Climate-driven specialisation in plant–pollinator networks peaks outside the tropics

Climate-driven specialisation in plant–pollinator networks peaks outside the tropics | 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 Climate-driven specialisation in plant–pollinator networks peaks outside the tropics View ORCID Profile Sailee P. Sakhalkar , View ORCID Profile Nico Blüthgen , View ORCID Profile Laura A. Burkle , View ORCID Profile Paul CaraDonna , View ORCID Profile Bo Dalsgaard , View ORCID Profile Carsten F. Dormann , View ORCID Profile Christopher N. Kaiser-Bunbury , View ORCID Profile Tiffany M. Knight , View ORCID Profile Jeff Ollerton , View ORCID Profile Julian Resasco , View ORCID Profile Matthias Schleuning , View ORCID Profile Diego P. Vázquez , View ORCID Profile Rita N. Afagwu , View ORCID Profile Ruben Alarcón , View ORCID Profile Felipe W. Amorim , View ORCID Profile Marsal D. Amorim , View ORCID Profile Dominik Anýž , View ORCID Profile Blanca Arroyo-Correa , View ORCID Profile Maddi Artamendi , View ORCID Profile Zeynep Atalay , View ORCID Profile Justin A. Bain , View ORCID Profile Katherine C. R. Baldock , View ORCID Profile Gavin Ballantyne , View ORCID Profile Caio S. Ballarin , Behnaz Balmaki , View ORCID Profile Michael Bartoš , View ORCID Profile Vasuki Belavadi , View ORCID Profile Paolo Biella , View ORCID Profile Anne D. Bjorkman , View ORCID Profile João Paulo Raimundo Borges , View ORCID Profile Jordi Bosch , View ORCID Profile Camila Bosenbecker , View ORCID Profile Stephane Boyer , View ORCID Profile Karin T. Burghardt , View ORCID Profile Edson Cardona , View ORCID Profile Quebin Casiá , View ORCID Profile Anthony M. T. Castagna , View ORCID Profile Ferhat Celep , Melanie N. Chisté , View ORCID Profile Yann Clough , View ORCID Profile James M. Cook , Liam P. Crowther , View ORCID Profile Fabiana D. Reis , View ORCID Profile Luis P. da Silva , View ORCID Profile Wesley Dáttilo , View ORCID Profile Valeska De Cárdenas , View ORCID Profile Joan Díaz-Calafat , View ORCID Profile Lynn V. Dicks , Rye Dickson , View ORCID Profile Marion L. Donald , View ORCID Profile Lee A. Dyer , View ORCID Profile Fairo F. Dzekashu , View ORCID Profile Natalia Escobedo Kenefic , View ORCID Profile Tom M. Fayle , View ORCID Profile Laura L. Figueroa , View ORCID Profile Jan Filip , View ORCID Profile Raúl García-Camacho , View ORCID Profile Amy-Marie Gilpin , View ORCID Profile Luis Giménez-Benavides , View ORCID Profile Ingrid N. Gomes , View ORCID Profile Heather Grab , View ORCID Profile Ingo Grass , View ORCID Profile Travis J. Guy , View ORCID Profile Veronica Hederström , View ORCID Profile Carlos Hernández-Castellano , View ORCID Profile Sandra Hervías-Parejo , View ORCID Profile Andrea Holzschuh , View ORCID Profile Sebastian Hopfenmüller , View ORCID Profile José M. Iriondo , View ORCID Profile Štěpán Janeček , View ORCID Profile Jana Jersáková , View ORCID Profile Shalene Jha , View ORCID Profile Aphrodite Kantsa , View ORCID Profile Tamar Keasar , View ORCID Profile Liam Kendall , View ORCID Profile Yannick Klomberg , View ORCID Profile Ishmeal N. Kobe , View ORCID Profile Theresia Krausl , View ORCID Profile Patricia Landaverde , View ORCID Profile Carlos Lara-Romero , View ORCID Profile H. Michael G. Lattorff , View ORCID Profile Felipe Librán-Embid , View ORCID Profile Tial C. Ling , View ORCID Profile Sissi Lozada-Gobilard , Francis Ewome Luma , View ORCID Profile Pedro Luna , View ORCID Profile Ludmilla M. S. Aguiar , View ORCID Profile Ana Carolina Pereira Machado , View ORCID Profile Isabel C. Machado , View ORCID Profile Ainhoa Magrach , View ORCID Profile Fabienne Maihoff , View ORCID Profile Gabriel Marcacci , View ORCID Profile Carlos Martínez-Núñez , View ORCID Profile Pietro K. Maruyama , Natsuki Matsubara , Maggie Mayberry , View ORCID Profile Marco A. R. Mello , View ORCID Profile Ugo Mendes Diniz , View ORCID Profile Marcos Méndez , View ORCID Profile Jan E. J. Mertens , View ORCID Profile Rubén Milla , View ORCID Profile Javier Morente-López , View ORCID Profile Tarcila L. Nadia , View ORCID Profile Georgios Nakas , View ORCID Profile Anders Nielsen , View ORCID Profile Alon Ornai , View ORCID Profile Sergio Osorio-Canadas , View ORCID Profile Wilhelm H. A. Osterman , View ORCID Profile Todd M. Palmer , View ORCID Profile Theodora Petanidou , View ORCID Profile Christian W. W. Pirk , View ORCID Profile Kit S. Prendergast , Richard Primack , View ORCID Profile Luis M. Primo , View ORCID Profile Marina Querejeta , View ORCID Profile Zelma Quirino , View ORCID Profile André Rodrigo Rech , View ORCID Profile Sara Reverté , View ORCID Profile Pedro J. Rey , View ORCID Profile Léo C. Rocha-Filho , View ORCID Profile Anselm Rodrigo Domínguez , Masoud A. Rostami , View ORCID Profile Avery L. Russell , View ORCID Profile Silvia Santamaría , View ORCID Profile Francisco A. R. Santos , View ORCID Profile Takehiro Sasaki , View ORCID Profile Manu E. Saunders , View ORCID Profile Victor H. D. Silva , View ORCID Profile Michael P. Simanonok , Antigoni Sounapoglou , View ORCID Profile Ingolf Steffan-Dewenter , Sam Tarrant , View ORCID Profile Rubén Torices , View ORCID Profile Anna Traveset , View ORCID Profile Teja Tscharntke , View ORCID Profile Guillermo Uceda-Gómez , View ORCID Profile Katherine R. Urban-Mead , View ORCID Profile Casper J. van der Kooi , View ORCID Profile Catrin Westphal , View ORCID Profile Abdullahi Yusuf , View ORCID Profile Robert Tropek doi: https://doi.org/10.1101/2025.10.08.680666 Sailee P. Sakhalkar 1 Department of Ecology, Faculty of Science, Charles University , Prague, Czechia 3 Institute of Microbiology of the Czech Academy of Sciences , Prague, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sailee P. Sakhalkar For correspondence: sailee.sakha{at}gmail.com robert.tropek{at}gmail.com Nico Blüthgen 4 Ecological Networks Lab, Department of Biology, Technische Universität Darmstadt , Darmstadt, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nico Blüthgen Laura A. Burkle 5 Department of Ecology, Montana State University , Bozeman, MT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura A. Burkle Paul CaraDonna 6 Chicago Botanic Garden, Negaunee Institute for Plant Science and Action , Glencoe, IL, USA 7 Rocky Mountain Biological Laboratory , Crested Butte, CO, USA 8 Plant Biology & Conservation, Northwestern University , Evanston, IL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul CaraDonna Bo Dalsgaard 9 Section for Molecular Ecology & Evolution, Globe Institute, University of Copenhagen , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bo Dalsgaard Carsten F. Dormann 10 Biometry & Environmental System Analysis, University of Freiburg , Freiburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carsten F. Dormann Christopher N. Kaiser-Bunbury 11 Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University of Exeter , Cornwall Campus, Penryn, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher N. Kaiser-Bunbury Tiffany M. Knight 12 Department of Species Interaction Ecology, Helmholtz Centre for Environmental Research-UFZ , Leipzig, Germany 13 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , Leipzig, Germany 14 Department of Science and Conservation, National Tropical Botanical Garden , Kalāheo, HI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tiffany M. Knight Jeff Ollerton 15 Faculty of Arts, Science and Technology, University of Northampton , Waterside Campus, Northampton, UK 16 Key Laboratory for Plant Diversity and Biogeography of East Asia, Chinese Academy of Sciences, Kunming Institute of Botany , Kunming, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jeff Ollerton Julian Resasco 7 Rocky Mountain Biological Laboratory , Crested Butte, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julian Resasco Matthias Schleuning 17 Senckenberg Biodiversity and Climate Research Centre (SBiK-F) , Frankfurt (Main), Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthias Schleuning Diego P. Vázquez 18 Argentine Institute for Dryland Research, CONICET & National University of Cuyo , Mendoza, Argentina 19 Faculty of Exact and Natural Sciences, National University of Cuyo , Mendoza, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Diego P. Vázquez Rita N. Afagwu 20 Department of Biology, Missouri State University , Springfield, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rita N. Afagwu Ruben Alarcón 21 Department of Biology, California State University Channel Islands , Camarillo, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruben Alarcón Felipe W. Amorim 22 Laboratório de Ecologia da Polinização e Interações (LEPI), Department of Biodiversity and Biostatistics, Institute of Biosciences, São Paulo State University , Botucatu, SP, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Felipe W. Amorim Marsal D. Amorim 23 Centro de Síntese Ecológica e Conservação, Departamento de Genética, Ecologia e Evolução - ICB, Universidade Federal de Minas Gerais , Belo Horizonte, MG, Brazil 24 Programa de Pós-Graduação em Ecologia, Conservação e Manejo da Vida Silvestre, Universidade Federal de Minas Gerais , Belo Horizonte, MG, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marsal D. Amorim Dominik Anýž 1 Department of Ecology, Faculty of Science, Charles University , Prague, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dominik Anýž Blanca Arroyo-Correa 25 Department of Ecology and Evolution, Estación Biológica de Doñana , EBD-CSIC, Seville, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Blanca Arroyo-Correa Maddi Artamendi 26 University of the Basque Country (UPV-EHU) , Leioa, Spain 27 Basque Centre for Climate Change (BC3) , Leioa, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maddi Artamendi Zeynep Atalay 28 Tübitak (The Scientific and Technological Research Council of Türkiye), Department of Science Fellowships and Grant Programs , Ankara, Türkiye Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zeynep Atalay Justin A. Bain 6 Chicago Botanic Garden, Negaunee Institute for Plant Science and Action , Glencoe, IL, USA 8 Plant Biology & Conservation, Northwestern University , Evanston, IL, USA 29 Oklahoma Biological Survey, University of Oklahoma , Norman, OK, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Justin A. Bain Katherine C. R. Baldock 30 Department of Geography and Environmental Sciences, Northumbria University , Newcastle upon Tyne, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine C. R. Baldock Gavin Ballantyne 31 Centre for Conservation and Restoration Science, Edinburgh Napier University , Edinburgh, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gavin Ballantyne Caio S. Ballarin 22 Laboratório de Ecologia da Polinização e Interações (LEPI), Department of Biodiversity and Biostatistics, Institute of Biosciences, São Paulo State University , Botucatu, SP, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Caio S. Ballarin Behnaz Balmaki 32 Division of Environmental Science, Texas Woman’s University , Denton, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael Bartoš 33 Institute of Botany of the Czech Academy of Sciences , Třeboň, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael Bartoš Vasuki Belavadi 34 Department of Entomology, University of Agricultural Sciences , Bangalore, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vasuki Belavadi Paolo Biella 35 Department of Biotechnology and Biosciences, University of Milano-Bicocca , Milano, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paolo Biella Anne D. Bjorkman 36 Department of Biological and Environmental Sciences, University of Gothenburg , Gothenburg, Sweden 37 Gothenburg Global Biodiversity Centre , Gothenburg, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anne D. Bjorkman João Paulo Raimundo Borges 38 Programa de Pós-Graduação em Ciência Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri , Diamantina, MG, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for João Paulo Raimundo Borges Jordi Bosch 39 CREAF , Bellaterra, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jordi Bosch Camila Bosenbecker 23 Centro de Síntese Ecológica e Conservação, Departamento de Genética, Ecologia e Evolução - ICB, Universidade Federal de Minas Gerais , Belo Horizonte, MG, Brazil 40 Ecologia, Conservação e Biodiversidade, Universidade Federal de Uberlândia , Belo Horizonte, MG, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Camila Bosenbecker Stephane Boyer 41 Institut de Recherche sur la Biologie de l’Insecte, CNRS/Université de Tours , Tours, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephane Boyer Karin T. Burghardt 42 Department of Entomology, University of Maryland , College Park, MD, USA 43 Yale School of the Environment , New Haven, CT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karin T. Burghardt Edson Cardona 44 Unidad de Investigación para el Conocimiento, Uso y Valoración de la Biodiversidad, Centro de Estudios Conservacionistas, Facultad de Ciencias Químicas y Farmacia, Universidad de San Carlos de Guatemala , Guatemala City, Guatemala Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Edson Cardona Quebin Casiá 44 Unidad de Investigación para el Conocimiento, Uso y Valoración de la Biodiversidad, Centro de Estudios Conservacionistas, Facultad de Ciencias Químicas y Farmacia, Universidad de San Carlos de Guatemala , Guatemala City, Guatemala Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Quebin Casiá Anthony M. T. Castagna 20 Department of Biology, Missouri State University , Springfield, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anthony M. T. Castagna Ferhat Celep 45 Department of Biology, Faculty of Engineering and Natural Sciences, Kırıkkale University , Kırıkkale, Türkiye Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ferhat Celep Melanie N. Chisté 46 Naturpark Fränkische Schweiz - Frankenjura , Pottenstein/Kirchenbirkig, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yann Clough 47 Centre for Environmental and Climate Science, Lund University , Lund, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yann Clough James M. Cook 48 Hawkesbury Institute for the Environment, Western Sydney University , Penrith, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James M. Cook Liam P. Crowther 49 School of Biological Sciences, University of East Anglia , Norwich, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fabiana D. Reis 50 Programas de Pós-Graduação em Biologia Animal, Universidade Federal dos Vales do Jequitinhonha e Mucuri , Diamantina, MG, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fabiana D. Reis Luis P. da Silva 51 CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto , Vila do Conde, Portugal 52 BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO , Campus de Vairão, Vila do Conde, Portugal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luis P. da Silva Wesley Dáttilo 53 Red de Ecoetología, Instituto de Ecología AC , Xalapa, Veracruz, Mexico 54 Laboratorio Nacional de Biología del Cambio Climático , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wesley Dáttilo Valeska De Cárdenas 55 Universidad Mayor de San Andrés , La Paz, Bolivia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valeska De Cárdenas Joan Díaz-Calafat 56 Department of Biology, University of the Balearic Islands , Palma, Spain 57 MUCBO – Museu Balear de Ciències Naturals (FJBS-MBCN) , Sóller, Illes Balears, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joan Díaz-Calafat Lynn V. Dicks 58 Department of Zoology, University of Cambridge , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lynn V. Dicks Rye Dickson 7 Rocky Mountain Biological Laboratory , Crested Butte, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marion L. Donald 59 Bioeconomy Science Institute , Lincoln, New Zealand 60 School of Biological Sciences, University of Canterbury , Christchurch, New Zealand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marion L. Donald Lee A. Dyer 61 Biology Department, University of Nevada Reno , NV, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lee A. Dyer Fairo F. Dzekashu 62 International Centre of Insect Physiology and Ecology (icipe) , Nairobi, Kenya 63 Social Insects Research Group (SIRG), Department of Zoology & Entomology, University of Pretoria , Pretoria, Republic of South Africa 64 Department of Biological Sciences, University of Lethbridge , Lethbridge, AB, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fairo F. Dzekashu Natalia Escobedo Kenefic 44 Unidad de Investigación para el Conocimiento, Uso y Valoración de la Biodiversidad, Centro de Estudios Conservacionistas, Facultad de Ciencias Químicas y Farmacia, Universidad de San Carlos de Guatemala , Guatemala City, Guatemala 65 Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Natalia Escobedo Kenefic Tom M. Fayle 2 Biology Centre of the Czech Academy of Sciences, Institute of Entomology , České Budějovice, Czechia 66 School of Biological and Behavioural Sciences, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tom M. Fayle Laura L. Figueroa 67 Department of Environmental Conservation, University of Massachusetts Amherst , Amherst, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura L. Figueroa Jan Filip 1 Department of Ecology, Faculty of Science, Charles University , Prague, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jan Filip Raúl García-Camacho 68 Global Change Research Institute (IICG-URJC), Rey Juan Carlos University (URJC) , Madrid, Spain 69 Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos , Móstoles, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Raúl García-Camacho Amy-Marie Gilpin 48 Hawkesbury Institute for the Environment, Western Sydney University , Penrith, NSW, Australia 70 School of Science, Western Sydney University , Penrith, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Amy-Marie Gilpin Luis Giménez-Benavides 68 Global Change Research Institute (IICG-URJC), Rey Juan Carlos University (URJC) , Madrid, Spain 69 Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos , Móstoles, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luis Giménez-Benavides Ingrid N. Gomes 23 Centro de Síntese Ecológica e Conservação, Departamento de Genética, Ecologia e Evolução - ICB, Universidade Federal de Minas Gerais , Belo Horizonte, MG, Brazil 71 Laboratório de Genética da Conservação de Abelhas (LaBee), Instituto de Ciências Biológicas e da Saúde, Universidade Federal de Viçosa , Campus Florestal, Florestal, MG, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ingrid N. Gomes Heather Grab 72 Department of Entomology, Pennsylvania State University , University Park, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Heather Grab Ingo Grass 73 Ecology of Tropical Agricultural Systems, University of Hohenheim , Stuttgart, Germany 74 Center for Biodiversity and Integrative Taxonomy (KomBioTa), University of Hohenheim , Stuttgart, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ingo Grass Travis J. Guy 75 Department of Biology, University of Florida , Gainesville, Florida, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Travis J. Guy Veronica Hederström 47 Centre for Environmental and Climate Science, Lund University , Lund, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Veronica Hederström Carlos Hernández-Castellano 76 Wildlife Ecology & Health Group (WE&H), Departament de Medicina i Cirurgia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB) , Cerdanyola del Vallès, Spain 77 Insect Ecology Group, Department of Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague (CZU) , Prague, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carlos Hernández-Castellano Sandra Hervías-Parejo 78 Global Change Research Group, Mediterranean Institute for Advanced Studies , Mallorca, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sandra Hervías-Parejo Andrea Holzschuh 79 Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea Holzschuh Sebastian Hopfenmüller 79 Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg , Würzburg, Germany 80 Cultural Landscape Günztal Foundation , Ottobeuren, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sebastian Hopfenmüller José M. Iriondo 68 Global Change Research Institute (IICG-URJC), Rey Juan Carlos University (URJC) , Madrid, Spain 69 Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos , Móstoles, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for José M. Iriondo Štěpán Janeček 1 Department of Ecology, Faculty of Science, Charles University , Prague, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Štěpán Janeček Jana Jersáková 81 Department of Biology of Ecosystems, Faculty of Science, University of South Bohemia , České Budějovice, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jana Jersáková Shalene Jha 82 Department of Integrative Biology and Lady Bird Johnson Wildflower Center, University of Texas at Austin , Austin, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shalene Jha Aphrodite Kantsa 83 Department of Environmental Systems Science, ETH Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aphrodite Kantsa Tamar Keasar 84 Department of Biology and the Environment, University of Haifa – Oranim , Tivon, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tamar Keasar Liam Kendall 47 Centre for Environmental and Climate Science, Lund University , Lund, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Liam Kendall Yannick Klomberg 1 Department of Ecology, Faculty of Science, Charles University , Prague, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yannick Klomberg Ishmeal N. 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Kobe Theresia Krausl 47 Centre for Environmental and Climate Science, Lund University , Lund, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Theresia Krausl Patricia Landaverde 44 Unidad de Investigación para el Conocimiento, Uso y Valoración de la Biodiversidad, Centro de Estudios Conservacionistas, Facultad de Ciencias Químicas y Farmacia, Universidad de San Carlos de Guatemala , Guatemala City, Guatemala 85 General Zoology, Institute for Biology, Martin Luther University Halle-Wittenberg , Halle, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patricia Landaverde Carlos Lara-Romero 68 Global Change Research Institute (IICG-URJC), Rey Juan Carlos University (URJC) , Madrid, Spain 69 Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos , Móstoles, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carlos Lara-Romero H. Michael G. Lattorff 86 School of Life Sciences, University of KwaZulu-Natal , Westville Campus, Durban, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for H. Michael G. Lattorff Felipe Librán-Embid 87 Justus Liebig University of Gießen, Institute of Animal Ecology and Systematics , Gießen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Felipe Librán-Embid Tial C. Ling 88 CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences , Mengla, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tial C. 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Mertens Rubén Milla 68 Global Change Research Institute (IICG-URJC), Rey Juan Carlos University (URJC) , Madrid, Spain 69 Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos , Móstoles, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rubén Milla Javier Morente-López 68 Global Change Research Institute (IICG-URJC), Rey Juan Carlos University (URJC) , Madrid, Spain 69 Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos , Móstoles, Spain 102 Plant Evolutionary Ecology, Institute of Ecology, Evolution and Diversity, Faculty of Biological Sciences, Goethe University Frankfurt , Frankfurt am Main, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Javier Morente-López Tarcila L. 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Nadia Georgios Nakas 104 Department of Geography, University of the Aegean , Mytilene, Greece Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Georgios Nakas Anders Nielsen 105 Department of Landscape and Biodiversity, Norwegian Institute for Bioeconomy Research (NIBIO) , Ås, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anders Nielsen Alon Ornai 106 Department of Evolutionary and Environmental Biology, University of Haifa , Haifa, Israel 107 Department of Natural Science and Environmental Education, Oranim College of Education , Tivon, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alon Ornai Sergio Osorio-Canadas 108 Instituto de Investigaciones en Ecología y Sustentabilidad (IIES), Laboratorio de Interacciones Bióticas en Habitats Alterados, Universidad Nacional Autónoma de México , Campus Morelia, Morelia, México Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sergio Osorio-Canadas Wilhelm H. A. Osterman 36 Department of Biological and Environmental Sciences, University of Gothenburg , Gothenburg, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wilhelm H. A. Osterman Todd M. Palmer 75 Department of Biology, University of Florida , Gainesville, Florida, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Todd M. Palmer Theodora Petanidou 109 Laboratory of Biogeography & Ecology, Department of Geography, University of the Aegean , Mytilene, Greece Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Theodora Petanidou Christian W. W. Pirk 63 Social Insects Research Group (SIRG), Department of Zoology & Entomology, University of Pretoria , Pretoria, Republic of South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian W. W. Pirk Kit S. Prendergast 110 Centre for Sustainable Agricutural Systems, University of Southern Queensland , Toowoomba, QLD, Australia 111 School of Molecular and Life Science, Curtin University , Bentley, WA, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kit S. Prendergast Richard Primack 112 Biology Department, Boston University , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luis M. Primo 113 Facultad de Ciencias Agrarias, Universidad Nacional de Jujuy (FCA-UNJu) , San Salvador de Jujuy, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luis M. Primo Marina Querejeta 114 UMR CNRS 7267, Ecologie et Biologie des Interactions, Université de Poitiers , Poitiers, France 115 UMR CNRS 7261, Institut de Recherche sur la Biologie de l’Insecte, Université de Tours , Tours, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marina Querejeta Zelma Quirino 116 Departamento Engenharia e Meio Ambiente, Universidade Federal da Paraíba , PB, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zelma Quirino André Rodrigo Rech 117 Programas de Pós-Graduação em Biologia Animal, Ciência Florestal e Estudos Rurais, Universidade Federal dos Vales do Jequitinhonha e Mucuri , Diamantina, MG, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for André Rodrigo Rech Sara Reverté 118 Laboratory of Zoology, Research Institute for Biosciences, University of Mons , Mons, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sara Reverté Pedro J. Rey 119 Instituto Interuniversitario de Investigación del Sistema Tierra de Andalucía, Universidad de Jaén , Jaén, Spain 120 Departmento de Biología Animal, Biología Vegetal y Ecología, Universidad de Jaén , Jaén, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pedro J. Rey Léo C. Rocha-Filho 121 Instituto de Biologia – INBIO, Universidade Federal de Uberlândia , Uberlândia, MG, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Léo C. Rocha-Filho Anselm Rodrigo Domínguez 39 CREAF , Bellaterra, Spain 122 Autonomous University of Barcelona (UAB) , Bellaterra, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anselm Rodrigo Domínguez Masoud A. Rostami 123 Data Science Division, University of Texas at Arlington , Arlington, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Avery L. Russell 20 Department of Biology, Missouri State University , Springfield, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Avery L. Russell Silvia Santamaría 69 Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos , Móstoles, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Silvia Santamaría Francisco A. R. Santos 124 Department of Biological Sciences, Universidade Estadual de Feira de Santana , Feira de Santana, BA, Brasil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Francisco A. R. Santos Takehiro Sasaki 99 Graduate School of Environment and Information Sciences, Yokohama National University , Yokohama, Japan 125 Institute for Multidisciplinary Sciences, Yokohama National University , Yokohama, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Takehiro Sasaki Manu E. Saunders 126 School of Environmental and Rural Science, University of New England , Armidale, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manu E. Saunders Victor H. D. Silva 23 Centro de Síntese Ecológica e Conservação, Departamento de Genética, Ecologia e Evolução - ICB, Universidade Federal de Minas Gerais , Belo Horizonte, MG, Brazil 24 Programa de Pós-Graduação em Ecologia, Conservação e Manejo da Vida Silvestre, Universidade Federal de Minas Gerais , Belo Horizonte, MG, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Victor H. D. Silva Michael P. Simanonok 5 Department of Ecology, Montana State University , Bozeman, MT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael P. Simanonok Antigoni Sounapoglou 1 Department of Ecology, Faculty of Science, Charles University , Prague, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ingolf Steffan-Dewenter 79 Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ingolf Steffan-Dewenter Sam Tarrant 127 The Woodland Trust , Grantham, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rubén Torices 68 Global Change Research Institute (IICG-URJC), Rey Juan Carlos University (URJC) , Madrid, Spain 69 Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos , Móstoles, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rubén Torices Anna Traveset 78 Global Change Research Group, Mediterranean Institute for Advanced Studies , Mallorca, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anna Traveset Teja Tscharntke 128 Functional Agrobiodiversity & Agroecology, University of Göttingen , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Teja Tscharntke Guillermo Uceda-Gómez 1 Department of Ecology, Faculty of Science, Charles University , Prague, Czechia 129 Department of Zoology, Faculty of Science, University of South Bohemia, České Budějovice , Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guillermo Uceda-Gómez Katherine R. Urban-Mead 130 The Xerces Society for Invertebrate Conservation , Columbus, NJ, USA 131 Yale College, Yale University , New Haven, CT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine R. Urban-Mead Casper J. van der Kooi 132 Groningen Institute for Evolutionary Life Sciences, University of Groningen , Groningen, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Casper J. van der Kooi Catrin Westphal 128 Functional Agrobiodiversity & Agroecology, University of Göttingen , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catrin Westphal Abdullahi Yusuf 63 Social Insects Research Group (SIRG), Department of Zoology & Entomology, University of Pretoria , Pretoria, Republic of South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Abdullahi Yusuf Robert Tropek 1 Department of Ecology, Faculty of Science, Charles University , Prague, Czechia 2 Biology Centre of the Czech Academy of Sciences, Institute of Entomology , České Budějovice, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Robert Tropek For correspondence: sailee.sakha{at}gmail.com robert.tropek{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Pollination is a key ecological process sustaining biodiversity and food security, yet global patterns of plant–pollinator specialisation have remained unresolved. Using the largest global dataset of quantitative networks (>3,400 networks, >110,000 interactions), we show that the latitudinal specialisation gradient (LSG) exists, but it is non-linear, hemispherically asymmetric, and strongly taxon-dependent. Network-level and pollinator specialisation were lowest in the tropics and peaked at northern mid-latitudes, whereas plants tended to become more specialised toward higher latitudes. Climate consistently outperformed latitude, species richness, and environmental productivity as a predictor of these patterns. Specialisation declined with increasing temperature, rose with moderate rainfall before declining at the wettest sites, and increased with temperature seasonality, but plants and pollinators responded differently to these drivers. Functional groups diverged strongly: ectothermic insects were most specialised in cooler, seasonal climates, while birds showed weaker links to latitude but reduced specialisation in wetter regions. These findings demonstrate that climate, rather than latitude or species richness, structures global variation in specialisation. Because warmer and less seasonal climates promote generalisation, climate change is likely to disrupt the most specialised pollination systems, unevenly across taxa and regions, with important consequences for biodiversity and ecosystem stability. Introduction Specialisation of plant–pollinator interactions fundamentally influences pollination efficiency, plant reproduction, and ecosystem functioning and stability. The extent to which species rely on limited partners shapes resource partitioning and coexistence, thereby structuring communities, affecting species persistence, and driving evolutionary processes that generate and maintain biodiversity ( Armbruster & Muchhala 2009 ; Ollerton 2017 ; Van Der Niet & Johnson 2012 ). Specialised pollination is also hypothesised to facilitate reproductive isolation and speciation, promoting niche differentiation, and reducing competition, thus contributing to the exceptional diversity of plants and pollinators ( Kay & Sargent 2009 ; Van Der Niet & Johnson 2012 ). Yet, despite its ecological and evolutionary importance, broad-scale patterns in interaction specialisation remain unresolved. Latitudinal Specialisation Gradient (LSG), a systematic geographic pattern in the degree of interaction specialisation, remains a fundamentally unresolved question in ecology ( Hargreaves 2024 ; Pinheiro et al . 2023 ). Clarifying whether and why LSG exists is central to understanding the origin and maintenance of biodiversity gradients, community assembly, and the evolution of species interactions ( Moles & Ollerton 2016 ; Schleuning et al . 2012 ). LSG is closely linked to the well-established latitudinal diversity gradient (LDG), which describes increasing species richness toward the equator ( Armbruster & Muchhala 2009 ; Dobzhansky 1950 ; Pauw 2013 ; Schemske et al . 2009 ). Because specialisation and species richness are theoretically coupled, testing the existence of LSG and revealing its patterns and drivers is essential for our understanding of mechanisms structuring global biodiversity and ecosystem functioning, as well as their responses to global change ( Brown 2014 ; MacArthur 1972 ; Schemske et al . 2009 ). Pollination networks are ideal for testing LSG, because plant–pollinator interactions occur across all terrestrial biomes, encompass broad phylogenetic and functional diversity, and represent mutualisms integral to community assembly and diversification ( Schemske et al . 2009 ; Schleuning et al . 2012 ; Vázquez & Stevens 2004 ). Theoretical predictions and empirical evidence for LSG conflict sharply. Two prominent theoretical frameworks predict stronger specialisation in the tropics ( Hargreaves 2024 ; Schemske et al . 2009 ): the latitude–niche breadth hypothesis, attributing narrower niches to competition and benign climates ( MacArthur 1972 ), and the biotic interactions hypothesis, linking co-evolutionary specialisation to long-term climatic stability ( Dobzhansky 1950 ). Supporting empirical evidence for plant-pollinator LSG includes increased specialisation in plant-hummingbird networks toward the equator ( Dalsgaard et al . 2011 ) and a global synthesis of 54 pollination networks reporting higher specialisation at lower latitudes ( Trøjelsgaard & Olesen 2013 ). In direct contrast, optimal foraging theory predicts more generalised interactions in the tropics because high diversity often coincides with low local abundances of individual interacting species ( Schleuning et al . 2012 ; Vázquez & Stevens 2004 ). This prediction is consistent with a global analysis of 58 pollination networks showing decreasing network-level specialisation toward the tropics ( Schleuning et al . 2012 ). Further, Moles and Ollerton (2016) argued against any universal LSG, proposing that specialisation patterns vary with interaction type, interacting taxa, and ecosystem context, as supported by studies finding no consistent LSG ( Luna et al . 2022 ; e.g. Ollerton & Cranmer 2002 ; Rahimi & Jung 2024 ). Much of the variation in the existing studies can be attributed to geographic, taxonomic, and methodological biases. The global analyses have been dominated by temperate networks from the Americas and Europe, leaving critical gaps elsewhere ( Ollerton 2017 ; Vizentin-Bugoni et al . 2018 ), and have often ignored regional differences, despite the described strong ecosystem-specific effects on specialisation ( Pauw & Stanway 2015 ). Many studies examined a single group (bees in Cirtwill et al . 2025 ; e.g. hummingbirds in Dalsgaard et al . 2011 ; cacti in Gorostiague et al . 2023 ) or combined groups indiscriminately ( Luna et al . 2022 ; e.g., Olesen & Jordano 2002 ; Trøjelsgaard & Olesen 2013 ), potentially masking group-specific patterns shaped by contrasting diversity gradients, evolutionary histories, and resource-use strategies ( Armbruster 2017 ; Ollerton et al . 2007 ). Methodologically, cross-study comparisons are further hampered by the use of non-standardised specialisation metrics, uneven sampling effort, and the mixing of complete community networks with taxon-constrained partial networks ( Armbruster 2017 ; Brimacombe et al . 2022 ; Doré et al . 2021 ; Ollerton et al . 2007 ; Vizentin-Bugoni et al . 2018 ). Overcoming these biases is essential for the rigorous testing of plant-pollinator LSG. Here, we assemble the most comprehensive global dataset of quantitative plant– pollinator networks to date ( Fig. 1 ), comprising 3,415 networks from 162 studies, covering 110,571 pairwise interactions among 5,343 pollinator and 6,126 plant species, spanning all major terrestrial biomes across a broad latitudinal range ( Fig. 2 ). We address the limitations of prior syntheses by standardising sampling completeness, applying robust specialisation metrics (network-level H 2 ’ and species-level d’ ), and accounting for methodological differences. Crucially, we analyse LSGs separately for plants and functional pollinator groups, testing predictions that latitudinal patterns reflect their ecological and evolutionary differences. Finally, we test whether specialisation is driven by plant and pollinator diversity, climate, or environmental productivity, providing deeper insight into processes structuring global variation in plant–pollinator specialisation ( Fig. 1 ). By overcoming these geographic, taxonomic, and methodological constraints and explicitly analysing functional-group-specific patterns, our study advances ecological theory on biodiversity patterns, evolutionary ecology, and the architecture of mutualistic networks. Download figure Open in new tab Figure 1. Conceptual workflow for analysing the latitudinal specialisation gradient (LSG) in plant-pollinator interactions. Phase 1: (a) compilation of available plant–pollinator networks, (b) application of predefined selection criteria, and (c) pooling of spatiotemporal network replicates from the same site to minimize non-focal variation. Phase 2: (d–f) characterisation of networks with 28 descriptors (Table S2). Phase 3: (g) taxonomic standardisation and assignment of pollinators to functional groups (grey icons indicate groups excluded from functional-group analyses due to data insufficiency), followed by (h) construction of three complementary datasets and (i) calculation of three specialisation metrics and sampling completeness for each sampling network. Phase 4: (j–l) multi-stage modelling of LSG, including (j) evaluation of potential methodological biases, (k) fitting latitudinal models with significant biases as random effects, and (m) testing climatic, environmental, and biotic predictors to identify LSG drivers. See Online Methods for details. Download figure Open in new tab Figure 2. Global coverage and taxonomic composition of the analysed plant–pollinator networks. (a) Geographic distribution of networks, grouped into the New World and Old World. Symbols denote network completeness: taxonomically restricted (partial) and unrestricted (full). Network latitudinal ranges per region are indicated below the map. Marginal density plots show the distributions of network locations by their completeness along latitude (right) and longitude (top). (b) Pollinator composition by functional group, with bars indicating the percentage of all pollinator species in the dataset. (c) Plant composition, showing the 12 most speciose orders and their percentages in the dataset. Results Latitudinal specialisation gradient (LSG) We first tested whether interaction specialisation follows systematic latitudinal patterns, directly addressing the LSG hypothesis ( Fig. 1j–k ). Using generalised additive mixed models (GAMMs) and hierarchical GAMMs (HGAMs) on the three datasets (all networks, full networks, and functional-group networks), we modelled the three specialisation metrics (network-level H 2 ’ , and community-mean species-level d’ plants and d’ pollinators ) against latitude, while accounting for potential methodological biases of the general patterns from previous studies ( Fig. 3 ; Tables S4–S5). Geographic region and habitat openness most frequently showed significant effects (Table S4; Fig. S1) and were therefore retained as random effects in the final models (Table S5). Download figure Open in new tab Figure 3. Latitudinal patterns in specialisation of plant–pollinator interactions across datasets and metrics. Rows show datasets: (a–c) all networks; (d–f) full networks; (g–h) functional-group networks. Columns show specialisation metrics: network-level H 2 ’ (left); and community-mean species-level separated for pollinators ( d’ pollinators , middle) and for plants ( d’ plants , right; not calculated for the functional-group networks dataset). Curves are GAMM/HGAM fitted smooths: black curves indicate overall latitudinal trends with 95% confidence intervals (grey shading), coloured curves represent significant functional group-specific trends (see legend). Coloured points are raw data. Curves are drawn only when the smooth term for latitude is significant; otherwise only points are shown. Adjusted R 2 and significance (**p < 0.01; ***p < 0.001; n.s.: not significant) are reported in each panel, model summaries are reported in Table S5. Across models, latitudinal patterns in specialisation were generally non-linear and hemispherically asymmetric, with several models showing a mid-latitude maximum around ∼25–40°N ( Fig. 3 ). Latitude explained little variation in the all networks and full networks datasets ( R 2 ≤ 0.13), but resolving networks by functional groups consistently increased explanatory power of the models (functional-group networks dataset: R 2 = 0.21–0.34; d’ plants in the all networks dataset: R 2 = 0.24) and revealed taxon-dependent LSGs (Table S5; Fig. 3 ). In the all networks dataset, H 2 ’ and d’ plants showed a nearly U-shaped pattern, lowest in the tropics and increased toward higher latitudes, especially in the Northern Hemisphere ( Fig. 3a,c ). d’ pollinators was bimodal, with maxima in the northern subtropical and temperate latitudes ( Fig. 3b ; Table S5). Among functional pollinator groups, only bees and birds exhibited significant d’ patterns (Table S5): bees followed a generally hump-shaped pattern with dips in the tropics and northern temperate latitudes, whereas birds showed an almost flat pattern with an apparent dip in the northern temperate zone ( Fig 3b ). In the full-network dataset, latitude significantly predicted both d’ metrics but not H 2 ’ ( Fig. 3d–f ; Table S5). d’ pollinators followed a broadly hump-shaped pattern peaking in the northern subtropics, with more complex variation at higher northern latitudes; butterflies and moths broadly mirrored this pattern, although moths showed an additional peak in the southern tropics ( Fig. 3e ). d’ plants increased approximately linearly from the south to the north but with very low explained variation ( Fig. 3f ). In the functional-group networks dataset, latitude significantly influenced both H 2 ’ and d’ pollinators ( Fig. 3g–h ; Table S5). H 2 ’ followed a broadly U-shaped pattern with highest values in the northern subtropics, particularly for butterflies, moths, hoverflies, and bees, although some of these groups also exhibited mild secondary rises toward the lowest and highest latitudes ( Fig. 3g ). d’ pollinators showed a broadly hump-shaped pattern peaking at northern mid-latitudes and declining slightly toward the tropics and strongly toward the northern subarctic ( Fig. 3h ). Nevertheless, functional pollinator groups deviated from this pattern: butterflies generally mirrored it, whereas moths, hoverflies, bees, and birds showed irregular responses, mostly with multiple maxima and minima ( Fig. 3h ). Drivers of LSG We next tested whether forward selected climatic (mean annual temperature, MAT; mean annual precipitation, MAP; and temperature seasonality, SD_MMT), environmental (NDVI and SD_NDVI), and biotic (12 measures of predicted and observed species richness of plants and pollinators) variables explained global variation in specialisation ( H 2 ’, d’ pollinators , and d’ plants ). Using the same GAMM/HGAM framework ( Fig. 1i ), we compared individual predictors (and the MAT×MAP interaction) among each other and against the corresponding latitudinal models ( Fig. 4 ; Tables S6 and S7). All three potential methodological biases of the general patterns (geographic region, sampling focus, and habitat openness) showed significant effects in some models (Table S4) and were therefore retained as random effects where applicable. Download figure Open in new tab Figure 4. Most important climatic predictors for plant–pollinator specialisation across datasets and metrics. Panels show, for each metric–dataset combination, the predictors from the best-supported GAMM/HGAM model (lowest ΔAIC relative to the latitude-only baseline; see Table S7). Rows broadly correspond to datasets: (a–d) all networks; (e–h, k) full networks; (i–j) functional-group networks. Columns present specialisation metrics: network-level H 2 ’ (left); and community-mean species-level separated for pollinators ( d’ pollinators , middle) and for plants ( d’ plants , right; not calculated for the functional-group networks dataset). Curves are GAMM/HGAM fitted smooths: black curves indicate overall latitudinal trends with 95% confidence intervals (grey shading), coloured curves represent significant functional group-specific trends (see legend). Coloured points are raw data. The functional-group networks dataset model for H 2 ’ is omitted because latitude was the best predictor and is shown in Fig. 3g. Adjusted R 2 is reported in each panel; model details are in Table S7. Across models, predictor–response relationships were frequently non-linear. Except for H 2 ’ in the functional-group networks dataset, where latitude remained the strongest predictor ( Fig. 3g ), climatic variables (especially MAT, MAP, or their interaction) most consistently explained variation in interaction specialisation, whereas environmental and biotic predictors never did ( Fig. 4 ). However, even the best climate-based models often explained less variance than the corresponding latitudinal models (Tables S6–S7). For H 2 ’ , MAT was the best predictor in the all networks and full networks datasets, showing a non-linear but generally declining relationship with increasing temperature ( Fig. 4a,e ; Tables S6–S7). For d’ pollinators , MAT×MAP consistently provided the best-fitting models across all datasets ( Fig. 4b,c,f,g,I,j ). d’ pollinators responses to MAT were linear but varied in direction: increasing in the all networks and functional-group networks datasets but declining in the full network dataset. Functional groups diverged further, with bees and beetles generally increasing in specialisation with MAT, hoverflies declining, and other groups showing less consistent responses. In contrast, MAP effects were more uniform, with d’ pollinators generally increasing with precipitation but dipping near 3000 mm annually ( Fig. 4c,j,g ). For d’ plants , SD_MMT was the strongest predictor in the all networks dataset, with specialisation generally increasing under greater temperature seasonality but showing local minima around ±3°C and ±10°C ( Fig. 4d ). MAT×MAP was the most plausible predictor of d’ plants in the full networks dataset, with an S-shaped response to MAT dipping just below 0°C and peaking near 12°C ( Fig. 4h ), and a nearly hump-shaped relationship with MAP with the highest values at ∼3000 mm annually ( Fig. 4k ). Discussion Our global analyses of >3,400 quantitative plant–pollinator networks provide the strongest evidence to date that the latitudinal specialisation gradient (LSG) exists, but it is neither simple nor universal. Latitudinal patterns in specialisation were non-linear, differed between hemispheres, and varied across taxa. Most importantly, specialisation of plant–pollinator interactions did not peak in the tropics: plants tended to be more specialised toward higher latitudes, network-level and pollinator specialisation peaked in northern subtropical and temperate regions, and functional pollinator groups partly diverged in responses. These patterns held after accounting for methodological biases ( Moles & Ollerton 2016 ; e.g. Vázquez et al . 2009 ), and resolving pollinators by functional group substantially increased the variation explained by latitude. Across the specialisation metrics and datasets, climate consistently provided stronger explanatory power than latitude or diversity, with temperature, precipitation, and their interaction emerging as the dominant drivers of global variation in specialisation, while environmental productivity and species richness played no significant role. None of the main theoretical frameworks fully explain the revealed global patterns in plant–pollinator specialisation. The latitude–niche breadth ( MacArthur 1972 ) and biotic interactions ( Dobzhansky 1950 ) hypotheses both predict highest specialisation in the tropics, yet our analyses revealed both plants and pollinators to be most generalised at tropical latitudes. This pattern is consistent with the optimal foraging theory, which predicts that species-rich tropical communities favour generalisation because high diversity reduces per-species abundance and encounter rates, thereby promoting broader diets ( Schleuning et al . 2012 ; Vázquez & Stevens 2004 ). The additional declines in pollinator specialisation toward the highest latitudes may reflect physiological constraints in extreme climates. We lacked data on pollinator abundance and floral rewards, the resources for plants and pollinators respectively, to test the proposed mechanisms of optimal foraging theory directly. Nevertheless, because neither species richness nor environmental productivity (a potential proxy for resource availability; Bailey et al . 2004 ; Pauw 2013 ) outperformed climate predictors in our models, these mechanisms are unlikely to underlie the observed LSG patterns ( Hargreaves 2024 ; MacArthur 1972 ; Schemske et al . 2009 ; Vázquez & Stevens 2004 ). Climate emerged as the primary driver of the global variation in pollination specialisation. The declining network-level and pollinator specialisation towards warmer environments confirms that the predicted simple latitudinal gradients are in fact modified by elevation and regional context. These patterns also contradict expectations that warmer, less seasonal ecosystems foster higher specialisation in plant–pollinator interactions ( Granot & Belmaker 2020 ; Petanidou et al . 2018 ; Schemske et al . 2009 ). Instead, in warmer environments, pollinators may extend their activity despite the known high phenological turnover of tropical plants ( Du et al . 2024 ; e.g. Song et al . 2022 ), so an average pollinator species must rely on more plant species over their lifetimes or activity periods. Conversely, colder and more seasonal climates compress both flowering and foraging into shorter phenological windows, which may promote stronger specialisation ( Glaum et al . 2021 ). Precipitation exerted a non-linear influence: specialisation generally increased from dry to intermediate–high rainfall but declined at the wettest sites, consistent with moderate rainfall supporting flowering and foraging, in contrast to excessive rainfall suppressing flight and diluting nectar ( Aizen 2003 ; Klomberg et al . 2022 ; Maicher et al . 2018 ). Plant specialisation showed different climatic responses, with a broadly positive association with temperature seasonality, consistent with synchronous flowering and tighter pairings where seasonal forcing is strong ( Altermatt 2010 ; Glaum et al . 2021 ). By showing that climate, rather than latitude or biodiversity alone, structures specialisation worldwide, we bridge decades of debate on the LSG and reveal the mechanisms underlying interaction diversity. The functional-group differences demonstrate that LSG arises from group-specific traits related to climate. Plants became more specialised in highly seasonal ecosystems at higher latitudes, likely due to tighter flowering synchrony ( Du et al . 2024 ; Rathcke & Lacey 1985 ), whereas pollinators responded more directly to temperature and precipitation ( Dalsgaard et al . 2011 ). Several insect groups substantially contributed to the northern mid-latitude peak in specialisation, while birds showed only a shallow specialisation increase toward higher latitudes. These contrasts reflect differences in thermal physiology, where relatively colder and seasonal climates may select for more efficient specialised foraging in insects ( Brown 2014 ; Dzekashu et al . 2023 ), whereas mostly tropical nectarivorous birds can adapt behaviourally to year-round available but phenologically shifting resources ( Chmel et al . 2021 ). Weak or absent climatic trends in specialisation in some insect groups, including beetles, flies, and wasps, probably reflect their heterogeneous diets, although some clades are highly selective nectar or pollen feeders ( Ollerton 2017 ; Willmer 2011 ). Warmer and less seasonal climates promote generalisation over specialisation, with major implications for the fate of pollination networks under climate change. Projected reductions in seasonal differences in temperate regions ( Adam et al . 2023 ; Xu et al . 2013 ) and intensified precipitation extremes in the subtropics ( Wang et al . 2024 ) are likely to affect the most specialised plant–pollinator communities. Specialised interactions are particularly vulnerable to phenological mismatches, as even small shifts in flowering or pollinator activity can cause breakdowns, whereas generalists may adjust or rewire their interactions ( Bartomeus et al . 2011 ; Kudo & Ida 2013 ). Resulting losses of specialised species could shift communities toward generalists, with uncertain consequences for ecosystem functioning and stability ( Burkle et al . 2013 ). Responses may be expected to differ among groups: ectothermic insects, most specialised in cooler, seasonal climates, may be highly sensitive to resource shifts under climate warming, while bird specialisation is more strongly linked to precipitation and thus vulnerable to altered rainfall regimes. Geographic context also matters, since tropical networks are already relatively generalised, while more specialised temperate and subtropical communities may be more prone to species loss or phenological disruption. Overall, our results caution that climate change will not affect all plant–pollinator networks equally, underscoring the need to account for both climatic drivers and functional-group differences when assessing the future of pollination services and biodiversity. Author contributions S.P.S. and R.T. conceived and designed the study, with substantial input from N.B. S.P.S. collated and processed the datasets, and performed the analyses under the supervision of R.T., with feedback from N.B., B.D., C.D., C.K., D.V., J.O., J.R., L.B., M.S., P.C., and T.M.K. S.P.S and R.T. prepared all visualisations, tables, and supplementary materials, with contributions from D.A., J.F., and G.U. S.P.S. and R.T. wrote the manuscript draft, with contributions from N.B., B.D., C.D., C.K., D.V., J.O., J.R., L.B., M.S., P.C., T.M.K., and T.F. All authors contributed data, reviewed the manuscript draft, and approved its submission. Online Methods All analyses, including data preparation, were performed using R 4.5.0 ( R Core Team 2025 ). Dataset compilation We compiled a global dataset of plant–pollinator interaction networks ( Fig. 1a ) through a systematic search of Web of Science and Google Scholar between January and May 2021 (and we extensively monitored for suitable new studies until September 2024). Search strings combined terms pollin*, network*, flower visit*, plant*. All retrieved publications were manually screened for relevance, and we also examined their cited and citing references. Publicly available datasets were incorporated from the Mangal Interaction Database ( Poisot et al . 2016 ), the Web of Life database ( Fortuna et al . 2014 ), and a smaller set of data from the currently non-public LifeWebs project ( Butterill et al . 2021 ). To maximise geographic and methodological coverage, we also contacted authors of studies without open data and requested both published and unpublished datasets from the international community of pollination ecologists (Table S1). Datasets were included if they met the following criteria ( Fig. 1b ): originated from natural or semi-natural ecosystems (excluding agricultural, urban, and other anthropogenic habitats); contained at least five plant and five pollinator species; provided directly sampled interaction data (excluding compilations over broad spatial or temporal scales); and reported quantitative interaction strengths (i.e. excluding binary datasets). Coordinates, elevation, sampling details, and other metadata were obtained from publications and/or databases when available. When essential information was missing, we contacted the original authors; studies lacking such key data were excluded. Because direct measures of pollination efficiency were rarely available, we treated all floral visitors as potential pollinators and all flower visits as pollination interactions, which is standard practice in large-scale pollination network analyses. Our compiled dataset contained 3,415 quantitative networks from 162 studies, spanning 43.60°S to 81.00°N in latitude and 1 to 4,241 m in elevation, representing virtually all terrestrial biogeographic regions and major ecosystem types ( Fig. 2a , Table S1). To reduce variation irrelevant to our aims, we pooled temporally replicated networks sampled at the same sites and merged networks representing small-scale spatial units (such as transects or plots) within the same study site ( Fig. 1c ). However, networks from studies explicitly examining elevational or environmental gradients were retained separately, as were networks from different publications to account for methodological differences. After these steps, our dataset contained 739 unique networks. Network descriptors Each plant–pollinator network was characterised using 28 descriptors ( Fig. 1d-f ) grouped into four categories: study and methodological details, geographic information, environmental conditions, and local species richness (Table S2; latitudinal trends in Extended Data 2 ). Study and methodological details ( Fig. 1d ) included the source of network data, rationale for splitting datasets into separate networks (e.g. distinct sites, elevations, habitats, or vertical strata), sampling design and field methods, sampling focus (zoocentric or phytocentric), network completeness (full or partial networks), and lower-level (plants) and upper-level (pollinators) constraints (specified if sampling was taxonomically biased). Geographic information comprised country, geographic coordinates, absolute latitude, elevation, and region (New World or Old World, Fig. 1d ). Environmental conditions included habitat openness (forest or open habitat). Seven climatic and environmental variables ( Fig. 1e ) were extracted from geographic coordinates with the terra package (Hijmans et al. 2025 a ). These climatic variables were mean annual temperature (MAT, °C), standard deviation of mean monthly temperature (SD_MMT, ±°C), mean temperature of the three warmest months (°C), mean annual precipitation (MAP, mm), and the standard deviation of mean monthly precipitation (SD_MMP, ±mm) ( Fick & Hijmans 2017 ). Environmental productivity was quantified as annual mean Normalised Difference Vegetation Index (mean NDVI; Didan 2015 )and the standard deviation of monthly NDVI (SD_NDVI). Species richness descriptors ( Fig. 1f ) included predicted local species richness for plants ( Cai et al . 2023 ), birds ( Jenkins et al . 2013 ), nectarivorous birds (range maps from BirdLife, filtered using an unpublished species list by B. Dalsgaard and J. Ollerton, pers. comm.), and butterflies ( Daru 2024 ), retrieved using the raster package ( Hijmans et al . 2025b ). In addition, we included observed species richness directly recorded in the original network data: plants, pollinators, and total species richness, as well as richness of individual functional pollinator groups (see Table S2 for definitions). Taxonomy and functional groups We standardised the taxonomy and nomenclature for plants and pollinators across all studies ( Fig. 1g ) using the taxize package ( Chamberlain & Szöcs 2013 ) with the GBIF database (2023). Species names were verified by exact matching, unmatched or partially matched (“fuzzy”) species names were corrected to fix typos and re-matched. Taxa identified to morphospecies were retained as unique entities (called species hereinafter) within individual datasets but were not comparable across different studies. Pollinator species were assigned to functional groups reflecting distinct floral resource use and ecological roles, following commonly accepted pollination syndromes ( Willmer 2011 ): bees, wasps (incl. sawflies), butterflies, hawkmoths, settling moths, beetles, birds, bats, non-flying mammals, and reptiles ( Fig. 1g ). Flies were further separated into hoverflies, known for their relatively specialised floral associations and high dataset representation, and all other flies without further subdivision of specialised fly groups due to low abundance and generally coarse taxonomic identification in our data. Other floral visitors (such as spiders, ants, orthopterans, hemipterans) were excluded as mostly accidental floral visitors and/or unimportant pollinators (although some species can be important in unique pollination systems; Ollerton 2021 ). Plants were not subdivided into functional groups. After the taxonomic standardisation and restricting the networks to the selected pollinator functional groups, the dataset comprised 110,571 pairwise interactions among 5,343 pollinator species and 6,126 plant species, spanning a broad diversity of pollination systems ( Fig. 2b–c ). Datasets for analyses To reflect the dataset heterogeneity and robustly test latitudinal specialisation patterns, we created three distinct data subsets for further analyses ( Fig. 1h ). All networks dataset (739 networks): all quantitative networks after the above-described filtering and merging, regardless of original taxonomic scope or sampling methods. This allowed evaluation of global specialisation patterns analogous to previous syntheses not correcting for methodological or taxonomic differences. Full networks dataset (322 networks): only networks covering plant–pollinator communities without intentional taxonomic focus (i.e. not necessarily sampling all potential interactions in the community, which is usually impossible even with substantial effort). We excluded partial networks explicitly restricted to plant or pollinator groups, such as hummingbird-visited flowers or bee-plant interactions. This dataset provides more robust insights into community-level specialisation patterns by reducing bias from taxonomic sampling scope. Functional-group networks dataset (926 networks, varying by functional groups; Table S3): to test within-group specialisation patterns, we used networks originally sampled with a taxonomic bias or subnetworks extracted from entire-community networks. These subnetworks were created separately for each functional pollinator group, retaining only those with ≥5 plant and ≥5 pollinator species (Table S3). Due to insufficient data, reptiles, bats, non-flying mammals, and hawkmoths were excluded from this dataset analyses ( Fig. 1g ). Specialisation metrics To comprehensively characterise specialisation and allow reliable comparisons across networks of different size, we used two complementary size-independent metrics capturing network-level and species-level specialisation ( Fig. 1i ), using the bipartite package ( Dormann et al . 2008 ). Network-level specialisation H 2 ’ quantifies the deviation of observed interaction frequencies from random partner choice, reflecting overall interaction specialisation ( Blüthgen et al . 2006 ). Values range from 0 (complete generalisation; interactions randomly distributed) to 1 (maximum specialisation; interactions highly restricted). This metric is standardised to account for variation in network size and species frequencies ( Blüthgen et al . 2006 ). Community-mean species-level specialisation d’ represents the average specialisation of individual species within networks, allowing separation for particular functional groups. Analogous to H 2 ’ , this metric quantifies deviation of species from random interactions, ranging from 0 (generalist) to 1 (specialist; Blüthgen et al . 2006 ). Although d’ is correlated with H 2 ’ when calculated for all species in the network ( Blüthgen et al . 2006 ), it allows specialisation quantification for a part of species, such as plants, pollinators, or their functional groups. We calculated mean d’ separately for pollinators ( d’ pollinators ) and plants ( d’ plants ). d’ pollinators was calculated across all three datasets and further partitioned by functional group. d’ plants was calculated only for the all networks and full networks datasets, but not for the functional-group network dataset to avoid biases introduced by restricting networks to a single pollinator group. Analyses of specialisation patterns We fitted separate models for each of the three specialisation metrics ( H 2 ’, d’ pollinators , and d’ plants ), using beta distribution bounded between 0 and 1. To minimise sampling bias ( Blüthgen & Staab 2024 ), all analyses were weighted by network-specific sampling completeness. This was estimated as the proportion of observed to total interactions (completeness at order q = 0) using rarefaction and extrapolation implemented in the Completeness . link function from the iNEXT package ( Chiu et al . 2023 ). Networks were generally well sampled, with 75% exceeding 0.75 completeness ( Extended Data 3 ). The estimated completeness value of each network was directly used as its model weight, ensuring that each network contributed appropriately to the analyses. To analyse patterns in interaction specialisation, we constructed eight model sets for each of the three specialisation metrics across the three datasets (with d’ plants not calculated for the functional-group networks dataset), employing two complementary modelling approaches. Generalised additive mixed models (GAMMs; Pedersen et al . 2019 ) were applied to H 2 ’ and d’ plants in the all networks and full networks datasets to capture overall patterns, as GAMMs allow flexible non-linear relationships without assuming a predefined shape. To incorporate functional group-specific responses, we fitted hierarchical generalised additive models (HGAMs) to H 2 ’ in the functional-group networks dataset and to d’ pollinators in all three datasets, as HGAMs extend GAMMs by modelling both global and group-specific responses to predictors. Following Pedersen et al. (2019) , we constructed five HGAM types: global effect model (G: single common response across functional groups); group-specific common-shape model (S: functional groups’ responses differ in magnitude but share shape); group-specific different-shape model (I: each group has a distinct response curve); and two combined models with a global response alongside group-specific deviations: global and group-specific common shape model (GS), and global and group-specific different-shapes model (GI). All models were fitted using the gam function with beta family ( betar , logit link) in the mgcv package ( Wood 2011 ). Predictors were modelled with thin-plate regression splines (k = 10). For functional group-specific effects, we applied factor smooths (bs = “fs”) to obtain separate estimates for each group. Smoothing parameters were estimated by restricted maximum likelihood (REML), with a shrinkage penalty to allow splines to collapse to a straight line when the effective degrees of freedom approached one (Wood 2017). Model diagnostics were checked with the gam . check function to ensure no systematic patterns in the residuals. To assess whether latitude predicts variation in interaction specialisation, we implemented a multi-stage modelling approach ( Fig. 1j–l ). First, we evaluated potential sampling biases (in the meaning of potential biases complicating interpretation of previous published results) by testing effects of three network descriptors (sampling focus, habitat openness, and geographic region), each as a fixed effect in separate models alongside latitude, for each specialisation metric and network dataset. Second, network descriptors with significant effects on latitudinal specialisation patterns were incorporated as random effects into the main latitudinal models ( Fig. 1j ). We then re-evaluated each random effect and sequentially removed those that did not remain significant. This resulted in the final set of eight models with latitude as the key predictor and only network descriptors that significantly influenced LSG retained as random effects (Table S4). This multi-stage approach accounts for dataset-specific variability in methodological biases while isolating the general latitudinal trend in specialisation. To further evaluate drivers of LSG, we tested the importance of climatic, environmental, and biotic variables ( Fig. 1l ; Table S2). We first calculated pairwise correlations among all variables (Spearman’s ρ; Fig. S2) and removed highly correlated predictors (r > 0.7) to reduce redundancy. Because elevational variation can disrupt latitudinal patterns, we also tested the relationship of MAT to elevation, latitude, and their interaction using a linear model ( lm function). MAT was significantly predicted by this model (p < 0.001; R 2 = 0.89; Extended Data 1 ), and we therefore excluded elevation from subsequent analyses. Among the biotic variables, we excluded predicted species richness for birds, nectarivorous birds, and butterflies, as these predictors showed very limited variability within the network datasets, probably because of geographical clustering of datasets containing these functional groups, making them unsuitable for robust model testing. We then evaluated the remaining climatic (MAT, SD_MMT, MAP), environmental (NDVI and SD_NDVI), and biotic (one predicted and eleven observed species richness measures) variables. Because biotic predictors are meaningful at different metric–dataset scales, we deployed them selectively: observed plant species richness was considered broadly for all metrics and datasets; predicted plant species richness, observed pollinator species richness and observed total species richness were considered for H 2 ’ and d’ plants in the all networks and full networks datasets; and observed richness of functional pollinator groups was used for species-level metrics ( d’ plants , d’ pollinators ) across datasets and additionally for H 2 ’ in the functional-group dataset (see Table S6 for details). Following the procedure for the latitudinal models (see above), we selected relevant methodological biases as random effects for each model and retained only those with significant effects. Consequently, each predictor was tested in a separate model, with MAT and MAP also examined as an interaction term. Within each of the eight model sets we compared models by AIC against the latitudinal model, treated as a null baseline. AIC values were standardised within each model set by setting the latitudinal model to zero, and we report ΔAIC values relative to this baseline (Table S6). Extended data Download figure Open in new tab Extended Data 1. Mean annual temperature as a function of absolute latitude and elevation for the analysed plant-pollinator interaction networks. Sampling sites are coloured by their elevation. Adjusted R 2 and p-value (***p < 0.001) originate from a linear model predicting mean annual temperature by latitude and elevation. Download figure Open in new tab Extended Data 2. Latitudinal distribution of plant–pollinator network descriptors. Points show descriptor values for individual networks in the all networks dataset, coloured by site elevation. See Table S2 for descriptor definitions, abbreviations, and units. Download figure Open in new tab Extended Data 3. Kernel density distributions of sampling completeness (%). Panels show sampling completeness for plant–pollinator networks in the (a) all networks dataset, (f) full networks dataset, and (b–e, g–j) for individual functional groups from the functional-group networks dataset. Panel labels report the number of networks in each dataset (n). Acknowledgements We are grateful to everybody who assisted with sampling of the analysed datasets, especially to Elisângela L. S. Bezerra, Kryštof Chmel, Joel Queiroz, and Pat Willmer; to Ondřej Mottl for priceless statistical advice; and to Sara D. Leonhardt, Martin Volf, Tereza Kočárková, Fotoula Papandreou, Javier Oñate-Casado, Riccardo Pernice, and Constantinos Charalambous for feedback on earlier versions of this manuscript. We used GPT large language model (ChatGPT, OpenAI, models 4.5, 4o, 4-turbo, and 5, June 2024–September 2025) to assist in improving the clarity, readability, and stylistic refinement of the manuscript text. This study was funded by the Czech Science Foundation project 21-24186M (to S.P.S., D.A., J.F., Š.J., I.K., A.S., R.T.). Individual datasets and co-authors were supported by Alexander von Humboldt Foundation (1134644), São Paulo Research Foundation (2023/03083-6, 2023/02881-6, 2023/17728-9), and Consulate General of France in São Paulo (all previous to M.A.M.); Bavarian State Ministry of Science and Art (to F.M.); Biotechnology and Biological Sciences Research Council (to L.V.D.); Center for Research on Biodiversity Dynamics and Climate Change, CEPID–FAPESP (2021/10639-5; to F.A. and C.S.B.); National Council for Scientific and Technological Development, CNPq (308559/2022-3 to F.A., C.S.B.; 141736/2020-8 to A.C.M.; 311665/2022-5, 400904/2019-5, and 423939/2021-1 to J.P.B.; 310508/2019-3 to I.M.; 309893/2023-2 to L.M.; 177005/2024-6 to V.S.; 305204/2024-6 to M.A.M); CAPES (Finance Code 001; COOPBRASS: 88887.947041/2024-00 to A.C.M.; 177005/2024-6 to V.S.; PROEX 88882.347259/2019-01 to U.M.); Brazilian Biodiversity Fund, FunBio (004/2021 to I.N.G.; 029/2022 to V.S.); Rufford Foundation (377031 to I.N.G.; 28478-1 to U.M.); Czech Science Foundation (19-14620S; T.F.); German Research Foundation DFG (152112243 to F.L.); Dirección General de Investigación, Universidad de San Carlos de Guatemala (4.8.63.2.27–2012 to E.C. and N.E.; 4.8.63.8.60–2018 and 4.8.63.4.41–2020 to Pa.L.); FAPEMIG (RED-00039-23 to P.M. and A.R.R.); INCT Pollination (CNPq/CAPES/FAPERJ Call 58/2022 to P.M. and A.R.R.); Faculty for Future, Schlumberger Foundation (Q.C., Pa.L.); the Human Frontier Science Program (RGP023/2023 to C.v.); European Research Council ERC (101054177 to Sa.H. and A.T.; 819374 to Y.C., V.H., and Th.K.); Knut and Alice Wallenberg Foundation (KAW 2019.0202 to A.B. and W.O.); LIFE project Olivares Vivos+ (LIFE20 NAT/ES/001487 to P.R.); Missouri Department of Conservation (K02442-PI0242-022 to R.N.A., Mag.M., and A.L.R.); National Science Foundation (DGE-2244337 to L.A.D.); OAPN project 014/2009 (to Mar.M.); CONAHCYT (CBF2023-2024-216 to W.D.); Spanish Ministry of Science, Innovation and Universities (PID2021-127900NB-I00, PGC2018-098498-A-100, and RYC2021-032351-I to A.M.); Israel Ministry of Environmental Protection (grant no. 121-5-13 to Ta.K.); and funding of iDiv via the German Research Foundation DFG (FZT 118, 202548816 to T.M.K.). Funder Information Declared Czech Science Foundation, https://ror.org/01pv73b02 , 21-24186M , 19-14620S Alexander von Humboldt Foundation, https://ror.org/012kf4317 , 1134644 São Paulo Research Foundation , 2023/03083-6 , 2023/02881-6 , 2023/17728-9 Consulate General of France in São Paulo Bavarian State Ministry of Science and Art Biotechnology and Biological Sciences Research Council Center for Research on Biodiversity Dynamics and Climate Change CEPID-FAPESP , 2021/10639-5 National Council for Scientific and Technological Development, CNPq , 308559/2022-3 , 141736/2020-8 , 311665/2022-5 , 400904/2019-5 , 423939/2021-1 , 310508/2019-3, 309893/2023-2, 177005/2024-6, 305204/2024-6 CAPES , Finance Code 001; COOPBRASS: 88887.947041/2024-00, 177005/2024-6 , PROEX 88882.347259/2019-01 Brazilian Biodiversity Fund, FunBio , 004/2021 , 029/2022 Rufford Foundation, https://ror.org/02bxrrf91 , 377031 , 28478-1 German Research Foundation DFG , 152112243 Dirección General de Investigación, Universidad de San Carlos de Guatemala , 4.8.63.2.27-2012 , 4.8.63.8.60-2018 , 4.8.63.4.41-2020 FAPEMIG , RED-00039-23 INCT Pollination (CNPq/CAPES/FAPERJ Call 58/2022) Faculty for Future, Schlumberger Foundation the Human Frontier Science Program , RGP023/2023 European Research Council ERC , 101054177 , 819374 Knut and Alice Wallenberg Foundation , KAW 2019.0202 LIFE project Olivares Vivos+ , LIFE20 NAT/ES/001487 Missouri Department of Conservation , K02442-PI0242-022 National Science Foundation , DGE-2244337 OAPN , 014/2009 CONAHCYT , CBF2023-2024-216 Spanish Ministry of Science, Innovation and Universities , PID2021-127900NB-I00 , PGC2018-098498-A-100 , RYC2021-032351-I Israel Ministry of Environmental Protection , 121-5-13 German Research Foundation DFG , FZT 118 , 202548816 References ↵ Adam , O. , Liberty-Levi , N. , Byrne , M. & Birner , T. 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Pirk , Kit S. Prendergast , Richard Primack , Luis M. Primo , Marina Querejeta , Zelma Quirino , André Rodrigo Rech , Sara Reverté , Pedro J. Rey , Léo C. Rocha-Filho , Anselm Rodrigo Domínguez , Masoud A. Rostami , Avery L. Russell , Silvia Santamaría , Francisco A. R. Santos , Takehiro Sasaki , Manu E. Saunders , Victor H. D. Silva , Michael P. Simanonok , Antigoni Sounapoglou , Ingolf Steffan-Dewenter , Sam Tarrant , Rubén Torices , Anna Traveset , Teja Tscharntke , Guillermo Uceda-Gómez , Katherine R. Urban-Mead , Casper J. van der Kooi , Catrin Westphal , Abdullahi Yusuf , Robert Tropek bioRxiv 2025.10.08.680666; doi: https://doi.org/10.1101/2025.10.08.680666 Share This Article: Copy Citation Tools Climate-driven specialisation in plant–pollinator networks peaks outside the tropics Sailee P. Sakhalkar , Nico Blüthgen , Laura A. Burkle , Paul CaraDonna , Bo Dalsgaard , Carsten F. Dormann , Christopher N. Kaiser-Bunbury , Tiffany M. Knight , Jeff Ollerton , Julian Resasco , Matthias Schleuning , Diego P. Vázquez , Rita N. Afagwu , Ruben Alarcón , Felipe W. Amorim , Marsal D. Amorim , Dominik Anýž , Blanca Arroyo-Correa , Maddi Artamendi , Zeynep Atalay , Justin A. Bain , Katherine C. R. Baldock , Gavin Ballantyne , Caio S. Ballarin , Behnaz Balmaki , Michael Bartoš , Vasuki Belavadi , Paolo Biella , Anne D. Bjorkman , João Paulo Raimundo Borges , Jordi Bosch , Camila Bosenbecker , Stephane Boyer , Karin T. Burghardt , Edson Cardona , Quebin Casiá , Anthony M. T. Castagna , Ferhat Celep , Melanie N. Chisté , Yann Clough , James M. Cook , Liam P. Crowther , Fabiana D. Reis , Luis P. da Silva , Wesley Dáttilo , Valeska De Cárdenas , Joan Díaz-Calafat , Lynn V. Dicks , Rye Dickson , Marion L. Donald , Lee A. Dyer , Fairo F. Dzekashu , Natalia Escobedo Kenefic , Tom M. Fayle , Laura L. Figueroa , Jan Filip , Raúl García-Camacho , Amy-Marie Gilpin , Luis Giménez-Benavides , Ingrid N. Gomes , Heather Grab , Ingo Grass , Travis J. Guy , Veronica Hederström , Carlos Hernández-Castellano , Sandra Hervías-Parejo , Andrea Holzschuh , Sebastian Hopfenmüller , José M. Iriondo , Štěpán Janeček , Jana Jersáková , Shalene Jha , Aphrodite Kantsa , Tamar Keasar , Liam Kendall , Yannick Klomberg , Ishmeal N. Kobe , Theresia Krausl , Patricia Landaverde , Carlos Lara-Romero , H. Michael G. Lattorff , Felipe Librán-Embid , Tial C. Ling , Sissi Lozada-Gobilard , Francis Ewome Luma , Pedro Luna , Ludmilla M. S. Aguiar , Ana Carolina Pereira Machado , Isabel C. Machado , Ainhoa Magrach , Fabienne Maihoff , Gabriel Marcacci , Carlos Martínez-Núñez , Pietro K. Maruyama , Natsuki Matsubara , Maggie Mayberry , Marco A. R. Mello , Ugo Mendes Diniz , Marcos Méndez , Jan E. J. Mertens , Rubén Milla , Javier Morente-López , Tarcila L. Nadia , Georgios Nakas , Anders Nielsen , Alon Ornai , Sergio Osorio-Canadas , Wilhelm H. A. Osterman , Todd M. Palmer , Theodora Petanidou , Christian W. W. Pirk , Kit S. Prendergast , Richard Primack , Luis M. Primo , Marina Querejeta , Zelma Quirino , André Rodrigo Rech , Sara Reverté , Pedro J. Rey , Léo C. Rocha-Filho , Anselm Rodrigo Domínguez , Masoud A. Rostami , Avery L. Russell , Silvia Santamaría , Francisco A. R. Santos , Takehiro Sasaki , Manu E. Saunders , Victor H. D. Silva , Michael P. Simanonok , Antigoni Sounapoglou , Ingolf Steffan-Dewenter , Sam Tarrant , Rubén Torices , Anna Traveset , Teja Tscharntke , Guillermo Uceda-Gómez , Katherine R. 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