Ancient origin of an urban underground mosquito

Ancient origin of an urban underground mosquito | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Ancient origin of an urban underground mosquito View ORCID Profile Yuki Haba , Matthew L. Aardema , Maria O. Afonso , Natasha M. Agramonte , John Albright , Ana Margarida Alho , Antonio P.G. Almeida , Haoues Alout , Bulent Alten , View ORCID Profile Mine Altinli , Raouf Amara Korba , Stefanos S. Andreadis , Vincent Anghel , Soukaina Arich , Arielle Arsenault-Benoit , Célestine Atyame , Fabien Aubry , Frank W. Avila , Diego Ayala , Rasha S. Azrag , Lilit Babayan , Allon Bear , Norbert Becker , Anna G. Bega , Sophia Bejarano , Ira Ben-Avi , Joshua B. Benoit , Saïd C. Boubidi , William E. Bradshaw , Daniel Bravo-Barriga , Rubén Bueno-Marí , Nataša Bušić , Viktoria Čabanová , Brittany Cabeje , Beniamino Caputo , Maria V. Cardo , Simon Carpenter , Elena Carreton , Mouhamadou S. Chouaïbou , Michelle Christian , Maureen Coetzee , William R. Conner , Anton Cornel , C. Lorna Culverwell , Aleksandra I. Cupina , Katrien De Wolf , Isra Deblauwe , Brittany Deegan , Sarah Delacour-Estrella , Alessandra della Torre , Debora Diaz , Serena E. Dool , Vitor L dos Anjos , Sisay Dugassa , Babak Ebrahimi , Samar Y.M. Eisa , Nohal Elissa , Sahar A.B. Fallatah , Ary Faraji , Marina V. Fedorova , Emily Ferrill , View ORCID Profile Dina M. Fonseca , Kimberly A. Foss , Cipriano Foxi , Caio M. França , Stephen R. Fricker , Megan L. Fritz , Eva Frontera , Hans-Peter Fuehrer , Kyoko Futami , Enas H.S. Ghallab , Romain Girod , Mikhail I. Gordeev , David Greer , Martin Gschwind , Milehna M. Guarido , Teoh Guat Ney , Filiz Gunay , Eran Haklay , Alwia A.E. Hamad , View ORCID Profile Jun Hang , Christopher M. Hardy , Jacob W. Hartle , Jenny C. Hesson , Yukiko Higa , Christina M. Holzapfel , Ann-Christin Honnen , Angela M. Ionica , Laura Jones , Përparim Kadriaj , Hany A. Kamal , Colince Kamdem , Dmitry A. Karagodin , Shinji Kasai , Mihaela Kavran , Emad I.M. Khater , Frederik Kiene , Heung-Chul Kim , Ilias Kioulos , Annette Klein , Marko Klemenčić , Ana Klobučar , Erin Knutson , Constantianus J.M. Koenraadt , Linda Kothera , Pauline Kreienbühl , Pierrick Labbé , Itay Lachmi , View ORCID Profile Louis Lambrechts , Nediljko Landeka , Christopher H. Lee , Bryan D. Lessard , Ignacio Leycegui , Jan O. Lundström , Yoav Lustigman , Caitlin MacIntyre , Andrew J. Mackay , View ORCID Profile Krisztian Magori , Carla Maia , Colin A. Malcolm , Ralph-Joncyn O. Marquez , Dino Martins , Reem A. Masri , Gillian McDivitt , Rebekah J. McMinn , Johana Medina , Karen S. Mellor , Jason Mendoza , Enrih Merdić , Stacey Mesler , Camille Mestre , Homer Miranda , Martina Miterpáková , Fabrizio Montarsi , Anton V. Moskaev , Tong Mu , Tim W.R. Möhlmann , Alice Namias , Ivy Ng’iru , Marc F. Ngangué , Maria T. Novo , Laor Orshan , José A. Oteo , Yasushi Otsuka , Rossella Panarese , Claudia Paredes-Esquivel , Lusine Paronyan , Steven T. Peper , Dušan V. Petrić , Kervin Pilapil , Cristina Pou-Barreto , Sebastien J. Puechmaille , Ute Radespiel , Nil Rahola , Vivek K Raman , Hamadouche Redouane , Michael H. Reiskind , Nadja M. Reissen , Benjamin L. Rice , Vincent Robert , Ignacio Ruiz-Arrondo , Ryan Salamat , Amy Salamone , M’hammed Sarih , Giuseppe Satta , Kyoko Sawabe , View ORCID Profile Francis Schaffner , Karen E. Schultz , Elena V. Shaikevich , View ORCID Profile Igor V. Sharakhov , Maria V. Sharakhova , Nader Shatara , Anuarbek K. Sibataev , Mathieu Sicard , Evan Smith , Ryan C. Smith , Nathalie Smitz , Nicolas Soriano , Christos G. Spanoudis , Christopher M. Stone , Liora Studentsky , Tatiana Sulesco , Luciano M. Tantely , La K. Thao , Noor Tietze , Ryan E. Tokarz , Kun-Hsien Tsai , Yoshio Tsuda , Nataša Turić , Melissa R. Uhran , Isik Unlu , View ORCID Profile Wim Van Bortel , Haykuhi Vardanyan , Laura Vavassori , Enkelejda Velo , Marietjie Venter , Goran Vignjević , Chantal B.F. Vogels , Tatsiana Volkava , John Vontas , Heather M. Ward , Nazni Wasi Ahmad , Mylène Weill , Jennifer D. West , Sarah S. Wheeler , Gregory S. White , Nadja C. Wipf , Tai-Ping Wu , Kai-Di Yu , Elke Zimmermann , Carina Zittra , View ORCID Profile Petra Korlević , Erica McAlister , Mara K.N. Lawniczak , View ORCID Profile Molly Schumer , View ORCID Profile Noah H. Rose , View ORCID Profile Carolyn S. McBride doi: https://doi.org/10.1101/2025.01.26.634793 Yuki Haba 1 Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA 2 Princeton Neuroscience Institute, Princeton University , Princeton, NJ 08544, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yuki Haba For correspondence: yhaba{at}princeton.edu csm7{at}princeton.edu Matthew L. Aardema 3 Department of Biology, Montclair State University , Montclair, NJ, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria O. Afonso 4 Global Health and Tropical Medicine, GHTM, LA-REAL, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa , Lisboa, Portugal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natasha M. Agramonte 5 DeKalb County Board of Health , Decatur, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Albright 6 Shasta Mosquito and Vector Control District , Anderson, CA 96007, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ana Margarida Alho 7 Public Health Unit USP Francisco George, Primary Medical Healthcare Cluster Lisbon North, Largo Professor Arnaldo Sampaio , 1549-010 Lisboa, Portugal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antonio P.G. Almeida 4 Global Health and Tropical Medicine, GHTM, LA-REAL, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa , Lisboa, Portugal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Haoues Alout 8 ASTRE, UMR 117, INRAE-CIRAD , Montpellier, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bulent Alten 9 Hacettepe University, Faculty of Science, Department of Biology, VERG Laboratories , Beytepe, Ankara, Turkey Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mine Altinli 10 Institut des Sciences de l’Évolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier , Montpellier, 34095, France 11 Bernhard-Nocht-Institute for Tropical Medicine , Bernhard Nocht Str. 74, 20359, Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mine Altinli Raouf Amara Korba 12 Laboratory of Health and Environment, Faculty of Life and Natural Sciences and of Earth and Universe Sciences, Mohamed El Bachir El Ibrahimi University , Bordj Bou Arreridj, 34030, Algeria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stefanos S. Andreadis 13 Institute of Plant Breeding and Genetic Resources, Hellenic Agricultural Organization – DIMITRA , 57001 Thermi, Greece Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vincent Anghel 14 Southern Nevada Health District , Las Vegas, NV 89107, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Soukaina Arich 10 Institut des Sciences de l’Évolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier , Montpellier, 34095, France 15 Service de Parasitologie et des Maladies Vectorielles, Institut Pasteur du Maroc , Casablanca 20360, Morocco Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arielle Arsenault-Benoit 16 Department of Entomology, University of Maryland , College Park, MD 20742, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Célestine Atyame 17 University of Reunion Island, UMR PIMIT (Processus Infectieux en Milieu Insulaire Tropical) CNRS 9192, INSERM 1187, IRD 249, University of Reunion Island , Reunion Island, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fabien Aubry 18 Institut Pasteur, Université Paris Cité, CNRS UMR2000, Insect-Virus Interactions Unit , 75015 Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frank W. Avila 19 Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia , Medellín, 050010, Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Diego Ayala 20 MIVEGEC, University of Montpellier, CNRS, IRD , 34394 Montpellier, France 21 CIRMF , Franceville, Gabon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rasha S. Azrag 22 Department of Zoology, Faculty of Science, University of Khartoum , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lilit Babayan 23 National Center of Disease Control and Prevention, Ministry of Health , Yerevan 0025, Republic of Armenia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Allon Bear 24 E7 Ministry of Environmental Protection , Ramla, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Norbert Becker 25 Center for Organismal Studies, University of Heidelberg , Heidelberg, Germany 26 German Mosquito Control Association , Speyer, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna G. Bega 27 Laboratory of Experimental Biology and Biotechnology, Scientific and Educational Center in Chernogolovka, Federal State University of Education , Moscow 105005, Russia 28 Vavilov Institute of General Genetics, Russian Academy of Sciences , 119991 Moscow, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sophia Bejarano 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ira Ben-Avi 30 Ministry of Health , Jerusalem, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joshua B. Benoit 31 Department of Biological Sciences, University of Cincinnati , Cincinnati, OH 45211, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Saïd C. Boubidi 32 Entomology Unit, Laboratory of Parasitology, Pasteur Institute of Algeria , Algiers, Algeria Find this author on Google Scholar Find this author on PubMed Search for this author on this site William E. Bradshaw 33 Institute of Ecology and Evolution, 5289 University of Oregon , Eugene, OR 97403-5289, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Bravo-Barriga 34 Parasitología, Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad de Extremadura , Cáceres, Spain 35 Department of Animal Health Department (Parasitology and Parasitic Diseases), Faculty of Veterinary Medicine, University of Córdoba, Sanidad Animal Building , Rabanales Campus, Córdoba, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rubén Bueno-Marí 36 European Center of Excellence for Vector Control, Laboratorios Lokímica - Rentokil Initial , Valencia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nataša Bušić 37 Department of Biology, Josip Juraj Strossmayer University of Osijek , Osijek, 31000, Croatia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Viktoria Čabanová 38 Department of Virus Ecology, Institute of Virology, Biomedical Research Center Slovak Academy of Sciences , Bratislava, 845 05, Slovakia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brittany Cabeje 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Beniamino Caputo 39 Department of Public Health and Infectious Diseases, Sapienza University , Rome, 00185, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria V. Cardo 40 Ecología de Enfermedades Transmitidas por Vectores, Instituto de Investigación e Ingeniería Ambiental, UNSAM, CONICET , Buenos Aires, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simon Carpenter 41 The Pirbright Institute , Ash Road, Woking, Surrey, GU24 0NF, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elena Carreton 42 Faculty of Veterinary Medicine, Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mouhamadou S. Chouaïbou 43 Centre Suisse de Recherches Scientifiques en Côte d’Ivoire , Abidjan 1301, Côte d’Ivoire Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michelle Christian 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maureen Coetzee 44 Wits Research Institute for Malaria, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand , Johannesburg, South Africa 45 Centre for Emerging Zoonotic & Parasitic Diseases, National Institute for Communicable Diseases , Johannesburg, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site William R. Conner 46 Department of Evolution and Ecology, University of California , Davis, CA 95616, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anton Cornel 47 Kearney Agricultural Research and Extension Center , Parlier, CA 93648, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site C. Lorna Culverwell 48 University of Helsinki, Medicum, Department of Virology , Helsinki 00014, Finland 49 Department of Life Sciences, The Natural History Museum , London, SW7 5BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aleksandra I. Cupina 50 Faculty of Agriculture, Centre of Excellence One Health Vectors and Climate, Laboratory for Medical and Veterinary Entomology, University of Novi Sad , 21101 Novi Sad, Serbia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katrien De Wolf 51 Entomology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine , 2000 Antwerp, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isra Deblauwe 51 Entomology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine , 2000 Antwerp, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brittany Deegan 52 Consolidated Mosquito Abatement District , Parlier, CA 93648, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah Delacour-Estrella 53 Department of Animal Pathology, Faculty of Veterinary Medicine at the University of Zaragoza , Zaragoza, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alessandra della Torre 39 Department of Public Health and Infectious Diseases, Sapienza University , Rome, 00185, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Debora Diaz 30 Ministry of Health , Jerusalem, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Serena E. Dool 54 Zoological Institute and Museum, University of Greifswald , Greifswald, 17489, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vitor L dos Anjos 1 Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sisay Dugassa 55 Aklilu Lemma Institute of Pathobiology, Addis Ababa University , Addis Ababa, Ethiopia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Babak Ebrahimi 56 Santa Clara County Vector Control District , San Jose, CA 95112, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samar Y.M. Eisa 22 Department of Zoology, Faculty of Science, University of Khartoum , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nohal Elissa 57 Département Faune et Actions de Salubrité, Service Parisien de Santé Environnementale, Direction de la Santé Publique , Ville de Paris, Paris 75019, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sahar A.B. Fallatah 58 Biology Department, College of Science, Imam Abdulrahman bin Faisal University , Dammam 31113, Kingdom of Saudi Arabia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ary Faraji 59 Salt Lake City Mosquito Abatement District , Salt Lake City, UT 84116, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marina V. Fedorova 60 Сentral Research Institute for Epidemiology , Moscow, 111123, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily Ferrill 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dina M. Fonseca 61 Center for Vector Biology, Rutgers University , New Brunswick, NJ, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dina M. Fonseca Kimberly A. Foss 62 Northeast Massachusetts Mosquito Control District , 118 Tenney Street, Georgetown, MA 01833, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cipriano Foxi 63 Istituto Zooprofilattico Sperimentale della Sardegna , 07100, Sassari, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Caio M. França 64 Department of Biology, Southern Nazarene University , Bethany, OK, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephen R. Fricker 65 STEM, University of South Australia , Adelaide, South Australia, 5000, Australia 66 Medical Entomology, Centre for Disease Control and Environmental Health , NT Health, NT, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Megan L. Fritz 16 Department of Entomology, University of Maryland , College Park, MD 20742, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eva Frontera 34 Parasitología, Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad de Extremadura , Cáceres, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hans-Peter Fuehrer 67 Institute of Parasitology, Department of Pathobiology, University of Veterinary Medicine , 1210 Vienna, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kyoko Futami 68 Department of Vector Ecology and Environment, Institute of Tropical Medicine, Nagasaki University , Nagasaki, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Enas H.S. Ghallab 69 Department of Entomology, Faculty of Science, Ain Shams University , Abbassia, Cairo 11566, Egypt Find this author on Google Scholar Find this author on PubMed Search for this author on this site Romain Girod 70 Medical Entomology Unit, Institut Pasteur de Madagascar , Antananarivo, Madagascar Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mikhail I. Gordeev 71 Department of General Biology and Ecology, Federal State University of Education , 105005 Moscow, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Greer 14 Southern Nevada Health District , Las Vegas, NV 89107, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Gschwind 72 Swiss Tropical and Public Health Institute , Allschwil, Switzerland 73 University of Basel , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Milehna M. Guarido 74 Centre for Emerging and Reemerging Arbo and Respiratory Virus Research (CEARV), Department Medical Virology, University of Pretoria , South Africa 75 Department of Production Animal Studies, Faculty of Veterinary Science, University of Pretoria , Onderstepoort, 0110, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Teoh Guat Ney 76 Medical Entomology Unit, Infectious Disease Research Centre, Institute For Medical Research, National Institutes of Health , Jalan Pahang 50588, Kuala Lumpur, Malaysia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Filiz Gunay 9 Hacettepe University, Faculty of Science, Department of Biology, VERG Laboratories , Beytepe, Ankara, Turkey Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eran Haklay 77 Ministry of Environmental Protection , Jerusalem, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alwia A.E. Hamad 22 Department of Zoology, Faculty of Science, University of Khartoum , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jun Hang 78 Walter Reed Army Institute of Research , Silver Spring, MD 20910, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jun Hang Christopher M. Hardy 79 Environment, CSIRO , Canberra, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacob W. Hartle 80 Placer Mosquito & Vector Control District , Roseville, CA 95678, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jenny C. Hesson 81 Department of Medical Biochemistry and Microbiology/Zoonosis Science Center, Uppsala University , Uppsala, SE-75123, Sweden 82 Biologisk Myggkontroll, Nedre Dalälven Utvecklings AB, Uppsala , SE-75646, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yukiko Higa 83 Department of Medical Entomology, National Institute of Infectious Diseases , Tokyo 162-8640, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christina M. Holzapfel 33 Institute of Ecology and Evolution, 5289 University of Oregon , Eugene, OR 97403-5289, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ann-Christin Honnen 72 Swiss Tropical and Public Health Institute , Allschwil, Switzerland 73 University of Basel , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Angela M. Ionica 84 Department of Parasitology and Parasitic Diseases, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca , 400372, Cluj-Napoca, Romania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Jones 41 The Pirbright Institute , Ash Road, Woking, Surrey, GU24 0NF, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Përparim Kadriaj 85 Vector Control Unit, Department of Epidemiology and Control of Infectious Diseases, Institute of Public Health , Tirana, Albania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hany A. Kamal 86 Department of Pest Control Projects, Dallah Company , Jeddah, Kingdom of Saudi Arabia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Colince Kamdem 87 Department of Biological Sciences, The University of Texas at El Paso , 500 W. University Ave., El Paso, TX 79968, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dmitry A. Karagodin 88 Institute of Cytology and Genetics , Novosibirsk, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shinji Kasai 83 Department of Medical Entomology, National Institute of Infectious Diseases , Tokyo 162-8640, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mihaela Kavran 50 Faculty of Agriculture, Centre of Excellence One Health Vectors and Climate, Laboratory for Medical and Veterinary Entomology, University of Novi Sad , 21101 Novi Sad, Serbia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emad I.M. Khater 69 Department of Entomology, Faculty of Science, Ain Shams University , Abbassia, Cairo 11566, Egypt Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frederik Kiene 89 Institute of Zoology and Institute of Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover , 30559 Hanover, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Heung-Chul Kim 90 Force Health Protection and Preventive Medicine, Medical Department Activity-Korea/65th Medical Brigade , Unit 15281, APO AP 96271-5281, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ilias Kioulos 91 Department of Crop Science, Agricultural University of Athens , 11855, Athens, Greece Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annette Klein 89 Institute of Zoology and Institute of Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover , 30559 Hanover, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marko Klemenčić 92 Croatian Institute for Public Health of Međimurje County , 40000 Čakovec, Croatia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ana Klobučar 93 Department of Epidemiology, Andrija Stampar Teaching Institute of Public Health , Zagreb, 10000, Croatia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Erin Knutson 94 Washington State Department of Health , Olympia, WA 98504, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Constantianus J.M. Koenraadt 95 Laboratory of Entomology, Wageningen University & Research , Wageningen, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Linda Kothera 96 Centers for Disease Control and Prevention , Fort Collins, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pauline Kreienbühl 18 Institut Pasteur, Université Paris Cité, CNRS UMR2000, Insect-Virus Interactions Unit , 75015 Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pierrick Labbé 10 Institut des Sciences de l’Évolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier , Montpellier, 34095, France 97 Institut Universitaire de France , 75005, Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Itay Lachmi 98 Nature and Parks Authority , Jerusalem, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Louis Lambrechts 18 Institut Pasteur, Université Paris Cité, CNRS UMR2000, Insect-Virus Interactions Unit , 75015 Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Louis Lambrechts Nediljko Landeka 99 Institute of Public Health of the Istrian County , 52100 Pula, Croatia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher H. Lee 100 Department of Plant Pathology, Entomology and Microbiology Iowa State University , Ames, IA 50011, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bryan D. Lessard 101 Australian National Insect Collection, National Research Collections Australia , CSIRO, Canberra, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ignacio Leycegui 14 Southern Nevada Health District , Las Vegas, NV 89107, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jan O. Lundström 81 Department of Medical Biochemistry and Microbiology/Zoonosis Science Center, Uppsala University , Uppsala, SE-75123, Sweden 82 Biologisk Myggkontroll, Nedre Dalälven Utvecklings AB, Uppsala , SE-75646, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yoav Lustigman 98 Nature and Parks Authority , Jerusalem, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Caitlin MacIntyre 102 Zoonotic Arbo- and Respiratory Virus Program, Centre for Viral Zoonoses, Faculty of Health Sciences, University of Pretoria , Pretoria, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew J. Mackay 103 Illinois Natural History Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign , Champaign, IL 61820, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Krisztian Magori 104 Department of Biology, Eastern Washington University , Cheney, WA 99004, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Krisztian Magori Carla Maia 4 Global Health and Tropical Medicine, GHTM, LA-REAL, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa , Lisboa, Portugal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Colin A. Malcolm 105 School of Health, Medicine and Life Sciences, University of Hertfordshire , Hatfield, Hertfordshire, AL10 9AB, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ralph-Joncyn O. Marquez 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dino Martins 106 Mpala Research Centre , 555-10400, Nanyuki, Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site Reem A. Masri 107 Department of Entomology and Fralin Life Sciences Institute, Virginia Polytechnic and State University , Blacksburg, VA 24061, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gillian McDivitt 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rebekah J. McMinn 108 Department of Microbiology, Immunology, and Pathology, Colorado State University , Fort Collins, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Johana Medina 109 Miami-Dade County Mosquito Control , Miami, FL 33178, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karen S. Mellor 110 Antelope Valley Mosquito & Vector Control District , Lancaster, CA 93535, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jason Mendoza 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Enrih Merdić 37 Department of Biology, Josip Juraj Strossmayer University of Osijek , Osijek, 31000, Croatia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stacey Mesler 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Camille Mestre 10 Institut des Sciences de l’Évolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier , Montpellier, 34095, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Homer Miranda 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martina Miterpáková 111 Institute of Parasitology, Slovak Academy of Sciences , Košice, Slovakia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fabrizio Montarsi 112 Laboratorio di Entomologia Sanitaria e Patogeni Trasmessi da Vettori, Istituto Zooprofilattico Sperimentale delle Venezie , 35020, Legnaro, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anton V. Moskaev 27 Laboratory of Experimental Biology and Biotechnology, Scientific and Educational Center in Chernogolovka, Federal State University of Education , Moscow 105005, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tong Mu 1 Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tim W.R. Möhlmann 95 Laboratory of Entomology, Wageningen University & Research , Wageningen, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alice Namias 10 Institut des Sciences de l’Évolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier , Montpellier, 34095, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ivy Ng’iru 106 Mpala Research Centre , 555-10400, Nanyuki, Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marc F. Ngangué 21 CIRMF , Franceville, Gabon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria T. Novo 4 Global Health and Tropical Medicine, GHTM, LA-REAL, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa , Lisboa, Portugal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laor Orshan 30 Ministry of Health , Jerusalem, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site José A. Oteo 113 Department of Infectious Diseases, Center of Rickettsiosis and Arthropod-Borne Diseases (CRETAV), San Pedro University Hospital-Center for Biomedical Research from La Rioja (CIBIR) , Logroño, 26006, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yasushi Otsuka 114 Research Center for the Pacific Islands, Kagoshima University , Kagoshima, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rossella Panarese 115 Dipartimento di Medicina Veterinaria, Università degli Studi di Bari , 70010, Valenzano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claudia Paredes-Esquivel 116 Parasitology and Mediterranean Ecoepidemiology Research Group, University of the Balearic Islands , 07122 Palma, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lusine Paronyan 23 National Center of Disease Control and Prevention, Ministry of Health , Yerevan 0025, Republic of Armenia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steven T. Peper 117 Anastasia Mosquito Control District of St. Johns County , Augustine, FL 32092, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dušan V. Petrić 50 Faculty of Agriculture, Centre of Excellence One Health Vectors and Climate, Laboratory for Medical and Veterinary Entomology, University of Novi Sad , 21101 Novi Sad, Serbia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kervin Pilapil 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cristina Pou-Barreto 118 Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias. Universidad de La Laguna , Tenerife, 38206, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sebastien J. Puechmaille 10 Institut des Sciences de l’Évolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier , Montpellier, 34095, France 97 Institut Universitaire de France , 75005, Paris, France 119 Applied Zoology and Nature Conservation, University of Greifswald , 17489, Greifswald, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ute Radespiel 120 Institute of Zoology, University of Veterinary Medicine Hannover , 30559 Hanover, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nil Rahola 20 MIVEGEC, University of Montpellier, CNRS, IRD , 34394 Montpellier, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vivek K Raman 14 Southern Nevada Health District , Las Vegas, NV 89107, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hamadouche Redouane 121 Regional Health Observatories , Oran, Algeria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael H. Reiskind 122 Department of Entomology and Plant Pathology, North Carolina State University , Raleigh, NC, 27695 USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nadja M. Reissen 59 Salt Lake City Mosquito Abatement District , Salt Lake City, UT 84116, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin L. Rice 1 Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA 123 Madagascar Health and Environmental Research (MAHERY) , Maroantsetra, Madagascar Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vincent Robert 20 MIVEGEC, University of Montpellier, CNRS, IRD , 34394 Montpellier, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ignacio Ruiz-Arrondo 113 Department of Infectious Diseases, Center of Rickettsiosis and Arthropod-Borne Diseases (CRETAV), San Pedro University Hospital-Center for Biomedical Research from La Rioja (CIBIR) , Logroño, 26006, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ryan Salamat 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amy Salamone 94 Washington State Department of Health , Olympia, WA 98504, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site M’hammed Sarih 15 Service de Parasitologie et des Maladies Vectorielles, Institut Pasteur du Maroc , Casablanca 20360, Morocco Find this author on Google Scholar Find this author on PubMed Search for this author on this site Giuseppe Satta 63 Istituto Zooprofilattico Sperimentale della Sardegna , 07100, Sassari, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kyoko Sawabe 83 Department of Medical Entomology, National Institute of Infectious Diseases , Tokyo 162-8640, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Francis Schaffner 124 Francis Schaffner Consultancy , Riehen, Switzerland 125 National Centre for Vector Entomology, Institute of Parasitology, Vetsuisse Faculty, University of Zürich , Zürich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Francis Schaffner Karen E. Schultz 126 Mosquito and Vector Management District of Santa Barbara County , Summerland, CA 93067, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elena V. Shaikevich 28 Vavilov Institute of General Genetics, Russian Academy of Sciences , 119991 Moscow, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Igor V. Sharakhov 107 Department of Entomology and Fralin Life Sciences Institute, Virginia Polytechnic and State University , Blacksburg, VA 24061, USA 127 Department of Genetics and Cell Biology, Tomsk State University , Tomsk 634050, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Igor V. Sharakhov Maria V. Sharakhova 107 Department of Entomology and Fralin Life Sciences Institute, Virginia Polytechnic and State University , Blacksburg, VA 24061, USA 128 Laboratory of Cell Differentiation Mechanisms, Institute of Cytology and Genetics , 10, Ac. Lavrentieva ave., Novosibirsk 630090, Russia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nader Shatara 129 San Francisco Department of Public Health , San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anuarbek K. Sibataev 130 Department of Biology, Plant Protection and Quarantine, S.Seifullin Kazakh Agrotechnical Research University , Astana, Kazakhstan 131 Department of General Biology and Genomics, Eurasian National University , Astana, Kazakhstan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mathieu Sicard 10 Institut des Sciences de l’Évolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier , Montpellier, 34095, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Evan Smith 31 Department of Biological Sciences, University of Cincinnati , Cincinnati, OH 45211, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ryan C. Smith 100 Department of Plant Pathology, Entomology and Microbiology Iowa State University , Ames, IA 50011, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nathalie Smitz 132 Royal Museum for Central Africa , Leuvensesteenweg 13, 3080, Tervuren, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicolas Soriano 29 County of San Diego, Vector Control Program , San Diego, CA 92123, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christos G. Spanoudis 133 Faculty of Agriculture, Forestry and Natural Environment, School of Agriculture, Laboratory of Applied Zoology and Parasitology, Aristotle University of Thessaloniki , 54124 Thessaloniki, Greece Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher M. Stone 103 Illinois Natural History Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign , Champaign, IL 61820, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liora Studentsky 30 Ministry of Health , Jerusalem, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tatiana Sulesco 11 Bernhard-Nocht-Institute for Tropical Medicine , Bernhard Nocht Str. 74, 20359, Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luciano M. Tantely 134 Unité d’Entomologie Médicale, Institut Pasteur de Madagascar , Antananarivo 101, Madagascar Find this author on Google Scholar Find this author on PubMed Search for this author on this site La K. Thao 135 Kern Mosquito & Vector Control District , Bakersfield, CA 93314, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Noor Tietze 56 Santa Clara County Vector Control District , San Jose, CA 95112, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ryan E. Tokarz 136 Department of International and Global Studies, Mercer University , Macon, GA 31207, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kun-Hsien Tsai 137 Department of Public Health, Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University , No. 17, Xu-Zhou Road, Taipei 100025, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yoshio Tsuda 83 Department of Medical Entomology, National Institute of Infectious Diseases , Tokyo 162-8640, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nataša Turić 37 Department of Biology, Josip Juraj Strossmayer University of Osijek , Osijek, 31000, Croatia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa R. Uhran 31 Department of Biological Sciences, University of Cincinnati , Cincinnati, OH 45211, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isik Unlu 109 Miami-Dade County Mosquito Control , Miami, FL 33178, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wim Van Bortel 51 Entomology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine , 2000 Antwerp, Belgium 138 Outbreak Research Team, Department of Biomedical Sciences, Institute of Tropical Medicine , 2000 Antwerp, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wim Van Bortel Haykuhi Vardanyan 23 National Center of Disease Control and Prevention, Ministry of Health , Yerevan 0025, Republic of Armenia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Vavassori 72 Swiss Tropical and Public Health Institute , Allschwil, Switzerland 73 University of Basel , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Enkelejda Velo 85 Vector Control Unit, Department of Epidemiology and Control of Infectious Diseases, Institute of Public Health , Tirana, Albania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marietjie Venter 74 Centre for Emerging and Reemerging Arbo and Respiratory Virus Research (CEARV), Department Medical Virology, University of Pretoria , South Africa 139 Emerging Viral Threats, One Health Vaccines and Surveillance (EVITOH) Division, Infectious Disease and Oncology Research Institute (IDORI), University of the Witwatersrand , Johannesburg, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Goran Vignjević 37 Department of Biology, Josip Juraj Strossmayer University of Osijek , Osijek, 31000, Croatia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chantal B.F. Vogels 95 Laboratory of Entomology, Wageningen University & Research , Wageningen, The Netherlands 140 Department of Epidemiology of Microbial Diseases, Yale School of Public Health , New Haven, CT 06510, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tatsiana Volkava 141 Laboratory of Parasitology, The State Scientific and Production Amalgamation, The Scientific and Practical Center of the National Academy of Sciences of Belarus for Biological Resources , Belarus, Minsk Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Vontas 91 Department of Crop Science, Agricultural University of Athens , 11855, Athens, Greece 142 Institute Molecular Biology Biotechnology Foundation for Research and Technology , 70013, Heraklion, Crete, Greece Find this author on Google Scholar Find this author on PubMed Search for this author on this site Heather M. Ward 117 Anastasia Mosquito Control District of St. Johns County , Augustine, FL 32092, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nazni Wasi Ahmad 76 Medical Entomology Unit, Infectious Disease Research Centre, Institute For Medical Research, National Institutes of Health , Jalan Pahang 50588, Kuala Lumpur, Malaysia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mylène Weill 10 Institut des Sciences de l’Évolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier , Montpellier, 34095, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer D. West 80 Placer Mosquito & Vector Control District , Roseville, CA 95678, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah S. Wheeler 143 Sacramento-Yolo Mosquito & Vector Control District , Elk Grove, CA, 95624, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gregory S. White 59 Salt Lake City Mosquito Abatement District , Salt Lake City, UT 84116, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nadja C. Wipf 72 Swiss Tropical and Public Health Institute , Allschwil, Switzerland 73 University of Basel , Basel, Switzerland 144 Vector Biology Department, Liverpool School of Tropical Medicine , Liverpool, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tai-Ping Wu 145 Wuhan Center for Disease Control and Prevention , Wuhan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kai-Di Yu 146 Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University , Taipei 100, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elke Zimmermann 120 Institute of Zoology, University of Veterinary Medicine Hannover , 30559 Hanover, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Carina Zittra 147 Division Limnology, Department of Functional and Evolutionary Ecology, University of Vienna , 1030 Vienna, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Petra Korlević 148 Wellcome Sanger Institute , Hinxton CB10 1SA, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Petra Korlević Erica McAlister 49 Department of Life Sciences, The Natural History Museum , London, SW7 5BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mara K.N. Lawniczak 148 Wellcome Sanger Institute , Hinxton CB10 1SA, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Molly Schumer 149 Department of Biology and Howard Hughes Medical Institute, Stanford University , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Molly Schumer Noah H. Rose 1 Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA 2 Princeton Neuroscience Institute, Princeton University , Princeton, NJ 08544, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Noah H. Rose Carolyn S. McBride 1 Department of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544, USA 2 Princeton Neuroscience Institute, Princeton University , Princeton, NJ 08544, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carolyn S. McBride For correspondence: yhaba{at}princeton.edu csm7{at}princeton.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Understanding how life is adapting to urban environments represents an important challenge in evolutionary biology. Here we investigate a widely cited example of urban adaptation, Culex pipiens form molestus , also known as the London Underground Mosquito. Population genomic analysis of ∼350 contemporary and historical samples counter the popular hypothesis that molestus originated belowground in London less than 200 years ago. Instead, we show that molestus first adapted to human environments aboveground in the Middle East over the course of >1000 years, likely in concert with the rise of agricultural civilizations. Our results highlight the role of early human society in priming taxa for contemporary urban evolution and have important implications for understanding arbovirus transmission. Main The rise of modern cities is rapidly reshaping our planet and imposing novel selective pressure on the living organisms around us. Many species have begun to adapt to these unique challenges. A review of the literature highlights at least 130 examples of animals, plants, and microbes that have evolved responses to dense urban environments ( 1 ). Yet how such adaptations occur and the amount of time they require remain poorly understood. As urbanization accelerates over the coming decades ( 2 ), there is a pressing need to better understand the mechanisms and timescale of urban adaptation. One of the most widely cited examples of urban adaptation involves the northern house mosquito Culex pipiens Linnaeus, 1758 ( Fig. 1A ). Cx. pipiens is common in temperate zones across the world ( 3 , 4 ). In Europe and North America, an ancestral form has long been appreciated as a bird-biting mosquito that requires open space for mating and pauses reproduction (i.e. diapauses) during the cold northern winter ( Fig. 1B–C , blue) ( 3 ). However, a derived, human-biting form thrives in urban belowground environments, such as subways, cellars, and cesspits, and differs from its aboveground counterpart in ways that seem perfectly suited to subterranean life ( Fig. 1B–C , red) ( 3 ). The belowground mosquitoes are able to mate in confined spaces and remain active in winter. Adult females readily bite humans (and other mammals). Yet if hosts are scarce, they can develop a first clutch of eggs without taking any blood, a trait known as autogeny. Strikingly, despite this array of genetically based behavioral and physiological differences, the two mosquitoes show no consistent morphological differences ( 3 ). They are formally considered distinct forms: the bird-biting Cx. pipiens f. pipiens Linnaeus, 1758 and the human-biting Cx. pipiens f. molestus Forskål, 1775 ( 5 ), hereafter referred to as simply pipiens and molestus , respectively. Download figure Open in new tab Figure 1. Culex pipiens form molestus behavior, ecology, and hypothetical origin. ( A ) Female Cx. pipiens complex mosquito. ( B ) Behavioral and physiological characteristics of Cx. pipiens forms in northern Eurasia. At warmer latitudes molestus can breed aboveground. ( C ) Example microhabitats: a city park ( pipiens) and the flooded basement of an apartment complex ( molestus ). ( D ) Two hypotheses describing molestus ’ origin. Hypothesis 1 (left) posits that belowground molestus evolved from local aboveground pipiens in situ within the past 100–200 years. Hypothesis 2 (right) posits that molestus first evolved in an aboveground context thousands of years ago, possibly in association with early agricultural societies of the Mediterranean basin, with colonization of belowground habitats (dotted arrow) occurring much later ( 22 , 23 ). [Image credits: Lawrence Reeves (mosquito); Yuki Haba (city park); Colin Malcolm (flooded basement)] The sophisticated adaptations of molestus to urban belowground environments have led to much speculation over when and where it originated. A widely cited hypothesis suggests that molestus evolved in the London Underground subway system, where it first became famous in the 1940s during World War II ( 1 , 6 – 11 ). At that time, many Londoners took nightly refuge in the city’s subway system to escape intense Nazi bombing. Sleeping on subway platforms protected people from bombs but made them easy targets for molestus , which became known as the ‘London Underground Mosquito’ and was hypothesized to have evolved there during the ∼100 year period between subway tunnel construction and mosquito discovery ( Fig. 1D , left) ( 6 , 12 ). Form molestus was reported in cellars and cesspits in France, Denmark, Germany, and the former USSR 10–25 years before its discovery in London ( 3 , 13 – 15 ), but an urban, belowground origin in northern Europe within the past few hundred years remains possible. Recent reviews have pointed to molestus as one of the best candidates for rapid urban adaptation ( 1 , 7 – 11 ), and major science news outlets treat this hypothesis as fact ( 16 – 21 ). The idea that such an array of traits could emerge de novo in just a few hundred years is striking and sets a new bar for the number and complexity of changes we might expect to occur in modern cities over short timescales. An alternative hypothesis, which is mentioned but less prominent in the literature, posits that molestus first adapted to humans in an aboveground context, long before the rise of modern cities ( Fig. 1D , right) ( 22 , 23 ). While molestus is confined to belowground habitats in cold regions, it thrives aboveground in warmer climates, particularly in the Mediterranean basin ( 23 ). Moreover, early records document molestus -like mosquitoes breeding and biting humans aboveground in Egypt, Croatia, and Italy 50–100 years before they were discovered in basements and subways ( 24 – 26 ). According to this alternative scenario, many of the traits that allow molestus to thrive in urban belowground environments would represent exaptations, or traits that first arose in a different time and context ( 27 ). An aboveground Mediterranean origin could also push the timing of molestus ’ origin back thousands of years, to an era when humans first started forming dense agricultural communities. Early allozyme and microsatellite studies indicate that contemporary molestus populations from aboveground and belowground habitats are genetically related ( 22 , 28 ), but the validity and timing of a putative aboveground origin remain to be tested. Here we leverage the first large population genomic dataset for Cx. pipiens to infer when, where, and in what ecological context molestus first evolved. Beyond its enigmatic origins, molestus is a competent disease vector, implicated in the transmission of West Nile virus and other arboviruses across Eurasia and North America over the past several decades ( 29 , 30 ). Solving the mystery of molestus ’ origins thus has important implications for understanding both rapid urban adaptation and emerging threats to human health. Form molestus is genetically isolated from pipiens across the Western Palearctic Multiple lines of evidence indicate that molestus first split from pipiens somewhere in the Western Palearctic (a region that includes Europe, North Africa, and western Asia) ( 31 ), before spreading to other parts of the world. However, the structure of populations across this region has been difficult to decipher due to the absence of morphological differences. Analysis of one or a small number of genetic loci shows that the two forms are isolated in northern Europe, where harsh winters confine molestus to belowground environments ( 22 , 23 ). However, molestus and pipiens appear to be more genetically similar in southern Europe, where both breed aboveground, and may even collapse into a single panmictic population in North Africa ( 22 , 23 ). To better resolve the situation with high-resolution genomic data, we sequenced the whole genomes of 357 Cx. pipiens individuals collected in 77 locations scattered across the Western Palearctic ( Fig. 2A ; n = ∼5 individuals per population at 12.9X median coverage). These data are part of a larger collection of 840 genomes to be presented in a companion study of the deeper evolutionary history of Cx. pipiens across its entire global range ( 32 ). Download figure Open in new tab Fig. 2. Form molestus is genetically isolated from pipiens across the Western Palearctic. ( A ) Sampled populations, colored by average PC1 value. Circles and triangles represent aboveground and belowground locations, respectively. Half-circles indicate that both pipiens and molestus were collected in the same or nearby aboveground sites. ( B ) PCA of genetic variation across all samples in (A) ( n = 357). ( C ) PC1 values plotted against latitude, with marginal frequency histogram at top. The gray dashed line indicates a natural break in the histogram, inferred to separate pipiens and molestus (PC1 = 0.04). Thick outlines mark individuals from North Africa and the Middle East. Asterisk marks a putative F1 hybrid from southwest Russia (Stavropol). Inset shows position of historical London samples in a combined PCA with contemporary mosquitoes ( n =22, collected 1940-1985; see also fig. S1 ). ( D ) PC1 values for pipiens and molestus individuals collected in the exact same day and trap (green lines) or in the same general location (within 5–45 km; grey lines). We used a Principal Component Analysis (PCA) to assess variation across the Western Palearctic using 504,000 high quality SNPs ( 32 ) ( Fig. 2B ). The first major axis (PC1) accounted for by far the most variation (39.5%, fig. S2A ) and was thus likely to represent divergence between pipiens and molestus . Consistent with this prediction, belowground and aboveground samples from northern latitudes were clustered at opposite ends of the PC1 axis ( Fig. 2C ). Sequenced mosquitoes with known biting or egg-laying behavior ( n = 13), including those from lower latitudes, were also arrayed across PC1 according to expected form ( fig. S1A ). PC2 explained ∼4% of genetic variation across the sample ( Fig. 2B ) and was strongly correlated with longitude ( fig. S2B ). Our sample included aboveground mosquitoes from London, which clustered with other northern European pipiens , but we were not given permission to collect mosquitoes in the London Underground. To confirm that the genetic picture today reflects the one present when iconic WWII populations were first discovered, we used a minimally destructive approach ( 33 ) to extract and sequence DNA from 22 museum specimens collected at 15 sites in London between 1940 and 1985 (table S2, mean genome-wide coverage = 5.8X). Metadata for most samples did not specify microhabitat, but the sampling locations included the sites of major underground stations, including Paddington, Monument, and Barking. A joint PCA with contemporary samples placed the historical London specimens in the same two genetic clusters that characterize mosquitoes at that latitude today ( Fig. 2C , inset; fig. S1B ). We conclude that the genetic character of pipiens and molestus populations in northern Europe has been stable for the past 75 years. Form pipiens and molestus are genetically well separated in the north, but our data confirm that they are less distinct at southern latitudes. Mosquitoes on both the molestus and pipiens ends of the PC1 axis have increasingly intermediate values as one moves from northern Europe towards Africa, creating a striking U-shaped pattern when PC1 is plotted against latitude ( Fig. 2C ). Importantly, however, they never completely merge; even southern populations fall into two discrete genetic clusters with a break at PC1 ∼0.04 ( Fig. 2C , dashed line). Moreover, individuals from these two clusters were frequently collected in the same traps, highlighting the absence of microgeographic barriers ( Fig. 2D ). Our whole genome data thus show unequivocally that pipiens and molestus are able to coexist in sympatry across the region ( 34 , 35 ). They are genetically closer in the south, and several individuals in our sample may represent early generation hybrids (e.g. see asterisk in Fig. 2C ). Despite this, we see no evidence of collapse into a panmictic population. Ancestral latitudinal gradient within pipiens suggests molestus arose at the southern edge of the Western Palearctic The genetic similarity of molestus and pipiens at southern latitudes is believed to result from increased gene flow ( 22 , 23 ); hybridization should be rare in the north where the two forms occupy different microhabitats, but increasingly common in the south where both breed aboveground ( Fig. 3A ). To test this hypothesis we examined the latitudinal cline within pipiens , which is much stronger than that within molestus ( Fig. 2C ). More specifically, we used genome-wide f3 statistics ( 36 ) to model each pipiens population as a mix of ‘pure’ pipiens and molestus reference populations taken from their northern extremes. Many European and west Asian populations showed evidence of mixing ( Fig. 3B ), but the signal was not latitudinal ( Fig. 3C ; Pearson’s r = −0.022, P = 0.90). Moreover, North African pipiens populations, which are genetically closest to molestus , showed no signs of admixture ( Fig. 3B ). These results cast doubt on the longstanding hypothesis that latitudinal variation within pipiens is driven by hybridization with molestus . Download figure Open in new tab Fig. 3. Ancestral latitudinal gradient within pipiens suggests molestus arose at the southern edge of the Western Palearctic ( A ) Hybridization gradient hypothesis: the genetic gradient within pipiens may result from increasing levels of gene flow with molestus as one moves from north to south. ( B ) Z scores of genome-wide f3 values for each pipiens population when modeled as a mixture of northern pipiens (Sweden) and northern molestus (Belgium). Significantly negative f3 values ( Z < −3, green outlines) are consistent with the presence of admixture. ( C ) f3 statistics of pipiens populations with significant signs of admixture, plotted against latitude. ( D ) Ancestral gradient hypothesis: the genetic gradient within pipiens may be ancestral, with molestus evolving from southern pipiens populations. ( E ) Fraction of derived alleles shared by each pipiens population with northern pipiens versus northern molestus. Culex torrentium was used as the outgroup. Light brown in the map shows the Mediterranean climate zone. ( F ) Fraction of derived alleles shared with northern pipiens vs. molestus , plotted against latitude. Both (C) and (F) include linear regression line with 95% confidence interval and Pearson’s correlation test statistics. Across all analyses, only populations with four or more individuals were included. An alternative hypothesis, which has not been explored in the literature, is that the latitudinal gradient within pipiens predates the evolution of molestus . In this case, southern pipiens could be genetically closer to molestus not because they mix with molestus , but because they gave rise to molestus ( Fig. 3D ). Consistent with this idea, we found that southern pipiens —and especially pipiens populations in the Mediterranean basin—share as many, or more, derived alleles with a reference molestus population from the north than they do with a reference pipiens population from the north ( Fig. 3E ). Moreover, the overall signal of relative allele sharing was strongly latitudinal ( Fig. 3F , Pearson’s r = 0.88, P = 2.5 x 10 -14 ). Taken together, we conclude that the latitudinal gradient within pipiens is ancestral—perhaps reflecting adaptation to variation in temperature and/or precipitation—and that molestus is most likely derived from populations in the south. Form molestus evolved thousands of years ago in the Middle East We further explored the geography of molestus’ origin by constructing a distance-based ( Dxy ) tree for Cx. pipiens individuals from the full global sample ( 32 ). Contemporary gene flow can obscure ancestral relationships in phylogenetic trees. We were therefore careful to exclude any individual or population that showed signs of recent introgression from the other form ( fig. S3 – S4 , S8 ) or from Culex quinquefasciatus , a tropical sibling species that hybridizes with Cx. pipiens in the Americas and Asia ( 32 ). We also excluded low coverage samples (<10X), leaving a total of 205 individuals. The resulting tree provided strong support for a southern origin of molestus . First, all molestus samples formed a monophyletic clade that was nested within Mediterranean pipiens ( Fig. 4A , fig. S5 ). Second, the earliest branching lineages within molestus corresponded to aboveground mosquitoes from the eastern Mediterranean—specifically Egypt, Israel, and Greece ( Fig. 4A ). Egyptian and Israeli samples were also among the most genetically diverse, together with two populations from the Caucasus region (Armenia and southern Russia) ( Fig. 4B ). Finally, while belowground molestus from northern latitudes formed tight, derived clades, aboveground molestus populations from North Africa, the Middle East, and southern Europe were scattered across the base of the tree ( Fig. 4A ). These results support the hypothesis that molestus first evolved in an aboveground context in the greater Mediterranean basin, and more specifically in the Middle East. Download figure Open in new tab Fig. 4. Form molestus evolved thousands of years ago in the Middle East. ( A ) molestus clade excerpted from neighbor-joining tree based on pairwise genetic distance ( Dxy ) among putatively unadmixed pipiens and molestus individuals from the global sample ( 32 ). Terminal branches are collapsed at the root of each population, with a symbol and number indicating microhabitat and sample size, respectively ( 32 ). Map inset shows distribution of two subgroups of molestus from the tree (orange/red) and pipiens (dark grey). Only molestus is present in Egypt, marked by an asterisk. Black circles mark nodes with >95% bootstrap support. See fig. S5 for the full tree. ( B ) Genome-wide nucleotide diversity (π) of populations shown in (A). ( C ) Relative cross-coalescence (rCC) rate between Moroccan pipiens (MAK) and Egyptian molestus (ADR) inferred from phased, whole genome sequences ( 32 ). rCC rate is expected to plateau at 1, going backwards in time, when populations have merged into a single ancestral population. Rapid divergence is observed between ∼10K and 1K years ago, with a split time (T; rCC rate=50%) of 2,141 years (black dashed arrow) based on estimates of generation time and mutation rate ( 32 ). Biologically reasonable upper and lower bounds for these parameters give minimum and maximum split times of 1,298 and 12,468 years (grey arrowheads). Thick black line shows genome-wide result, and light grey lines show 100 bootstrap replicates. Illustration credits: Wheat, Freepik.com. Pharaoh and Glacier, Vecteezy.com. The Middle East is a compelling location for the emergence of molestus not only because it harbors diverse, early branching populations ( Fig. 4A–B ), but also because it is the only place within the Western Palearctic where molestus is known to occur on its own, in the absence of pipiens ( Fig. 2A ) ( 28 , 37 ). The Middle East was also home to some of the earliest agricultural societies, which were thriving in Mesopotamia and Egypt by 3000 BCE ( 38 ). A Middle Eastern origin thus raises the possibility that molestus first adapted to human hosts and habitats in isolation from pipiens and on a timescale of thousands, rather than hundreds, of years ( Fig. 1D , right) ( 22 , 23 ). We explored the timing of molestus ’ origin using a cross-coalescent analysis of DNA haplotypes from Middle Eastern molestus (Egypt) and Mediterranean pipiens (Morocco) ( n = 2 individuals with ∼50X coverage from each population) ( 39 ). As expected, the relative cross coalescence (rCC) rate starts near zero in the recent past, when the two populations are isolated, but rises monotonically and eventually plateaus near one, going backward in time, when they merge into a single ancestral population ( Fig. 4C ). Accurate assignment of dates to this rCC curve requires knowledge of the de novo mutation rate ( μ ) and generation time ( g ), neither of which has been directly measured for Cx. pipiens in nature. However, plausible literature estimates for μ (4.85×10 −9 ) and g (20 days) ( 32 ) suggest that peak rates of divergence occurred ∼2,000 years ago ( Fig. 4C , dashed arrow), while minimum and maximum reasonable values ( 32 ) lead to split times anywhere between 1,300 and 12,500 years ago ( Fig. 4C , grey arrowheads). We obtained a similar range of split times when using an alternative pipiens population from the southern Caucasus region (Armenia; fig. S7 ). Taken together, these results are inconsistent with a post-industrial origin for molestus in northern Europe ( Fig. 1D , left) and instead support an ancient origin associated with early agricultural civilizations of the Middle East ( Fig. 1D , right). Introgression from molestus into aboveground pipiens is associated with human density Recent urbanization did not drive initial evolution of molestus , but it may have driven its expansion and increased contact with pipiens across the northern hemisphere—contact that is thought to have contributed to the emergence of West Nile virus (WNV) in human populations over the last several decades ( 29 , 30 ). WNV is a mosquito-borne virus that primarily infects birds and is effectively amplified within avian populations by bird-biting pipiens ( Fig. 5A ). Spillover to dead-end human hosts can only occur if local pipiens mosquitoes are also willing to bite humans, a broadening of biting behavior that may be driven by gene flow from molestus in urban areas ( 40 – 42 ). This idea has spurred efforts to detect and quantify admixture between pipiens and molestus in natural populations ( 23 ), yet we show above that much of the genetic signal previously attributed to mixing between forms instead represents ancestral variation ( Fig. 3 ). Form pipiens likely receives genetic input from molestus in some places, but exactly where and to what extent are not known. Download figure Open in new tab Fig. 5. Introgression from molestus into pipiens is associated with human density. ( A ) Schematic of West Nile virus transmission dynamics. Form pipiens - molestus hybrids, which have intermediate biting preference ( 42 ), are implicated in the spillover of WNV from birds to humans ( 40 , 41 ). ( B ) Base tree used to simultaneously estimate three potential sources of introgression into focal pipiens populations (X pip ): northern pipiens (Sweden, SWE), Middle Eastern molestus (Egypt, ADR), and northern molestus (Belgium, BVR). ( C – D ) Population-specific estimates of ‘gene flow’ from northern pipiens (C), which accounts for the ancestral latitudinal gradient, and northern molestus (D). We did not detect gene flow into any population from Middle Eastern molestus ( fig. S8 ). ( E ) Correlation between gene flow from northern molestus (D) and human population density within circles of varying radius around each collection site. ( F ) Gene flow from northern molestus as a function of human density within a 3-km radius of each collection site. Three outliers in grey (Cook’s distance > 4) were excluded, but regression remains significant if included ( P = 0.005, R 2 = 0.18). [Image credit: Phylopic.com (bird and human)] We used f -branch statistics to reassess levels of gene flow from molestus into pipiens while controlling for ancestral variation. f -branch statistics allow simultaneous quantification of gene flow among multiple branches in a tree ( 43 ). To account for the sister relationship between forms in the south ( Fig. 3D ), we specified a fixed tree in which focal pipiens populations were genetically closer to molestus than to a reference pipiens population from the far north ( Fig 5B ). Deviations from this topology (i.e. for focal populations from the north) can then be modeled as significant ‘gene flow’ into the focal pipiens population from the northern reference ( Fig. 5B , arrow 1). As expected, the resulting signal was strongly latitudinal ( Fig. 5C , fig. S8A ). The tree also included two potential sources of molestus introgression, allowing us to distinguish them. We could not detect gene flow into any pipiens population from the early branching molestus lineage in Egypt ( Fig. 5B arrow 2; fig. S8C ), consistent with its isolated location at the southern edge of the contemporary range. However, we detected substantial gene flow into some pipiens populations from a derived molestus lineage present in the north ( Fig. 5B arrow 3, Fig. 5D , fig. S8B ). Levels of gene flow from northern molestus into pipiens did not covary with latitude ( Fig. 5D , fig. S8B ), but showed a positive association with human population density ( 44 – 46 ). The more humans living within 1–10 km of each sampling location, the more likely we were to observe introgression ( Fig. 5E ). This relationship was most significant when averaging human density across an area with 3 km radius (linear regression P = 0.003, R 2 = 0.21, Fig. 5E–F ), suggesting that levels of urbanization immediately around collection sites are most predictive of hybridization. Moreover, this signal was driven primarily by the consistent presence of ∼5% introgression in truly ‘urban’ areas, defined by the European Commission as having >1500 people per km 2 ( Fig. 5F ) ( 47 ). Introgression was less predictable at rural sites ( P = 0.07, R 2 = 0.11 excluding urban centers). Inclusion of three statistical outliers in this analysis ( Fig. 5F , grey dots) slightly weakened the trend, but still indicated a strong association (regression P = 0.005, R 2 = 0.18). Interestingly, a site in Paris showed ∼15% introgression, as one might expect based on its density, but we could not detect any genetic input from molestus in London. Such geographic variability in levels of gene flow may in part reflect whether local pipiens and molestus populations are infected by compatible or incompatible strains of Wolbachia pipientis bacteria ( 48 , 49 ). Taken together, our results counter the longstanding idea that gene flow between pipiens and molestus is greatest at southern latitudes (where both forms breed aboveground) and instead reveal a complex landscape of introgression that is modestly associated with levels of human activity. Discussion Understanding how life can adapt to rapid urbanization is an important challenge in evolutionary biology. As examples accumulate in the literature, each new case provides a reference for the potential speed and character of adaptation. Here, we revisit one of the most iconic such examples using high resolution population genomic data. Instead of evolving in the subway system of a northern European city over the course of 100–200 years, our results suggest that Cx. pipiens f. molestus first adapted to human hosts and habitats in the Middle East over the course of 1000 or more years, possibly in association with early agricultural societies ( Fig. 6 ). Early agricultural settlements would have provided a novel ‘human’ niche in a region that might otherwise have been too arid to support robust Cx. pipiens populations. Irrigation systems offer rich breeding sites for larval stages, while abundant humans and domestic animals offer a reliable source of blood for adult females. We cannot say where exactly within the Middle East adaptation first occurred, as our sampling in the region is limited and ranges shift over time. However, molestus is particularly abundant in Egypt’s Nile basin, and ancient pharaonic artifacts and papyrus are consistent with the idea that molestus was spreading filarial worms among humans there as many as 2000 years ago ( 23 ). Download figure Open in new tab Fig. 6. Inferred evolutionary history of molestus . Three sequential panels show the inferred history of molestus . Left : The latitudinal genetic gradient characterizing extant pipiens populations in the Western Palearctic predates molestus . Middle : The rise of dense, settled, agricultural communities in the Middle East 2000–10,000 years ago, including along the Nile River in Egypt, would have provided new ecological opportunities for mosquitoes that could adapt to human hosts and habitats—driving the evolution of molestus . Right : Eventually molestus must have spread north, where it became established alongside pipiens in the warm Mediterranean region by the 1800s (but possibly earlier) and in belowground microhabitats of cold, northern cities by the early 1900s. Our distance tree ( Fig. 4A ) suggests that molestus was further spread (most likely by humans) overland all the way to East Asia and from the Mediterranean region overseas to America and Australia. Rather than benchmarking the speed and complexity of urban evolution, this updated history highlights the role of preexisting traits, or exaptations, in adaptation to urban environments ( 27 ). Three of the key behaviors that allow molestus to thrive belowground are present in contemporary Egyptian populations and almost certainly arose in ancient times: mammal-biting, the ability to mate in confined spaces, and the ability to lay a first clutch of eggs without a blood meal ( 37 , 50 ). A fourth trait, lack of diapause, which limits molestus to belowground environments at northern latitudes, is also present in the Middle East today ( 37 ). Form molestus was thus primed to take advantage of northern, belowground environments before they arose. Indeed, it joins a host of other ‘urban’ taxa that first became dependent on humans thousands of years ago, including brown rats ( 51 ), house mice ( 52 ), cockroaches ( 53 ), house sparrows ( 54 ), and the dengue mosquito Aedes aegypti ( 55 ). Ancient origins do not preclude additional, contemporary evolution ( 51 , 53 ). Once established belowground, molestus was likely exposed to a new suite of challenges. For example, many belowground habitats lack vertebrate hosts altogether, providing a competitive edge to females that can develop eggs without a bloodmeal. This trait, called autogeny, is present in contemporary Middle Eastern molestus , but only at low frequency ( 37 , 50 ). In contrast, it occurs at high frequency in some aboveground molestus from southern Europe ( 34 ) and is nearly fixed in northern belowground populations ( Fig. 1B ). Future work should explore whether increased autogeny may provide a bona fide example of rapid, urban evolution in belowground environments and whether this change arose just once, or many times in parallel ( 56 ). Our distance tree ( Fig. 4A ) suggests that belowground populations from northern Eurasia are all closely related, but that those from the east coast of North America (and possibly Australia) represent independent colonization events. Our findings also carry public health implications. The emergence and spread of WNV over the last two decades has triggered an intense interest in quantifying admixture between molestus and pipiens , as hybridization is thought to drive spillover from birds to humans ( 40 , 41 ). Yet we show that true patterns of admixture are obscured by ancestral relationships at southern latitudes. In particular, it is currently standard practice to identify ‘hybrids’ using a single locus marker called CQ11 ( 57 ). Pure pipiens and molestus were thought to be fixed for different alleles at this locus such that heterozygotes must be ‘hybrids’. Our data instead suggest that pipiens harbors ancestral variation at this locus. The ‘ molestus ’ allele was likely present at moderate frequency in the Mediterranean basin before molestus arose, and remains present in ‘pure’ Mediterranean pipiens today. Accurate inferences of gene flow will require more substantial genomic data and relatively complex analytical methods (e.g. Fig. 5 ). Future work should also consider the possibility that Mediterranean pipiens populations are somewhat intermediate between canonical northern forms at the phenotypic, as well as genetic, level ( 58 ). They may be effective bridge vectors even in the absence of genetic input from molestus . WNV represents an increasing threat to public health across the northern hemisphere, with many of the most severe outbreaks occurring within the last 5 years ( 59 , 60 ). Taken together, we hope that our work opens the door to more incisive investigation of the potential links between urbanization, gene flow, ancestral variation, and viral spillover. Funding This work was supported by grants or fellowships from the Masason Foundation (YH), Honjo International Scholarship Foundation (YH), Pacific Southwest Center of Excellence in Vector-borne Diseases (CSM), Society for the Study of Evolution (Rosemary Grant Advance Award to YH), Princeton High Meadows Environmental Institute (Walbridge Fund Graduate Award to YH), the American Philosophical Society (Lewis and Clark Field Scholar Award to YH), and the New York Stem Cell Foundation. CSM was a New York Stem Cell Foundation—Robertson Investigator. MKNL and PK were supported by core funding from Wellcome (220540/Z/20/A), which also supported the historic mosquito sequencing costs. LL was funded by the French Government’s Investissement d’Avenir program, Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases (ANR-10-LABX-62-IBEID). AdT and BC were supported by Ministero della Ricerca (Italy), Piano Nazionale di Ripresa e Resilienza, and the EU’s Extended Partnership initiative on Emerging Infectious Diseases (project number PE00000007). EF was supported by grants from Consejería de Economía, Comercio e Innovación of the Extremadura regional Governments, Spain (IB10044 and IB16135). CM was supported by grants by Fundação para a Ciência e a Tecnologia (Starting Grant IF/01302/2015; GHTM UID/Multi/04413/2020; LA-REAL LA/P/0117/2020). JB was supported by the US National Institute of Allergy and Infectious Diseases (R01AI148551). Sampling and species identification in Belgium was supported by the Flemish, Walloon, and Brussels regional governments and the Federal Public Service in the context of NEHAP (MEMO project, CES-2016-02) as well as the Belgian Science Policy Office and the Department of Economy, Science and Innovation of the Flemish government. MOA, APGA, CM and MTN were funded by Global Health and Tropical Medicine, a unit of the Instituto de Higiene e Medicina Tropical of the Universidade NOVA de Lisboa. Author Contribution YH conceived the study, performed and analyzed experiments, and wrote the manuscript. PK, EM, and MKNL helped conceive, collect, and analyze data associated with archival samples from London. MS helped advise YH and analyze experiments. NHR helped conceive the study and analyze experiments. CSM conceived and supervised the study, helped analyze experiments, and wrote the manuscript. All other authors collected and identified mosquitoes. All authors provided feedback on the manuscript. Competing interests None declared. Data and materials availability All genome resequencing data associated with this study is available in the NCBI SRA (790 contemporary genomes PRJNA1209100, 22 genomes from archival museum specimens ERS10924505–ERS10924526). Associated scripts will be available on GitHub (github.com/YukiHaba). Supplementary Materials Materials and Methods Figs. S1 to S8 Tables S1 and S2 References ( 61 ) - ( 93 ) Materials and Methods Culex pipiens Population Genomics Project This study is one of two flagship studies associated with the Culex pip iens Population Genomic Project, also known as PipPop. Both studies make use of 840 individual whole-genome sequences of Culex pipiens complex mosquitoes ( Cx. pipiens sensu lato ) and outgroups (Table S1). Within the complex, we specifically targeted Cx. pipiens s. s . Linnaeus, 1758 and hybrids ( n =688), but also sequenced smaller numbers of Cx. quinquefasciatus Say, 1823 ( n =101), Cx. pallens Coquillett, 1898 ( n =33), and Cx. australicus Dobrotworsky & Drummond, 1953 ( n =5). Cx. torrentium Martini, 1925 ( n =9) was included as an outgroup, and a handful of sequenced mosquitoes were inferred to belong to more distant, unknown taxa ( n =4; Table S1). A total of 790 genomes were sequenced for PipPop, while 50 were previously published (40 from ( 61 ) and 10 from ( 62 )). Full details on sampling of the 790 PipPop genomes, as well as variant calling and sample filtering for the full dataset, are provided in the companion study and summarized briefly here. Mosquito collection and sequencing We collected and sequenced 790 mosquitoes from 163 populations spread across 44 countries in the Americas, Europe, Africa, Asia, and Australia, targeting n ∼ 5 individuals per population. 752 mosquitoes (95%) were collected from 2014–2021, and the remaining 38 (5%) were collected from 2003–2012. Mosquitoes were mostly sampled as adults and identified as Cx. pipiens s. l . or Cx. torrentium using standard metrics, but a subset came from dense larval pools. They were collected from both aboveground (87%) and belowground (13%) sites. Belowground sites included basements of residential buildings, manholes, stormwater drains, cesspits, subway systems, and underground floors of a parking garage. Aboveground sites spanned a variety of habitats, from dense urban environments to residential areas to natural parks. Detailed sample metadata, including individual and population IDs, GPS coordinates, collection date, life stage, sex, and trapping method can be found in Table S1. Genomic DNA was extracted using the NucleoSpin 96 DNA RapidLyse kit (Macherey-Nagel, Germany). We confirmed that samples belonged to the Cx. pipiens complex or Cx. torrentium (outgroup) via a multiplex PCR targeting the ace-2 locus ( 63 ) and visual inspection of amplicon sizes on a gel. Samples with no bands or unexpected band sizes were excluded. DNA sequencing libraries were prepared using Illumina DNA Prep Kits (Illumina, USA) with custom dual-unique barcodes. Approximately 80 barcoded libraries were pooled and sequenced on individual S4 lanes of a Novaseq 6000 PE150 sequencer (Illumina, USA), with a target genome-wide coverage of 10-15X. One pool including Mediterranean and Middle Eastern mosquitoes was sequenced across four lanes (a full S4 flow cell) to achieve higher coverage (∼60X) for use in cross-coalescence analyses. Read processing and mapping Raw reads were assessed for quality using FastQC v.0.11.8 ( 64 ), and low-quality bases and adapters were trimmed using Trimmomatic ( 65 ). Trimmed reads were mapped onto the recently updated, chromosome-scale CpipJ5 assembly ( 62 ). We used BWA-MEM v.0.7.17 ( 66 ) to map the reads with default settings and identified and removed optical and PCR duplicates with Picard MarkDuplicates v.2.20.2 ( 67 ). We then used GATK v.3.8 ( 68 ) to perform local realignment around small insertions and deletions. We calculated genome-wide coverage after deduplication using Mosdepth v.0.3.3 ( 69 ). We used the deduplicated, realigned reads for all the analyses below. Accessible regions and variant calling We used 100 high-coverage individuals (50 Cx. pipiens s. s . with >20X coverage and 50 Cx. quinquefasciatus with >10X coverage) to characterize variation in coverage across the genome and mask ‘inaccessible’ regions for variant calling. More specifically, we masked sites with 1.5X normalized coverage in either species. We also masked putative repeat elements regardless of coverage ( 62 ). This left us with ∼131 million ‘accessible’ sites or approximately 23% of the 559 Mb genome. We then called single nucleotide variants in all 840 individuals using BCFtools v1.13 ( 70 ). Variant calling was parallelized across multiple 20 Mb chunks of the genome. In addition to masking the inaccessible sites and repeat elements described above, we also masked multiallelic SNPs, indels, and SNPs falling within 5 bp of indels. We calculated key statistics for each SNP and further removed those with QUAL<50, MQ10% individuals with missing genotypes, average mean depth across all samples of 30X, and alleles of GQ<20. These cutoffs were chosen after visual inspection of the distribution of each statistic, following GATK hard-filtering best practices for non-model species ( 71 ). After filtering, we were left with 30.6M high-quality, accessible, biallelic SNPs (of ∼131M total accessible sites). This full SNP set was used for all analyses except where otherwise specified. Individual filtering We removed two samples from Raleigh, USA with 50% genotype missingness (RAL5, RAL6). We also filtered the full sample set for kin based on pairwise KING kinship coefficients computed in NgsRelate v.2.0 ( 72 ). The vast majority of pairs showed low relatedness as expected (mean kinship coefficient 0.00026). However, a subset of pairs showed higher values, including a subset of mosquitoes collected as larvae in the same pools. We identified all pairs with kinship > 0.09 and excluded the individual with lower coverage. We additionally excluded 3 individuals (PAR4, OSJi4, KAV5) that showed unexpectedly high relatedness to many individuals from other populations and one individual from Malaysia (MEL5) that clustered with North American samples. The unexpected relatedness of PAR4, OSJi4, and KAV5 to many other individuals could not be explained by their position in the 96-well plates used to process samples nor by low sequence coverage. While the Malaysian sample could conceivably be a migrant, we chose to remove it out of an abundance of caution. Final sample set After filtering low quality individuals and kin, we were left with data for 743 unrelated mosquitoes. A few analyses presented here address the full global sample. However, unless otherwise specified, this study focuses on the subset of 357 individuals collected in the Western Palearctic (Europe, North Africa, and western Asia). Analysis of population structure We conducted a principal component analysis (PCA) of variation among Western Palearctic individuals ( n = 357; Fig. 2 and fig. S1 ). As excessive linkage disequilibrium (LD) among genetic markers can lead to PC(s) of LD structure rather than population structure ( 73 ), we used Plink v.1.90 ( 74 ) to select a subset of 503,921 unlinked SNPs (--indep-pairwise 200 20 0.2). We then used PCAngsd v.1.10 ( 75 ) to estimate a covariance matrix and the princomp function in the R package stats v.3.6.2 to conduct the PCA ( 76 ). Sequencing and analysis of historical specimens from London To understand the relationship between historical and contemporary molestus populations, we extracted genomic DNA from 22 pinned Culex specimens in the National History Museum, London (Table S2) using a recently published, minimally destructive protocol ( 33 ). Briefly, pinned specimens were removed from the main label pins and put in a styrofoam box filled with wet paper towels for rehydration at 37 °C for 3 hours. Each rehydrated sample was then dipped in 200 µl of Lysis Buffer C (200 mM Tris, 25 mM EDTA, 0.05% Tween-20, and 0.4 mg/ml Proteinase K) and incubated at 37 °C for 2 hours. Genomic DNA in the lysis buffer was then purified using a modified MinElute (Qiagen) silica column approach. After extraction, intact mosquito specimens were rinsed in increasing percentages of ethanol (30% and 50%) and sent back to the museum for critical point drying. Libraries of the purified genomic DNA were created using NEB Next Ultra II DNA Library Prep Kit (New England Biolabs) with no shearing and then purified using 2.2x SPRI (Beckman Coulter Agencourt AMPure XP) beads post library ligation and two times 1x SPRI post PCR amplification using a KAPA HiFi HotStart Uracil+ ReadyMix PCR Kit. The final libraries were sequenced on one lane of NovaSeq PE75 (Illumina). Raw reads were run though the ancient DNA pipeline EAGER ( 77 ), with the following processing parameters: trimming adapter sequence, trimming bases of quality score < 20, removing sequences shorter than 30 bp, merging overlapping paired reads (with default minimum 11 bp overlap), aligning to the CpipJ5 assembly ( 62 ) using BWA-MEM, removing PCR duplicates and unaligned reads for final BAM files, and performing DamageProfiler to summarize ancient DNA characteristics (50 C > T and 30 G > A substitutions, read length in base pairs). We calculated genome-wide coverage after deduplication using Mosdepth ( 69 ) (mean = 5.77X, range = 1.05–9.22X). We used ANGSD v.0.936 ( 78 ) to call genotype likelihoods (angsd -GL 1, SAMtools model) for the historical samples at the subset of 503,921 unlinked, biallelic SNPs used for PCA of contemporary genomes (see above). We then merged these samples with the contemporary Western Palearctic sample and conducted a joint PCA as described above (PCAngsd followed by princomp ). Analysis of latitudinal gradient We modeled each pipiens population in the Western Palearctic as a mix of northern pipiens and molestus populations using genome-wide f3 statistics ( Fig. 3A–C ). Specifically, we used the threepop function in Treemix v1.13 ( 79 ) to calculate f3 (X; pipiens, molestus ), where X represents a focal pipiens population, and the pipiens and molestus reference populations came from Sweden (SWE) and Belgium (BVR), respectively. We used a block jackknife approach to obtain the standard error and compute Z-scores, dividing the genome into blocks of 500 SNPs (-k 500). A Z-score of −3 was used as a significance threshold ( 79 ). We also estimated the number of derived alleles shared by focal pipiens populations with the same northern pipiens and molestus reference populations using Dsuite Dtrios ( 80 ), with Cx. torrentium as an outgroup. We specifically calculated the number of derived alleles shared with molestus as a fraction of those shared with either pipiens or molestus : n (ABBA) / ( n (ABBA) + n (BBAA)) where A represents the ancestral allele and B represents the derived allele as in the tree shown in Fig. 3D . Distance ( Dxy ) tree inference We inferred a distance tree for pipiens and molestus mosquitoes with >10X genome wide coverage from the full global sample based on the number of pairwise nucleotide differences ( Dxy ). Sequenced mosquitoes from early branching Cx. pipiens lineages native to southern Africa (named ‘juppi’ and ‘mada’), as well as Cx. torrentium were included as outgroups, but Cx. quinquefasciatus was excluded. As hybridization can confound relationships in distance trees, we used a variety of methods to identify and exclude populations or individuals that showed signs of admixture. Using f3 tests we found that no molestus populations were well modeled as a mixture of pipiens and molestus , suggesting that introgression from pipiens into molestus is generally rare ( fig. S3 ). However, a more sensitive 4-population test (Patterson’s D ) found small yet significant signs of introgression into some Mediterranean molestus populations ( fig. S4 ), which we then excluded from the tree. Identification of pipiens populations that have received genetic input from molestus is more challenging because introgression is confounded by the ancestral genetic gradient ( Fig. 3 ). To overcome this, we used the F-branch statistics ( 43 ) as presented in Fig. 5A (see Quantifying gene flow from molestus into pipiens for details) and then excluded all pipiens populations that showed non-zero introgression. Finally, we excluded any pipiens or molestus individual with >2% inferred ancestry from sibling species Cx. quinquefasciatus based on an NGSadmix ( 75 ) analysis presented in the companion study (Table S1 column X). Such introgression is rare in the Western Palearctic ( 81 ), but extremely common in the Americas, leading to exclusion of most American samples. After filtering, we moved forward with Dxy tree inference for 99 molestus , 96 pipiens , and 10 individuals from outgroups. We used pixy v.1.2.7 ( 82 ) to estimate pairwise genome-wide Dxy among the remaining samples. We included invariant accessible sites in addition to the full set of 30.6M biallelic SNPs as exclusion of invariant sites is known to generate bias ( 82 ). We bootstrapped genome-wide Dxy estimates 100 times by sampling 1Mb windows with replacement. We then built the genome-wide neighbor-joining tree as well as bootstrapped trees based on the resulting matrices of Dxy values using the R packages ape v.5.6.2 ( 83 ) and ggtree v3.6.2 ( 84 ). We annotated populations in the tree based on microhabitat of origin—aboveground, belowground, or “suspected belowground”. Suspected belowground populations included one Belgian population (BVR) and one Chinese population (BEJ). The Belgian individuals were collected aboveground in a heavily industrialized zone and suspected of having escaped from a nearby tire factory. The Chinese individuals were collected trying to bite the collector inside a residential building in Tangshan, near Beijing. Genetic diversity (π) We calculated genome-wide nucleotide diversity (π) for all molestus populations included in the Dxy tree analysis using pixy v.1.2.7 ( 82 ). A potential concern in doing so was that the eastern Mediterranean molestus populations, including key populations from Egypt and Israel, might have experienced introgression from Cx. quinquefasciatus below the 2% threshold we used for exclusion from the tree ( 81 ). Even a small amount of introgression from the divergent Cx. quinquefasciatus could inflate diversity estimates. To identify putatively introgressed genomic regions, we used Dsuite Dtrios to calculate f4 admixture ratios in non-overlapping windows of fixed size (50kb, 150kb, 250kb, 500kb, 1Mb) using the following tree: ((( pipiens , X), quinquefasciatus ), outgroup). Reference pipiens and quinquefasciatus populations came from Sweden (SWE) and Saudi Arabia (JED). Cx. torrentium was used as the outgroup. The vast majority of windows in most individuals showed 0 introgression, but we observed a minor peak at f4 ∼ 0.5 in some samples ( fig. S6 ), likely representing the heterozygous state for introgressed haplotypes. Homozygous quinquefasciatus haplotypes ( f4 ∼ 1) were also sometimes present, but extremely rare. After comparing signal to noise ratios, we settled on a window size of 150kb and an f4 cutoff of 0.2 for calling introgression ( fig. S6 ). When computing diversity (π), we masked every 150kb locus for which any of the 99 molestus individuals showed significant introgression from quinquefasciatus . In total, we masked ∼5% of all 30.6M accessible sites. Cross-coalescent analysis of pipiens - molestus split time To estimate the divergence time between pipiens and molestus , we carried out cross-coalescent analyses using MSMC2 following published best practices ( 39 , 85 ). As MSMC2 requires phased genomes, we assembled a genome phasing panel using 551 individuals with >10X coverage that represent all major geographic regions where pipiens and molestus occur (mean coverage = 19.6X, range = 10–87.3X). We considered the full set of 30.6M biallelic SNPs but further filtered out genotypes with DP < 8. We first individually phased nearby heterozygous sites based on information present in sequencing reads using HAPCUT2 ( 86 ). This read-based phasing alone was able to phase up to ∼90% of variants in the highest-coverage samples (range = 0.8–90.2%, median = 22.9%). We then carried out statistical phasing with the pre-phased variants across all individuals using SHAPEIT4 v2.2 ( 87 ) with a phase set error rate of 0.0001. To increase accuracy, we increased the MCMC iterations in SHAPEIT4 from the default value of 15 to 27 ( --mcmc-iterations 10b + 1p + 1b + 1p + 1b + 1p + 1b + 1p + 10m ), and we increased PBWT depth from the default value of 4 to 8. We phased variants on each chromosome separately. We selected two high-coverage individuals from an Egyptian molestus population (ADR, 47.5X and 56X coverage) and another two from a Moroccan pipiens population (MAK, 56.1X and 67.5X). We first extracted phased genomes of focal individuals using BCFtools and generated chromosome-specific masks based on average coverage using bamCaller.py ( 85 ). We also masked every 150kb locus at which the individuals showed signs of introgression from quinquefasciatus (see above, f4 > 0.2; fig. S6 ). We then ran MSMC2 to characterize rates of cross-coalescence within and between the two populations. The time at which the relative rate of cross-coalescence exceeded 50% was used as a point estimate of the split time ( 39 ). We bootstrapped MSMC2 analyses using 100 replicates of three 200 Mb ‘chromosomes’, each composed of resampled blocks of 10 Mb. To explore the robustness of our results to sample selection, we reran the analysis with an alternative Mediterranean pipiens population for which high-coverage genomes were available (MEG, Armenia; 50.8X and 20.7X) ( fig. S7 ). MSMC2 generates split time estimates in coalescent units ( 85 ), which can be converted to years given a taxon-specific mutation rate ( μ ) and generation time ( g ). Since μ and g have not been directly measured in natural Cx. pipiens populations, we used plausible, literature-based, ‘best-guess’ values, as well as biologically reasonable minima and maxima. For μ , we considered published data from mosquitoes and other insects and set the reasonable range at 1.0–8.0×10 −9 ( 88 – 90 ). Our best-guess of μ was 4.85×10 −9 , taken from a recent estimate in Ae. aegypti , a well-studied mosquito from the same subfamily ( 55 ). For g , our best-guess was 20 days, based on a study of an autogenous molestus lab colony (20–21.3 days) ( 91 ). However, lab conditions are often better than those found in nature (e.g., unlimited food) and pipiens mosquitoes might be delayed in finding bloodmeals. We therefore extended the reasonable range up to 30 days. Taken together, we used the following combinations of parameters for conversion of coalescent units to our best-guess, minimum, and maximum chronological split times ( Fig. 4D ): μ = 4.85×10 −9 and g = 20 (best-guess split time), μ = 8×10 −9 and g = 20 (minimum split time), μ = 1×10 −9 and g = 30 (maximum split time). Quantifying gene flow from molestus into pipiens To quantify gene flow from molestus into pipiens across the Western Palearctic while accounting for the ancestral genetic gradient, we used Dsuite Fbranch to calculate branch-specific f4 admixture ratios ( 43 ) ( Fig. 5 ). We specified the tree shown in Fig. 5B and estimated gene flow into focal pipiens populations from the three other branches. Latitudinally varying gene flow from a northern pipiens population (SWE, Sweden, arrow 1) accounted for the ancestral gradient. Gene flow from a Middle Eastern molestus population (ADR, Egypt, arrow 2) and a northern molestus population (BVR, Belgium, arrow 3) allowed us to isolate genetic input from molestus subsequent to the split with pipiens . We used a linear modeling framework to explore a potential association between molestus gene flow ( Fig. 5D ) and human population density (a proxy for urbanization). We first downloaded 30-second resolution population density data from the Gridded Population of the World v4 ( 92 ) and compared the effect of density on introgression when averaging density within circles of the following radii (centered around collection sites): 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 60, 70, 80, 90, and 100 km. In a simple linear regression excluding three outlier populations (PAR, LND, FON, Cook’s distance > 4), human density had a significant effect using radii of 1–10 km, but not across larger distances ( Fig. 5E ). The model with human density averaged across a 3 km buffer explained the most variance ( R 2 = 0.21) and was used in the analysis shown in Fig. 5F . We also asked whether climate could explain additional variance in molestus introgression across populations by adding WorldClim2 bioclimatic variables (Bio1–19) ( 93 ) to the human density only model one at a time, again in a linear modeling framework. None of the bioclimatic variables significantly improved the model. Bio8 (mean temperature of the wettest quarter) was the only variable that had a marginal effect (linear model P = 0.09). Supplementary Figures Download figure Open in new tab Fig. S1. Location of phenotyped and historical samples in PCA analyses. ( A ) Individuals with known phenotypes highlighted in PC1 x latitude plot for contemporary Western Palearctic samples (same analysis as Fig. 2C ; n = 357). The autogenous samples (able to lay eggs without a blood meal) correspond to lab strains derived from belowground sites in Amsterdam (∼52 N°) and Athens (∼38 N°), as well as field-collected samples from a flooded area on the belowground floor of a parking complex in Paris (∼49 N°). The anautogenous samples (require a blood meal for egg development) came from various outdoor locations in Paris (∼49 N°; cemetery, school, and hospital). The human-biting mosquitoes were collected trying to bite the collector in a residence in Rome (∼42 N°). ( B ) Combined analysis of genetic variation across contemporary Western Palearctic samples ( n = 357) and historical London samples ( n = 22, see also Fig. 2C inset). Historical samples were collected between 1940–1985 and archived in the Natural History Museum in London. See Table S1 and S2 for detailed sample metadata. Download figure Open in new tab Figure S2. Second major axis of genetic variation across Western Palearctic samples is correlated with longitude. ( A ) Variance explained by the first 10 principal components in PCA of all Western Palearctic samples ( Fig. 2B ; n = 357 individuals). ( B ) Plot of PC2 vs Longitude showing a strong and significant correlation (Pearson’s correlation test, R = 0.80, P = 7.8 x 10 -82 ). North African samples are separated from others at the same longitude, most likely due to limited gene flow across the Mediterranean Sea. Download figure Open in new tab Figure S3. f3 statistics for molestus populations modeled as a mixture of pipiens and molestus reference populations from the north. Each population was modeled as a mixture of Swedish pipiens (SWE) and Belgian molestus (BVR). Y axis labels list country, 3-letter population code (see table S1), and sample size (number of individuals). Error bars indicate 95% block-jackknife confidence intervals. None of the populations showed significant signs of mixture ( f3 < 0). This analysis includes all global molestus samples that showed <2% quinquefasciatus ancestry, inferred via NGSadmix in a companion paper (Table S1, column X). Only a few samples outside the Western Palearctic met this criterion. Download figure Open in new tab Figure S4. Patterson’s D statistics testing molestus populations for signs of introgression from pipiens . Each population was modeled as sister to Belgian molestus (BVR) and receiving potential genetic input from Swedish pipiens (SWE). Y axis labels list country, 3-letter population code (see table S1), and sample size (number of individuals). Error bars indicate 95% block-jackknife confidence intervals. Blue outlines highlight populations showing small, but significant, introgression from pipiens ( D >0). These were excluded from the neighbor-joining Dxy tree in Fig. 4A and S5 . This analysis includes all Western Palearctic molestus samples as well as any global molestus sample that showed <2% quinquefasciatus ancestry inferred via NGSadmix in a companion paper (Table S1, column X). Only a few samples outside the Western Palearctic met this criterion. Download figure Open in new tab Figure S5. Neighbor-Joining tree based on whole genome Dxy , related to Figure 4A . Analysis only included individuals of ‘pure’ ancestry and >10X genome-wide coverage. pipiens and molestus individuals were deemed ‘pure’ if they showed no signs of introgression from either Cx. quinquefasciatus or each other (see Materials and Methods for details). A zoom version of the molestus clade appears in Figure 4A . Cx. torrentium was used as an outgroup, with branch cut short for visualization. Each tip is labeled with country of origin and population code (table S1). Download figure Open in new tab Figure S6. Identification of local Cx. quinquefasciatus introgression using f4 ratio. We looked for stretches of introgression in the genomes of select molestus and pipiens samples so that they could be masked in analyses of genetic diversity ( Fig. 4B ) and cross-coalescence ( Fig. 4C and S7). The figure shows example data from chromosome 2 of an Egyptian molestus individual (ADR1). f4 was calculated in sliding windows of between 50kb (bottom) and 1MB (top). A , Full results for each window size. B , Summary histograms. Elevated f4 values indicate quinquefasciatus introgression. Red outline and dashed lines show the chosen window size (150kb) and threshold ( f4 ratio = 0.2) chosen for masking. Download figure Open in new tab Figure S7. Relative cross-coalescence (rCC) rate for Armenian pipiens (MEG) and Egyptian molestus (ADR) inferred by MSMC2, related to Figure 4D . The black line indicates the genome-wide result and gray lines indicate 100 bootstrap results. Chronological time along the x-axis corresponds to a scaling factor derived from best-guess values of the de novo mutation rate and generation time for Cx. pipiens (32) . This scaling factor gives a split time (rCC = 50%) of ∼2.2K years (red dashed line and arrowhead). Minimum and maximum scaling factors (derived from biologically reasonable bounds on the mutation rate and generation time ( 32 ) give split times of ∼1.3K and 16.1K years (grey arrowheads). Download figure Open in new tab Figure S8. Sources of introgression into pipiens , related to Figure 5 . Full results from f -branch analysis used to assess three sources of introgression into focal pipiens populations: ( A ) northern pipiens (Sweden, SWE), ( B ) northern molestus (Belgium, BVR), and ( C ) Egyptian molestus (ADR). pipiens populations are ordered along the x -axis by latitude from south (left) to north (right). Table S1. Sample metadata . (Excel file) Each row represents a mosquito sequenced for this study ( n =790) or previously published ( n =50, see column AA). Columns A–S contain basic collection information. Columns T and S list average sequence coverage (T) and whether or not the sample was included in the final set of 743 unrelated mosquitoes (U). Column V lists the species identity, which was usually inferred from genomic analysis but also cross-checked with information on male genitalia when available. Mosquitoes with ancestry from both Cx. pipiens and Cx. quinquefasciatus are listed as ‘hybrids’ if they showed 11–89% Cx. quinquefasciatus ancestry inferred using NGSadmix (column X, see below) and as one or the other ‘pure’ species otherwise. Cx. pallens mosquitoes from East Asia are listed as hybrids with either Cx. quinquefasciatus (or Cx. pipiens ) if they showed more (or less) Cx. quinquefasciatus ancestry than expected given the species’ hybrid origin (cutoffs: >40% or <20% respectively). Column W lists the intraspecific Cx. pipiens lineage ( pipiens, molestus, juppi , or mada ) to which any Cx. pipiens or Cx. pipiens hybrid belongs. This inference is based on PCA and is only available for samples that passed the NGSrelate filter listed in column U. Columns X–Z show the results of an NGSadmix analysis of samples from outside sub-Saharan Africa with k=3 clusters. Note that the third cluster (Mediterranean pipiens / molestus ) combines signatures of both pipiens and molestus ancestry and does not easily translate into assignment of a mosquito to the pipiens vs molestus lineage. Table S2. Museum specimens collected in London from 1940–1985 . (Excel file) Each row represents one mosquito individual. Column A shows sample ID. Column B shows collection year. Column C shows locality within London, if known. Columns D and E show GPS coordinates (Latitude and Longitude). Note the coordinates are approximate, inferred from the location names on the collection note. Column F shows additional collection notes, if any. Column G shows mean genome-wide coverage after de-duplication. Column H shows inferred ecotype of each sample based on the PCA with contemporary Western Palearctic samples ( fig. S1B ). Acknowledgements We thank members of the McBride Lab for discussion. Footnotes We revised the Discussion text for clarity and corrected a few formatting and stylistic matters. Main References 1. M. T. J. Johnson , J. Munshi-South , Evolution of life in urban environments . Science 358 , eaam8327 ( 2017 ). OpenUrl Abstract / FREE Full Text 2. H. Ritchie , V. Samborska , M. Roser , Urbanization . Our world in data ( 2024 ). 3. E. B. Vinogradova , Culex Pipiens Pipiens Mosquitoes: Taxonomy, Distribution, Ecology, Physiology, Genetics, Applied Importance and Control (Pensoft, Sofia, Bulgaria , 2000 ) Pensoft series parasitologica . 4. A. Farajollahi , D. M. Fonseca , L. D. Kramer , A. Marm Kilpatrick , “Bird biting” mosquitoes and human disease: a review of the role of Culex pipiens complex mosquitoes in epidemiology . Infect. Genet. Evol . 11 , 1577 – 1585 ( 2011 ). OpenUrl CrossRef PubMed 5. R. Harbach , Culex pipiens: Species versus species complex – taxonomic history and perspective . J. Am. Mosq. Control Assoc . 28 , 10 – 23 ( 2012 ). OpenUrl CrossRef PubMed 6. K. Byrne , R. A. Nichols , Culex pipiens in London Underground tunnels: differentiation between surface and subterranean populations . Heredity (Edinb .) 82 ( Pt 1 ), 7 – 15 ( 1999 ). OpenUrl CrossRef PubMed 7. A. P. Hendry , K. M. Gotanda , E. I. Svensson , Human influences on evolution, and the ecological and societal consequences . Philos. Trans. R. Soc. Lond. B Biol. Sci . 372 , 20160028 ( 2017 ). OpenUrl CrossRef PubMed 8. K. A. Thompson , L. H. Rieseberg , D. Schluter , Speciation and the City . Trends Ecol. Evol . 33 , 815 – 826 ( 2018 ). OpenUrl CrossRef PubMed 9. S. P. Otto , Adaptation, speciation and extinction in the Anthropocene . Proc. Biol. Sci . 285 , 20182047 ( 2018 ). OpenUrl CrossRef PubMed 10. L. R. Rivkin , J. S. Santangelo , M. Alberti , M. F. J. Aronson , C. W. de Keyzer , S. E. Diamond , M.-J. Fortin , L. J. Frazee , A. J. Gorton , A. P. Hendry , Y. Liu , J. B. Losos , J. S. MacIvor , R. A. Martin , M. J. McDonnell , L. S. Miles , J. Munshi-South , R. W. Ness , A. E. M. Newman , M. R. Stothart , P. Theodorou , K. A. Thompson , B. C. Verrelli , A. Whitehead , K. M. Winchell , M. T. J. Johnson , A roadmap for urban evolutionary ecology . Evol. Appl . 12 , 384 – 398 ( 2019 ). OpenUrl CrossRef PubMed 11. D. N. Reznick , J. Losos , J. Travis , From low to high gear: there has been a paradigm shift in our understanding of evolution . Ecol. Lett . 22 , 233 – 244 ( 2019 ). OpenUrl CrossRef PubMed 12. P. G. Shute , Culex molestus . Trans. R. Entomol. Soc. Lond . 102 , 380 – 382 ( 1951 ). OpenUrl CrossRef 13. C. Wesenberg-Lund , Contributions to the Biology of the Danish Culicidae (AF Høst & søn , 1920 )vol. 8 . 14. J. Legendre , Le moustique cavernicole ou l’adaptation de “Culex pipiens” a l’urbanisme moderne . Bull. Acad. Med . 106 , 86 – 89 ( 1931 ). OpenUrl 15. O. Hecht , Experimentelle beiträge zur biologie der stechmücken II . Z. Angew. Entomol . 19 , 579 – 607 ( 1932 ). OpenUrl 16. D. N. Reznick , The “Origin” Then and Now: An Interpretive Guide to the “Origin of Species” ( Princeton University Press , Princeton, NJ , 2012 ; https://play.google.com/store/books/details?id=vBB6db03MrkC ). 17. B. Nye , “ Chapter 18: Mosquito in the Tube ” in Undeniable: Evolution and the Science of Creation ( Macmillan , 2014 ), pp. 137 – 144 . 18. J. W. Bull , Humans artificially drive evolution of new species , EurekAlert! ( 2016 ). https://www.eurekalert.org/pub_releases/2016-06/nhmo-had062716.php . 19. E. Blakemore , The London Underground Has Its Own Mosquito Subspecies , Smithsonian Magazine ( 2016 ). https://www.smithsonianmag.com/smart-news/london-underground-has-its-own-mosquito-subspecies-180958566/ . 20. K. Silver , Earth, BBC ( 2016 ). https://www.bbc.com/earth/story/20160323-the-unique-mosquito-that-lives-in-the-london-underground . 21. M. Schilthuizen , Darwin Comes to Town: How the Urban Jungle Drives Evolution ( St Martin’s Press , New York, NY , 2019 ). 22. D. M. Fonseca , N. Keyghobadi , C. A. Malcolm , C. Mehmet , F. Schaffner , M. Mogi , R. C. Fleischer , R. C. Wilkerson , Emerging vectors in the Culex pipiens complex . Science 303 , 1535 – 1538 ( 2004 ). OpenUrl Abstract / FREE Full Text 23. Y. Haba , L. McBride , Origin and status of Culex pipiens mosquito ecotypes . Curr. Biol . 32 , R237 – R246 ( 2022 ). OpenUrl CrossRef PubMed 24. P. Forskål , Descriptiones animalium, avium, amphibiorum, piscium, insectorum, vermium: quae in itinere orientali observavit (ex officina Mölleri , 1775 ). 25. E. F. Germar , Reise nach Dalmatien und in das Gebiet von Ragusa ( F. A. Brockhaus , 1817 ). 26. E. Ficalbi , Notizie preventive sulle zanzare Italiane . Boll. Soc. Entomol. Ital . 21 , 124 – 131 ( 1890 ). OpenUrl 27. K. M. Winchell , J. B. Losos , B. C. Verrelli , Urban evolutionary ecology brings exaptation back into focus . Trends Ecol. Evol . 38 , 719 – 726 ( 2023 ). OpenUrl CrossRef PubMed 28. F. Villani , S. Urbanelli , A. Gad , S. Nudelman , L. Bullini , Electrophoretic variation of Culex pipiens from Egypt and Israel . Biol. J. Linn. Soc. Lond . 29 , 49 – 62 ( 1986 ). OpenUrl CrossRef Web of Science 29. L. R. Petersen , A. C. Brault , R. S. Nasci , West Nile virus: review of the literature . JAMA 310 , 308 – 315 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 30. C. Giesen , Z. Herrador , B. Fernandez-Martinez , J. Figuerola , L. Gangoso , A. Vazquez , D. Gómez-Barroso , A systematic review of environmental factors related to WNV circulation in European and Mediterranean countries . One Health 16 , 100478 ( 2023 ). OpenUrl CrossRef PubMed 31. M. L. Aardema , S. K. Olatunji , D. M. Fonseca , The enigmaticCulex pipiens(Diptera: Culicidae) species complex: Phylogenetic challenges and opportunities from a notoriously tricky mosquito group . Ann. Entomol. Soc. Am . 115 , 95 – 104 ( 2022 ). OpenUrl CrossRef 32. Materials and methods are available as supplementary materials . 33. P. Korlević , E. McAlister , M. Mayho , A. Makunin , P. Flicek , M. K. N. Lawniczak , A minimally morphologically destructive approach for DNA retrieval and whole-genome shotgun sequencing of pinned historic dipteran vector species . Genome Biol. Evol . 13 ( 2021 ). 34. B. Gomes , C. A. Sousa , M. T. Novo , F. B. Freitas , R. Alves , A.R. Côrte-Real , P. Salgueiro , M. J. Donnelly , A. P. G. Almeida , J. Pinto , Asymmetric introgression between sympatric molestus and pipiens forms of Culex pipiens (Diptera: Culicidae) in the Comporta region, Portugal . BMC Evol. Biol . 9 , 262 ( 2009 ). OpenUrl CrossRef PubMed 35. It. E. , Moroni , A. , Ravera , O. and Anelli , A. S. Urbanelli , R. Cianchi , V. Petrarca , M. Sabatinelli , M. Coluzzi , L. Bullini , “ Adattamento all’ambiente urbano nella zanzara Culex pipiens (Diptera, Culicidae) ” in Ecologia Atti I Congressi Nazionali S . It. E. , Moroni , A. , Ravera , O. and Anelli , A. , Ed. ( Zara , 1981 ), pp. 305 – 316 . 36. N. Patterson , P. Moorjani , Y. Luo , S. Mallick , N. Rohland , Y. Zhan , T. Genschoreck , T. Webster , D. Reich , Ancient admixture in human history . Genetics 192 , 1065 – 1093 ( 2012 ). OpenUrl Abstract / FREE Full Text 37. S. Nudelman , R. Galun , U. Kitron , A. Spielman , Physiological characteristics of Culex pipiens populations in the Middle East . Med. Vet. Entomol . 2 , 161 – 169 ( 1988 ). OpenUrl CrossRef PubMed 38. O. Olsson , “ First states I: Mesopotamia and Egypt ” in Paleoeconomics ( Springer Nature Switzerland, Cham , 2024 ), pp. 285 – 308 . 39. S. Schiffels , K. Wang , MSMC and MSMC2: The Multiple Sequentially Markovian Coalescent . Methods Mol. Biol . 2090 , 147 – 166 ( 2020 ). OpenUrl CrossRef PubMed 40. S. Huang , G. L. Hamer , G. Molaei , E. D. Walker , T. L. Goldberg , U. D. Kitron , T. G. Andreadis , Genetic variation associated with mammalian feeding in Culex pipiens from a West Nile virus epidemic region in Chicago, Illinois . Vector Borne Zoonotic Dis . 9 , 637 – 642 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 41. A. M. Kilpatrick , L. D. Kramer , M. J. Jones , P. P. Marra , P. Daszak , D. M. Fonseca , Genetic influences on mosquito feeding behavior and the emergence of zoonotic pathogens . Am. J. Trop. Med. Hyg . 77 , 667 – 671 ( 2007 ). OpenUrl Abstract / FREE Full Text 42. M. L. Fritz , E. D. Walker , J. R. Miller , D. W. Severson , I. Dworkin , Divergent host preferences of above- and below-ground Culex pipiens mosquitoes and their hybrid offspring . Med. Vet. Entomol . 29 , 115 – 123 ( 2015 ). OpenUrl CrossRef PubMed 43. M. Malinsky , H. Svardal , A. M. Tyers , E. A. Miska , M. J. Genner , G. F. Turner , R. Durbin , Whole-genome sequences of Malawi cichlids reveal multiple radiations interconnected by gene flow . Nat. Ecol. Evol . 2 , 1940 – 1955 ( 2018 ). OpenUrl CrossRef PubMed 44. M. Di Luca , L. Toma , D. Boccolini , F. Severini , G. La Rosa , G. Minelli , G. Bongiorno , F. Montarsi , D. Arnoldi , G. Capelli , A. Rizzoli , R. Romi , Ecological distribution and CQ11 genetic structure of Culex pipiens complex (Diptera: Culicidae) in Italy . PLoS One 11 , e0146476 ( 2016 ). OpenUrl CrossRef PubMed 45. H. C. Osório , L. Zé-Zé , F. Amaro , A. Nunes , M. J. Alves , Sympatric occurrence of Culex pipiens (Diptera, Culicidae) biotypes pipiens, molestus and their hybrids in Portugal, Western Europe: feeding patterns and habitat determinants . Med. Vet. Entomol . 28 , 103 – 109 ( 2014 ). OpenUrl CrossRef PubMed 46. J. Martínez-de la Puente , M. Ferraguti , S. Ruiz , D. Roiz , R. C. Soriguer , J. Figuerola , Culex pipiens forms and urbanization: effects on blood feeding sources and transmission of avian Plasmodium . Malar. J . 15 , 589 ( 2016 ). OpenUrl CrossRef PubMed 47. Eurostat, Glossary:Urban centre ( 2018 ). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Urban_centre . 48. O. Duron , J. Lagnel , M. Raymond , K. Bourtzis , P. Fort , M. Weill , Transposable element polymorphism of Wolbachia in the mosquito Culex pipiens: evidence of genetic diversity, superinfection and recombination . Mol. Ecol . 14 , 1561 – 1573 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 49. E. Dumas , C. M. Atyame , P. Milesi , D. M. Fonseca , E. V. Shaikevich , S. Unal , P. Makoundou , M. Weill , O. Duron , Population structure of Wolbachia and cytoplasmic introgression in a complex of mosquito species . BMC Evol. Biol . 13 , 181 ( 2013 ). OpenUrl CrossRef PubMed 50. K. Knight , A. Malek , A morphological and biological study of Culex pipiens in the Cairo area of Egypt (Diptera-culioidae) . Bulletin de la Société Fouad 1er d’entomologie 35 , 175 – 185 ( 1951 ). OpenUrl 51. A. Harpak , N. Garud , N. A. Rosenberg , D. A. Petrov , M. Combs , P. S. Pennings , J. Munshi-South , Genetic adaptation in New York City rats . Genome Biol. Evol . 13 , evaa247 ( 2021 ). OpenUrl CrossRef PubMed 52. L. Weissbrod , F. B. Marshall , F. R. Valla , H. Khalaily , G. Bar-Oz , J.-C. Auffray , J.-D. Vigne , T. Cucchi , Origins of house mice in ecological niches created by settled hunter-gatherers in the Levant 15,000 y ago . Proc. Natl. Acad. Sci. U. S. A . 114 , 4099 – 4104 ( 2017 ). OpenUrl Abstract / FREE Full Text 53. A. Wada-Katsumata , J. Silverman , C. Schal , Changes in taste neurons support the emergence of an adaptive behavior in cockroaches . Science 340 , 972 – 975 ( 2013 ). OpenUrl Abstract / FREE Full Text 54. M. Ravinet , T. O. Elgvin , C. Trier , M. Aliabadian , A. Gavrilov , G.-P. Sætre , Signatures of human-commensalism in the house sparrow genome . Proc. Biol. Sci . 285 , 20181246 ( 2018 ). OpenUrl CrossRef PubMed 55. N. H. Rose , A. Badolo , M. Sylla , J. Akorli , S. Otoo , A. Gloria-Soria , J. R. Powell , B. J. White , J. E. Crawford , C. S. McBride , Dating the origin and spread of specialization on human hosts in Aedes aegypti mosquitoes . Elife 12 ( 2023 ). 56. J. A. Rioux , J. M. Pech , Le biotype autogene de Culex pipiens L. ne doit pasetre nomme Culex molestus Forskal (Diptera Culicidae) . Cahiers Des Nat . 15 , 115 – 117 ( 1959 ). OpenUrl 57. C. M. Bahnck , D. M. Fonseca , Rapid assay to identify the two genetic forms of Culex (Culex) pipiens L. (Diptera: Culicidae) and hybrid populations . Am. J. Trop. Med. Hyg . 75 , 251 – 255 ( 2006 ). OpenUrl Abstract / FREE Full Text 58. E. Roubaud , Le pouvoir autogène chez le biotype nord-africain du moustique commun Culex pipiens (L .). Bull. Soc. Pathol. Exot . 36 , 172 – 175 ( 1939 ). OpenUrl 59. M. Naddaf , Mosquito-borne diseases are surging in Europe - how worried are scientists? Nature 633 , 749 ( 2024 ). OpenUrl CrossRef PubMed 60. CDC, West Nile Virus Current Year Data, 2024, CDC ( 2024 ). https://www.cdc.gov/west-nile-virus/data-maps/current-year-data.html . References 1. ↵ M. T. J. Johnson , J. Munshi-South , Evolution of life in urban environments . Science 358 , eaam8327 ( 2017 ). OpenUrl Abstract / FREE Full Text 2. ↵ H. Ritchie , V. Samborska , M. Roser , Urbanization . Our world in data ( 2024 ). 3. ↵ E. B. Vinogradova , Culex Pipiens Pipiens Mosquitoes: Taxonomy, Distribution, Ecology, Physiology, Genetics, Applied Importance and Control (Pensoft, Sofia, Bulgaria , 2000 ) Pensoft series parasitologica . 4. ↵ A. Farajollahi , D. M. Fonseca , L. D. Kramer , A. Marm Kilpatrick , “Bird biting” mosquitoes and human disease: a review of the role of Culex pipiens complex mosquitoes in epidemiology . Infect. Genet. Evol . 11 , 1577 – 1585 ( 2011 ). OpenUrl CrossRef PubMed 5. ↵ R. Harbach , Culex pipiens: Species versus species complex – taxonomic history and perspective . J. Am. Mosq. Control Assoc . 28 , 10 – 23 ( 2012 ). OpenUrl CrossRef PubMed 6. ↵ K. Byrne , R. A. Nichols , Culex pipiens in London Underground tunnels: differentiation between surface and subterranean populations . Heredity (Edinb .) 82 ( Pt 1 ), 7 – 15 ( 1999 ). OpenUrl CrossRef PubMed 7. ↵ A. P. Hendry , K. M. Gotanda , E. I. Svensson , Human influences on evolution, and the ecological and societal consequences . Philos. Trans. R. Soc. Lond. B Biol. Sci . 372 , 20160028 ( 2017 ). OpenUrl CrossRef PubMed 8. K. A. Thompson , L. H. Rieseberg , D. Schluter , Speciation and the City . Trends Ecol. Evol . 33 , 815 – 826 ( 2018 ). OpenUrl CrossRef PubMed 9. S. P. Otto , Adaptation, speciation and extinction in the Anthropocene . Proc. Biol. Sci . 285 , 20182047 ( 2018 ). OpenUrl CrossRef PubMed 10. L. R. Rivkin , J. S. Santangelo , M. Alberti , M. F. J. Aronson , C. W. de Keyzer , S. E. Diamond , M.-J. Fortin , L. J. Frazee , A. J. Gorton , A. P. Hendry , Y. Liu , J. B. Losos , J. S. MacIvor , R. A. Martin , M. J. McDonnell , L. S. Miles , J. Munshi-South , R. W. Ness , A. E. M. Newman , M. R. Stothart , P. Theodorou , K. A. Thompson , B. C. Verrelli , A. Whitehead , K. M. Winchell , M. T. J. Johnson , A roadmap for urban evolutionary ecology . Evol. Appl . 12 , 384 – 398 ( 2019 ). OpenUrl CrossRef PubMed 11. ↵ D. N. Reznick , J. Losos , J. Travis , From low to high gear: there has been a paradigm shift in our understanding of evolution . Ecol. Lett . 22 , 233 – 244 ( 2019 ). OpenUrl CrossRef PubMed 12. ↵ P. G. Shute , Culex molestus . Trans. R. Entomol. Soc. Lond . 102 , 380 – 382 ( 1951 ). OpenUrl CrossRef 13. ↵ C. Wesenberg-Lund , Contributions to the Biology of the Danish Culicidae (AF Høst & søn , 1920 )vol. 8 . 14. J. Legendre , Le moustique cavernicole ou l’adaptation de “Culex pipiens” a l’urbanisme moderne . Bull. Acad. Med . 106 , 86 – 89 ( 1931 ). OpenUrl 15. ↵ O. Hecht , Experimentelle beiträge zur biologie der stechmücken II . Z. Angew. Entomol . 19 , 579 – 607 ( 1932 ). OpenUrl 16. ↵ D. N. Reznick , The “Origin” Then and Now: An Interpretive Guide to the “Origin of Species” ( Princeton University Press , Princeton, NJ , 2012 ; https://play.google.com/store/books/details?id=vBB6db03MrkC ). 17. B. Nye , “ Chapter 18: Mosquito in the Tube ” in Undeniable: Evolution and the Science of Creation ( Macmillan , 2014 ), pp. 137 – 144 . 18. J. W. Bull , Humans artificially drive evolution of new species , EurekAlert! ( 2016 ). https://www.eurekalert.org/pub_releases/2016-06/nhmo-had062716.php . 19. E. Blakemore , The London Underground Has Its Own Mosquito Subspecies , Smithsonian Magazine ( 2016 ). https://www.smithsonianmag.com/smart-news/london-underground-has-its-own-mosquito-subspecies-180958566/ . 20. K. Silver , Earth, BBC ( 2016 ). https://www.bbc.com/earth/story/20160323-the-unique-mosquito-that-lives-in-the-london-underground . 21. ↵ M. Schilthuizen , Darwin Comes to Town: How the Urban Jungle Drives Evolution ( St Martin’s Press , New York, NY , 2019 ). 22. ↵ D. M. Fonseca , N. Keyghobadi , C. A. Malcolm , C. Mehmet , F. Schaffner , M. Mogi , R. C. Fleischer , R. C. Wilkerson , Emerging vectors in the Culex pipiens complex . Science 303 , 1535 – 1538 ( 2004 ). OpenUrl Abstract / FREE Full Text 23. ↵ Y. Haba , L. McBride , Origin and status of Culex pipiens mosquito ecotypes . Curr. Biol . 32 , R237 – R246 ( 2022 ). OpenUrl CrossRef PubMed 24. ↵ P. Forskål , Descriptiones animalium, avium, amphibiorum, piscium, insectorum, vermium: quae in itinere orientali observavit (ex officina Mölleri , 1775 ). 25. E. F. Germar , Reise nach Dalmatien und in das Gebiet von Ragusa ( F. A. Brockhaus , 1817 ). 26. E. Ficalbi , Notizie preventive sulle zanzare Italiane . Boll. Soc. Entomol. Ital . 21 , 124 – 131 ( 1890 ). OpenUrl 27. ↵ K. M. Winchell , J. B. Losos , B. C. Verrelli , Urban evolutionary ecology brings exaptation back into focus . Trends Ecol. Evol . 38 , 719 – 726 ( 2023 ). OpenUrl CrossRef PubMed 28. ↵ F. Villani , S. Urbanelli , A. Gad , S. Nudelman , L. Bullini , Electrophoretic variation of Culex pipiens from Egypt and Israel . Biol. J. Linn. Soc. Lond . 29 , 49 – 62 ( 1986 ). OpenUrl CrossRef Web of Science 29. ↵ L. R. Petersen , A. C. Brault , R. S. Nasci , West Nile virus: review of the literature . JAMA 310 , 308 – 315 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 30. ↵ C. Giesen , Z. Herrador , B. Fernandez-Martinez , J. Figuerola , L. Gangoso , A. Vazquez , D. Gómez-Barroso , A systematic review of environmental factors related to WNV circulation in European and Mediterranean countries . One Health 16 , 100478 ( 2023 ). OpenUrl CrossRef PubMed 31. ↵ M. L. Aardema , S. K. Olatunji , D. M. Fonseca , The enigmaticCulex pipiens(Diptera: Culicidae) species complex: Phylogenetic challenges and opportunities from a notoriously tricky mosquito group . Ann. Entomol. Soc. Am . 115 , 95 – 104 ( 2022 ). OpenUrl CrossRef 32. ↵ Materials and methods are available as supplementary materials . 33. ↵ P. Korlević , E. McAlister , M. Mayho , A. Makunin , P. Flicek , M. K. N. Lawniczak , A minimally morphologically destructive approach for DNA retrieval and whole-genome shotgun sequencing of pinned historic dipteran vector species . Genome Biol. Evol . 13 ( 2021 ). 34. ↵ B. Gomes , C. A. Sousa , M. T. Novo , F. B. Freitas , R. Alves , A.R. Côrte-Real , P. Salgueiro , M. J. Donnelly , A. P. G. Almeida , J. Pinto , Asymmetric introgression between sympatric molestus and pipiens forms of Culex pipiens (Diptera: Culicidae) in the Comporta region, Portugal . BMC Evol. Biol . 9 , 262 ( 2009 ). OpenUrl CrossRef PubMed 35. ↵ It. E. , Moroni , A. , Ravera , O. and Anelli , A. S. Urbanelli , R. Cianchi , V. Petrarca , M. Sabatinelli , M. Coluzzi , L. Bullini , “ Adattamento all’ambiente urbano nella zanzara Culex pipiens (Diptera, Culicidae) ” in Ecologia Atti I Congressi Nazionali S . It. E. , Moroni , A. , Ravera , O. and Anelli , A. , Ed. ( Zara , 1981 ), pp. 305 – 316 . 36. ↵ N. Patterson , P. Moorjani , Y. Luo , S. Mallick , N. Rohland , Y. Zhan , T. Genschoreck , T. Webster , D. Reich , Ancient admixture in human history . Genetics 192 , 1065 – 1093 ( 2012 ). OpenUrl Abstract / FREE Full Text 37. ↵ S. Nudelman , R. Galun , U. Kitron , A. Spielman , Physiological characteristics of Culex pipiens populations in the Middle East . Med. Vet. Entomol . 2 , 161 – 169 ( 1988 ). OpenUrl CrossRef PubMed 38. ↵ O. Olsson , “ First states I: Mesopotamia and Egypt ” in Paleoeconomics ( Springer Nature Switzerland, Cham , 2024 ), pp. 285 – 308 . 39. ↵ S. Schiffels , K. Wang , MSMC and MSMC2: The Multiple Sequentially Markovian Coalescent . Methods Mol. Biol . 2090 , 147 – 166 ( 2020 ). OpenUrl CrossRef PubMed 40. ↵ S. Huang , G. L. Hamer , G. Molaei , E. D. Walker , T. L. Goldberg , U. D. Kitron , T. G. Andreadis , Genetic variation associated with mammalian feeding in Culex pipiens from a West Nile virus epidemic region in Chicago, Illinois . Vector Borne Zoonotic Dis . 9 , 637 – 642 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 41. ↵ A. M. Kilpatrick , L. D. Kramer , M. J. Jones , P. P. Marra , P. Daszak , D. M. Fonseca , Genetic influences on mosquito feeding behavior and the emergence of zoonotic pathogens . Am. J. Trop. Med. Hyg . 77 , 667 – 671 ( 2007 ). OpenUrl Abstract / FREE Full Text 42. ↵ M. L. Fritz , E. D. Walker , J. R. Miller , D. W. Severson , I. Dworkin , Divergent host preferences of above- and below-ground Culex pipiens mosquitoes and their hybrid offspring . Med. Vet. Entomol . 29 , 115 – 123 ( 2015 ). OpenUrl CrossRef PubMed 43. ↵ M. Malinsky , H. Svardal , A. M. Tyers , E. A. Miska , M. J. Genner , G. F. Turner , R. Durbin , Whole-genome sequences of Malawi cichlids reveal multiple radiations interconnected by gene flow . Nat. Ecol. Evol . 2 , 1940 – 1955 ( 2018 ). OpenUrl CrossRef PubMed 44. ↵ M. Di Luca , L. Toma , D. Boccolini , F. Severini , G. La Rosa , G. Minelli , G. Bongiorno , F. Montarsi , D. Arnoldi , G. Capelli , A. Rizzoli , R. Romi , Ecological distribution and CQ11 genetic structure of Culex pipiens complex (Diptera: Culicidae) in Italy . PLoS One 11 , e0146476 ( 2016 ). OpenUrl CrossRef PubMed 45. H. C. Osório , L. Zé-Zé , F. Amaro , A. Nunes , M. J. Alves , Sympatric occurrence of Culex pipiens (Diptera, Culicidae) biotypes pipiens, molestus and their hybrids in Portugal, Western Europe: feeding patterns and habitat determinants . Med. Vet. Entomol . 28 , 103 – 109 ( 2014 ). OpenUrl CrossRef PubMed 46. ↵ J. Martínez-de la Puente , M. Ferraguti , S. Ruiz , D. Roiz , R. C. Soriguer , J. Figuerola , Culex pipiens forms and urbanization: effects on blood feeding sources and transmission of avian Plasmodium . Malar. J . 15 , 589 ( 2016 ). OpenUrl CrossRef PubMed 47. ↵ Eurostat, Glossary:Urban centre ( 2018 ). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Urban_centre . 48. ↵ O. Duron , J. Lagnel , M. Raymond , K. Bourtzis , P. Fort , M. Weill , Transposable element polymorphism of Wolbachia in the mosquito Culex pipiens: evidence of genetic diversity, superinfection and recombination . Mol. Ecol . 14 , 1561 – 1573 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 49. ↵ E. Dumas , C. M. Atyame , P. Milesi , D. M. Fonseca , E. V. Shaikevich , S. Unal , P. Makoundou , M. Weill , O. Duron , Population structure of Wolbachia and cytoplasmic introgression in a complex of mosquito species . BMC Evol. Biol . 13 , 181 ( 2013 ). OpenUrl CrossRef PubMed 50. ↵ K. Knight , A. Malek , A morphological and biological study of Culex pipiens in the Cairo area of Egypt (Diptera-culioidae) . Bulletin de la Société Fouad 1er d’entomologie 35 , 175 – 185 ( 1951 ). OpenUrl 51. ↵ A. Harpak , N. Garud , N. A. Rosenberg , D. A. Petrov , M. Combs , P. S. Pennings , J. Munshi-South , Genetic adaptation in New York City rats . Genome Biol. Evol . 13 , evaa247 ( 2021 ). OpenUrl CrossRef PubMed 52. ↵ L. Weissbrod , F. B. Marshall , F. R. Valla , H. Khalaily , G. Bar-Oz , J.-C. Auffray , J.-D. Vigne , T. Cucchi , Origins of house mice in ecological niches created by settled hunter-gatherers in the Levant 15,000 y ago . Proc. Natl. Acad. Sci. U. S. A . 114 , 4099 – 4104 ( 2017 ). OpenUrl Abstract / FREE Full Text 53. ↵ A. Wada-Katsumata , J. Silverman , C. Schal , Changes in taste neurons support the emergence of an adaptive behavior in cockroaches . Science 340 , 972 – 975 ( 2013 ). OpenUrl Abstract / FREE Full Text 54. ↵ M. Ravinet , T. O. Elgvin , C. Trier , M. Aliabadian , A. Gavrilov , G.-P. Sætre , Signatures of human-commensalism in the house sparrow genome . Proc. Biol. Sci . 285 , 20181246 ( 2018 ). OpenUrl CrossRef PubMed 55. ↵ N. H. Rose , A. Badolo , M. Sylla , J. Akorli , S. Otoo , A. Gloria-Soria , J. R. Powell , B. J. White , J. E. Crawford , C. S. McBride , Dating the origin and spread of specialization on human hosts in Aedes aegypti mosquitoes . Elife 12 ( 2023 ). 56. ↵ J. A. Rioux , J. M. Pech , Le biotype autogene de Culex pipiens L. ne doit pasetre nomme Culex molestus Forskal (Diptera Culicidae) . Cahiers Des Nat . 15 , 115 – 117 ( 1959 ). OpenUrl 57. ↵ C. M. Bahnck , D. M. Fonseca , Rapid assay to identify the two genetic forms of Culex (Culex) pipiens L. (Diptera: Culicidae) and hybrid populations . Am. J. Trop. Med. Hyg . 75 , 251 – 255 ( 2006 ). OpenUrl Abstract / FREE Full Text 58. ↵ E. Roubaud , Le pouvoir autogène chez le biotype nord-africain du moustique commun Culex pipiens (L .). Bull. Soc. Pathol. Exot . 36 , 172 – 175 ( 1939 ). OpenUrl 59. ↵ M. Naddaf , Mosquito-borne diseases are surging in Europe - how worried are scientists? Nature 633 , 749 ( 2024 ). OpenUrl CrossRef PubMed 60. ↵ CDC, West Nile Virus Current Year Data, 2024, CDC ( 2024 ). https://www.cdc.gov/west-nile-virus/data-maps/current-year-data.html . 61. ↵ A. A. Yurchenko , R. A. Masri , N. V. Khrabrova , A. K. Sibataev , M. L. Fritz , M. V. Sharakhova , Genomic differentiation and intercontinental population structure of mosquito vectors Culex pipiens pipiens and Culex pipiens molestus . Sci. Rep . 10 , 7504 ( 2020 ). OpenUrl CrossRef PubMed 62. ↵ S. S. Ryazansky , C. Chen , M. Potters , A. N. Naumenko , V. Lukyanchikova , R. A. Masri , I. I. Brusentsov , D. A. Karagodin , A. A. Yurchenko , V. L. Dos Anjos , Y. Haba , N. H. Rose , J. Hoffman , R. Guo , T. Menna , M. Kelley , E. Ferrill , K. E. Schultz , Y. Qi , A. Sharma , S. Deschamps , V. Llaca , C. Mao , T. D. Murphy , E. M. Baricheva , S. Emrich , M. L. Fritz , J. B. Benoit , I. V. Sharakhov , C. S. McBride , Z. Tu , M. V. Sharakhova , The chromosome-scale genome assembly for the West Nile vector Culex quinquefasciatus uncovers patterns of genome evolution in mosquitoes . BMC Biol . 22 , 16 ( 2024 ). OpenUrl CrossRef PubMed 63. ↵ J. L. Smith , D. M. Fonseca , Rapid assays for identification of members of the Culex (Culex) pipiens complex, their hybrids, and other sibling species (Diptera: culicidae) . Am. J. Trop. Med. Hyg . 70 , 339 – 345 ( 2004 ). OpenUrl Abstract / FREE Full Text 64. ↵ S. Andrews , Others, FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics, Babraham Institute, Cambridge, United Kingdom [Preprint] ( 2010 ). 65. ↵ A. M. Bolger , M. Lohse , B. Usadel , Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics 30 , 2114 – 2120 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 66. ↵ H. Li , R. Durbin , Fast and accurate short read alignment with Burrows-Wheeler transform . Bioinformatics 25 , 1754 – 1760 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 67. ↵ Picard . http://broadinstitute.github.io/picard/ . 68. ↵ G. van der Auwera , B. D. O’Connor , Genomics in the Cloud: Using Docker, GATK, and WDL in Terra ( O’Reilly Media, Sebastopol, CA , ed. 1, 2020 ). 69. ↵ B. S. Pedersen , A. R. Quinlan , Mosdepth: quick coverage calculation for genomes and exomes . Bioinformatics 34 , 867 – 868 ( 2018 ). OpenUrl CrossRef PubMed 70. ↵ P. Danecek , J. K. Bonfield , J. Liddle , J. Marshall , V. Ohan , M. O. Pollard , A. Whitwham , T. Keane , S. A. McCarthy , R. M. Davies , H. Li , Twelve years of SAMtools and BCFtools . Gigascience 10 ( 2021 ). 71. ↵ Hard-filtering germline short variants, GATK . https://gatk.broadinstitute.org/hc/en-us/articles/360035890471-Hard-filtering-germline-short-variant s. 72. ↵ K. Hanghøj , I. Moltke , P. A. Andersen , A. Manica , T. S. Korneliussen , Fast and accurate relatedness estimation from high-throughput sequencing data in the presence of inbreeding . Gigascience 8 ( 2019 ). 73. ↵ A. Abdellaoui , J.-J. Hottenga , P. de Knijff , M. G. Nivard , X. Xiao , P. Scheet , A. Brooks , E. A. Ehli , Y. Hu , G. E. Davies , J. J. Hudziak , P. F. Sullivan , T. van Beijsterveldt , G. Willemsen , E. J. de Geus , B. W. J. H. Penninx , D. I. Boomsma , Population structure, migration, and diversifying selection in the Netherlands . Eur. J. Hum. Genet . 21 , 1277 – 1285 ( 2013 ). OpenUrl CrossRef PubMed 74. ↵ S. Purcell , B. Neale , K. Todd-Brown , L. Thomas , M. A. R. Ferreira , D. Bender , J. Maller , P. Sklar , P. W. de Bakker , M. J. Daly , P. C. Sham , PLINK: a tool set for whole-genome association and population-based linkage analyses . Am. J. Hum. Genet . 81 , 559 – 575 ( 2007 ). OpenUrl CrossRef PubMed 75. ↵ L. Skotte , T. S. Korneliussen , A. Albrechtsen , Estimating individual admixture proportions from next generation sequencing data . Genetics 195 , 693 – 702 ( 2013 ). OpenUrl Abstract / FREE Full Text 76. ↵ R Core Team , R: A Language and Environment for Statistical Computing ( R Foundation for Statistical Computing , Vienna, Austria , 2020 ; https://www.R-project.org/ ). 77. ↵ J. A. Fellows Yates , T. C. Lamnidis , M. Borry , A. Andrades Valtueña , Z. Fagernäs , S. Clayton , M. U. Garcia , J. Neukamm , A. Peltzer , Reproducible, portable, and efficient ancient genome reconstruction with nf-core/eager . PeerJ 9 , e10947 ( 2021 ). OpenUrl CrossRef PubMed 78. ↵ T. S. Korneliussen , A. Albrechtsen , R. Nielsen , ANGSD: Analysis of next generation sequencing data . BMC Bioinformatics 15 , 356 ( 2014 ). OpenUrl CrossRef PubMed 79. ↵ J. K. Pickrell , J. K. Pritchard , Inference of population splits and mixtures from genome-wide allele frequency data . PLoS Genet . 8 , e1002967 ( 2012 ). OpenUrl CrossRef PubMed 80. ↵ M. Malinsky , M. Matschiner , H. Svardal , Dsuite - Fast D-statistics and related admixture evidence from VCF files . Mol. Ecol. Resour . 21 , 584 – 595 ( 2021 ). OpenUrl CrossRef PubMed 81. ↵ E. V. Shaikevich , E. B. Vinogradova , A. Bouattour , A. P. Gouveia de Almeida , Genetic diversity of Culex pipiens mosquitoes in distinct populations from Europe: contribution of Cx. quinquefasciatus in Mediterranean populations . Parasit. Vectors 9 , 47 ( 2016 ). OpenUrl CrossRef PubMed 82. ↵ K. L. Korunes , K. Samuk , pixy: Unbiased estimation of nucleotide diversity and divergence in the presence of missing data . Mol. Ecol. Resour . 21 , 1359 – 1368 ( 2021 ). OpenUrl CrossRef PubMed 83. ↵ E. Paradis , K. Schliep , ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R . Bioinformatics 35 , 526 – 528 ( 2019 ). OpenUrl CrossRef PubMed 84. ↵ G. Yu , D. K. Smith , H. Zhu , Y. Guan , T. T.-Y. Lam , Ggtree: An r package for visualization and annotation of phylogenetic trees with their covariates and other associated data . Methods Ecol. Evol . 8 , 28 – 36 ( 2017 ). OpenUrl CrossRef 85. ↵ K. Wang , I. Mathieson , J. O’Connell , S. Schiffels , Tracking human population structure through time from whole genome sequences . PLoS Genet . 16 , e1008552 ( 2020 ). OpenUrl CrossRef PubMed 86. ↵ P. Edge , V. Bafna , V. Bansal , HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies . Genome Res . 27 , 801 – 812 ( 2017 ). OpenUrl Abstract / FREE Full Text 87. ↵ O. Delaneau , J.-F. Zagury , M. R. Robinson , J. L. Marchini , E. T. Dermitzakis , Accurate, scalable and integrative haplotype estimation . Nat. Commun . 10 , 5436 ( 2019 ). OpenUrl CrossRef PubMed 88. ↵ P. D. Keightley , R. W. Ness , D. L. Halligan , P. R. Haddrill , Estimation of the spontaneous mutation rate per nucleotide site in a Drosophila melanogaster full-sib family . Genetics 196 , 313 – 320 ( 2014 ). OpenUrl Abstract / FREE Full Text 89. P. D. Keightley , A. Pinharanda , R. W. Ness , F. Simpson , K. K. Dasmahapatra , J. Mallet , J. W. Davey , C. D. Jiggins , Estimation of the spontaneous mutation rate in Heliconius melpomene . Mol. Biol. Evol . 32 , 239 – 243 ( 2015 ). OpenUrl CrossRef PubMed 90. ↵ I. Rashid , M. Campos , T. Collier , M. Crepeau , A. Weakley , H. Gripkey , Y. Lee , H. Schmidt , G. C. Lanzaro , Spontaneous mutation rate estimates for the principal malaria vectors Anopheles coluzzii and Anopheles stephensi . Sci. Rep . 12 , 226 ( 2022 ). OpenUrl CrossRef PubMed 91. ↵ Q. Gao , F. Su , Y.-B. Zhou , W. Chu , H. Cao , L.-L. Song , J.-J. Zhou , P.-E. Leng , Autogeny, Fecundity, and Other Life History Traits of Culex pipiens molestus (Diptera: Culicidae) in Shanghai, China . J. Med. Entomol . 56 , 656 – 664 ( 2019 ). OpenUrl CrossRef PubMed 92. ↵ Center for International Earth Science Information Network-CIESIN - Columbia University, Gridded Population of the World, Version 4 (GPWv4): Population Density, Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC) ( 2016 ); doi: 10.7927/H4NP22DQ . OpenUrl CrossRef 93. ↵ S. E. Fick , R. J. Hijmans , WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas: NEW CLIMATE SURFACES FOR GLOBAL LAND AREAS . Int. J. Climatol . 37 , 4302 – 4315 ( 2017 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted February 05, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Ancient origin of an urban underground mosquito Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Ancient origin of an urban underground mosquito Yuki Haba , Matthew L. Aardema , Maria O. Afonso , Natasha M. Agramonte , John Albright , Ana Margarida Alho , Antonio P.G. Almeida , Haoues Alout , Bulent Alten , Mine Altinli , Raouf Amara Korba , Stefanos S. Andreadis , Vincent Anghel , Soukaina Arich , Arielle Arsenault-Benoit , Célestine Atyame , Fabien Aubry , Frank W. Avila , Diego Ayala , Rasha S. Azrag , Lilit Babayan , Allon Bear , Norbert Becker , Anna G. Bega , Sophia Bejarano , Ira Ben-Avi , Joshua B. Benoit , Saïd C. Boubidi , William E. Bradshaw , Daniel Bravo-Barriga , Rubén Bueno-Marí , Nataša Bušić , Viktoria Čabanová , Brittany Cabeje , Beniamino Caputo , Maria V. Cardo , Simon Carpenter , Elena Carreton , Mouhamadou S. Chouaïbou , Michelle Christian , Maureen Coetzee , William R. Conner , Anton Cornel , C. Lorna Culverwell , Aleksandra I. Cupina , Katrien De Wolf , Isra Deblauwe , Brittany Deegan , Sarah Delacour-Estrella , Alessandra della Torre , Debora Diaz , Serena E. Dool , Vitor L dos Anjos , Sisay Dugassa , Babak Ebrahimi , Samar Y.M. Eisa , Nohal Elissa , Sahar A.B. Fallatah , Ary Faraji , Marina V. Fedorova , Emily Ferrill , Dina M. Fonseca , Kimberly A. Foss , Cipriano Foxi , Caio M. França , Stephen R. Fricker , Megan L. Fritz , Eva Frontera , Hans-Peter Fuehrer , Kyoko Futami , Enas H.S. Ghallab , Romain Girod , Mikhail I. Gordeev , David Greer , Martin Gschwind , Milehna M. Guarido , Teoh Guat Ney , Filiz Gunay , Eran Haklay , Alwia A.E. Hamad , Jun Hang , Christopher M. Hardy , Jacob W. Hartle , Jenny C. Hesson , Yukiko Higa , Christina M. Holzapfel , Ann-Christin Honnen , Angela M. Ionica , Laura Jones , Përparim Kadriaj , Hany A. Kamal , Colince Kamdem , Dmitry A. Karagodin , Shinji Kasai , Mihaela Kavran , Emad I.M. Khater , Frederik Kiene , Heung-Chul Kim , Ilias Kioulos , Annette Klein , Marko Klemenčić , Ana Klobučar , Erin Knutson , Constantianus J.M. Koenraadt , Linda Kothera , Pauline Kreienbühl , Pierrick Labbé , Itay Lachmi , Louis Lambrechts , Nediljko Landeka , Christopher H. Lee , Bryan D. Lessard , Ignacio Leycegui , Jan O. Lundström , Yoav Lustigman , Caitlin MacIntyre , Andrew J. Mackay , Krisztian Magori , Carla Maia , Colin A. Malcolm , Ralph-Joncyn O. Marquez , Dino Martins , Reem A. Masri , Gillian McDivitt , Rebekah J. McMinn , Johana Medina , Karen S. Mellor , Jason Mendoza , Enrih Merdić , Stacey Mesler , Camille Mestre , Homer Miranda , Martina Miterpáková , Fabrizio Montarsi , Anton V. Moskaev , Tong Mu , Tim W.R. Möhlmann , Alice Namias , Ivy Ng’iru , Marc F. Ngangué , Maria T. Novo , Laor Orshan , José A. Oteo , Yasushi Otsuka , Rossella Panarese , Claudia Paredes-Esquivel , Lusine Paronyan , Steven T. Peper , Dušan V. Petrić , Kervin Pilapil , Cristina Pou-Barreto , Sebastien J. Puechmaille , Ute Radespiel , Nil Rahola , Vivek K Raman , Hamadouche Redouane , Michael H. Reiskind , Nadja M. Reissen , Benjamin L. Rice , Vincent Robert , Ignacio Ruiz-Arrondo , Ryan Salamat , Amy Salamone , M’hammed Sarih , Giuseppe Satta , Kyoko Sawabe , Francis Schaffner , Karen E. Schultz , Elena V. Shaikevich , Igor V. Sharakhov , Maria V. Sharakhova , Nader Shatara , Anuarbek K. Sibataev , Mathieu Sicard , Evan Smith , Ryan C. Smith , Nathalie Smitz , Nicolas Soriano , Christos G. Spanoudis , Christopher M. Stone , Liora Studentsky , Tatiana Sulesco , Luciano M. Tantely , La K. Thao , Noor Tietze , Ryan E. Tokarz , Kun-Hsien Tsai , Yoshio Tsuda , Nataša Turić , Melissa R. Uhran , Isik Unlu , Wim Van Bortel , Haykuhi Vardanyan , Laura Vavassori , Enkelejda Velo , Marietjie Venter , Goran Vignjević , Chantal B.F. Vogels , Tatsiana Volkava , John Vontas , Heather M. Ward , Nazni Wasi Ahmad , Mylène Weill , Jennifer D. West , Sarah S. Wheeler , Gregory S. White , Nadja C. Wipf , Tai-Ping Wu , Kai-Di Yu , Elke Zimmermann , Carina Zittra , Petra Korlević , Erica McAlister , Mara K.N. Lawniczak , Molly Schumer , Noah H. Rose , Carolyn S. McBride bioRxiv 2025.01.26.634793; doi: https://doi.org/10.1101/2025.01.26.634793 Share This Article: Copy Citation Tools Ancient origin of an urban underground mosquito Yuki Haba , Matthew L. Aardema , Maria O. Afonso , Natasha M. Agramonte , John Albright , Ana Margarida Alho , Antonio P.G. Almeida , Haoues Alout , Bulent Alten , Mine Altinli , Raouf Amara Korba , Stefanos S. Andreadis , Vincent Anghel , Soukaina Arich , Arielle Arsenault-Benoit , Célestine Atyame , Fabien Aubry , Frank W. Avila , Diego Ayala , Rasha S. Azrag , Lilit Babayan , Allon Bear , Norbert Becker , Anna G. Bega , Sophia Bejarano , Ira Ben-Avi , Joshua B. Benoit , Saïd C. Boubidi , William E. Bradshaw , Daniel Bravo-Barriga , Rubén Bueno-Marí , Nataša Bušić , Viktoria Čabanová , Brittany Cabeje , Beniamino Caputo , Maria V. Cardo , Simon Carpenter , Elena Carreton , Mouhamadou S. Chouaïbou , Michelle Christian , Maureen Coetzee , William R. Conner , Anton Cornel , C. Lorna Culverwell , Aleksandra I. Cupina , Katrien De Wolf , Isra Deblauwe , Brittany Deegan , Sarah Delacour-Estrella , Alessandra della Torre , Debora Diaz , Serena E. Dool , Vitor L dos Anjos , Sisay Dugassa , Babak Ebrahimi , Samar Y.M. Eisa , Nohal Elissa , Sahar A.B. Fallatah , Ary Faraji , Marina V. Fedorova , Emily Ferrill , Dina M. Fonseca , Kimberly A. Foss , Cipriano Foxi , Caio M. França , Stephen R. Fricker , Megan L. Fritz , Eva Frontera , Hans-Peter Fuehrer , Kyoko Futami , Enas H.S. Ghallab , Romain Girod , Mikhail I. Gordeev , David Greer , Martin Gschwind , Milehna M. Guarido , Teoh Guat Ney , Filiz Gunay , Eran Haklay , Alwia A.E. Hamad , Jun Hang , Christopher M. Hardy , Jacob W. Hartle , Jenny C. Hesson , Yukiko Higa , Christina M. Holzapfel , Ann-Christin Honnen , Angela M. Ionica , Laura Jones , Përparim Kadriaj , Hany A. Kamal , Colince Kamdem , Dmitry A. Karagodin , Shinji Kasai , Mihaela Kavran , Emad I.M. Khater , Frederik Kiene , Heung-Chul Kim , Ilias Kioulos , Annette Klein , Marko Klemenčić , Ana Klobučar , Erin Knutson , Constantianus J.M. Koenraadt , Linda Kothera , Pauline Kreienbühl , Pierrick Labbé , Itay Lachmi , Louis Lambrechts , Nediljko Landeka , Christopher H. Lee , Bryan D. Lessard , Ignacio Leycegui , Jan O. Lundström , Yoav Lustigman , Caitlin MacIntyre , Andrew J. Mackay , Krisztian Magori , Carla Maia , Colin A. Malcolm , Ralph-Joncyn O. Marquez , Dino Martins , Reem A. Masri , Gillian McDivitt , Rebekah J. McMinn , Johana Medina , Karen S. Mellor , Jason Mendoza , Enrih Merdić , Stacey Mesler , Camille Mestre , Homer Miranda , Martina Miterpáková , Fabrizio Montarsi , Anton V. Moskaev , Tong Mu , Tim W.R. Möhlmann , Alice Namias , Ivy Ng’iru , Marc F. Ngangué , Maria T. Novo , Laor Orshan , José A. Oteo , Yasushi Otsuka , Rossella Panarese , Claudia Paredes-Esquivel , Lusine Paronyan , Steven T. Peper , Dušan V. Petrić , Kervin Pilapil , Cristina Pou-Barreto , Sebastien J. Puechmaille , Ute Radespiel , Nil Rahola , Vivek K Raman , Hamadouche Redouane , Michael H. Reiskind , Nadja M. Reissen , Benjamin L. Rice , Vincent Robert , Ignacio Ruiz-Arrondo , Ryan Salamat , Amy Salamone , M’hammed Sarih , Giuseppe Satta , Kyoko Sawabe , Francis Schaffner , Karen E. Schultz , Elena V. Shaikevich , Igor V. Sharakhov , Maria V. Sharakhova , Nader Shatara , Anuarbek K. Sibataev , Mathieu Sicard , Evan Smith , Ryan C. Smith , Nathalie Smitz , Nicolas Soriano , Christos G. Spanoudis , Christopher M. Stone , Liora Studentsky , Tatiana Sulesco , Luciano M. Tantely , La K. Thao , Noor Tietze , Ryan E. Tokarz , Kun-Hsien Tsai , Yoshio Tsuda , Nataša Turić , Melissa R. Uhran , Isik Unlu , Wim Van Bortel , Haykuhi Vardanyan , Laura Vavassori , Enkelejda Velo , Marietjie Venter , Goran Vignjević , Chantal B.F. Vogels , Tatsiana Volkava , John Vontas , Heather M. Ward , Nazni Wasi Ahmad , Mylène Weill , Jennifer D. West , Sarah S. Wheeler , Gregory S. White , Nadja C. Wipf , Tai-Ping Wu , Kai-Di Yu , Elke Zimmermann , Carina Zittra , Petra Korlević , Erica McAlister , Mara K.N. Lawniczak , Molly Schumer , Noah H. Rose , Carolyn S. 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