More stories

  • in

    Regreening: green is not always gold

    CORRESPONDENCE
    05 April 2022

    Regreening: green is not always gold

    Michael C. Orr

    0
    &

    Alice C. Hughes

    1

    Michael C. Orr

    Institute of Zoology, Chinese Academy of Sciences, Beijing, China.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Alice C. Hughes

    University of Hong Kong, Hong Kong, China.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Twitter

    Facebook

    Email

    As the upcoming United Nations Biodiversity Conference in Kunming, China, ushers in the UN decade of ecosystem restoration, regreening efforts are sprouting worldwide. Adding vegetation — expedited by new technologies such as EcoFit, which predicts what trees will thrive in a given environment — can salvage highly disturbed habitats, benefiting native species and offsetting climate change. But when aimed at halting desertification, regreening can have a devastating cost for native ecosystems.

    Access options

    Access through your institution

    Change institution

    Buy or subscribe

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

    Additional access options:

    Log in

    Learn about institutional subscriptions

    Nature 604, 40 (2022)
    doi: https://doi.org/10.1038/d41586-022-00944-4

    Competing Interests
    The authors declare no competing interests.

    Related Articles

    See more letters to the editor

    Subjects

    Biodiversity

    Conservation biology

    Climate change

    Latest on:

    Biodiversity

    China: protect black soil for biodiversity
    Correspondence 05 APR 22

    Funding battles stymie ambitious plan to protect global biodiversity
    News 31 MAR 22

    Are there limits to economic growth? It’s time to call time on a 50-year argument
    Editorial 16 MAR 22

    Climate change

    The microbiologist working to understand how oceans absorb carbon dioxide
    Spotlight 05 APR 22

    By the numbers: China’s net-zero ambitions
    Spotlight 05 APR 22

    Turning industrial CO2 into battery fuel
    Spotlight 05 APR 22

    Jobs

    Junior group leader position in Human immunology, Pathophysiology and Immunotherapy at Inserm-Université Paris Cité Unit 976

    National Institute for Health and Medical Research (INSERM)
    Paris, France

    Senior Assistant Editor

    Elsevier Inc.
    London, Greater London, United Kingdom

    Dean, Gordon W. Davis College of Agricultural Sciences and Natural Resources

    Texas Tech University (TTU)
    Lubbock, TX, United States

    Postdoctoral fellow positions in cancer stem cells, single cell genomics, and tumor immunology

    Houston Methodist in “ affiliation with Weill- Cornell Medical College
    Houston, United States More

  • in

    The genetic architecture underlying body-size traits plasticity over different temperatures and developmental stages in Caenorhabditis elegans

    Andersen EC, Bloom JS, Gerke JP, Kruglyak L (2014) A variant in the neuropeptide receptor npr-1 is a major determinant of Caenorhabditis elegans growth and physiology. PLoS Genet 10(2):e1004156. https://doi.org/10.1371/journal.pgen.1004156Andersen EC, Shimko TC, Crissman JR, Ghosh R, Bloom JS, Seidel HS et al. (2015) A powerful new quantitative genetics platform, combining caenorhabditis elegans high-throughput fitness assays with a large collection of recombinant strains. G3 5(5):911–920. https://doi.org/10.1534/g3.115.017178Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Angilletta MJ, Dunham AE (2003) The temperature-size rule in ectotherms: simple evolutionary explanations may not be general. Am Naturalist 162:3
    Google Scholar 
    Atkinson D (1994) Temperature and organism size–a biological law for ectotherms? Adv Ecol Res 25:1–58Azevedo RBR, French V, Partridge L (2002) Temperature modulates epidermal cell size in Drosophila melanogaster. J Insect Physiol 48:231–237CAS 
    Article 

    Google Scholar 
    Azevedo RBR, James AC, McCabe J, Partridge L (1998) Latitudinal variation of wing: thorax size and wing-aspect ration in Drosophila melanogaster. Evolution 52(5):1353–1362PubMed 

    Google Scholar 
    Bates D, Mächler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48. https://doi.org/10.18637/jss.v067.i01Beldade P, Mateus ARA, Keller RA (2011) Evolution and molecular mechanisms of adaptive developmental plasticity. Mol Ecol 20:1347–1363. https://doi.org/10.1111/j.1365-294X.2011.05016.xArticle 
    PubMed 

    Google Scholar 
    Bochdanovits Z, Van Der Klis H, De Jong G (2003) Covariation of larval gene expression and adult body size in natural populations of Drosophila melanogaster. Mol Biol Evolution 20(11):1760–1766. https://doi.org/10.1093/molbev/msg179CAS 
    Article 

    Google Scholar 
    Brenner S (1974) Genetics of the Caenorhabditis elegans. ChemBioChem 4(8):683–687. https://doi.org/10.1002/cbic.200300625CAS 
    Article 

    Google Scholar 
    Callahan HS, Dhanoolal N, Ungerer MC (2005) Plasticity genes and plasticity costs: a new approach using an Arabidopsis recombinant inbred population. N Phytologist 166(1):129–140. https://doi.org/10.1111/j.1469-8137.2005.01368.xCAS 
    Article 

    Google Scholar 
    Carta D, Villanova L, Costacurta S, Patelli A, Poli I, Vezzù S et al. (2011) Method for optimizing coating properties based on an evolutionary algorithm approach. Anal Chem 83(16):6373–6380. https://doi.org/10.1021/ac201337eCAS 
    Article 
    PubMed 

    Google Scholar 
    Czarnoleski M, Kramarz P, Malek D, Drobniak SM (2017) Genetic components in a thermal developmental plasticity of the beetle Tribolium castaneum. J Therm Biol 68:55–62. https://doi.org/10.1016/j.jtherbio.2017.01.015Article 
    PubMed 

    Google Scholar 
    Dupuis J, Siegmund D (1999) Statistical methods for mapping quantitative trait loci from a dense set of markers. Genetics 151(1):373–386CAS 
    Article 

    Google Scholar 
    Ellers J, Driessen G (2011) Genetic correlation between temperature-induced plasticity of life-history traits in a soil arthropod. Evolut Ecol 25:473–484. https://doi.org/10.1007/s10682-010-9414-1Article 

    Google Scholar 
    Fischer K, Bauerfeind SS, Fiedler K (2006) Temperature-mediated plasticity in egg and body size in egg size-selected lines of a butterfly. J Therm Biol 31:347–354. https://doi.org/10.1016/j.jtherbio.2006.01.006Article 

    Google Scholar 
    Gaertner BE, Phillips PC (2010) Caenorhabditis elegans as a platform for molecular quantitative genetics and the systems biology of natural variation. Genet Res 92(5–6):331–348. https://doi.org/10.1017/S0016672310000601CAS 
    Article 

    Google Scholar 
    Gao AW, Sterken MG, uit de Bos J, van Creij J, Kamble R, Snoek BL et al. (2018) Natural genetic variation in C. elegans identified genomic loci controlling metabolite levels. Genome Res 28(9):1296–1308. https://doi.org/10.1101/gr.232322.117CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ghosh SM, Testa ND, Shingleton AW (2013) Temperature-size rule is mediated by thermal plasticity of critical size in Drosophila melanogaster. Proc Biol Sci 280(1760):20130174. https://doi.org/10.1098/rspb.2013.0174Gutteling EW, Doroszuk A, Riksen JAG, Prokop Z, Reszka J, Kammenga JE (2007a) Environmental influence on the genetic correlations between life-history traits in Caenorhabditis elegans. Heredity 98:206–213. https://doi.org/10.1038/sj.hdy.6800929CAS 
    Article 
    PubMed 

    Google Scholar 
    Gutteling EW, Riksen JAG, Bakker J, Kammenga JE (2007b) Mapping phenotypic plasticity and genotype – environment interactions affecting life-history traits in Caenorhabditis elegans. Heredity 98:28–37. https://doi.org/10.1038/sj.hdy.6800894CAS 
    Article 
    PubMed 

    Google Scholar 
    Jovic K, Sterken MG, Grilli J, Bevers RPJ, Rodriguez M, Riksen JAG, et al. (2017) Temporal dynamics of gene expression in heat-stressed Caenorhabditis elegans. PLoS One 12:e0189445Kammenga JE, Doroszuk A, Riksen JAG, Hazendonk E, Spiridon L, Petrescu AJ et al. (2007) A Caenorhabditis elegans wild type defies the temperature-size rule owing to a single nucleotide polymorphism in tra-3. PLoS Genet 3(3):0358–0366. https://doi.org/10.1371/journal.pgen.0030034CAS 
    Article 

    Google Scholar 
    Kang MH, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ et al. (2008) Efficient control of population structure in model organism association mapping. Genetics 178(3):1709–1723. https://doi.org/10.1534/genetics.107.080101Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Klok CJ, Harrison JF (2013) The temperature size rule in Arthropods: independent of macro-environmental variables but size dependent. Integr Comp Biol 53(4):557–570. https://doi.org/10.1093/icb/ict075Article 
    PubMed 

    Google Scholar 
    Kruijer W, Boer MP, Malosetti M, Flood PJ, Engel B, Kooke R et al. (2014) Marker-based estimation of heritability in immortal populations. Genetics 199(2):379–398. https://doi.org/10.1534/genetics.114.167916Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lafuente E, Beldade P (2019) Genomics of developmental plasticity in animals. Front Genet 10:1–18. https://doi.org/10.3389/fgene.2019.00720CAS 
    Article 

    Google Scholar 
    Lafuente E, Duneau D, Beldade P (2018) Genetic basis of thermal plasticity variation in Drosophila melanogaster body size. PLoS Genet 14(9):1–24. https://doi.org/10.1371/journal.pgen.1007686CAS 
    Article 

    Google Scholar 
    Li Y, Álvarez OA, Gutteling EW, Tijsterman M, Fu J, Riksen JAG et al. (2006) Mapping determinants of gene expression plasticity by genetical genomics in C. elegans. PLoS Genet 2(12):2155–2161. https://doi.org/10.1371/journal.pgen.0020222CAS 
    Article 

    Google Scholar 
    Nagashima T, Ishiura S, Suo S (2017) Regulation of body size in Caenorhabditis elegans: effects of environmental factors and the nervous system. Int J Developmental Biol 61(6–7):367–374. https://doi.org/10.1387/ijdb.160352ssCAS 
    Article 

    Google Scholar 
    Nakad R, Snoek LB, Yang W, Ellendt S, Schneider F, Mohr TG et al. (2016) Contrasting invertebrate immune defense behaviors caused by a single gene, the Caenorhabditis elegans neuropeptide receptor gene npr-1. BMC Genomics 17(1):1–20. https://doi.org/10.1186/s12864-016-2603-8CAS 
    Article 

    Google Scholar 
    Norry FM, Loeschcke VR (2002) Longevity and resistance to cold stress in cold-stress selected lines and their controls in Drosophila melanogaster. J Evolut Biol 15:775–783Article 

    Google Scholar 
    Paaby AB, Rockman MV (2014) Cryptic genetic variation: evolution’ s hidden substrate. Nat Rev Genet 15(4):247–258. https://doi.org/10.1038/nrg3688CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Peng IF, Berke BA, Zhu Y, Lee WH, Chen W, Wu CF (2007) Temperature-dependent developmental plasticity of drosophila neurons: cell-autonomous roles of membrane excitability, Ca2+ influx, and cAMP signaling. J Neurosci 27(46):12611–12622. https://doi.org/10.1523/JNEUROSCI.2179-07.2007CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Petersen C, Dirksen P, Schulenburg H (2015) Why we need more ecology for genetic models such as C. elegans. Trends Genet 31(3):120–127. https://doi.org/10.1016/j.tig.2014.12.001CAS 
    Article 
    PubMed 

    Google Scholar 
    R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Reddy KC, Andersen EC, Kruglyak L, Kim DH (2009) A polymorphism in npr-1 is a behavioral determinant of pathogen susceptibility in C. elegans. Science 323(5912):382–384. https://doi.org/10.1126/science.1166527CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rieseberg LH, Archer MA, Wayne RK (1999) Transgressive segregation, adaptation and speciation. Heredity 83:363–372Rockman MV, Skrovanek SM, Kruglyak L (2010) Selection at linked sites shapes. Science 330:372–376. https://doi.org/10.1126/science.1194208Rodriguez M, Snoek LB, Riksen JAG, Bevers RP, Kammenga JE (2012) Genetic variation for stress-response hormesis in C. elegans lifespan. Exp Gerontol 47(8):581–587. https://doi.org/10.1016/j.exger.2012.05.005CAS 
    Article 
    PubMed 

    Google Scholar 
    Saltz JB, Bell AM, Flint J, Gomulkiewicz R, Hughes KA, Keagy J (2018) Why does the magnitude of genotype-by-environment interaction vary? Ecol Evolution 8(12):6342–6353. https://doi.org/10.1002/ece3.4128Article 

    Google Scholar 
    Scheiner S (1993) Plasticity as a selectable trait: reply to via. Am Soc Naturalist 142(2):371–373Article 

    Google Scholar 
    Sgro C, Hoffmann A (2004) Genetic correlations, tradeoffs and environmental variation. Heredity 93:241–248. https://doi.org/10.1038/sj.hdy.6800532Snoek BL, Sterken MG, Bevers RPJ, Volkers RJM, van Hof A, Brenchley R, et al. (2017) Contribution of trans regulatory eQTL to cryptic genetic variation in C. elegans. BMC Genomics 18:500. https://doi.org/10.1186/s12864-017-3899-8Snoek BL, Volkers RJM, Nijveen H, Petersen C, Dirksen P, Sterken MG, et al. (2019) A multi-parent recombinant inbred line population of C. elegans allows identification of novel QTLs for complex life history traits. BMC Biol 17:24Snoek LB, Orbidans HE, Stastna JJ, Aartse A, Rodriguez M, Riksen JAG et al. (2014) Widespread genomic incompatibilities in Caenorhabditis elegans. G3: Genes, Genomes, Genet 4(10):1813–1823. https://doi.org/10.1534/g3.114.013151Article 

    Google Scholar 
    Snoek LB, Sterken MG, Hartanto M, van Zuilichem AJ, Kammenga JE, de Ridder D et al. (2020) WormQTL2: an interactive platform for systems genetics in Caenorhabditis elegans. Database 2020:baz149. https://doi.org/10.1093/database/baz149Steigenga MJ, Zwaan BJ, Brakefield PM, Fischer K (2005) The evolutionary genetics of egg size plasticity in a butterfly. J Evolut Biol 18:281–289. https://doi.org/10.1111/j.1420-9101.2004.00855.xCAS 
    Article 

    Google Scholar 
    Sterken MG, Bevers RPJ, Volkers RJM, Riksen JAG, Kammenga JE, Snoek BL (2020) Dissecting the eQTL micro-architecture in Caenorhabditis elegans. Front Genet 11(Nov):1–15. https://doi.org/10.3389/fgene.2020.501376CAS 
    Article 

    Google Scholar 
    Sterken MG, Plaat LVB, Van Der Riksen JAG, Rodriguez M, Schmid T, Hajnal A, et al. (2017) Ras/MAPK modifier loci revealed by eQTL in Caenorhabditis elegans. G3 (Bethesda) 7:3185–3193. https://doi.org/10.1534/g3.117.1120Sterken MG, Snoek LB, Kammenga JE, Andersen EC (2015) The laboratory domestication of Caenorhabditis elegans. Trends Genet 31(5):224–231. https://doi.org/10.1016/j.tig.2015.02.009CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Têtard-jones C, Kertesz M, Preziosi R (2011) Quantitative trait loci mapping of phenotypic plasticity and genotype-environment interactions in plant and insect performance. Philos Trans R Soc B: Biol Sci 366:1569Article 

    Google Scholar 
    Thompson OA, Snoek LB, Nijveen H, Sterken MG, Volkers RJM, Brenchley R et al. (2015) Remarkably divergent regions punctuate the genome assembly of the Caenorhabditis elegans Hawaiian strain CB4856. Genetics 200(3):975–989. https://doi.org/10.1534/genetics.115.175950CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Van Voorhies WA (1996) Bergmann size clines: a simple explanation for their occurrence in ectotherms. Evolution 50(3):1259–1264. https://doi.org/10.1111/j.1558-5646.1996.tb02366.xArticle 
    PubMed 

    Google Scholar 
    Via S, Gomulkiewicz R, de Jong G, Scheiner SM, Schlichting CD, van Tienderen PH (1995) Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol Evolution 10:5Article 

    Google Scholar 
    Viñuela A, Snoek LB, Riksen JAG, Kammenga JE (2010) Genome-wide gene expression regulation as a function of genotype and age in C. elegans. Genome Res 20(7):929–937. https://doi.org/10.1101/gr.102160.109CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Viñuela A, Snoek LB, Riksen JAG, Kammenga JE (2011) Gene expression modifications by temperature-toxicants interactions in Caenorhabditis elegans. PLoS One 6(9):e24676. https://doi.org/10.1371/journal.pone.0024676Wickham H (2011) Ggplot2. Wiley Interdiscip Rev: Computational Stat 3(2):180–185. https://doi.org/10.1002/wics.147Article 

    Google Scholar 
    Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R et al. (2019) Welcome to the Tidyverse. J Open Source Softw 4(43):1686. https://doi.org/10.21105/joss.01686Article 

    Google Scholar  More

  • in

    Winter torpor expression varies in four bat species with differential susceptibility to white-nose syndrome

    Wang, L.C.H. Ecological, physiological, and biochemical aspects of torpor in mammals and birds in Advances in comparative and environmental physiology (ed. Wang, L.C.H.) 361–401 (Springer, 1989).Humphries, M.M., Speakman, J.R., & Thomas, D.W. Temperature, hibernation energetics, and the cave and continental distributions of little brown myotis in Functional and Evolutionary Ecology of Bats (ed. Zubaid, A., McCracken, G.F., & Kunz, T.H.) 23–37 (Oxford Press, 2005).Hudson, J.W. Torpidity in mammals in Comparative physiology of thermoregulation. (ed. Whittow, G.C., Hudson, J.W., & Deavers, D.R.) 97–165 (Academic Press, 1973).Geiser, F. & Ruf, T. Hibernation versus daily torpor in mammals and birds: Physiological variables and classification of torpor patterns. Phys. Zool. 68, 935–966 (1995).Article 

    Google Scholar 
    Davis, W. H. & Hitchcock, H. B. Biology and migration of the bat, Myotis lucifugus, New England. J. Mamm. 46, 296–313. https://doi.org/10.2307/1377850 (1965).Article 

    Google Scholar 
    Speakman, J. R. & Rowland, A. Preparing for inactivity: How insectivorous bats deposit a fat store for hibernation. Proc. Nutr. Soc. 58, 123–131. https://doi.org/10.1079/PNS19990017 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    Boyles, J. G., Johnson, J. S., Blomberg, A. & Lilley, T. M. Optimal hibernation theory. Mammal Rev. 50, 91–100. https://doi.org/10.1111/mam.12181 (2020).Article 

    Google Scholar 
    Britzke, E. R., Sewell, P., Hohmann, M. G., Smith, R. & Scott, R. Use of temperature-sensitive transmitters to monitor the temperature profiles of hibernating bats affected with white-nose syndrome. Northeast. Nat. 17, 239–246. https://doi.org/10.1656/045.017.0207 (2010).Article 

    Google Scholar 
    Halsall, A. L., Boyles, J. G. & Whitaker, J. O. Jr. Body temperature patterns of big browns during winter in a building hibernaculum. J. Mamm. 93, 497–503. https://doi.org/10.1644/11-MAMM-A-262.1 (2012).Article 

    Google Scholar 
    Johnson, J. S., Lacki, M. J., Thomas, S. C. & Grider, J. F. Frequent arousals from winter torpor in Rafinesque’s big-eared bat (Corynorhinus rafinesquii). PLoS ONE 7, e49754. https://doi.org/10.1371/journal.pone.0049754 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jonasson, K. A. & Willis, C. K. R. Hibernation energetics of free-ranging little brown bats. J. Exp. Bio. 215, 2141–2149. https://doi.org/10.1242/jeb.066514 (2012).Article 

    Google Scholar 
    Day, K. M. & Tomasi, T. E. Winter energetics of female Indiana bats Myotis sodalis. Physiol. Biochem. Zool. 87, 56–64. https://doi.org/10.1086/671563 (2014).Article 
    PubMed 

    Google Scholar 
    Meierhofer, M. B. et al. Winter habitats of bats in Texas. PLoS ONE 14, e0220839. https://doi.org/10.1371/journal.pone.0220839 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boyles, J. G. Benefits of knowing the costs of disturbance to hibernating bats. Wildl. Soc. Bull. 41, 388–392. https://doi.org/10.1002/wsb.755 (2017).Article 

    Google Scholar 
    Frick, W. F. et al. Pathogen dynamics during invasion and establishment of white-nose syndrome explain mechanisms of host persistence. Ecology 98, 624–631. https://doi.org/10.1002/ecy.1706 (2017).Article 
    PubMed 

    Google Scholar 
    Cheng, T. L. et al. The scope and severity of white-nose syndrome on hibernating bats in North America. Conserv. Biol. 35, 1586–1597. https://doi.org/10.1111/cobi.13739 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cryan, P. M., Meteyer, C. U., Boyles, J. G. & Blehert, D. S. Wing pathology of white-nose syndrome in bats suggests life-threatening disruption of physiology. BMC Biol. 8, 1–8 (2010).Article 

    Google Scholar 
    Cryan, P. M. et al. Electrolyte depletion in white-nose syndrome bats. J. Wild. Dis. 49, 398–402 (2013).CAS 
    Article 

    Google Scholar 
    Frick, W. F. et al. Disease alters macroecological patterns of North American bats. Glob. Ecol. Biogeogr. 24, 741–749. https://doi.org/10.1111/geb.12290 (2015).Article 

    Google Scholar 
    Bernard, R.F., Willcox, E.V., Parise, K.L., Foster, J.T., & McCracken, G.F. White-nose syndrome fungus, Pseudogymnoascus destructans, on bats captured emerging from caves during winter in the southeastern United States. BMC Zool. https://doi.org/10.1186/s40850-017-0021-2 (2017).Davy, C. M. et al. The other white-nose syndrome transcriptome: Tolerant and susceptible hosts respond differently to the pathogen Pseudogymnoascus destructans. Ecol. Evol. 7, 7161–7170. https://doi.org/10.1002/ece3.3234 (2015).Article 

    Google Scholar 
    Lilley, T. M. et al. Resistance is futile: RNA-sequencing reveals differing responses to bat fungal pathogen in Nearctic Myotis lucifugus and Palearctic Myotis myotis. Oecologia 191, 295–309. https://doi.org/10.1007/s00442-019-04499-6 (2019).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bernard, R. F. & McCracken, G. F. Winter behavior of bats and the progression of white-nose syndrome in the southeastern United States. Ecol. Evol. 7, 1487–1496. https://doi.org/10.1002/ece3.2772 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moosman, P.R., Warner, D.P., Hendren, R.H., & Hosler, M.J. Potential for monitoring eastern small-footed bats on talus slopes. Northeast. Nat. https://www.jstor.org/stable/26453719 (2015).Bernard, R. F. et al. Identifying research needs to inform white-nose syndrome management decisions. Cons. Sci. Prac. 2(e220), 2020. https://doi.org/10.1111/csp2.220 (2020).Article 

    Google Scholar 
    Reynolds, D. S., Shoemaker, K., Oettingen, S. V. & Najjar, S. High rates of winter activity and arousals in two New England bat species: implications for a reduced white-nose syndrome impact?. Northeast. Nat. 24, B188–B208. https://doi.org/10.1656/045.024.s720 (2017).Article 

    Google Scholar 
    Bernard, R. F., Willcox, E. V., Jackson, R. T., Brown, V. A. & McCracken, G. F. Feasting, not fasting: Winter diets of cave hibernating bats in the United States. Front. Zool. 18, 1–13. https://doi.org/10.1186/s12983-021-00434-9 (2021).Article 

    Google Scholar 
    Prendergast, B. J., Freeman, D. A., Zucker, I. & Nelson, R. J. Periodic arousal from hibernation is necessary for initiation of immune responses in ground squirrels. Am. J. Phys. 282, 1054–1062. https://doi.org/10.1152/ajpregu.00562.2001 (2002).Article 

    Google Scholar 
    Dobony, C. A. et al. Little brown myotis persist despite exposure to white-nose syndrome. J. Fish Wild. 2, 190–195. https://doi.org/10.3996/022011-JFWM-014 (2011).Article 

    Google Scholar 
    Rowley, J. J. & Alford, R. A. Hot bodies protect amphibians against chytrid infection in nature. Sci. Rep. 3, 1–4. https://doi.org/10.1038/srep01515 (2013).CAS 
    Article 

    Google Scholar 
    Verant, M. L. et al. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiol. 14, 10 (2014).Article 

    Google Scholar 
    Verant, M. L. et al. Temperature-dependent growth of Geomyces destructans, the fungus that causes bat white-nose syndrome. PLoS ONE 7, e46280. https://doi.org/10.1371/journal.pone.0046280 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brownlee-Bouboulis, S. A. & Reeder, D. M. White-nose syndrome-affected little brown Myotis (Myotis lucifugus) increase grooming and other active behaviors during arousals from hibernation. J. Wildl. Dis. 49, 850–859. https://doi.org/10.7589/2012-10-242 (2013).Article 
    PubMed 

    Google Scholar 
    Campbell, J. Tennessee winter bat population and white-nose syndrome monitoring report for 2018–2019. TWRA Wildlife Technical Report 16-4. http://www.tnbwg.org/2019%20Annual%20Monitoring%20Report.pdf (2019).Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Eco. Let. 15, 1050–1057. https://doi.org/10.1111/j.1461-0248.2012.01829.x (2012).Article 

    Google Scholar 
    Langwig, K.E., et al. Drivers of variation in species impacts for a multi-host fungal disease of bats. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150456. https://doi.org/10.1098/rstb.2015.0456 (2016).Aldridge, H.D.J.N., & Brigham, R.M. Load carrying and maneuverability in an insectivorous bat: A test of the 5% ‘rule’ of radio-telemetry. J. Mamm. 69, 379–382. https://doi.org/10.2307/1381393 (1988)Sikes, R. S. et al. Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mamm. 97, 663–688. https://doi.org/10.1093/jmammal/gyw078 (2016).Article 

    Google Scholar 
    Barclay, R. M. R. et al. Can external radiotransmitters be used to assess body temperature and torpor in bats?. J. Mammal. 77, 1102–1106. https://doi.org/10.2307/1382791 (1996).Article 

    Google Scholar 
    Turbill, C., & Geiser, F. Hibernation by tree-roosting bats. J. Comp. Physiol. B. 178, 597–605. https://doi.org/10.1007/s00360-007-0249-1 (2008)Park, K.J., Jones, G., & Ransome, R.D. Torpor, arousal and activity of hibernating greater horseshoe bats (Rhinolophus ferrumequinum). Funct. Ecol. 14, 580–588 (2000).Reeder, D. M. et al. Frequent arousal from hibernation linked to severity of infection and mortality in bats with white-nose syndrome. PLoS ONE 7, e38920. https://doi.org/10.1371/journal.pone.0038920 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sirajuddin, P. Vulnerability of tri-colored bats (Perimyotis subflavus) to white-nose syndrome in the southeastern United States. M.S thesis at https://www.proquest.com/dissertations-theses/vulnerability-tri-colored-bats-i-perimyotis/docview/2185672601/se-2?accountid=8361 (2018).R Development Core Team 3.6.1. A Language and Environment for Statistical Computing. http://www.r-project.org (2019).Bates, D., Maechler, M., Bolker, B., & Walker, S. lme4: Linear mixed-effects models using ‘Eigen’ and S4. R Packag. version 1.1-13. ftp://cran.r-project.org/pub/R/web/packages/lme4/lme4.pdf. (2017)Czenze, Z. J. & Willis, C. K. R. Warming up and shipping out: Arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). J. Comp. Physiol. B-Biochem. Syst. Environ. Physiol. 185, 575–586. https://doi.org/10.1007/s00360-015-0900-1 (2015).Article 

    Google Scholar 
    Conover, W.J. Practical Nonparametric Statistics. (Wiley, 1999).Crawley, M.J. The R Book: Second Edition. (Wiley, 2013)Lenth, R., Singmann, H., Love, J., Buerkner, P., & Herve, M. Package ‘emmeans’. R Packag. version 1.7.0. https://cran.r-project.org/web/packages/emmeans/emmeans.pdf (2021)Best, T.L. & Jennings, J.B. Myotis leibii. Mammalian Species 547, 1–6. https://doi.org/10.2307/3504255 (1997)Frank, C.L., Herzog, C.,. Laske, J.P., & Cardino V. The role of skin temperature in the resistance of Myotis leibii to white-nose syndrome presented at the 49th Symposium of the North American Society for Bat Research, Kalamazoo, Michigan https://www.nasbr.org/resources/docs/meetings/year/2019/NASBR-2019-Abstracts-Draft-191001.pdf (2019)Johnson, J.S., Scafini, M.R., Sewall, B.J., & Turner, G.G. Hibernating bat species in Pennsylvania use colder winter habitats following the arrival of white-nose syndrome in Conservation and Ecology of Pennsylvania’s Bats (ed. Butchkoski, C.M., Reeder, D.M., Turner, G.G., & Whidden, H.P) 181–199 (The Pennsylvania Academy of Science, 2016)Haase, C.G., et al. Body mass and hibernation microclimate may predict bat susceptibility to white‐nose syndrome. Eco. Evo. 11, 506–515 https://doi.org/10.1002/ece3.7070 (2021)Veilleux, J.P. A Noteworthy Hibernation Record of Myotis leibii (Eastern Small-footed Bat) in Massachusetts. Northeast. Nat. 14, 501–502 https://doi.org/10.1656/1092-6194(2007)14[501:ANHROM]2.0.CO;2 (2007)Boyles, J. G., Dunbar, M. B. & Whitaker, J. O. Activity following arousal in winter in North American vespertilionid bats. Mamm. Rev. 36, 267–280. https://doi.org/10.1111/j.1365-2907.2006.00095.x (2006).Article 

    Google Scholar 
    Webb, P. I., Speakman, J. R. & Racey, P. A. How hot is a hibernaculum? A review of the temperatures at which bats hibernate. Can. J. Zool. 74, 761–765. https://doi.org/10.1139/z96-087 (1996).Article 

    Google Scholar 
    Jackson, R.T., Willcox, E.V., Zobel, J.M., & Bernard, R.F. Hibernation behavior of four bat species with differing susceptibility to white-nose syndrome. In review (2021).Fujita, M. S. & Kunz, T. H. Pipstrellus subflavus. Mamm. Spec. 228, 1–6. https://doi.org/10.2307/3504021 (1984).Article 

    Google Scholar 
    Bohn, S. J. et al. Evidence of ‘sickness behaviour’ in bats with white-nose syndrome. Behaviour 152, 981–1003. https://doi.org/10.1163/1568539X-00003384 (2016).Article 

    Google Scholar 
    Tuttle, M. D. Status, causes of decline, and management of endangered gray bats. J. Wild. Manag. 43, 1–17. https://doi.org/10.2307/3800631 (1979).Article 

    Google Scholar 
    Harvey, M.J., Altenbach, J.S., & Best, T.L. Bats of the Eastern United States. (JHU Press, 2011).Klüg-Baerwald, B. J., Lausen, C. L., Willis, C. K. & Brigham, R. M. Home is where you hang your bat: Winter roost selection by prairie-living big brown bats. J. Mamm. 98, 752–760. https://doi.org/10.1093/jmammal/gyx039 (2017).Article 

    Google Scholar 
    Sandel, J. K. et al. Use and selection of winter hibernacula by the eastern pipistrelle (Pipistrellus subflavus) in Texas. J. Mamm. 82, 173–178. https://doi.org/10.1644/1545-1542(2001)082%3c0173:UASOWH%3e2.0.CO;2 (2001).Article 

    Google Scholar 
    Hayes, J. P., Ober, H. K., Sherwin, R. E. Survey and monitoring of bats in Ecological and behavioral methods for the study of bats (ed. Kunz, T. H., Parsons, S.). 120–129 (JHU Press, 2009). More

  • in

    Influence of infrastructure, ecology, and underpass-dimensions on multi-year use of Standard Gauge Railway underpasses by mammals in Tsavo, Kenya

    Polyzos, S. & Tsiotas, D. The contribution of transport infrastructures to the economic and regional development: A review of the conceptual framework. Theor. Empir. Res. Urban Manag. 15, 5–23 (2020).
    Google Scholar 
    Ledec, G. & Posas, P. J. Biodiversity conservation in road projects: Lessons from World Bank experience in Latin America. Transp. Res. Rec. 1819, 198–202 (2003).Article 

    Google Scholar 
    Hughes, A. C. Understanding and minimizing environmental impacts of the Belt and Road Initiative. Conserv. Biol. 33, 883–894 (2019).Article 

    Google Scholar 
    Seiler, A. in COST 341—habitat fragmentation due to transportation infrastructure: the European review (eds Trocmé, M. et al.) Ch. 3, 31–50 (Office for Official Publications of the European Communities, 2002).Marcantonio, M., Rocchini, D., Geri, F., Bacaro, G. & Amici, V. Biodiversity, roads, & landscape fragmentation: Two Mediterranean cases. Appl. Geogr. 42, 63–72. https://doi.org/10.1016/j.apgeog.2013.05.001 (2013).Article 

    Google Scholar 
    Plămădeal, V. & Slobodeaniuc, S. Negative impact of railway transport on the ambient environment. J. Eng. Sci. https://doi.org/10.5281/zenodo.2640044 (2019).Lala, F. et al. Wildlife roadkill in the Tsavo Ecosystem, Kenya: Identifying hotspots, potential drivers, and affected species. Heliyon 7, e06364 (2021).Article 

    Google Scholar 
    Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232. https://doi.org/10.1038/nature13717 (2014).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Laurance, W. F., Goosem, M. & Laurance, S. G. W. Impacts of roads and linear clearings on tropical forests. Trends Ecol. Evol. 24, 659–669. https://doi.org/10.1016/j.tree.2009.06.009 (2009).Article 
    PubMed 

    Google Scholar 
    Clair, C. C. S., Whittington, J., Forshner, A., Gangadharan, A. & Laskin, D. N. Railway mortality for several mammal species increases with train speed, proximity to water, and track curvature. Sci. Rep. 10, 20476. https://doi.org/10.1038/s41598-020-77321-6 (2020).CAS 
    Article 

    Google Scholar 
    Kušta, T., Ježek, M. & Keken, Z. Mortality of large mammals on railway tracks. Sci. Agric. Bohem. 42, 12–18 (2011).
    Google Scholar 
    Dorsey, B. & Olsson, M. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 26, 219–227 (Wiley, 2015).Barrientos, R. & Borda-de-Água, L. Railway Ecology (eds Borda-de-Água, L. et al.) Ch. 4, 43–64 (Springer Open, 2017).Lucas, P. S., de Carvalho, R. G. & Grilo, C. Railway Ecology Ch. Chapter 6, 81–99 (2017).Barrientos, R., Ascensão, F., Beja, P., Pereira, H. M. & Borda-de-Água, L. Railway ecology vs. road ecology: Similarities and differences. Eur. J. Wildl. Res. 65, 1–9. https://doi.org/10.1007/s10344-018-1248-0 (2019).Article 

    Google Scholar 
    Jasińska, K. D. et al. Linking habitat composition, local population densities and traffic characteristics to spatial patterns of ungulate-train collisions. J. Appl. Ecol. 56, 2630–2640. https://doi.org/10.1111/1365-2664.13495 (2019).Article 

    Google Scholar 
    Smith, D. J., Ree, R. v. d. & Rosell, C. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 21, 172–183 (Wiley, 2015).Gilhooly, P. S., Nielsen, S. E., Whittington, J. & Clair, C. C. S. Wildlife mortality on roads and railways following highway mitigation. Ecosphere 10, e02597 (2019).Article 

    Google Scholar 
    Clevenger, A. P., Chruszcz, B. & Gunson, K. E. Highway mitigation fencing reduces wildlife-vehicle collisions. Wildl. Soc. Bull. 29, 646–653 (2001).
    Google Scholar 
    Simpson, N. O. et al. Overpasses and underpasses: Effectiveness of crossing structures for migratory ungulates. J. Wildl. Manag. 80, 1370–1378. https://doi.org/10.1002/jwmg.21132 (2016).Article 

    Google Scholar 
    Seidler, R. G., Green, D. S. & Beckmann, J. P. Highways, crossing structures and risk: Behaviors of Greater Yellowstone pronghorn elucidate efficacy of road mitigation. Glob. Ecol. Conserv. 15, e00416. https://doi.org/10.1016/j.gecco.2018.e00416 (2018).Article 

    Google Scholar 
    Huijser, M. P. et al. Effectiveness of short sections of wildlife fencing and crossing structures along highways in reducing wildlife–vehicle collisions and providing safe crossing opportunities for large mammals. Biol. Conserv. 197, 61–68. https://doi.org/10.1016/j.biocon.2016.02.002 (2016).Article 

    Google Scholar 
    Olsson, M. P. O. & Widen, P. Effects of highway fencing and wildlife crossings on moose Alces alces movements and space use in southwestern Sweden. Wildl. Biol. 14, 111–117 (2008).Article 

    Google Scholar 
    Donaldson, B. Use of highway underpasses by large mammals and other wildlife in Virginia: Factors influencing their effectiveness. Transp. Res. Rec. 157–164, 2007. https://doi.org/10.3141/2011-17 (2011).Article 

    Google Scholar 
    Foster, M. L. & Humphrey, S. R. Use of highway underpasses by Florida panthers and other wildlife. Wildl. Soc. Bull. 23, 95–100 (1995).
    Google Scholar 
    Caldwell, M. R. & Klip, J. M. K. Wildlife interactions within highway underpasses. J. Wildl. Manag. 84, 227–236. https://doi.org/10.1002/jwmg.21801 (2019).Article 

    Google Scholar 
    Clevenger, A. P. & Waltho, N. Performance indices to identify attributes of highway crossing structures facilitating movement of large mammals. Biol. Conserv. 121, 453–464. https://doi.org/10.1016/j.biocon.2004.04.025 (2005).Article 

    Google Scholar 
    Mcdonald, W. & Clair, C. C. S. Elements that promote highway crossing structure use by small mammals in Banff National Park. J. Appl. Ecol. 41, 82–93 (2004).Article 

    Google Scholar 
    Mata Estacio, C., Hervás Bengoechea, I., Herranz Barrera, J., Suárez Cardona, F. & Arrazola, J. E. M. International Conference on Ecology and Transportation (ICOET 2003) Federal Highway Administration.Sawyer, H., Lebeau, C. & Hart, T. Mitigating roadway impacts to migratory mule deer—A case study with underpasses and continuous fencing. Wildl. Soc. Bull. 36, 492–498. https://doi.org/10.1002/wsb.166 (2012).Article 

    Google Scholar 
    Rodriguez, A., Crema, G. & Delibes, M. Use of non-wildlife passages across a high speed railway by terrestrial vertebrates. J. Appl. Ecol. 33, 1527–1540 (1996).Article 

    Google Scholar 
    Yanes, M., Velasco, J. M. & Sufirez, F. Permeability of roads and railways to vertebrates: The importance of culverts. Biol. Conserv. 71, 217–222 (1995).Article 

    Google Scholar 
    Rodriguez, A., Crema, G. & Delibes, M. Factors affecting crossing of red foxes and wildcats through non-wildlife passages across a high-speed railway. Ecography 2, 287–294 (1997).Article 

    Google Scholar 
    Weeks, S. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 43, 353–356 (Wiley, 2015).Okita-Ouma, B. et al. Effectiveness of wildlife underpasses and culverts in connecting elephant habitats: A case study of new railway through Kenya’s Tsavo National Parks. Afr. J. Ecol. 59(3), 624–640 (2021).Article 

    Google Scholar 
    Collinson, W., Davies-Mostert, H., Roxburgh, L. & van der Ree, R. Status of road ecology research in Africa: Do we understand the impacts of roads, and how to successfully mitigate them?. Front. Ecol. Evol. 7, 479. https://doi.org/10.3389/fevo.2019.00479 (2019).ADS 
    Article 

    Google Scholar 
    Wang, Y., Guan, L., Chen, J. & Kong, Y. Influences on mammals frequency of use of small bridges and culverts along the Qinghai-Tibet railway, China. Ecol. Res. 33, 879–887. https://doi.org/10.1007/s11284-018-1578-0 (2018).Article 

    Google Scholar 
    Ng, S. J., Dole, J. W., Sauvajot, R. M., Riley, S. P. D. & Valone, T. J. Use of highway undercrossings by wildlife in southern California. Biol. Conserv. 115, 499–507. https://doi.org/10.1016/s0006-3207(03)00166-6 (2004).Article 

    Google Scholar 
    Mata, C., Hervas, I., Herranz, J., Suarez, F. & Malo, J. E. Are motorway wildlife passages worth building? Vertebrate use of road-crossing structures on a Spanish motorway. J. Environ. Manag. 88, 407–415. https://doi.org/10.1016/j.jenvman.2007.03.014 (2008).CAS 
    Article 

    Google Scholar 
    Mata, C., Herranz, J. & Malo, J. E. Attraction and avoidance between predators and prey at wildlife crossings on roads. Diversity 12, 166. https://doi.org/10.3390/d12040166 (2020).Article 

    Google Scholar 
    Stewart, L., Russell, B., Zelig, E., Patel, G. & Whitney, K. S. Wildlife crossing design influences effectiveness for small and large mammals in Banff National Park. Case Stud. Environ. 4, 1231752. https://doi.org/10.1525/cse.2020.1231752 (2020).Article 

    Google Scholar 
    Mysłajek, R. W., Nowak, S., Kurek, K., Tołkacz, K. & Gewartowska, O. Utilisation of a wide underpass by mammals on an expressway in the Western Carpathians, S Poland. Folia Zool. 65, 225–232. https://doi.org/10.25225/fozo.v65.i3.a8.2016 (2016).Article 

    Google Scholar 
    Clevenger, A. P. & Waltho, N. factors influencing the effectiveness of wildlife underpasses in Banff National Park, Alberta, Canada. Conserv. Biol. 14, 47–56 (2000).Article 

    Google Scholar 
    Laurance, W. F., Sloan, S., Weng, L. & Sayer, J. A. Estimating the environmental costs of Africa’s massive “development corridors”. Curr. Biol. 25, 3202–3208. https://doi.org/10.1016/j.cub.2015.10.046 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    van der Ree, R., Gagnon, J. W. & Smith, D. J. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 20, 159–171 (Wiley, 2015).Ascensão, F. & Mira, A. Factors affecting culvert use by vertebrates along two stretches of road in southern Portugal. Ecol. Res. 22, 57–66. https://doi.org/10.1007/s11284-006-0004-1 (2006).Article 

    Google Scholar 
    Hepenstrick, D., Thiel, D., Holderegger, R. & Gugerli, F. Genetic discontinuities in roe deer (Capreolus capreolus) coincide with fenced transportation infrastructure. Basic Appl. Ecol. 13, 631–638. https://doi.org/10.1016/j.baae.2012.08.009 (2012).Article 

    Google Scholar 
    Wilson, R. E., Farley, S. D., McDonough, T. J., Talbot, S. L. & Barboza, P. S. A genetic discontinuity in moose (Alces alces) in Alaska corresponds with fenced transportation infrastructure. Conserv. Genet. 16, 791–800. https://doi.org/10.1007/s10592-015-0700-x (2015).Article 

    Google Scholar 
    Jaeger, J. A. G. & Fahrig, L. Effects of road fencing on population persistence. Conserv. Biol. 18, 1651–1657 (2004).Article 

    Google Scholar 
    Ngene, S., Lala, F., Nzisa, M., Kimitei, K., Mukeka, J., Kiambi, S., Davidson, Z., Bakari, S., Lyimo, E. & Khayale, C. (eds Arusha Kenya Wildlife Service (KWS) and Tanzania Wildlife Research Institute (TAWIRI)) (2017).World Resources Institute, Department of Resource Surveys and Remote Sensing Ministry of Environment and Natural Resources Kenya, Central Bureau of Statistics Ministry of Planning and National Development Kenya & International Livestock Research Institute. Nature’s Benefits in Kenya, An Atlas of Ecosystems and Human Well-Being (World Resources Institute, 2007).Wijngaarden, W. V. Elephants, trees, grass, grazers: relationships between climate, soils, vegetation, and large herbivores in a semi-arid savanna ecosystem (Tsavo, Kenya) Doctor of Philosophy thesis, Landbouwhogeschool te Wageningen (1985).Stuart, C. Field Guide to Tracks & Signs of Southern, Central & East African Wildlife (Penguin Random House South Africa, 2013).
    Google Scholar 
    Murie, O. J. & Elbroch, M. A Field Guide to Animal Tracks Vol. 3 (Houghton Mifflin Harcourt, 2005).
    Google Scholar 
    Kerley, G. I. H., Pressey, R. L., Cowling, R. M., Boshoff, A. F. & Sims-Castley, R. Options for the conservation of large and medium-sized mammals in the Cape Floristic Region hotspot, South Africa. Biol. Conserv. 112, 169–190. https://doi.org/10.1016/S0006-3207(02)00426-3 (2003).Article 

    Google Scholar 
    R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2021).Hayward, M. W., Hayward, G. J., Tambling, C. J. & Kerley, G. I. Do lions Panthera leo actively select prey or do prey preferences simply reflect chance responses via evolutionary adaptations to optimal foraging?. PLoS ONE 6, e23607 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    De Boer, W. F. et al. Spatial distribution of lion kills determined by the water dependency of prey species. J. Mammal. 91, 1280–1286 (2010).Article 

    Google Scholar 
    Hayward, M. W. & Kerley, G. I. H. Prey preferences of the lion (Panthera leo). J. Zool. 267, 309–322. https://doi.org/10.1017/S0952836905007508 (2005).Article 

    Google Scholar 
    Davidson, Z. et al. Seasonal diet and prey preference of the African lion in a waterhole-driven semi-arid Savanna. PLoS ONE 8, e55182. https://doi.org/10.1371/journal.pone.0055182 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Patterson, B. D., Kasiki, S. M., Selempo, E. & Kays, R. W. Livestock predation by lions (Panthera leo) and other carnivores on ranches neighboring Tsavo National ParkS, Kenya. Biol. Conserv. 119, 507–516. https://doi.org/10.1016/j.biocon.2004.01.013 (2004).Article 

    Google Scholar 
    Hayward, M. W. et al. Prey preferences of the leopard (Panthera pardus). J. Zool. 270, 298–313. https://doi.org/10.1111/j.1469-7998.2006.00139.x (2006).Article 

    Google Scholar 
    Ogara, W. O. et al. Determination of carnivores prey base by scat analysis in Samburu community group ranches in Kenya. Afr. J. Environ. Sci. Technol. 4, 540–546 (2010).
    Google Scholar 
    Hayward, M. W. Prey preferences of the spotted hyaena (Crocuta crocuta) and degree of dietary overlap with the lion (Panthera leo). J. Zool. 270, 606–614. https://doi.org/10.1111/j.1469-7998.2006.00183.x (2006).Article 

    Google Scholar 
    Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

    Google Scholar 
    Barton, K. & Barton, M. K. Package ‘MuMIn’. Version 1, 18 (2015).
    Google Scholar 
    Williams, E. M. Giraffe stature and neck elongation: Vigilance as an evolutionary mechanism. Biology 5, 35 (2016).Article 

    Google Scholar 
    Shorrocks, B. The Giraffe: Biology, Ecology, Evolution and Behaviour (Wiley, 2016).Book 

    Google Scholar 
    Mata, C., Bencini, R., Chambers, B. K. & Malo, J. E. Handbook of Road Ecology (eds Smith, D. J. & van der Ree, C. G. R.) Ch. 23, 190–197 (Wiley, 2015).Harris, I. M., Mills, H. R. & Bencini, R. Multiple individual southern brown bandicoots (Isoodonobesulus fusciventer) and foxes (Vulpes vulpes) use underpasses installed at a new highway in Perth, Western Australia. Wildl. Res. 37, 127–133 (2010).Article 

    Google Scholar 
    Fehlmann, G. et al. Extreme behavioural shifts by baboons exploiting risky, resource-rich, human-modified environments. Sci. Rep. 7, 1–8 (2017).CAS 
    Article 

    Google Scholar 
    McLennan, M. R., Spagnoletti, N. & Hockings, K. J. The implications of primate behavioral flexibility for sustainable human-primate coexistence in anthropogenic habitats. Int. J. Primatol. 38, 105–121. https://doi.org/10.1007/s10764-017-9962-0 (2017).Article 

    Google Scholar 
    Riley, E. P. Flexibility in diet and activity patterns of Macaca tonkeana in response to anthropogenic habitat alteration. Int. J. Primatol. 28, 107–133. https://doi.org/10.1007/s10764-006-9104-6 (2007).Article 

    Google Scholar 
    Johnson-Ulrich, L., Yirga, G., Strong, R. L. & Holekamp, K. E. The effect of urbanization on innovation in spotted hyenas. Anim. Cogn. 24, 1027–1038. https://doi.org/10.1007/s10071-021-01494-4 (2021).Article 
    PubMed 

    Google Scholar 
    Holekamp, K. E. & Dloniak, S. M. Intraspecific variation in the behavioral ecology of a tropical carnivore, the spotted hyena. Adv. Study Behav. 42, 189–229 (2010).Article 

    Google Scholar 
    Devens, C. H. et al. Estimating leopard density across the highly modified human-dominated landscape of the Western Cape, South Africa. Oryx 55, 34–45. https://doi.org/10.1017/S0030605318001473 (2021).Article 

    Google Scholar 
    Van Cleave, E. K. et al. Diel patterns of movement activity and habitat use by leopards (Panthera pardus pardus) living in a human-dominated landscape in central Kenya. Biol. Conserv. 226, 224–237. https://doi.org/10.1016/j.biocon.2018.08.003 (2018).Article 

    Google Scholar 
    Odden, M., Athreya, V., Rattan, S. & Linnell, J. D. C. Adaptable neighbours: Movement patterns of GPS-collared leopards in human dominated landscapes in India. PLoS ONE 9, e112044. https://doi.org/10.1371/journal.pone.0112044 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Athreya, V., Odden, M., Linnell, J. D. C., Krishnaswamy, J. & Karanth, K. U. A cat among the dogs: Leopard Panthera pardus diet in a human-dominated landscape in western Maharashtra, India. Oryx 50, 156–162. https://doi.org/10.1017/S0030605314000106 (2016).Article 

    Google Scholar 
    Suraci, J. P. et al. Behavior-specific habitat selection by African lions may promote their persistence in a human-dominated landscape. Ecology 100, e02644. https://doi.org/10.1002/ecy.2644 (2019).Article 
    PubMed 

    Google Scholar 
    Daniels, S. E., Fanelli, R. E., Gilbert, A. & Benson-Amram, S. Behavioral flexibility of a generalist carnivore. Anim. Cogn. 22, 387–396 (2019).Article 

    Google Scholar 
    Murray, M. H. & St. Clair, C. C. Individual flexibility in nocturnal activity reduces risk of road mortality for an urban carnivore. Behav. Ecol. 26, 1520–1527. https://doi.org/10.1093/beheco/arv102 (2015).Article 

    Google Scholar 
    Galanti, V., Preatoni, D., Martinoli, A., Wauter, L. A. & Tosi, G. Space and habitat use of the African elephant in the Tarangire-Manyara ecosystem, Tanzania: Implications for conservation. Mamm. Biol. 71, 99–114. https://doi.org/10.1016/j.mambio.2005.10.001 (2006).Article 

    Google Scholar 
    Douglas-Hamilton, I., Krink, T. & Vollrath, F. Movements and corridors of African elephants in relation to protected areas. Naturwissenschaften 92, 158–163. https://doi.org/10.1007/s00114-004-0606-9 (2005).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Coe, P. K. et al. Identifying migration corridors of mule deer threatened by highway development. Wildl. Soc. Bull. 39, 256–267. https://doi.org/10.1002/wsb.544 (2015).Article 

    Google Scholar 
    Spinage, C. A. Territoriality and social organization of the Uganda defassa waterbuck Kobus defassa ugandae. J. Zool. Lond. 159, 329–361 (1969).Article 

    Google Scholar 
    Mizutani, F. & Jewell, P. A. Home-range and movements of leopards (Panthera pardus) on a livestock ranch in Kenya. J. Zool. Lond. 244, 269–286 (1998).Article 

    Google Scholar 
    Riley, S. P. et al. A southern California freeway is a physical and social barrier to gene flow in carnivores. Mol. Ecol. 15, 1733–1741. https://doi.org/10.1111/j.1365-294X.2006.02907.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    Sells, S. N. & Mitchell, M. S. The economics of territory selection. Ecol. Model. 438, 109329. https://doi.org/10.1016/j.ecolmodel.2020.109329 (2020).Article 

    Google Scholar 
    Valls-Fox, H. et al. Water and cattle shape habitat selection by wild herbivores at the edge of a protected area. Anim. Conserv. 21, 365–375. https://doi.org/10.1111/acv.12403 (2018).Article 

    Google Scholar 
    Hibert, F. et al. Spatial avoidance of invading pastoral cattle by wild ungulates: Insights from using point process statistics. Biodivers. Conserv. 19, 2003–2024 (2010).Article 

    Google Scholar 
    Stewart, K. M., Bowyer, R. T., Kie, J. G., Cimon, N. J. & Johnson, B. K. Temporospatial distributions of elk, mule deer, and cattle: Resource partitioning and competitive displacement. J. Mammal. 83, 229–244. https://doi.org/10.1644/1545-1542(2002)083%3c0229:Tdoemd%3e2.0.Co;2 (2002).Article 

    Google Scholar 
    Leeuw, J. D. et al. Distribution and diversity of wildlife in northern Kenya in relation to livestock and permanent water points. Biol. Conserv. 100, 297–306 (2001).Article 

    Google Scholar 
    Donaldson, B. Use of highway underpasses by large mammals and other wildlife in Virginia. Transp. Res. Rec 157–164, 2007. https://doi.org/10.3141/2011-17 (2011).Article 

    Google Scholar 
    Dodd, N. L., Gagnon, J. W., Manzo, A. L. & Schweinsburg, R. E. Video surveillance to assess highway underpass use by elk in Arizona. J. Wildl. Manag. 71, 637–645. https://doi.org/10.2193/2006-340 (2007).Article 

    Google Scholar 
    Gordon, K. M. & Anderson, S. H. International Conference on Ecology and Transportation https://escholarship.org/uc/item/2wv1v6dz.Bond, A. R. & Jones, D. N. Temporal trends in use of fauna-friendly underpasses and overpasses. Wildl. Res. 35, 103–112. https://doi.org/10.1071/WR07027 (2008).Article 

    Google Scholar 
    Altmann, J., Schoeller, D., Altmann, S. A., Muruthi, P. & Sapolsky, R. M. Body size and fatness of free-living baboons reflect food availability and activity levels. Am. J. Primatol. 30, 149–161. https://doi.org/10.1002/ajp.1350300207 (1993).Article 
    PubMed 

    Google Scholar 
    Kiffner, C. et al. Road-based line distance surveys overestimate densities of olive baboons. PLoS ONE 17, e0263314. https://doi.org/10.1371/journal.pone.0263314 (2022).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Strandburg-Peshkin, A., Farine, D. R., Crofoot, M. C. & Couzin, I. D. Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement. Elife 6, e19505. https://doi.org/10.7554/eLife.19505 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bohrer, G., Beck, P. S., Ngene, S. M., Skidmore, A. K. & Douglas-Hamilton, I. Elephant movement closely tracks precipitation driven vegetation dynamics in a Kenyan forest-savanna landscape. Mov. Ecol. 2, 2 (2014).Article 

    Google Scholar 
    Merkle, J. A. et al. Large herbivores surf waves of green-up during spring. Proc. Biol. Sci. 283, 20160456. https://doi.org/10.1098/rspb.2016.0456 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Middleton, A. D. et al. Green-wave surfing increases fat gain in a migratory ungulate. Oikos 127, 1060–1068. https://doi.org/10.1111/oik.05227 (2018).Article 

    Google Scholar 
    Bartlam-Brooks, H. L. A., Beck, P. S. A., Bohrer, G. & Harris, S. In search of greener pastures: Using satellite images to predict the effects of environmental change on zebra migration. J. Geophys. Res. Biogeosci. 118, 1427–1437. https://doi.org/10.1002/jgrg.20096 (2013).Article 

    Google Scholar 
    Bischof, R. et al. A migratory northern ungulate in the pursuit of spring: Jumping or surfing the green wave?. Am. Nat. 180, 407–424. https://doi.org/10.1086/667590 (2012).Article 
    PubMed 

    Google Scholar 
    Aikens, E. O. et al. The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecol. Lett. 20, 741–750. https://doi.org/10.1111/ele.12772 (2017).Article 
    PubMed 

    Google Scholar 
    Mandinyenya, B., Monks, N., Mundy, P. J., Sebata, A. & Chirima, A. Habitat choices of African buffalo (Syncerus caffer) and plains zebra (Equus quagga) in a heterogeneous protected area. Wildl. Res. 47, 106–113. https://doi.org/10.1071/WR18201 (2020).Article 

    Google Scholar  More

  • in

    DarkCideS 1.0, a global database for bats in karsts and caves

    The DarkCideS database was initially conceptualised and developed by KCT, JAG, and ACH as part of the “Global Bat Cave Vulnerability and Conservation Mapping Initiative” in 2014, and later with the “Mapping Karst Biodiversity in Yunnan” and the “Southeast Asian Atlas of Biodiversity” projects. The initiative includes developing tools and methods (e.g., the Bat Cave Vulnerability Index14) and synthesis (e.g., the global bat cave vulnerability assessment11) to identify conservation priorities and important bat caves in the tropics. Since 2019, the initiative has expanded and potential collaborators and contributors were invited through scientific conferences (Association for Tropical Biology and Conservation 2018, International Bat Research Conference 2019), social media platforms, and personal correspondences. At present, the database has 36 collaborators from twenty countries on six continents with expertise and research interests in bat conservation. Four main datasets for all known cave-dwelling bats were built for the DarkCideS database version 1.0.Datasets and compilation for species checklistThe first dataset contains taxonomic checklists for all extant cave-dwelling bats species extracted from the expert-based International Union for the Conservation Union (IUCN) Red List database version 2020-1 (Table 1). We screened and included all bat species that were reported to use, roost in, or aggregate in “Caves”, “Underground”, and “Karsts” habitats in any part of their life histories. We also scanned major publicly available bat cave databases from expeditions such as “Bats in China” (http://www.bio.bris.ac.uk/research/bats/China/) and UNEP-EUROBATS (https://www.eurobats.org/) for European bats24 for additional information and datasets. In addition, the first dataset contains species ecological traits, distribution range, and threatening processes (Table 1).Table 1 DarkCideS 1.0 includes key traits for all living cave-dwelling bat species (N = 679). General metadata for traits included in the current version of the database: habitat preference, ecological status, feeding groups, geographical range, island endemism, geopolitical endemism, distribution range, biogeographical breadth, generation length, body mass, and threatening process.Full size tableInformation per species was pooled from the IUCN Red List versions 2020-125. Species taxonomy was then curated and updated (e.g., synonyms or merged species) using the nomenclature from Simmons and Cirranello12. The “checklist for global cave-dwelling bats” derived from the IUCN Red List includes 679 species. Meanwhile, the DarkCideS 1.0 dataset contains occurrence data for 402 species from 16 families representing 59% of all cave-dwelling species11 (Fig. 2). We found a marginally significant relationship between the species richness and proportion of threatened species between the IUCN-based global cave-dwelling bat and DarkCideS datasets (Kendall’s τ b = 0.60, P = 0.07). The highest completeness of sampled species is in the Neotropics (67.38%) and Indomalayan region (66.08%), and the greatest gaps are in Austral-Oceania (40.28%). Highest endemism was recorded in Austral-Oceania (58.62%) (χ2 = 227.32, df = 5, P  More

  • in

    Influence of wind and light on the floating and sinking process of Microcystis

    Paerl, H. W. & Huisman, J. Climate. Blooms like it hot. Science 320, 57–58 (2008).CAS 
    Article 

    Google Scholar 
    Yamamoto, Y., Shiah, F. K. & Chen, Y. L. Importance of large colony formation in bloom-forming cyanobacteria to dominate in eutrophic ponds. Ann. Limnol. Int. J Limnol. 47, 167–173 (2011).Article 

    Google Scholar 
    Chen, Y. W., Qin, B. Q., Teubner, K. & Dokulil, M. T. Long-term dynamics of phytoplankton assemblages: Microcystis-domination in Lake Taihu, a large shallow lake in China. J. Plankton Res. 25, 445–453 (2003).Article 

    Google Scholar 
    Walsby, A. E. The nuisance algae: Curiosities in the biology of planktonic blue-green algae. Water Treat. Exam. 19, 359–373 (1970).
    Google Scholar 
    Reynolds, C. S. & Walsby, A. E. Water-blooms. Biol. Rev. 50, 437–481 (1975).CAS 
    Article 

    Google Scholar 
    Yonggang, L., Wei, Z., Ming, L. I., Amp, D. X. & Man, X. Effect of colony size on Microcystis diurnal vertical migration. J. Lake Sci. 25(3), 386–391 (2013).Article 

    Google Scholar 
    Ibelings, B. W., Mur, L. & Walsby, A. Diurnal variations in buoyancy and vertical distribution in populations of Microcystis in two shallow lakes. J. Plankton Res. 13, 419–436 (1991).Article 

    Google Scholar 
    Kromkamp, J. C. & Mur, L. R. Buoyant density variations in the cyanobacterium Microcystis aeruginosa due to variations in the cellular carbohydrate content. FEMS Microbiol. Lett. 1, 105–109 (1984).Article 

    Google Scholar 
    Kromkamp, J. & Walsby, A. E. A computer model of buoyancy and vertical migration in cyanobacteria. J. Plankton Res. 12, 161–183 (1990).Article 

    Google Scholar 
    Visser, P. M., Passarge, J. & Mur, L. R. Modelling vertical migration of the cyanobacterium Microcystis. Hydrobiologia 349(1–3), 99–109 (1997).Article 

    Google Scholar 
    Medrano, E. A., Uittenbogaard, R. E., Pires, L. M. D., van de Wiel, B. J. H. & Clercx, H. J. H. Coupling hydrodynamics and buoyancy regulation in Microcystis aeruginosa for its vertical distribution in lakes. Ecol. Model. 248, 41–56 (2013).Article 

    Google Scholar 
    George, D. G. & Edwards, R. W. The effect of wind on the distribution of chlorophyll A and crustacean plankton in a shallow eutrophic reservoir. J. Appl. Ecol. 13, 667 (1976).CAS 
    Article 

    Google Scholar 
    Hutchinson, P. A. & Webster, I. T. On the distribution of blue-green algae in lakes: Wind-tunnel tank experiments. Limnol. Oceanogr. 9, 374–382 (1994).Article 

    Google Scholar 
    Ha, K., Kim, H. W., Jeong, K. S. & Joo, G. J. Vertical distribution of Microcystis population in the regulated Nakdong River, Korea. J. Limnol. 1, 225–230 (2000).Article 

    Google Scholar 
    Ma, X., Wang, Y., Feng, S. & Wang, S. Vertical migration patterns of different phytoplankton species during a summer bloom in Dianchi Lake, China. Environ. Earth Sci. 74, 3805–3814 (2015).CAS 
    Article 

    Google Scholar 
    Ndong, M. et al. A novel Eulerian approach for modelling cyanobacteria movement: Thin layer formation and recurrent risk to drinking water intakes. Water Res. 127, 191–203 (2017).CAS 
    Article 

    Google Scholar 
    Hozumi, A., Ostrovsky, I. S., Sukenik, A. & Gildor, H. Turbulence regulation of Microcystis surface scum formation and dispersion during a cyanobacteria bloom event. Inland Waters. 10, 51–70 (2020).CAS 
    Article 

    Google Scholar 
    Zhu, W., Chen, H., Xiao, M., Miquel, L. & Li, M. Wind induced turbulence caused colony disaggregation and morphological variations in the cyanobacterium Microcystis. J. Lake Sci. 33, 349 (2021).Article 

    Google Scholar 
    Wu, X. & Kong, F. Effects of light and wind speed on the vertical distribution of Microcystis aeruginosa colonies of different sizes during a summer bloom. Int. Rev. Hydrobiol. 94, 258–266 (2009).Article 

    Google Scholar 
    Xiao, M. et al. The influence of water oscillation on the vertical distribution of Microcystis colonies of different sizes. Fresenius Environ. Bull. 22, 3511–3518 (2013).CAS 

    Google Scholar 
    Zhao, H. et al. Numerical simulation of the vertical migration of Microcystis (cyanobacteria) colonies based on turbulence drag. J. Limnol. 76, 190–198 (2017).
    Google Scholar 
    Li, M., Xiao, M., Zhang, P. & Hamilton, D. P. Morphospecies-dependent disaggregation of colonies of the cyanobacterium Microcystis under high turbulent mixing. Water Res. 141, 340–348 (2018).CAS 
    Article 

    Google Scholar 
    Chien, Y. C., Wu, S. C., Chen, W. C. & Chou, C. C. Model simulation of diurnal vertical migration patterns of different-sized colonies of Microcystis employing a particle trajectory approach. Environ. Eng. Sci. 30, 179–186 (2013).CAS 
    Article 

    Google Scholar 
    Medrano, E. A., van de Wiel, B. J. H., Uittenbogaard, R. E., Pires, L. M. D. & Clercx, H. J. H. Simulations of the diurnal migration of Microcystis aeruginosa based on a scaling model for physical-biological interactions. Ecol. Model. 337, 200–210 (2016).Article 

    Google Scholar 
    Liu, H., Zheng, Z. C., Young, B. & Harris, T. D. Three-dimensional numerical modeling of the cyanobacterium Microcystis transport and its population dynamics in a large freshwater reservoir. Ecol. Model. 398, 20–34 (2019).CAS 
    Article 

    Google Scholar 
    Shih, T. H., Liou, W. W., Shabbir, A., Yang, Z. & Zhu, J. A new k-ε eddy viscosity model for high Reynolds number turbulent flows. Comput. Fluids. 24, 227–238 (1995).Article 

    Google Scholar 
    Geernaert, G. L., Larsen, S. E. & Hansen, F. Measurements of the wind stress, heat flux, and turbulence intensity during storm conditions over the North Sea. J. Geophys. Res. 92, 127–139 (1987).Article 

    Google Scholar 
    Large, W. G. & Pond, S. Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr. 11, 324–336 (1981).Article 

    Google Scholar 
    Sellers, H. Development and application of “U.S.E.D.”: A hydroclimate lake stratification model. Ecol. Model. 21, 233–246 (1984).Article 

    Google Scholar 
    Morsi, S. A. & Alexander, A. J. An investigation of particle trajectories in two-phase flow systems. J. Fluid Mech. 55, 193–208 (1972).Article 

    Google Scholar 
    Gosman, A. D. & Loannides, E. Aspects of computer simulation of liquid-fuelled combustor. AIAA J. 81, 482–490 (1981).
    Google Scholar 
    Li, M. et al. To increase size or decrease density? Different Microcystis species has different choice to form blooms. Sci. Rep. 6, 37056 (2016).CAS 
    Article 

    Google Scholar 
    Li, M., Zhu, W. & Gao, L. Analysis of cell concentration, volume concentration, and colony size of Microcystis via laser particle analyzer. Environ. Manag. 53, 947–958 (2014).Article 

    Google Scholar 
    Sun, D., Li, Y., Wang, Q. & Gao, J. Light scattering properties and their relation to the biogeochemical composition of turbid productive waters: A case study of Lake Taihu. Appl. Opt. 48(11), 1979–1989 (2009).CAS 
    Article 

    Google Scholar 
    Li, M., Zhu, W., Gao, L., Huang, J. & Li, L. Seasonal variations of morphospecies composition and colony size of Microcystis in a shallow hypertrophic lake (Lake Taihu, China). Fresenius Environ. Bull. 22, 3474–3483 (2013).CAS 

    Google Scholar 
    Zhu, W. et al. Vertical distribution of Microcystis colony size in Lake Taihu: Its role in algal blooms. J. Great Lakes Res. 40, 949–955 (2014).Article 

    Google Scholar 
    Chen, Y. Y. & Liu, Q. Q. On the horizontal distribution of algal-bloom in Chaohu Lake and its formation process. Acta Mech. Sinica-Prc. 30(005), 656–666 (2014).MathSciNet 
    Article 

    Google Scholar 
    Beletsky, D., Hawley, N., Rao, Y. R., Vanderploeg, H. A. & Ruberg, S. A. Summer thermal structure and anticyclonic circulation of Lake Erie. Geophys. Res. Lett. 39, 6605 (2012).Article 

    Google Scholar 
    Ishikawa, T. & Qian, X. Numerical simulation of wind-induced current and water exchange at the mouth of Takahamairi Bay of the Lake Kasumigaura during the formation of diurnal thermocline. Tohoku Univ. 2, 419–428 (1998).
    Google Scholar 
    Wu, H., Wu, X. & Yang, T. Feedback regulation of surface scum formation and persistence by self-shading of Microcystis colonies: Numerical simulations and laboratory experiments. Water Res. 194(3), 116908 (2021).CAS 
    Article 

    Google Scholar  More

  • in

    Vulnerability to climate change of species in protected areas in Thailand

    Study areaThe study area covers the total land area of Thailand. Where it is useful, we divided Thailand into six regions (Fig. 2a), the names and boundaries of which are widely used, although they have no official administrative status. We focused on the elements of Thailand’s protected area system that were concerned principally with the in-situ conservation of biodiversity: existing and proposed National Parks, Wildlife Sanctuaries, Non-hunting Areas, and Forest Parks, covering 111, 201 km2 or 21.7% of the country’s land area37 (Fig. 1).Environmental dataA set of environmental variables that were expected to be directly or indirectly related to species distributions in Thailand was used to model suitable habitat in the present and future (Supplementary Material Table S1). These variables were chosen to encompass ecologically relevant variables and enable consistent comparison between species, regardless of species-specific preferences. GIS layers for the whole of the study area were compiled using a variety of data sources at 1-km2 resolution. For variables originally at higher than 1-km resolutions, we used the plus function in ArcMap to combine them with a mask of the study area to use the mask dimensions for all cells.The physical variables, altitude, slope, aspect, and soil pH are widely used in species distribution modeling. Slope and aspect have biologically significant impacts on both temperature and rainfall at these latitudes8 and are particularly important at the poleward margins of species ranges where species may be confined to one aspect. Slope also affects soil maturity and depth. Soil pH is a consistently measured soil variable that broadly correlates with fertility in tropical soils8. Additional soil variables, particularly soil phosphorus, have been shown to be important filters of plant species distributions in the tropics38, but they are not available for Thailand with a useful accuracy and spatial resolution. Altitudes were downloaded from the CGIAR-Consortium for Spatial Information, CGIAR-CSI version 4.1. Slope and aspect were generated by using surface tools in ArcGIS. Soil pH was extracted from ISRIC-World Soil Information version 2.0.Unlike the temperate zone, where tolerances of winter cold and requirements for summer warmth dominate plant and animal distributions, our understanding of how tropical climates filter species distributions is still weak38,39. In Thailand, as in most of the tropics, there are two major climatic gradients which correlate with changes in species composition: a rainfall gradient in the lowlands, along which total rainfall declines and the length of the dry season increases, and a gradient of steadily declining temperature with elevation7. There is no simple relationship between elevation, and thus temperature, and rainfall. An additional complication is that temperature seasonality may be significant in northern Thailand (north of c.18° N), where cooler winters reduce dry-season water stress and extreme low temperatures at high altitudes may exceed physiological tolerances. We therefore chose 8 bioclimatic variables (Supplementary Material Table S1) related to precipitation and temperature, and their seasonality, all of which have previously been used in species distribution modelling in this region9,40. These are available at a resolution of 30 arc sec (approximately 1 km at the equator) from WorldClim ver. 1.4 based on averages of 1970–1990. These variables are available from the same source (and downscaled using the same methods) for the future climate projections.Vegetation structure is an additional major influence on plant and animal distributions in the tropics, both in intact natural vegetation38,39 and when the original vegetation has been degraded or cleared8. Vegetation structure was represented through the inclusion of two continuous variables, percentage forest cover and tree density, as most of the modelled species are known to be sensitive to both the presence of forest and the degree of intactness of the tree cover9. Mean tree density per km2 was extracted from Crowther et al.41 version 2 and percentage coverage of forest per km2 was extracted from the European Space Agency (ESA) GlobCover Version 2.3.Note that the mechanistic basis of the correlations between all these variables and the current distributions of tropical plants and animals are rarely known. Temperature has a direct physiological impact on all organisms, and water supply may be seasonally limiting for plants and some amphibians, but indirect links through biotic interactions are expected to be more important in the tropics, including pest pressure on plants38 and food supply for animals39. Competition is probably also important in shaping local species assemblies. For future projections, we assumed that temperature and precipitation were changing, and that other variables (topography, soil, and vegetation) were stable, so our analysis represents the impacts of climate alone. For 2070, we used the same variables projected by three CMIP5 Earth System Models, CNRM-CM5, GFDL-CM3 and HadGEM2-ES, which have been previously used in Southeast Asia9,42 and in Thailand7. We used two Representative Concentration Pathways, RCP2.6 and RCP8.5, representing low and high greenhouse-gas concentration scenarios, respectively, and thus the potential range of radiative forcing by the end of the century43. RCP2.6 is consistent with meeting the Paris Agreement’s 2 °C global warming target.Species occurrence dataMany locality records for vertebrates were supplied by the Department of National Parks, Wildlife and Plant Conservation (DNP). Trained DNP staff walked along trails throughout the protected areas in Thailand during 2017–2018. They recorded 271,695 locations for 70 mammal species, 18 locations for 3 amphibian species, 318 locations for 18 reptile species, and 43,057 locations for 65 bird species44. We supplemented this with data downloaded from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/) for 1960–2019 for amphibians (2063 localities for 86 species)45, reptiles (1722 localities from 196 species)46, mammals (2508 localities from 191 species)47, and birds (1,559,222 localities from 884 species)48. More than 95% of the bird records from GBIF were identified as coming from eBird49, which is popular among birders in Thailand. For plants, we used occurrence data from the DNP’s forest resource inventory project from 221 plots, including 24,605 localities for 363 species, the DNP’s Forest Herbarium, including 227 localities for 141 species, and locations for 12 rare and endangered forest species collected from all over Thailand. We also downloaded data from the Botanical Information and Ecology Network (BIEN, https://bien.nceas.ucsb.edu/bien/), including 7209 localities for 1422 species.We removed suspect records (coordinate issues, name problems, etc.), duplicates from the same locality (i.e., more than one individual of the same species recorded in a cell), and species with  0.5 as adequate, but since only five SDMs out of the 1457 generated in this study had values lower than this (0.3–0.5), we retained all the models.Assessment of climate change impactsThe estimated current distribution for each species from Maxent was used as the baseline for comparison with projected distributions of suitable habitat for these species by 2070, under the two emission scenarios and three ESMs, and with and without unlimited dispersal into newly available habitat. We then assessed the impacts of climate change, both on the spatial distribution of individual species and on the pattern of species richness. To generate a species richness map, the binary habitat suitability maps for all species were stacked to produce a consolidated map, which showed the number of species for each 1 km grid cell, and then classified them into five classes (lowest, low, moderate, high, and highest), using the mean ± standard deviation as a break class40.Current and future maps were then compared for each species to calculate the change in species richness, and contingency tables were generated containing the numbers of cells (each of 1 km2) in each richness class. Suitable habitat areas were calculated for the current climate and projected for the future climate. For each species we estimated gained habitat as the areas that will become suitable for a species in future under that scenario, lost habitat as the areas currently predicted as suitable now but projected to become unsuitable under future climatic change, and stable habitat as the areas predicted as suitable now which will remain suitable into the future.We then assessed the vulnerability of each species by estimating the projected change in its range over the next 50 years and using a criteria-based approach, which combined the mean of the suitable habitat area (interpreted as equivalent to extent of occurrence) in the three models and a simplified version of the IUCN Red List criteria51. For 2070, we modified criterion A3(c) as follows; Extinct (Ex) species are projected to lose 100% of suitable habitat by 2070, Critically Endangered (CR) species are projected to lose over 80%, Endangered (EN) species are projected to lose 50–80%; Vulnerable (VU) species are projected to lose 30–50%, Near Threatened (NT) species are projected to lose  More

  • in

    Hair cortisol concentration reflects the life cycle and management of grey wolves across four European populations

    Collection of wolf hair samplesHair samples were collected by researchers from opportunistically found-dead wolves upon standard necropsy (all the Alpine and part of the Iberian samples) or in the field (all the Dinaric-Balkan and most of the Iberian samples), or from legally harvested wolves (only in the Scandinavian population). At the time of sample collection, wolves were legally harvested in Sweden, Slovenia, and Spain, and under total protection in Portugal and Italy. Hair samples were collected from four body regions, when possible: lumbar (n = 133), dorsal cervical (n = 66), tail (n = 33) and ventral thorax (n = 27) (Tables S1 and S2). The hair was cut as close as possible to the skin with scissors to avoid collecting hair follicles, but in some samples, hairs were pulled from the carcass. Samples were stored at room temperature in paper envelopes. Age, sex, date, and cause of death/capture, geographical location, body mass, and total length were obtained for most of the wolves.Age was estimated by the dental eruption and wear or cementum age analysis and classified as ‘juveniles’ ( 2 years)40, or ‘unknown’. Sex was assessed by inspection of genitalia. Causes of death were classified as ‘acute’, likely lasting minutes to hours (vehicle accident and legal or illegal shooting); ‘subacute’, likely lasting hours to days (drowning, poisoning, trapping and intraspecific aggression); ‘chronic’, likely lasting several weeks (infectious diseases—canine distemper, canine parvovirosis, leptospirosis; sarcoptic mange; or neoplastic diseases) or ‘unknown’. Total length was obtained by measuring with metric tape (1 mm precision) the distance from snout to the distal end of the last tail vertebrae. The body mass was measured with 100 g precision with scales.The detailed protocol for the handling of wolves live trapped in the scope of ecological and conservation studies (n = 7, all from the Iberian population) has been previously described5. Traps were monitored twice every day, in the early morning and late afternoon, hence the duration of restraint after capture was unknown for 8 wolves, potentially up to 12 h. Trap-alarms were deployed in the capture of 2 wolves, with 41 and 70 min intervals between activation of the alarm and administration of the drugs. Live trapping was conducted under permits issued by the nature conservation authorities of Portugal (Instituto de Conservação da Natureza e das Florestas: 338/2007/CAPT, 258/2008/CAPT, 286/2008/CAPT, 260/2009/CAPT, 332/2010/MANU, 333/2010/CAPT, 336/2010/MANU, 26/2012/MANU, and 72/2014/CAPT) and Spain (Dirección Xeral de Conservación da Naturaleza, Xunta de Galicia: E-0020/13-PNPE, 095/2013; Consejería de Medio Ambiente, Principado de Asturias: 31/08/2017-BOPA 05/09/17) and according to European Union directives on the protection of animals used for scientific purposes (Directive 2010/63/EU) and international wildlife standards41,42. The study was undertaken in compliance with the ARRIVE guidelines43.Cortisol extractionThe protocol for the extraction of cortisol from the hair was adapted from previously described procedures15,27. Forty mg of guard hairs were separated from the undercoat and placed in 15 ml falcon tubes. Hair follicles were cut whenever found in the sample. For each sample, the length of three intact hairs was recorded. The samples were washed twice with 40 µl of distilled water/mg hair and three times with the same amount of isopropanol. In each washing step, the samples and washing solution were vortexed, the supernatant discarded, and the hair dried using clean paper towels. After the final wash, samples were dried overnight at room temperature and 30 mg of hair cut into a 2 ml polypropylene screw cap plastic tube with five 4 mm steel beads added to each tube.The hair was ground to a fine powder in a FastPrep sample homogenizer (MP Biomedicals, USA) for four times 1 min at 6.0 m/s. 50 µl methanol/mg hair were added to each sample and sonicated for 30 min at 50 Hz at 50 °C. The samples were incubated for 18 h at 50 °C in an orbital shaker at 160 rpm, centrifuged for 15 min at 14,000g at 20 °C, and 1000 µl of supernatant was collected to a screw cap glass chromatography vial and dried at room temperature in a gentle stream of nitrogen gas. Due to restrictions on laboratory use during the SARS-Cov-2 pandemic, some batches of samples were instead evaporated overnight on a suction hood. This unexpected change in the methanol evaporation protocol was recorded and accounted for in the statistical analysis.Cortisol quantificationA commercial competitive ELISA kit (Cortisol free in Saliva ELISA, Demeditec, Germany) was used to quantify the concentration of cortisol, following the manufacturer’s instructions. The kit plate wells are provided coated with polyclonal rabbit antibody against cortisol, and cortisol-horseradish peroxidase was used as conjugate. According to the manufacturer, the cross-reactivity of the test to selected steroids is low (Table S3), the intra-assay variation is 3.8–5.8% and the inter-assay variation is 6.2–6.4%. Samples, standards, and controls were tested in duplicate.The 4-parameter standard curve was calculated from the log-transformed cortisol concentration of the standard solutions and their measured OD45044. Standard curves were estimated using the software GraphPad Prism 6.04 (GraphPad Software, La Jolla, California USA), and yielded an average R2adjusted = 0.991 (range 0.968–0.999). The cortisol concentration of the reconstituted samples was estimated from the standard curve and converted to cortisol concentration as picograms (pg) of cortisol/mg of guard hair.Intra and inter-assay coefficients of variation were estimated for six ELISA assays of 37–40 samples each. The low and high controls included in the kit were used to estimate the inter-assay coefficient of variation and the duplicate runs of each sample were used to estimate the intra-assay coefficient of variation. Linearity was assessed by two-fold dilutions (1:1, 1:2, 1:4 and 1:8) of 4 extracted samples, comparing the expected and observed concentrations. Recovery was assessed by spiking 6 ground hair samples with known concentrations of cortisol (50, 25, 12.5, 6.25 pg/mg, and no spiking), comparing the expected and observed concentrations.The intra-assay coefficient of variation of the ELISA assays ranged from 6.50 to 9.97% (average 7.66%). The inter-assay coefficient of variation was 11.54% for the low concentration controls and 9.08% for the high concentration controls (average 10.31%). Assay linearity was 91% for the 1:2 dilution, 103% for 1:4, and 117% for 1:8 (average 103%). The recovery of cortisol averaged 94%, being 73% for the 50 pg/mg spiked samples, 74% for 25 pg/mg, 95% for 12.5 pg/mg, and 113% for 6.25 pg/mg.Determinants of hair cortisol concentrationThe potential determinants of HCC investigated included wolf intrinsic variables: sex, age, body condition, body structural size, month of death/capture, and wolf population. The scaled mass index was selected as a measure of body condition45 and estimated from the log-transformed body weight (g) and total length (mm). Log-transformed total length was used as an indicator of body structural size46. Samples were assigned to the Iberian, Alpine, Dinaric-Balkan, or Scandinavian wolf populations16 from the geographical location of the death or live-trapping sites (Fig. 1).The relationship between HCC and additional variables related to the sampling procedure or to the work conducted in the laboratory (length of hair used for cortisol extraction, sample storage time, body region, cause of death/capture, and methanol evaporation protocol), herein referred to as methodological variables, was also investigated as potential confounding variables. Sample storage time was the period in months between death/capture and cortisol extraction. In those samples for which only the year of death was available, 30 June was assigned as the date of death, solely to estimate storage time. All continuous variables were standardized to their z-scores.Statistical analysisFirst, the effect of body region was investigated by a linear mixed model with HCC as the dependent variable, and the independent variables body region, as a categorical fixed effect, and individual wolf, as a random effect. The lumbar region was set as the reference class as it was the most represented in our sample (Table S1). Data from 27 wolves for which samples were available from all 4 body regions were used in this analysis. Four outliers in the dataset violated the assumption of normality in the residuals of the model comparing HCC across body regions (Fig. S1A) and were excluded from this model’s dataset (Fig. S1B).Second, the effect of intrinsic and methodological variables on HCC from the lumbar body region was investigated by another linear mixed model with sex, age, body condition, body structural size (standardized log-transformed total length), cause of death/capture, wolf population, hair length, sample storage time, and methanol evaporation protocol as fixed effect independent variables. The month of death/capture was included as a random effect. Reference classes for the categorical variables were set as female, adult, acute death, Iberian population, and methanol evaporation by nitrogen gas stream. Two outliers in the dataset violated the assumption of normality in the residuals of the model (Full model, Table S4) and were excluded from this analysis (Fig. S1C,D).The goal of this analysis was to assess the relationship between HCC and wolf intrinsic variables, controlling for the potential confounding effect of the methodological variables. Starting from the full model (Table S4), models including all possible combinations of variables were ranked by their AICc using the package “MuMIn”47 in R 3.6.148. The most supported model was selected for inference and models with ΔAICc  More