshwater lake sediments across the Headwater Region of the Yellow River, China. Sci. Rep. 8, 4–11 (2018).
Google Scholar 
Rosales-Castillo, J. et al. Fungal community and ligninolytic enzyme activities in Quercus deserticola Trel. litter from forest fragments with increasing levels of disturbance. Forests 9, 11 (2017).
Google Scholar 
Kuhar, F., Barroetaveña, C. & Rajchenberg, M. New species of Tomentella (Thelephorales) from the Patagonian Andes forests. Mycologia 108, 780–790 (2016).CAS 
PubMed 

Google Scholar 
Alem, D., Dejene, T., Oria-de-Rueda, J. A., Geml, J. & Martín-Pinto, P. Soil fungal communities under Pinus patula Schiede ex Schltdl. & Cham. Plantation forests of different ages in Ethiopia. Forests 11, 1109 (2020).
Google Scholar 
Tedersoo, L. et al. Terrestrial and lignicolous macrofungi. ISME J. 10, 1228–1239 (2016).
Google Scholar 
Ruiz, R., Decock, C., Saikawa, M., Gene, J. & Guarro, J. Polyschema obclaviformis sp. Nov., and some new records of hyphomycetes from Cuba. Cryptogam. Mycol. 21, 215–220 (2000).
Google Scholar 
Kaygusuz, O. New locality records of Trichoglossum hirsutum (Geoglossales: Geoglossaceae) based on molecular analyses, and prediction of its potential distribution in Turkey. Curr. Res. Environ. Appl. Mycol. 10, 443–456 (2020).
Google Scholar 
Mayer, P. M. Ecosystem and decomposer effects on litter dynamics along an old field to old-growth forest successional gradient. Acta Oecol. 33, 222–230 (2008).ADS 

Google Scholar 
Krishna, M. P. & Mohan, M. Litter decomposition in forest ecosystems: A review. Energy Ecol. Environ. 2, 236–249 (2017).
Google Scholar 
Kirschbaum, M. U. F. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biol. Biochem. 27, 753–760 (1995).CAS 

Google Scholar 
Mayer, P. M., Tunnell, S. J., Engle, D. M., Jorgensen, E. E. & Nunn, P. Invasive grass alters litter decomposition by influencing macrodetritivores. Ecosystems 8, 200–209 (2005).
Google Scholar 
Epstein, H. E., Burke, I. C. & Lauenroth, W. K. Regional patterns of decomposition and primary production rates in the U.S. great plains. Ecology 83, 320 (2002).
Google Scholar 
Sharon, R., Degani, G. & Warburg, M. Comparing the soil macro-fauna in two oak-wood forests: Does community structure differ under similar ambient conditions?. Pedobiologia (Jena). 45, 355–366 (2001).
Google Scholar 
Clocchiatti, A., Hannula, S. E., van den Berg, M., Korthals, G. & de Boer, W. The hidden potential of saprotrophic fungi in arable soil: Patterns of short-term stimulation by organic amendments. Appl. Soil Ecol. 147, 103434 (2020).
Google Scholar 
Drenovsky, R., Vo, D., Graham, K. & Scow, K. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lauber, C., Hamady, M., Knigh, R. & Fierer, N. Pyrosequencing based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ullah, S. et al. The response of soil fungal diversity and community composition to long-term fertilization. Appl. Soil Ecol. 140, 35–41 (2019).
Google Scholar 
Bååth, E. & Anderson, T.-H. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol. Biochem. 35, 955–963 (2003).
Google Scholar 
Zhang, T., Wang, N.-F., Liu, H.-Y., Zhang, Y.-Q. & Yu, L.-Y. Soil pH is a key determinant of soil fungal community composition in the Ny-Ålesund Region, Svalbard (high arctic). Front. Microbiol. 7 (2016).Tian, D. et al. Effects of nitrogen deposition on soil microbial communities in temperate and subtropical forests in China. Sci. Total Environ. 607–608, 1367–1375 (2017).ADS 
PubMed 

Google Scholar 
Zhao, A. et al. Influences of canopy nitrogen and water addition on am fungal biodiversity and community composition in a mixed deciduous forest of China. Front. Plant Sci. 9 (2018).He, J. et al. Greater diversity of soil fungal communities and distinguishable seasonal variation in temperate deciduous forests compared with subtropical evergreen forests of eastern China. FEMS Microbiol. Ecol. 93, 1–12 (2017).
Google Scholar 
Shi, L. et al. Variation in forest soil fungal diversity along a latitudinal gradient. Fungal Divers. 64, 305–315 (2014).
Google Scholar 
Gebeyehu, G., Soromessa, T., Bekele, T. & Teketay, D. Plant diversity and communities along environmental, harvesting and grazing gradients in dry afromontane forests of Awi Zone, northwestern Ethiopia. Taiwania 64, 307–320 (2019).
Google Scholar 
Zegeye, H., Teketay, D. & Kelbessa, E. Diversity and regeneration status of woody species in Tara Gedam and Abebaye forests, northwestern Ethiopia. J. For. Res. 22, 315–328 (2011).
Google Scholar 
Abere, F., Belete, Y., Kefalew, A. & Soromessa, T. Carbon stock of Banja forest in Banja district, Amhara region, Ethiopia: An implication for climate change mitigation. J. Sustain. For. 36, 604–622 (2017).
Google Scholar 
Masresha, G., Soromessa, T. & Kelbessa, E. Status and species diversity of Alemsaga Forest, Northwestern Ethiopia 14 (2015).Rudolph, S., Maciá-Vicente, J. G., Lotz-Winter, H., Schleuning, M. & Piepenbring, M. Temporal variation of fungal diversity in a mosaic landscape in Germany. Stud. Mycol. 89, 95–104 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
De la Varga, H., Águeda, B., Martínez-Peña, F., Parladé, J. & Pera, J. Quantification of extraradical soil mycelium and ectomycorrhizas of Boletus edulis in a Scots pine forest with variable sporocarp productivity. Mycorrhiza 22, 59–68 (2012).PubMed 

Google Scholar 
Voříšková, J. & Baldrian, P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 7, 477–486 (2013).PubMed 

Google Scholar 
Reeuwijk, L. Procedures for Soil Analysis (International Soil Reference and Information Centre, 2002).
Google Scholar 
Walkley, A. & Black, I. A. An examination of the digestion method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 34, 29–38 (1934).ADS 

Google Scholar 
Kim, J., Kreller, C. R. & Greenberg, M. M. Preparation and analysis of oligonucleotides containing the C4’-oxidized abasic site and related mechanistic probes. J. Org. Chem. 70, 8122–8129 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kim, H. T. Soil sampling, preparation and analysis. 139–145 (1996).Bouyoucos, G. H. A reclamation of the hydrometer for making mechanical analysis. Soil. Agro. J. 43, 434–438 (1951).CAS 

Google Scholar 
Ihrmark, K., Bödeker, I. & Cruz-Martinez, K. New primers to amplify the fungal ITS2 region—evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).CAS 
PubMed 

Google Scholar 
White, T. ., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. in PCR Protocols: A Guide to Methods and Applications (eds. Innis, M. A., Gelfand, D. H., Sninsky, J. J. & White, T. J.) 315–322 (Academic Press, 1990).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).
Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).PubMed 

Google Scholar 
Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105 (2020).Hedberg, I. & Edwards, S. Flora of Ethiopia and Eritria (1989).Collins, C. G., Stajich, J. E., Weber, S. E., Pombubpa, N. & Diez, J. M. Shrub range expansion alters diversity and distribution of soil fungal communities across an alpine elevation gradient. Mol. Ecol. 27, 2461–2476 (2018).PubMed 
PubMed Central 

Google Scholar 
Schön, M. E., Nieselt, K. & Garnica, S. Belowground fungal community diversity and composition associated with Norway spruce along an altitudinal gradient. PLoS ONE 13, e0208493 (2018).PubMed 
PubMed Central 

Google Scholar 
Castaño, C. et al. Changes in fungal diversity and composition along a chronosequence of Eucalyptus grandis plantations in Ethiopia. Fungal Ecol. 39, 328–335 (2019).
Google Scholar 
Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, 1949).MATH 

Google Scholar 
Kent, M. & Coker, P. Vegetation Description and Analysis: A Practical Approach (Belhaven Press, 1993).
Google Scholar 
Magurran, A. E. Ecological Diversity and Its Measurement (Princeton University Press, 1988).
Google Scholar 
Jost, L., Chao, A. & Chazdon, R. Compositional similarity and β (beta) diversity. in Biological Diversity. Frontiers in Measurement and Assessment (eds. A.E., Magurran & B.J., M.) 66–84 (Oxford University Press, 2011).Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. (World Agroforestry Centre (ICRAF), 2005).R Core Team. A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2020).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1-128. http://CRAN.R-project.org/package=nlme (2016).Tóthmérész, B. Comparison of different methods for diversity ordering. J. Veg. Sci. 6, 283–290 (1995).
Google Scholar 
Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in marine communities: an approach to statistical analysis and interpretation. (PRIMER-E, Plymouth, 2014).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar  More