Ecology

Cairney, J. W. G. & Bastias, B. A. Influences of fire on forest soil fungal communities. Can. J. For. Res. 37, 207–215 (2007).Article 

Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. The effects of fuel reduction treatments on runoff, infiltration and erosion in two shrubland areas in the north of Spain. J. Environ. Manage. 105, 96–102 (2012).Article 

Google Scholar 
Reazin, C., Morris, S., Smith, J. E., Cowan, A. D. & Jumpponen, A. Fires of differing intensities rapidly select distinct soil fungal communities in a Northwest US ponderosa pine forest ecosystem. For. Ecol. Manage. 377, 118–127 (2016).Article 

Google Scholar 
Durán-Manual, F. et al. Prescribed burning in Pinus cubensis-dominated tropical natural forests: A myco-friendly fire-prevention tool. For. Syst. 31, e012 (2022).
Google Scholar 
Busse, M. D., Hubbert, K. R., Fiddler, G. O., Shestak, C. J. & Powers, R. F. Lethal soil temperatures during burning of masticated forest residues. Int. J. Wildl. Fire 14, 267–276 (2005).Article 

Google Scholar 
Frazão, D. F. et al. Cistus ladanifer (Cistaceae): A natural resource in Mediterranean-type ecosystems. Planta 247, 289–300 (2018).Article 

Google Scholar 
Keeley, J. E., Bond, W. J., Bradstock, R. A., Pausas, J. G. & Rundel, P. W. Fire in mediterranean ecosystems. Fire Medit. Ecosyst. https://doi.org/10.1017/cbo9781139033091 (2011).Article 

Google Scholar 
Louro, R., Peixe, A. & Santos-silva, C. New insights on Cistus salviifolius L. micropropagation. J. Bot. Sci. 6, 10–14 (2017).CAS 

Google Scholar 
Valbuena, L., Tarrega, R. & Luis, E. Influence of heat on seed germination of Cistus laurifolius and Cistus ladanifer. J. Wildl. Fire 2, 15–20 (1992).Article 

Google Scholar 
Martín-Pinto, P., Vaquerizo, H., Peñalver, F., Olaizola, J. & Oria-De-Rueda, J. A. Early effects of a wildfire on the diversity and production of fungal communities in Mediterranean vegetation types dominated by Cistus ladanifer and Pinus pinaster in Spain. For. Ecol. Manage. 225, 296–305 (2006).Article 

Google Scholar 
Comandini, O., Contu, M. & Rinaldi, A. C. An overview of Cistus ectomycorrhizal fungi. Mycorrhiza 16, 381–395 (2006).Article 
CAS 

Google Scholar 
Zuzunegui, M. et al. Growth response of Halimium halimifolium at four sites with different soil water availability regimes in two contrasted hydrological cycles. Plant Soil 247, 271–281 (2002).Article 

Google Scholar 
Civeyrel, L. et al. Molecular systematics, character evolution, and pollen morphology of Cistus and Halimium (Cistaceae). Plant Syst. Evol. 295, 23–54 (2011).Article 

Google Scholar 
Leonardi, M., Furtado, A. N. M., Comandini, O., Geml, J. & Rinaldi, A. C. Halimium as an ectomycorrhizal symbiont: New records and an appreciation of known fungal diversity. Mycol. Prog. 19, 1495–1509 (2020).Article 

Google Scholar 
Oria-De-Rueda, J. A., Martín-Pinto, P. & Olaizola, J. Bolete productivity of cistaceous scrublands in northwestern Spain. Econ. Bot. 62, 323–330 (2008).Article 

Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Does shrub recovery differ after prescribed burning, clearing and mastication in a Spanish heathland?. Plant Ecol. 216, 429–437 (2015).Article 

Google Scholar 
Ponte, E. D., Costafreda-Aumedes, S. & Vega-Garcia, C. Lessons learned from arson wildfire incidence in reforestations and natural stands in Spain. Forests 10, 1–18 (2019).Article 

Google Scholar 
Franco-Manchón, I., Salo, K., Oria-de-Rueda, J. A., Bonet, J. A. & Martín-Pinto, P. Are wildfires a threat to fungi in European Pinus forests? A case study of boreal and Mediterranean forests. Forests 10, 309 (2019).Article 

Google Scholar 
Mediavilla, O., Oria-de-Rueda, J. A. & Martin-Pinto, P. Changes in sporocarp production and vegetation following wildfire in a Mediterranean Forest Ecosystem dominated by Pinus nigra in Northern Spain. For. Ecol. Manage. 331, 85–92 (2014).Article 

Google Scholar 
Tomao, A., Antonio Bonet, J., Castaño, C. & De-Miguel, S. How does forest management affect fungal diversity and community composition? Current knowledge and future perspectives for the conservation of forest fungi. For. Ecol. Manage. 457, 117678 (2020).
Article 

Google Scholar 
Espinosa, J., Rodríguez de Rivera, O., Madrigal, J., Guijarro, M. & Hernando, C. Predicting potential cambium damage and fire resistance in Pinus nigra Arn. ssp. salzmannii. For. Ecol. Manage. 474, 118372 (2020).Article 

Google Scholar 
Potts, J. B. & Stephens, S. L. Invasive and native plant responses to shrubland fuel reduction: Comparing prescribed fire, mastication and treatment season. Biol. Conserv. 142, 1657–1664 (2009).Article 

Google Scholar 
Agee, J. K. & Skinner, C. N. Basic principles of forest fuel reduction treatments. For. Ecol. Manage. 211, 83–96 (2005).Article 

Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Fuel reduction at a Spanish heathland by prescribed fire and mechanical shredding: Effects on seedling emergence. J. Environ. Manage. 129, 621–627 (2013).Article 

Google Scholar 
Huggett, R. J., Abt, K. L. & Shepperd, W. Efficacy of mechanical fuel treatments for reducing wildfire hazard. For. Policy Econ. 10, 408–414 (2008).Article 

Google Scholar 
Fernández, C. & Vega, J. A. Shrub recovery after fuel reduction treatments and a subsequent fire in a Spanish heathland. Plant Ecol. 215, 1233–1243 (2014).Article 

Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Does fire severity influence shrub resprouting after spring prescribed burning?. Acta Oecologica 48, 30–36 (2013).Article 
ADS 

Google Scholar 
Ellsworth, J. W., Harrington, R. A. & Fownes, J. H. Seedling emergence, growth, and allocation of Oriental bittersweet: Effects of seed input, seed bank, and forest floor litter. For. Ecol. Manage. 190, 255–264 (2004).Article 

Google Scholar 
Castaño, C. et al. Resistance of the soil fungal communities to medium-intensity fire prevention treatments in a Mediterranean scrubland. For. Ecol. Manage. 472, 118217 (2020).Article 

Google Scholar 
Anderson, I. C., Bastias, B. A., Genney, D. R., Parkin, P. I. & Cairney, J. W. G. Basidiomycete fungal communities in Australian sclerophyll forest soil are altered by repeated prescribed burning. Mycol. Res. 111, 482–486 (2007).Article 
CAS 

Google Scholar 
Hernández-Rodríguez, M. et al. Soil fungal community composition in a Mediterranean shrubland is primarily shaped by history of major disturbance, less so by current fire fuel reduction treatments. Unpublished (2015).Oria de Rueda, J. A., Martín-Pinto, P. & Olaizola, J. Boletus edulis PRODUCTION IN XEROPHILIC AND PIROPHITIC SCHRUBS OF Cistus ladanifer AND Halimium lasianthum IN WESTERN SPAIN. in IV International Workshop on Edible Mycorrhizal Mushrooms (2005).Hart, B. T. N., Smith, J. E., Luoma, D. L. & Hatten, J. A. Recovery of ectomycorrhizal fungus communities fifteen years after fuels reduction treatments in ponderosa pine forests of the Blue Mountains. Oregon. For. Ecol. Manage. 422, 11–22 (2018).Article 

Google Scholar 
Hernández-Rodríguez, M., Oria-de-Rueda, J. A., Pando, V. & Martín-Pinto, P. Impact of fuel reduction treatments on fungal sporocarp production and diversity associated with Cistus ladanifer L. ecosystems. For. Ecol. Manage. 353, 10–20 (2015).Article 

Google Scholar 
Fernandes, P. M. Scientific support to prescribed underburning in southern Europe: What do we know?. Sci. Total Environ. 630, 340–348 (2018).Article 
ADS 
CAS 

Google Scholar 
Day, N. J. et al. Wildfire severity reduces richness and alters composition of soil fungal communities in boreal forests of western Canada. Glob. Chang. Biol. 25, 2310–2324 (2019).Article 
ADS 

Google Scholar 
Salo, K., Domisch, T. & Kouki, J. Forest wildfire and 12 years of post-disturbance succession of saprotrophic macrofungi (Basidiomycota, Ascomycota). For. Ecol. Manage. 451, 117454 (2019).Article 

Google Scholar 
Zakaria, A. J. & Boddy, L. Mycelial foraging by Resinicium bicolor: Interactive effects of resource quantity, quality and soil composition. FEMS Microbiol. Ecol. 40, 135–142 (2002).Article 
CAS 

Google Scholar 
Hul, S. et al. Fungal community shifts in structure and function across a boreal forest fire chronosequence. Appl. Environ. Microbiol. 81, 7869–7880 (2015).Article 
ADS 

Google Scholar 
Vázquez-Veloso, A. et al. Prescribed burning in spring or autumn did not affect the soil fungal community in Mediterranean Pinus nigra natural forests. For. Ecol. Manage. 512, 120161 (2022).Article 

Google Scholar 
Lindahl, B. D. et al. Spatial separation of litter decomposition and mycorrhizal nitrogen uptake in a boreal forest. New Phytol. 173, 611–620 (2007).Article 
CAS 

Google Scholar 
Salomón, R., Rodríguez-Calcerrada, J., González-Doncel, I., Gil, L. & Valbuena-Carabaña, M. On the general failure of coppice conversion into high forest in Quercus pyrenaica stands: A genetic and physiological approach. Folia Geobot. 52, 101–112 (2017).Article 

Google Scholar 
Williams, R. J., Hallgren, S. W. & Wilson, G. W. T. Frequency of prescribed burning in an upland oak forest determines soil and litter properties and alters the soil microbial community. For. Ecol. Manage. 265, 241–247 (2012).Article 

Google Scholar 
Semenova-Nelsen, T. A., Platt, W. J., Patterson, T. R., Huffman, J. & Sikes, B. A. Frequent fire reorganizes fungal communities and slows decomposition across a heterogeneous pine savanna landscape. New Phytol. 224, 916–927 (2019).Article 

Google Scholar 
Oliver, A. K., Callaham, M. A. & Jumpponen, A. Soil fungal communities respond compositionally to recurring frequent prescribed burning in a managed southeastern US forest ecosystem. For. Ecol. Manage. 345, 1–9 (2015).Article 

Google Scholar 
Sanz-Benito, I., Mediavilla, O., Casas, A., Oria-de-Rueda, J. A. & Martín-Pinto, P. Effects of fuel reduction treatments on the sporocarp production and richness of a Quercus/Cistus mixed system. For. Ecol. Manage. 503, 119798 (2022).Article 

Google Scholar 
Santos-Silva, C., Gonçalves, A. & Louro, R. Canopy cover influence on macrofungal richness and sporocarp production in montado ecosystems. Agrofor. Syst. 82, 149–159 (2011).Article 

Google Scholar 
Lin, W. R. et al. The impacts of thinning on the fruiting of saprophytic fungi in Cryptomeria japonica plantations in central Taiwan. For. Ecol. Manage. 336, 183–193 (2015).Article 

Google Scholar 
Aragón, G., López, R. & Martínez, I. Effects of Mediterranean dehesa management on epiphytic lichens. Sci. Total Environ. 409, 116–122 (2010).Article 
ADS 

Google Scholar 
Hämäläinen, A., Kouki, J. & Lohmus, P. The value of retained Scots pines and their dead wood legacies for lichen diversity in clear-cut forests: The effects of retention level and prescribed burning. For. Ecol. Manage. 324, 89–100 (2014).Article 

Google Scholar 
Schimmel, J. & Granstrom, A. Fire severity and vegetation response in the boreal Swedish. Ecol. Soc. Am. 77, 1436–1450 (1996).
Google Scholar 
Hinojosa, M. B., Albert-Belda, E., Gómez-Muñoz, B. & Moreno, J. M. High fire frequency reduces soil fertility underneath woody plant canopies of Mediterranean ecosystems. Sci. Total Environ. 752, 141877 (2021).Article 
ADS 
CAS 

Google Scholar 
Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. New Phytol. 205, 1525–1536 (2015).Article 
CAS 

Google Scholar 
Tedersoo, L. et al. Disentangling global soil fungal diversity. Science 346, 1052–1053 (2014).Article 

Google Scholar 
Adamo, I. et al. Sampling forest soils to describe fungal diversity and composition. Which is the optimal sampling size in Mediterranean pure and mixed pine oak forests?. Fungal Biol. https://doi.org/10.1016/j.funbio.2021.01.005 (2021).Article 

Google Scholar 
Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal diversity reveals strong pH and plant species effects in northern Europe. Front. Microbiol. 11, 1953 (2020).Article 

Google Scholar 
Peay, K., Garbelotto, M. & Bruns, T. Evidence of dispersal limitation in soil microorganisms: Isolation reduces species richness on mycorrhizal tree islands. Ecology 91, 3631–3640 (2010).Article 

Google Scholar 
Koivula, M. & Vanha-Majamaa, I. Experimental evidence on biodiversity impacts of variable retention forestry, prescribed burning, and deadwood manipulation in Fennoscandia. Ecol. Process. 9, 1–22 (2020).Article 

Google Scholar 
Fox, S. et al. Fire as a driver of fungal diversity—A synthesis of current knowledge. Mycologia 00, 1–27 (2022).
Google Scholar 
Raudabaugh, D. B. et al. Where are they hiding? Testing the body snatchers hypothesis in pyrophilous fungi. Fungal Ecol. 43, 100870 (2020).Article 

Google Scholar 
Izzo, A., Canright, M. & Bruns, T. D. The effects of heat treatments on ectomycorrhizal resistant propagules and their ability to colonize bioassay seedlings. Mycol. Res. 110, 196–202 (2006).Article 

Google Scholar 
Kipfer, T., Moser, B., Egli, S., Wohlgemuth, T. & Ghazoul, J. Ectomycorrhiza succession patterns in Pinus sylvestris forests after stand-replacing fire in the Central Alps. Oecologia 167, 219–228 (2011).Article 
ADS 

Google Scholar 
Glassman, S. I., Levine, C. R., Dirocco, A. M., Battles, J. J. & Bruns, T. D. Ectomycorrhizal fungal spore bank recovery after a severe forest fire: Some like it hot. ISME J. 10, 1228–1239 (2016).Article 

Google Scholar 
Buscardo, E. et al. Impact of wildfire return interval on the ectomycorrhizal resistant propagules communities of a Mediterranean open forest. Fungal Biol. 114, 628–636 (2010).Article 

Google Scholar 
Pringle, A., Vellinga, E. & Peay, K. The shape of fungal ecology: Does spore morphology give clues to a species’ niche?. Fungal Ecol. 17, 213–216 (2015).Article 

Google Scholar 
Zhang, K., Cheng, X., Shu, X., Liu, Y. & Zhang, Q. Linking soil bacterial and fungal communities to vegetation succession following agricultural abandonment. Plant Soil 431, 19–36 (2018).Article 
ADS 
CAS 

Google Scholar 
Xiang, X. et al. Arbuscular mycorrhizal fungal communities show low resistance and high resilience to wildfire disturbance. Plant Soil 397, 347–356 (2015).Article 
CAS 

Google Scholar 
Dove, N. C., Klingeman, D. M., Carrell, A. A., Cregger, M. A. & Schadt, C. W. Fire alters plant microbiome assembly patterns: Integrating the plant and soil microbial response to disturbance. New Phytol. 230, 2433–2446 (2021).Article 
CAS 

Google Scholar 
Fernandes, P. M. Fire-smart management of forest landscapes in the Mediterranean basin under global change. Landsc. Urban Plan. 110, 175–182 (2013).Article 

Google Scholar 
Fontúrbel, M. T., Fernández, C. & Vega, J. A. Prescribed burning versus mechanical treatments as shrubland management options in NW Spain: Mid-term soil microbial response. Appl. Soil Ecol. 107, 334–346 (2016).Article 

Google Scholar 
Geml, J. et al. Large-scale fungal diversity assessment in the Andean Yungas forests reveals strong community turnover among forest types along an altitudinal gradient. Mol. Ecol. 23, 2452–2472 (2014).Article 
CAS 

Google Scholar 
Chu, H. et al. Effects of slope aspects on soil bacterial and arbuscular fungal communities in a boreal forest in China. Pedosphere 26, 226–234 (2016).Article 

Google Scholar 
Geml, J. Soil fungal communities reflect aspect-driven environmental structuring and vegetation types in a Pannonian forest landscape. Fungal Ecol. 39, 63–79 (2019).Article 

Google Scholar 
Castaño, C. et al. Soil microclimate changes affect soil fungal communities in a Mediterranean pine forest. New Phytol. 220, 1211–1221 (2018).Article 

Google Scholar 
Collado, E. et al. Mushroom productivity trends in relation to tree growth and climate across different European forest biomes. Sci. Total Environ. 689, 602–615 (2019).Article 
ADS 
CAS 

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

Google Scholar 
White, T., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, 1990).
Google Scholar 
Kent, M. Vegetation Description and Data Analysis: A Practical Approach (Wiley, 2011).

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).Article 
CAS 

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

Google Scholar 
Abarenkov, K. et al. Plutof-a web based workbench for ecological and taxonomic research, with an online implementation for fungal its sequences. Evol. Bioinforma. 2010, 189–196 (2010).
Google Scholar 
Põlme, S. et al. FungalTraits: A user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

Google Scholar 
Agerer, R. Fungal relationships and structural identity of their ectomycorrhizae. Mycol. Prog. 5, 67–107 (2006).Article 

Google Scholar 
Tedersoo, L. & Smith, M. E. Lineages of ectomycorrhizal fungi revisited: Foraging strategies and novel lineages revealed by sequences from belowground. Fungal Biol. Rev. 27, 83–99 (2013).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1–128. http://CRAN.R-project.org/package=nlme (2016).Chao, A. & Chiu, C. Species richness: Estimation and comparison. Wiley StatsRef https://doi.org/10.1002/9781118445112.stat03432.pub2 (2016).Article 

Google Scholar 
Chiu, C. H., Wang, Y. T., Walther, B. A. & Chao, A. An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics 70, 671–682 (2014).Article 
MathSciNet 
MATH 

Google Scholar 
Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.4–2. https://CRAN.R-project.org/package=vegan. (2017).Oksanen, J., Blanchet, F., Kindt, R. & Al, E. vegan: Community Ecology Package. R package version 2.3–0. (2015). More

Research Group for Genomic Epidemiology, Technical University of Denmark, Kgs, Lyngby, DenmarkPatrick Munk, Christian Brinch, Frederik Duus Møller, Thomas N. Petersen, Rene S. Hendriksen, Anne Mette Seyfarth, Jette S. Kjeldgaard, Christina Aaby Svendsen & Frank M. AarestrupCentre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh, UKBram van Bunnik & Mark WoolhouseCentre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, SwedenFanny Berglund & D. G. Joakim LarssonDepartment of Viroscience, Erasmus MC, Rotterdam, The NetherlandsMarion KoopmansInstitute of Public Health, Tirana, AlbaniaArtan BegoUniversidad de Buenos Aires, Buenos Aires, ArgentinaPablo PowerMelbourne Water Corporation, Melbourne, AustraliaCatherine Rees & Kris CoventryCharles Darwin University, Darwin, AustraliaDionisia LambrinidisUniversity of Copenhagen, Frederiksberg C, DenmarkElizabeth Heather Jakobsen Neilson & Yaovi Mahuton Gildas HounmanouCharles Darwin University, Darwin Northern Territory, AustraliaKaren GibbCanberra Hospital, Canberra, AustraliaPeter CollignonALS Water, Scoresby, AustraliaSusan CassarAustrian Agency for Health and Food Safety (AGES), Vienna, AustriaFranz AllerbergerUniversity of Dhaka, Dhaka, BangladeshAnowara Begum & Zenat Zebin HossainEnvironmental Protection Department, Bridgetown, St. Michael, BarbadosCarlon WorrellLaboratoire Hospitalier Universitaire de Bruxelles (LHUB-ULB), Brussels, BelgiumOlivier VandenbergAQUAFIN NV, Aartselaar, BelgiumIlse PietersPolytechnic School of Abomey-Calavi, Abomey-Calavi, BeninDougnon Tamègnon VictorienUniversidad Catσlica Boliviana San Pablo, La Paz, BoliviaAngela Daniela Salazar Gutierrez & Freddy SoriaPublic Health Institute of the Republic of Srpska, Faculty of Medicine University of Banja Luka, Banja Luka, Bosnia and HerzegovinaVesna Rudić GrujićPublic Health Institute of the Republic of Srpska, Banja Luka, Bosnia and HerzegovinaNataša MazalicaBotswana International University of Science and Technology, Palapye, BotswanaTeddie O. RahubeUniversidade Federal de Minas Gerais, Belo Horizonte, BrazilCarlos Alberto Tagliati & Larissa Camila Ribeiro de SouzaOswaldo Cruz Institute, Rio de Janeiro, BrazilDalia RodriguesVale Institute of Technology, Belιm, PA, BrazilGuilherme OliveiraNational Center of Infectious and Parasitic Diseases, Sofia, BulgariaIvan IvanovUniversity of Ouagadougou, Ouagadougou, Burkina FasoBonkoungou Isidore Juste & Traoré OumarInstitut Pasteur du Cambodge, Phnom Penh, CambodiaThet Sopheak & Yith VuthyCentre Pasteur du Cameroun, Yaoundι, CameroonAntoinette Ngandjio, Ariane Nzouankeu & Ziem A. Abah Jacques OlivierUniversity of Regina, Regina, CanadaChristopher K. YostEau Terre Environnement Research Centre (INRS-ETE), Quebec City G1K 9A9, Canada and Indian Institute of Technology, Jammu, IndiaPratik KumarEau Terre Environnement Research Centre (INRS-ETE), Quebec City G1K 9A9, Canada and Lassonde School of Enginerring, York University, Toronto, CanadaSatinder Kaur BrarUniversity of N’Djamena, N’Djamena, ChadDjim-Adjim TaboEscuela de Medicina Veterinaria, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, ChileAiko D. AdellInstitute of Public Health, Santiago, ChileEsteban Paredes-Osses & Maria Cristina MartinezUniversidad Catolica del Maule, Centro de Biotecnología de los Recursos Naturales, Facultad de Ciencias Agrarias y Forestales, Talca, ChileSara Cuadros-OrellanaGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaChangwen Ke, Huanying Zheng & Li BaishengThe Hong Kong Polytechnic University, Hong Kong, ChinaLok Ting Lau & Teresa ChungShantou University Medical College, Shantou, ChinaXiaoyang JiaoNanjing University of Information Science and Technology, Nanjing, ChinaYongjie YuCenter for Disease Control and Prevention of Henan province, Zhengzhou, ChinaZhao JiaYongColombian Integrated Program for Antimicrobial Resistance Surveillance – Coipars, CI Tibaitatα, Corporaciσn Colombiana de Investigaciσn Agropecuaria (AGROSAVIA), Tibaitatα – Mosquera, Cundinamarca, ColombiaJohan F. Bernal Morales, Maria Fernanda Valencia & Pilar Donado-GodoyInstitut Pasteur de Côte d’Ivoire, Abidjan, Côte d’IvoireKalpy Julien CoulibalyUniversity of Zagreb, Zagreb, CroatiaJasna HrenovicAndrija Stampar Teaching Institute of Public Health, Zagreb, CroatiaMatijana JergovićVeterinary Research Institute, Brno, Czech RepublicRenáta KarpíškováCentre de Recherche en Sciences Naturelles de Lwiro (CRSN-LWIRO), Bukavu, Democratic Republic of CongoZozo Nyarukweba DeogratiasBIOFOS A/S, Copenhagen K, DenmarkBodil ElsborgTechnical University of Denmark, Kgs., Lyngby, DenmarkLisbeth Truelstrup Hansen & Pernille Erland JensenSuez Canal University, Ismailia, EgyptMohamed AbouelnagaUniversity of Sadat City, Sadat City, EgyptMohamed Fathy SalemMinistry of Health, Environmental Microbiology, Tallinn, EstoniaMarliin KoolmeisterAddis Ababa University, Addis Ababa, EthiopiaMengistu Legesse & Tadesse EgualeUniversity of Helsinki, Helsinki, FinlandAnnamari HeikinheimoFrench Institute Search Pour L’exploitation De La Mer (Ifremer), Nantes, FranceSoizick Le Guyader & Julien SchaefferInstituto Nacional de Investigaciσn en Salud Pϊblica-INSPI (CRNRAM), Galαpagos, Quito, EcuadorJose Eduardo VillacisNational Public Health Laboratories, Ministry of Health and Social Welfare, Kotu, GambiaBakary SannehNational Center for Disease Control and Public Health, Tbilisi, GeorgiaLile MalaniaRobert Koch Institute, Berlin, GermanyAndreas Nitsche & Annika BrinkmannTechnische Universitδt Dresden, Institute of Hydrobiology, Dresden, GermanySara Schubert, Sina Hesse & Thomas U. BerendonkUniversity for Development Studies, Tamale, GhanaCourage Kosi Setsoafia SabaUniversity of Ghana, Accra, GhanaJibril MohammedKwame Nkrumah University of Science and Technology, Kumasi, PMB, GhanaPatrick Kwame FegloCouncil for Scientific and Industrial Research Water Research Institute, Accra, GhanaRegina Ama BanuVeterinary Research Institute of Thessaloniki, Hellenic Agricultural Organisation-DEMETER, Thermi, GreeceCharalampos KotzamanidisAthens Water Supply and Sewerage Company (EYDAP S.A.), Athens, GreeceEfthymios LytrasUniversidad de San Carlos de Guatemala, Guatemala City, GuatemalaSergio A. LickesSemmelweis University, Institute of Medical Microbiology, Budapest, HungaryBela KocsisUniversity of Veterinary Medicine, Budapest, HungaryNorbert SolymosiUniversity of Iceland, Reykjavνk, IcelandThorunn R. ThorsteinsdottirCochin University of Science and Technology, Cochin, IndiaAbdulla Mohamed HathaKasturba Medical College, Manipal, IndiaMamatha BallalApollo Diagnostics, Mangalore, IndiaSohan Rodney BangeraShiraz University of Medical Sciences, Shiraz, IranFereshteh FaniShahid Beheshti University of Medical Sciences, Tehran, IranMasoud AlebouyehNational University of Ireland Galway, Galway, IrelandDearbhaile Morris, Louise O’Connor & Martin CormicanBen Gurion University of the Negev and Ministry of Health, Beer-Sheva, IsraelJacob Moran-GiladIstituto Zooprofilattico Sperimentale del Lazio e della Toscana, Rome, ItalyAntonio Battisti, Elena Lavinia Diaconu & Patricia AlbaCNR – Water Research Institute, Verbania, ItalyGianluca Corno & Andrea Di CesareNational Institute of Infectious Diseases, Tokyo, JapanJunzo Hisatsune, Liansheng Yu, Makoto Kuroda, Motoyuki Sugai & Shizuo KayamaNational Center of Expertise, Taldykorgan, KazakhstanZeinegul ShakenovaMount Kenya University, Thika, KenyaCiira KiiyukiaKenya Medical Research Institute, Nairobi, KenyaEric Ng’enoUniversity of Prishtina “Hasan Prishtina” & National Institute of Public Health of Kosovo, Pristina, KosovoLul RakaKuwait Institute for Scientific Research, Kuwait City, KuwaitKazi Jamil, Saja Adel Fakhraldeen & Tareq AlaatiInstitute of Food Safety, Riga, LatviaAivars Bērziņš, Jeļena Avsejenko, Kristina Kokina, Madara Streikisa & Vadims BartkevicsAmerican University of Beirut, Beirut, LebanonGhassan M. MatarCentral Michigan University & Michigan Health Clinics, Saginaw, MI, USAZiad DaoudNational Food and Veterinary Risk Assessment Institute, Vilnius, LithuaniaAsta Pereckienė & Ceslova Butrimaite-AmbrozevicieneLuxembourg Institute of Science and Technology, Belvaux, LuxembourgChristian PennyInstitut Pasteur de Madagascar, Antananarivo, MadagascarAlexandra Bastaraud & Jean-Marc CollardUniversity of Antananarivo, Centre d’Infectiologie Charles Mιrieux, Antananarivo, MadagascarTiavina Rasolofoarison, Luc Hervé Samison & Mala Rakoto AndrianariveloUniversity of Malawi, Blantyre, MalawiDaniel Lawadi BandaMalaysian Genomics Resource Centre Berhad, Kuala Lumpur, MalaysiaArshana AminAIMST University, COMBio, Kedah, MalaysiaHeraa Rajandas & Sivachandran ParimannanWater Services Corporation, Luqa, MaltaDavid SpiteriEnvironmental Health Directorate, St. Venera, MaltaMalcolm Vella HaberUniversity of Mauritius, Reduit, MauritiusSunita J. SantchurnInstitute for Public Health Montenegro, Podgorica, MontenegroAleksandar Vujacic & Dijana DjurovicInstitut Pasteur du Maroc, Casablanca, MoroccoBrahim Bouchrif & Bouchra KarraouanCentro de Investigaηγo em Saϊde de Manhiηa (CISM), Maputo, MozambiqueDelfino Carlos VubilAgriculture and Forestry University, Kathmandu, NepalPushkar PalNational Institute for Public, Health and the Environment (RIVM), Bilthoven, The NetherlandsHeike Schmitt & Mark van PasselUniversity of Otago, Dunedin, New ZealandGert-Jan Jeunen & Neil GemmellUniversity of Otago, Christchurch, New ZealandStephen T. ChambersUniversity of Central America, Managua, NicaraguaFania Perez Mendoza & Jorge Huete-PιrezUniversidad Nacional Autσnoma de Nicaragua-Leσn, Leσn, NicaraguaSamuel VilchezUniversity of Ilorin, Ilorin, NigeriaAkeem Olayiwola Ahmed, Ibrahim Raufu Adisa & Ismail Ayoade OdetokunUniversity of Ibadan, Ibadan, NigeriaKayode FashaeNorwegian Institute of Public Health, Oslo, NorwayAnne-Marie Sørgaard & Astrid Louise WesterVEAS, Slemmestad, NorwayPia Ryrfors & Rune HolmstadUniversity of Agriculture, Faisalabad, PakistanMashkoor MohsinAga Khan University, Karachi, PakistanRumina Hasan & Sadia ShakoorLaboratorio Central de Salud Publica, Asuncion, ParaguayNatalie Weiler Gustafson & Claudia Huber SchillInstituto Nacional de Salud, Lima, PeruMaria Luz Zamudio RojasUniversidad de Piura, Piura, PeruJorge Echevarria Velasquez & Felipe Campos YauceWHO Environmental and Occupational Health, Manila, PhilippinesBonifacio B. MagtibayMaynilad Water Services, Inc., Quezon City, PhilippinesKris Catangcatang & Ruby SibuloNational Veterinary Research Institute, Pulawy, PolandDariusz WasylUniversidade Catσlica Portuguesa, CBQF – Centro de Biotecnologia e Quνmica Fina – Laboratσrio Associado, Escola Superior de Biotecnologia, Porto, PortugalCelia Manaia & Jaqueline RochaAguas do Tejo Atlantico, Lisboa, PortugalJose Martins & Pedro ÁlvaroGwangju Institute of Science and Technology, Gwangju, Republic of KoreaDoris Di Yoong Wen, Hanseob Shin & Hor-Gil HurKorea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaSukhwan YoonInstitute of Public Health of the Republic of North Macedonia, Skopje, Republic of North MacedoniaGolubinka Bosevska & Mihail KochubovskiState Medical and Pharmaceutical University, Chișinău, Republic of MoldovaRadu CojocaruNational Agency for Public Health, Chișinău, Republic of MoldovaOlga BurduniucKing Abdullah University of Science and Technology, Thuwal, Saudi ArabiaPei-Ying HongUniversity of Edinburgh, Edinburgh, Scotland, UKMeghan Rose PerryInstitut Pasteur de Dakar, Dakar, SenegalAmy GassamaInstitute of Veterinary Medicine of Serbia, Belgrade, SerbiaVladimir RadosavljevicNanyang Technological University, Singapore, SingaporeMoon Y. F. Tay, Rogelio Zuniga-Montanez & Stefan WuertzPublic Health Authority of the Slovak Republic, Bratislava, SlovakiaDagmar Gavačová, Katarína Pastuchová & Peter TruskaNational Laboratory of Health, Environment and Food, Ljubljana, SloveniaMarija TrkovIndependent consultant, Johannesburg, South AfricaKaren KeddyDaspoort Waste Water Treatment Works, Pretoria, South AfricaKerneels EsterhuyseKorea Advanced Institute of Science and Technology, Daejeon, South KoreaMin Joon SongSchool of Veterinary Sciences, Lugo, SpainMarcos Quintela-BalujaLabaqua, Santiago de Compostela, SpainMariano Gomez LopezIRTA, Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autonoma de Barcelona, Bellaterra, SpainMarta Cerdà-CuéllarUniversity of Kelaniya, Ragama, Sri LankaR. R. D. P. Perera, N. K. B. K. R. G. W. Bandara & H. I. PremasiriMedical Research Institute, Colombo, Sri LankaSujatha PathirageCaribbean Public Health Agency, Catries, Saint LuciaKareem CharlemagneThe Sahlgrenska Academy at the University of Gothenburg, Gothenburg, SwedenCarolin RutgerssonSwedish University of Agricultural Sciences, Uppsala, SwedenLeif Norrgren & Stefan ÖrnFederal Food Safety and Veterinary Office (FSVO), Bern, SwitzerlandRenate BossAra Region Bern AG, Herrenschwanden, SwitzerlandTanja Van der HeijdenCenters for Disease Control, Taipei, TaiwanYu-Ping HongKilimanjaro Clinical Research Institute, Moshi, TanzaniaHappiness Houka KumburuSokoine University of Agriculture, Morogoro, TanzaniaRobinson Hammerthon MdegelaFaculty of Science and Technology, Suratthani Rajabhat University, Surat Thani, ThailandKaknokrat ChonsinFaculty of Public Health, Mahidol University, Bangkok, ThailandOrasa SuthienkulFaculty of Medicine Siriraj Hospital, Bangkok, ThailandVisanu ThamlikitkulNational Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsAna Maria de Roda HusmanNational Institute of Hygiene, Lomι, TogoBawimodom BidjadaAgence de Mιdecine Prιventive, Dapaong, TogoBerthe-Marie Njanpop-LafourcadeDivision of Integrated Surveillance of Health Emergencies and Response, Lomι, TogoSomtinda Christelle Nikiema-PessinabaPublic Health Institution of Turkey, Ankara, TurkeyBelkis LeventHatay Mustafa Kemal University, Hatay, TurkeyCemil KurekciMakerere University, Kampala, UgandaFrancis Ejobi & John Bosco KaluleAbu Dhabi Public Health Center, Abu Dhai, United Arab EmiratesJens ThomsenDubai municipality, WWTP Al Aweer, Dubai, UAEOuidiane ObaidiRashid Hospital, Dubai, UAELaila Mohamed JassimNorthumbrian Water, Northumbria House, Abbey Road, Pity Me, Durham, UKAndrew MooreUniversity of Exeter Medical School, Cornwall, UKAnne Leonard, Lihong Zhang & William H. GazeNewcastle University, Newcastle upon Tyne, UKDavid W. Graham & Joshua T. BunceBrightwater Treatment Plant, Woodinville, WA, USABrett LeforDepartment of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USADrew Capone & Joe BrownUniversity of North Carolina, Chapel Hill, USAEmanuele Sozzi & Mark D. SobseyUniversity of Washington, Seattle, WA, USAJohn Scott Meschke, Nicola Koren Beck, Pardi Sukapanpatharam & Phuong TruongBaylor University, Waco, USAMichael DavisColumbia Boulevard WWTP, Portland, USARonald LilienthalEastern Illinois University, Charleston, USASanghoon KangThe Ohio State University, Columbus Ohio, USAThomas E. WittumLaboratorio Tecnolσgico del Uruguay, Montevideo, UruguayNatalia Rigamonti & Patricia BaklayanInstitute of Public Health in Ho Chi Minh City, Ho Chi Minh, VietnamChinh Dang Van, Doan Minh Nguyen Tran & Nguyen Do PhucUniversity of Zambia, Lusaka, ZambiaGeoffrey Kwenda More

Pearson, P. N. & Palmer, M. R. Atmospheric carbon dioxide concentrations over the past 60 million years. Nature 406, 695–699 (2000).Article 
ADS 
CAS 

Google Scholar 
Wang, P. et al. The genome evolution and domestication of tropical fruit mango. Genome Biol. 21, 60 (2020).Article 

Google Scholar 
Yang, H. et al. Advances in the regulatory mechanisms of pollen response to heat stress in crops. Chin. Bull. Bot. 54(2), 157–167 (2019).CAS 

Google Scholar 
Liang, Q. Z. et al. Transcriptome and metabolome analyses reveal the involvement of multiple pathways in flowering intensity in mango. Front. Plant Sci. 13, 933923 (2022).Article 

Google Scholar 
Ranasinghe, C. S., Waidyarathna, K. P., Pradeep, A. P. C. & Meneripitiya, M. S. K. Approach to screen coconut varieties for high temperature tolerance by in-vitro pollen germination. COCOS. 19, 01–11 (2010).
Google Scholar 
Das, S., Krishnan, P., Nayak, M. & Ramakrishnan, B. High temperature stress effects on pollens of rice (Oryza sativa L.) genotypes. Environ. Exp. Bot. 101, 36–46 (2014).Article 

Google Scholar 
Balasubramanian, S., Sureshkumar, S., Lempe, J. & Weigel, D. Potent induction of Arabidopsis thaliana flowering by elevated growth temperature. PLoS Genet. 2(7), e106 (2006).Article 

Google Scholar 
Sakata, T., Takahashi, H., Nishiyama, I. & Higashitani, A. Effects of high temperature on the development of pollen mother cells and microspores in Barley Hordeum vulgare L.. J. Plant Res. 113(4), 395–402 (2000).Article 

Google Scholar 
Hedhly, A., Hormaza, J. I. & Herrero, M. The effect of temperature on pollen germination, pollen tube growth, and stigmatic receptivity in peach. Plant Biol. 7(5), 476–483 (2005).Article 
CAS 

Google Scholar 
Pirlak, L. The effects of temperature on pollen germination and pollen tube growth of apricot and sweet cherry. Gartenbauwissenschaft 67(2), 61–64 (2002).
Google Scholar 
Koti, S., Reddy, K. R., Reddy, V. R., Kakani, V. G. & Zhao, D. Interactive effects of carbon dioxide, temperature, and ultraviolet-B radiation on soybean (Glycine max L.) flower and pollen morphology, pollen production, germination, and tube lengths. J. Exp. Bot. 56(412), 725–736 (2004).Article 

Google Scholar 
Pham, V. T., Herrero, M. & Hormaza, J. I. Effect of temperature on pollen germination and pollen tube growth in longan (Dimocarpus longan Lour.). Sci. Hort. 197, 470–475 (2015).Article 

Google Scholar 
Meehl, T. G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).Article 
ADS 
CAS 

Google Scholar 
Reddy, K. R., Hodges, H. F. & Reddy, V. R. Temperature effects on cotton fruit retention. Agron. J. 84, 26–30 (1992).Article 

Google Scholar 
Reddy, K. R., Reddy, V. R. & Hodges, H. F. Effects of temperature on early season cotton growth and development. Agron. J. 84, 229–237 (1992).Article 

Google Scholar 
Stainforth, D. et al. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433, 403–406 (2005).Article 
ADS 
CAS 

Google Scholar 
Liu, Z., Yuan, Y., Liu, S., Yu, X. & Rao, L. Screening for high temperature tolerant cotton cultivars by testing in vitro pollen germination, pollen tube growth and boll retention. J. Integr. Plant Biol. 48, 706–714 (2006).Article 

Google Scholar 
Kakani, V. G., Prasad, P. V. V., Craufurd, P. Q. & Wheeler, T. R. Response of in vitro pollen germination and pollen tube growth of groundnut (Arachis hypogaea L.) genotypes to temperature. Plant Cell Environ. 25, 1651–1661 (2002).Article 

Google Scholar 
Kakani, V. G. et al. Differences in in vitro pollen germination and pollen tube growth of cotton cultivars in response to high temperature. Ann. Bot. 96(1), 59–67 (2005).Article 
CAS 

Google Scholar 
Hebbar, K. B. et al. Differences in in vitro pollen germination and pollen tube growth of coconut (Cocos nucifera L.) genotypes in response to high temperature stress. Environ. Ex. Bot. 153, 35–44 (2018).Article 

Google Scholar 
Aloni, B., Peet, M., Pharr, M. & Karmi, L. The effect of high temperaturare and high atmospheric CO2 on carbohydrate changes in bell pepper (Capsicum annuum) pollen in relation to its germination. Physiol. Plant 112, 505–512 (2001).Article 
CAS 

Google Scholar 
Dai, Q., Shaobing, P., Chavez, A. Q. & Vergara, B. S. Intraspecific responses of 188 rice cultivars to enhanced UVB radiation. Environ. Exp. Bot. 34(4), 433–442 (1994).Article 

Google Scholar 
Hepler, P. K., Vidali, L. & Cheung, A. Y. Polarized cell growth in higher plants. Annu. Rev. Cell Dev. Biol. 17(1), 159–187 (2001).Article 
CAS 

Google Scholar 
Prado, A. M., Porterfield, D. M. & Feijo, J. A. Nitric oxide is involved in growth regulation and re-orientation of pollen tubes. Development 131(11), 2707–2714 (2004).Article 
CAS 

Google Scholar 
Potocky, M., Jones, M. A., Bezvoda, R., Smirnoff, N. & Zarsky, V. Reactive oxygen species produced by NADPH oxidase are involved in pollen tube growth. New Phytol. 174(4), 742–751 (2007).Article 
CAS 

Google Scholar 
Lassig, R., Gutermuth, T., Bey, T. D., Konrad, K. R. & Romeis, T. Pollen tube NAD (P)H oxidases act as a speed control to dampen growth rate oscillations during polarized cell growth. Plant J. 78(1), 94–106 (2014).Article 
CAS 

Google Scholar 
McInnis, S. M., Desikan, R., Hancock, J. T. & Hiscock, S. J. Production of reactive oxygen species and reactive nitrogen species by angiosperm stigmas and pollen: potential signalling crosstalk?. New Phytol. 172(2), 221–228 (2006).Article 
CAS 

Google Scholar 
Duan, Q. et al. Reactive oxygen species mediate pollen tube rupture to release sperm for fertilization in Arabidopsis. Nat. Commun. 5, 3129 (2014).Article 
ADS 

Google Scholar 
You, J. & Chan, Z. ROS regulation during abiotic stress responses in crop plants. Front Plant Sci. 6, 1092 (2015).Article 

Google Scholar 
Apel, K. & Hirt, H. Reactive oxygen species: Metabolism, oxidative stress, and signal transduction. Annu. Rev. Plant Biol. 55, 373–399 (2004).Article 
CAS 

Google Scholar 
Pandhair, V. & Sekhon, B. S. Reactive oxygen species and antioxidants in plants: An overview. J. Plant Biochem. Biot. 15(2), 71–78 (2006).Article 
CAS 

Google Scholar 
Sharma, P., Jha, A. B., Dubey, R. S. & Pessarakli, M. Reactive oxygen species, oxidative damage, and antioxidative defense mechanism in plants under stressful conditions. J. Bot. https://doi.org/10.1155/2012/217037 (2012).Article 

Google Scholar 
Luo, C. et al. Construction of a high-density genetic map based on large-scale marker development in mango using specific-locus amplified fragment sequencing (SLAF-seq). Front. Plant Sci. 7, 1310 (2016).Article 

Google Scholar 
IPCC. IPCC Fourth Assessment Report. http://www.ipcc.ch/. Accessed 15 Jan 2010 (2007)Reddy, K. R. & Kakani, V. G. Screening Capsicum species of different origins for high temperature tolerance by in vitro pollen germination and pollen tube length. Sci. Hort. 112, 130–135 (2007).Article 

Google Scholar 
Armendariz, B. H. C., Oropeza, C., Chan, J. L., Maust, B., Aguilar, C. C. C., & Saenz, L. Pollen Fertility and Female Flower Anatomy of Micropropagated Coconut Palms. 373–378 (Revista Fitotecnia Mexicana, Sociedad Mexicana de Fitogenetica, A C. Mexico, 2006)Binelli, G., Manincor, E. V. & Ottaviano, E. Temperature effects on pollen germination and pollen tube growth in maize. Genetica Agraria 39, 269–281 (1985).
Google Scholar 
Matlob, A. N. & Kelly, W. C. Effect of high temperature on pollen tube growth of snake melon and cucumber. J. Am. Soc. Hortic. Sci. 98, 296–300 (1973).Article 

Google Scholar 
Zhou, Q. F. An Empirical Study on the Evolution of Mango Production in China. 1–53 (Hainan University, 2017)He, L. et al. Grafting trial on mango varieties in hot-dry region Jinsha River. Subtropic. Agric. Res. 6(3), 21–24 (2010) (in Chinese with English abstract).
Google Scholar 
Gong, D. Y., Liu, Q. G., Zhang, Y. & Zhang, X. B. Studies on adaptability and application of mango varieties in south subtropical regions of Guizhou. Acta Agricult. Jiangxi 24(7), 28–31 (2012) (in Chinese).CAS 

Google Scholar 
Liu, Z. T. Performance and cultivation techniques of coconut mango in Panxi hot area. Trop. Agricult. Guangxi 3(110), 11–12 (2007).
Google Scholar 
Gajanayake, B., Trader, B. W., Reddy, K. R. & Harkess, R. L. Screening ornamental pepper cultivars for temperature tolerance using pollen and physiological parameters. Hortic. Sci. 46, 878–884 (2011).
Google Scholar 
Salem, M. A., Kakani, V. G., Koti, S. & Reddy, K. R. Pollen-based screening of soybean genotypes for high temperatures. Crop Sci. 47, 219–231 (2007).Article 

Google Scholar 
Young, L. W., Wilen, R. W. & Bonham-Smith, P. C. High temperature stress of Brassica napus during flowering reduces micro- and megagametophyte fertility, induces fruit abortion, and disrupts seed production. J. Exp. Bot. 55, 485–495 (2004).Article 
CAS 

Google Scholar 
Kafizadeh, N., Carapetian, J. & Kalantari, K. M. Effects of heat stress on pollen viability and pollen tube growth in pepper. Res. J. Biol. Sci. 3, 1159–1162 (2008).
Google Scholar 
Pressman, E., Peet, M. M. & Pharr, D. M. The effect of heat stress on tomato pollen characteristics is associated with changes in carbohydrate concentration in the developing anthers. Ann. Bot. 90, 613–636 (2002).Article 

Google Scholar 
Sukhvibul, N. et al. Effect of temperature on pollen germination and pollen tube growth of four cultivars of mango (Mangifera indica L.). J. Hortic. Sci. Biotechnol. 75(2), 214–222 (2000).Article 

Google Scholar 
Koubouris, G. C., Metzidakis, I. T. & Vasilakakis, M. D. Impact of temperature on olive (Olea europaea L.) pollen performance in relation to relative humidity and genotype. Environ. Exp. Bot. 67(1), 209–214 (2009).Article 

Google Scholar 
Huang, J. H. et al. Effects of low temperatures on sexual reproduction of ‘Tainong 1’ mango (Mangifera indica). Sci. Horticult. 126(2), 109–119 (2010) (in Chinese with English abstract).Article 

Google Scholar 
Çetinbaş-Gença, A., Cai, G., Vardara, F. & Ünal, M. Differential effects of low and high temperature stress on pollen germination and tube length of hazelnut (Corylus avellana L.) genotypes. Sci. Horticult. 255, 61–69 (2019).Article 

Google Scholar 
Sorkheh, K. et al. Interactive effects of temperature and genotype on almond (Prunus dulcis L.) pollen germination and tube length. Sci. Hortic. 227, 162–168 (2018).Article 

Google Scholar 
Wang, L. et al. Analysis of common errors of custom enzyme activity units and suggestions for standardized use. Chin. J. Sci. Technol. 24(5), 1009–1011 (2013).
Google Scholar 
Wang, W. et al. Combined cytological and transcriptomic analysis reveals a nitric oxide signaling pathway involved in cold-inhibited Camellia sinensis pollen tube growth. Front. Plant Sci. 7, 456 (2016).
Google Scholar 
He, J. M., Bai, X. L., Wang, R. B., Cao, B. & She, X. P. The involvement of nitric oxide in ultraviolet-B-inhibited pollen germination and tube growth of Paulownia tomentosa in vitro. Physiol. Plant 131(2), 273–282 (2007).CAS 

Google Scholar 
Gao, Y. et al. Mitochondrial dysfunction mediated by cytoplasmic acidification results in pollen tube growth cessation in Pyrus pyrifolia. Physiol. Plant 153(4), 603–615 (2015).Article 
CAS 

Google Scholar 
Hall, A. E. Breading for heat tolerance. Plant Breed. Rev. (SAS Institute) 10, 129–168 (1999) (SAS/STAT user’s guide, version 9.2. SAS Institute, 1992).Mearns, L. O., Easterling, W., Hays, C. & Marx, D. Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios. Part I. The uncertainty due to spatial scale. Clim. Change. 51, 131–172 (2001).Article 

Google Scholar 
SAS Institute SAS/STAT User’s Guide, Version 9.1.3. (SAS Institute Inc., 2004).Li, H. S., Sun, Q., Zhao, S. J. & Zhang, W. H. Experiment Principle and Technology of Plant Physiology and Biochemistry (Higher Education Press, 2000).
Google Scholar 
Cai, Q. S. Plant Physiology Experiment. Vol. 4(1). 182–186 (China Agricultural University Press, 2013) (in Chinese).Jia, M. X. et al. ROS-induced oxidative stress is closely related to pollen deterioration following cryopreservation. In Vitro Cell Dev. Biol. Plant 53(4), 433–439 (2017).Article 
CAS 

Google Scholar  More

Strain assessmentWe did not detect any C-strain individual following analysis of 138 fully assembled mitochondrial DNA genomes (mitogenomes) from Australian samples. Our results, particularly that from Northern Territory, are not dissimilar to the finding of Piggott et al.56 who detected only two (i.e., 4.2%) C-strain mtCOI haplotype individuals from a much larger (i.e., n = 48) Northern Territory sample size. Proportions of C-strain to R-strain also varied significantly across the different SEA populations (Table S1) in contrast to the patterns observed in China, India, and African nations (e.g.,22,33,34,39,57). All Australian populations analysed for their corn or rice mitochondrial haplotypes via mitogenome assemblies of whole genome sequencing data therefore contrasted with the invasive populations from SEA where in some countries (e.g., Myanmar, Vietnam) FAW with the C-strain mtCOI haplotypes made up approximately 50% of the populations examined (see Table S1 for C- and R-strains mitogenome proportions, see also Fig. 1 ‘C-strain’ and ‘R-strain’ Maximum Likelihood cladograms).Figure 1Maximum Likelihood cladograms of unique Spodoptera frugiperda C-strain and R-strain partial mitochondrial genomes based on concatenation of the 13 PCGs (11,393 bp) using IQ-Tree with 1000 UFBoot replications. Individuals in clades I, II, III, and IV (C-strain) and in Clades I, II, V (R-strain) that are in the same colour scheme (i.e., green, orange, blue, or pinks) shared 100% nucleotide identity. Mitogenome haplotypes from native individuals for both C- and R-strains are in khaki green colour. Red and dark grey dots at branch nodes represent bootstrap values of 87–100% and 74–86%, respectively. Bootstrap values  Hetexp; see60) could likewise indicate recent mixing of distinct populations from SEA that suggest multiple introductions (e.g.,33,39 cf.46,47,61,62; i.e., due to a recent bottleneck from a recent western Africa founder event).Table 1 Population genetic differentiation via pairwise FST estimates between Spodoptera frugiperda populations from the invasive ranges of Africa (Uganda, Malawi, Benin), South Asia (India), East Asia (China (Cangyuan (CY), Xinping (XP), YuanJiang (YJ)), South Korea), Southeast Asia (Malaysia (Johor, Kedah, Penang States), Laos, Vietnam, Myanmar), and Pacific/Australia (Papua New Guinea (PNG), Australia—Kununurra (Western Australia, WA), Northern Territory (NT), Strathmore, Walkamin, Burdekin, Mackay (Queensland, Qld), Wee Waa (New South Wales, NSW).Full size tableThe observed heterozygosity excess detected in all invasive range populations could be further explained as due to population sub-structure and isolation breaking through periodic migration. Significant numbers of loci (ca. 30%) were also shown to not be in Hardy–Weinberg equilibrium (HWE) especially for the Malaysian (i.e., Kedah), but also Australian (i.e., Wee Waa, NT, Kununurra), Chinese (e.g., XP), South Korean, and Malawian populations. Taken as a whole, genetic diversity results from this study therefore suggested that the invasive Asian (i.e., SA, SEA, EA) FAW populations exhibited signatures of recent mixing of previously separated populations. Simulated patterns of moth migration of various invasive FAW populations such as between Myanmar and China (e.g.,41,42,55) and to Australia54 are incompatible with the population genomic data, which suggests these were likely discrete and non-panmictic FAW populations with the most probable explanation being due to multiple origins of founding populations.Genetic differentiation analysisEstimates of pairwise genetic differentiation (FST) between populations varied significantly (Table 1) and extended to between populations within a country (e.g., Mackay vs. rest of Australia; Kedah vs. rest of Malaysia). Of interest are the pairwise estimates between different Australian FAW populations from Kununurra (Western Australia), Northern Territory, Queensland (Strathmore, Walkamin, Burdekin, Mackay) and New South Wales (Wee Waa) that represented the most recently reported invasive populations in this study, and predominantly showed significant differentiation amongst themselves (with the exception of the two Queensland populations of Mackay and partially for Walkamin) and with other SEA/SA/EA countries. The majority of non-significant population genetic differentiation estimates were in SEA where the presence of FAW was reported earlier, i.e., since 2018 (e.g.,63,64 or as early as 200865,66; see also33), while across Asia (e.g., China) since 2016 but also potentially pre-2014 (16,67; see also33).Interestingly, significant genetic differentiation was observed between populations from Yunnan province in China and populations from Myanmar, Laos, and Vietnam. Penang and Johor (Malaysia) populations were not significantly differentiated from other SE Asian populations, nor with Ugandan and Malawian populations from east Africa. Individuals from Benin and Mackay (Queensland, Australia) showed non-significant genetic differentiation with all populations except with Kedah, and for Mackay also surprisingly with the Wee Waa population from New South Wales. The South Korean population exhibited significant genetic differentiation with SE Asian population except with Mackay, India and the Yuanjiang (YJ) population in Yunnan Province. Finally, the Kedah population, being one of the earliest collected samples from Malaysia and having been maintained as a laboratory population, showed strong differentiation with all populations (and lowest nucleotide diversity, π = 0.237; Table 2) further supporting unique, non-African, introduction events in SEA. Strong genetic differentiation suggested there was limited gene flow to breakdown sub-structure between populations, and the FST estimates from these invasive populations therefore failed to support a west-to-east spread pathway for the FAW. This observation instead suggested the widespread presence of genetically distinct FAW populations, likely due to independent introductions and therefore also highlighting likely biosecurity weaknesses especially in East Asia (e.g., China, South Korea) and SEA (e.g., Malaysia).Table 2 Population statistics for Spodoptera frugiperda populations from Southeast Asia (i.e., Malaysia (MYS; Johor, Kedah, Penang), Laos, Vietnam, Myanmar), East Asia (i.e., South Korea), and Pacific/Australia (i.e., Papua New Guinea (PNG), Australia).Full size tableThe genetic diversity of Australian populations identified surprisingly complex sub-structure patterns given the short time frame of population detections across different northern Australian regions. Significant genetic differentiation between, e.g., Kununurra (WA), Northern Territory (NT), Queensland (e.g., Strathmore, Burdekin), and Wee Waa (NSW) populations suggests these populations likely derived from separate establishment events. The WA Kununurra population was not significantly differentiated from the Johor State (Malaysia), India and the Cangyuan (CY) China populations, suggesting a potential south-eastern route from SA/SEA into north-western Australia. Contrasting this, Walkamin and Mackay populations showed non-significant genetic differentiation with the Madang (PNG) population, suggesting a potential second pathway for SEA individuals to arrive at the north-eastern region of Australia. Significant genetic differentiation between WA, NT, and Qld populations suggested that at least during the early stage of pest establishment in northern Australia, there was limited gene flow to homogenise the unique genetic background carried by these distinct individuals, some of which exhibited also distinct insecticide resistance profiles48,49.PCAWe selected specific populations to compare using Principal Component Analysis (PCA) as examples to support evidence of independent introductions, as seen from Fig. 3a between China (CY, YJ, XP) populations vs. Myanmar, in Fig. 3b (within Malaysian populations between those collected from Penang and Johor States vs. Kedah State), in Fig. 3c for between China and East Africa (e.g., Uganda, Malawi), and where Benin and India individuals that grouped with either China or east Africa; and in Fig. 3d between China, Malaysia (Kedah State), and Australia (NT, NSW)). Genetic variability between Australian populations (e.g., Strathmore (QLD) vs. NT and NSW) was also evident (Fig. 3d).Figure 3Principal component analysis (PCA) showing variability between selected FAW populations from their invasive ranges. (a) China and Myanmar; (b) Kedah and Johor/Penang populations from Malaysia, (c) China and east African (Uganda/Malawi) populations, (d) Australia (Strathmore, Qld/Northern Territory + New South Wales), China, and Malaysia (Kedah) populations, (e) Australia (Strathmore, Qld) and PNG (Madang Province) populations, (f) Lao PDR/Vietnam and South Korea populations, (g) China and SE Asian (Lao PDR/Vietnam/Myanmar/Philippines/Malaysia) and Pacific/Australia (PNG) populations, and (h) Australia, China and Malaysia (Kedah) populations. Note the overall population genomic variability between countries (e.g., a, c–g) and within countries (e.g., Malaysia (b), Australia (d)). Populations with similar genomic variability are also evident, e.g., for Strathmore (e) and South Korea (f); and for Madang (e) and Lao PDR/Vietnam (f), further supporting potential different population origins of various FAW populations across the current invasive regions. The Southeast Asian and Chinese populations are overall different (g), Australia’s FAW populations showed similarity with both Southeast Asia and China (g, h).Full size imagePCA also showed that differences existed between FAW populations from the Madang Province in PNG and with the Strathmore population from Qld (Fig. 3e). The SEA FAW populations from Lao PDR/Vietnam also exhibited diversity from the South Korean population (Fig. 3f), with the South Korean and Strathmore populations largely exhibiting similar diversity patterns, while the Madang population shared similarity with Laos and Vietnam populations. Plotting all SEA populations against China clearly showed that populations from SEA were distinct from the Chinese FAW populations (Fig. 3g), while in Australia, individuals from various populations shared similarity with both Chinese and SEA FAW. Despite the connectedness of the landscape between SEA and China, SEA largely appeared to have their own FAW populations, with FAW in SEA and in China differing in their genome compositions overall as shown via PCA.PCA further enabled visualisation of genetic diversity amongst Australia FAW populations, suggesting that arrival and establishment of FAW likely involved separate introduction events that followed closely after each other and over a short timeframe. While it had been anticipated that the southward spread of FAW from SEA would necessarily lead to Australia FAW and PNG FAW to share similar genetic backgrounds, the Madang Province FAW population appeared to be different from the Strathmore (Qld) population, with the Madang population being more similar to Lao PDR/Vietnam populations, and the Strathmore population more similar to FAW from South Korea.DivMigrate analysisDirectionality of gene flow between African, South Asia (Indian), East Asia (China) and SE Asian populations were predominantly from China to east African and SE Asian populations (e.g., Figs. 4a, b, S-1; see also Table 3), while movements of FAW in Laos and Vietnam (i.e., the Indochina region) were predominantly with other SEA countries (e.g., with Myanmar and East Africa; Figs. 4c, d, S-2; see also Table 3) but with no directional movements to the three Yunnan populations (CY, XP, YJ). Migration directionality with other SE Asian populations (e.g., Johor (JB; Fig. S-3) and Penang (PN, Fig. S-4)) showed that these two populations (but especially the Johor population) were predominantly source populations for Uganda, Malawi, Philippines, Vietnam, and PNG (Fig. S-3). Bidirectional migration between Myanmar and Laos PDR populations were also detected with the Johor population from Malaysia (Fig. S-3). When India was selected as the source population, bidirectional migration events were detected with Myanmar and with the Cangyuan (CY) populations (Fig. S-5) while unidirectional migration events from India to Uganda and Malawi and to Laos were detected, and the China Yuanjian (YJ) population showed unidirectional migration to India. Unidirectional migration events from CY and YJ populations to the PNG Madang population were detected, while bidirectional migration events between PNG and Myanmar, Laos PDR, Philippines, Vietnam, and with Uganda and Malawi were also detected (Fig. S-6). No migration events were detected between the West African Benin population and with the South Korean population.Figure 4Source populations are CY (a) and XP (b). (c, d) DivMigrate analyses with edge weight setting at 0.453 showing unidirectional (yellow arrow lines) and bidirectional (blue arrow lines) migration between countries in Africa and South Asia/East Asia/SE Asia. Migration rates between populations are as provided in Table 3. (c) Vietnam (VNM) as the source population identified an incidence of unidirectional migration from Malaysia (MYS) Johor state (JB) to Vietnam, while bidirectional migration events were detected from Vietnam to other SE Asian (e.g., Philippines (PHL), Lao PDR (Lao), Myanmar (MMR)), to Pacific/Australia (i.e., Papua New Guinea (PNG)), as well as to east Africa (Uganda (UGA), Malawi (MWI)). (d) Lao PDR (LAO) as source population identified bidirectional migration events between various SEA populations and east African populations, while unidirectional migration events were identified from India (IND) and China (CHN) Yunnan populations (CY, YJ) to Laos PDR. No migration events were evident from SE Asian populations to China.(a, b) DivMigrate analyses with edge weight setting at 0.453 showing unidirectional (yellow arrow lines) and bidirectional (blue arrow lines) gene flow between countries in Africa and South Asia/East Asia/SE Asia. Significant migration rates (at alpha = 0.5) are in red and as provided in Table 3. Incidences of unidirectional migration were predominantly detected from China (CHN) Yunnan populations (CY, XP) to SE Asian populations (e.g., Myanmar (MMR), Laos PDR (Lao), Philippines (PHL)) and to east African populations (e.g., Uganda (UGA), Malawi (MWI)) (a, b).Full size imageTable 3 DivMigrate matrix showing effective migration rates calculated using GST from source to target invasive populations.Full size tableAdmixture analysisAdmixture analyses involving all Australian, Southeast Asian and South Korean populations from this study; and native populations from the Americas and Caribbean Islands, and invasive populations from Africa (Benin, Uganda, Malawi), India, and China33, provided an overall complex picture of population structure that reflected the species’ likely introduction histories across its invasive ranges.Admixture analysis that excluded New World, African and Indian populations identified four genetic clusters (i.e., K = 4) to best describe these invasive populations from SEA, and EA (i.e., China, South Korea), and Pacific/Australia (Fig. 5a). At K = 4, Australian populations from NT and NSW, YJ population from China, South Korean, and Malaysia’s Kedah population, each showed unique admixture patterns (i.e., some individuals from NT and NSW populations lacked cluster 3; most of YJ (but also some CY and XP) individuals lacked clusters 1 and 2; South Korean (e.g., MF individuals) lacked cluster 2; Malaysia’s Kedah population lacked evidence of admixture (i.e., reflecting its laboratory culture history) and was made up predominantly by individuals that belonged to cluster 4. Populations from China also differed from most populations from SEA due to the overall absence of genetic cluster 4. Taken as a whole, establishment of the FAW populations in China, Malaysia, vs. other SE Asian populations, and between Australian populations (e.g., NT/NSW cf. WA/Qld), likely involved individuals from diverse genetic background (i.e., multiple introductions). At K = 4, the majority of Australian populations appeared to contain genetic clusters similar to China (i.e., cluster 3) and to SEA (i.e., cluster 2).Figure 5Admixture and corresponding CV plots for FAW populations from: (a) Australia, China, South Korea, Lao PDR, Myanmar, Malaysia, Philippines, PNG, and Vietnam, and (b) Benin, China, India, South Korea, Lao PDR, Myanmar, Malaysia, Philippines, PNG, Tanzania, and Vietnam. Optimal ancestral genetic clusters are K = 4 for both admixture plots. Boxed individuals have unique admixture patterns at K = 4 when compared with other populations. China FAW lacked Cluster 2 (navy blue colour; present in almost all SEA and Australian FAW), while in NSW and NT some individuals lacked cluster 3. South Korea ‘MF’ population generally lacked cluster 2, while Kedah (Malaysia) showed distinct (cluster 4) pattern for all individuals. The overall same observations are evident in the admixture plot in (b), with African FAW generally exhibiting admixture patterns similar to SEA populations than to Chinese FAW. With the exception of Kedah (Malaysia) and some Chinese FAW individuals, all FAW in the invasive range showed evidence of genomic admixture (i.e., hybrid signature). The figures were generated using the POPHELPER program  and further manipulated in Microsoft PowerPoint for Mac v16.54.Full size imageOverall admixture patterns at best K = 4 in China and SEA remained unchanged when analysed together with African and Indian individuals (Fig. 5b; excluded Australia). Benin individuals were either similar to China or to SEA, while eastern African populations (e.g., Uganda, Malawi) were similar to Southeast Asian populations from e.g., Vietnam, Laos, and is in agreement with the phylogenetic inference (Fig. 3) that identified these African individuals as having loci that were derived from Southeast Asian populations.Genome-wide SNP loci demonstrated that invasive FAW populations from SEA and Australia exhibited admixed genomic signatures similar to that observed in other invasive populations33,34. While the current invasive populations in Africa and Asia likely arrived already as hybrids as suggested by Yainna et al.68, the Malaysia Kedah State population was potentially established by offspring of a non-admixed female. Distinct admixture patterns in Malaysian FAW populations between Kedah and Johor/Penang states therefore suggested that establishment of these populations was likely as separate introduction events. As reported also in Tay et al.33, the Chinese YJ population appeared to have admixed signature that differed from XP and CY populations, and suggested that the YJ population could have a different introduction history than the XP and CY populations. Similar multiple genetic signatures based on lesser nuclear markers by Jiang et al.39 also supported likely multiple introductions of China Yunnan populations. More

Newmark, W. D. A land-bridge island perspective on mammalian extinctions in western North American parks. Nature 325, 430–432 (1987).Article 
ADS 
CAS 

Google Scholar 
Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).Article 

Google Scholar 
Radeloff, V. C. et al. Housing growth in and near United States protected areas limits their conservation value. Proc. Natl. Acad. Sci. U. S. A. 107, 940–945 (2010).Article 
ADS 
CAS 

Google Scholar 
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).Article 
CAS 

Google Scholar 
Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. https://doi.org/10.1126/sciadv.aay0814 (2020).Article 

Google Scholar 
Wasser, S. K. et al. Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science 349, 84–87 (2015).Article 
ADS 
CAS 

Google Scholar 
Davis, C. R. & Hansen, A. J. Trajectories in land use change around U,S. national parks and challenges and opportunities for management. Ecol. Appl. 21, 3299–3316 (2011).Article 

Google Scholar 
Newmark, W. D. Extinction of mammal populations in western North American national parks. Conserv. Biol. 9, 512–526 (1995).Article 

Google Scholar 
Newmark, W. D. Insularization of Tanzanian parks and the local extinction of large mammals. Conserv. Biol. 10, 1549–1556 (1996).Article 

Google Scholar 
Brashares, J. S., Arcese, P. & Sam, M. K. Human demography and reserve size predict wildlife extinction in West Africa. Proc. R. Soc. B Biol. Sci. 268, 2473–2478 (2001).Article 
CAS 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
ADS 
CAS 

Google Scholar 
Turner, M. G. & Dale, V. H. Comparing large, infrequent disturbances: What have we learned?. Ecosystems 1, 493–496 (1998).Article 

Google Scholar 
Berger, J. The last mile: How to sustain long-distance migration in mammals. Conserv. Biol. 18, 320–331 (2004).Article 

Google Scholar 
Bolger, D. T., Newmark, W. D., Morrison, T. A. & Doak, D. F. The need for integrative approaches to understand and conserve migratory ungulates. Ecol. Lett. 11, 63–77 (2008).
Google Scholar 
Sawyer, H., Kauffman, M. J., Nielson, R. M. & Horne, J. S. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecol. Appl. 19, 2016–2025 (2009).Article 

Google Scholar 
Tucker, M. A. et al. Moving in the anthropocene: Global reductions in terrestrial mammalian movements. Science 469, 466–469 (2018).Article 
ADS 

Google Scholar 
Soulé, M. E. & Terborgh, J. Conserving nature at regional and continental scales-a scientific program for North America. Bioscience 49, 809–817 (1999).Article 

Google Scholar 
Hilty, J. et al. Guidelines for conserving connectivity through ecological networks and corridors. Best Pract. Prot. Area Guidel. Ser. 30, 122 (2020).
Google Scholar 
Haddad, N. & Tewksbury, J. Impacts of corridors on populations and communities. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 390–415 (Cambridge University Press, 2010).
Google Scholar 
Ramiadantsoa, T., Ovaskainen, O., Rybicki, J. & Hanski, I. Large-scale habitat corridors for biodiversity conservation: A forest corridor in Madagascar. PLoS One 10, 1–18 (2015).Article 
CAS 

Google Scholar 
Newmark, W. D., Jenkins, C. N., Pimm, S. L., McNeally, P. B. & Halley, J. M. Targeted habitat restoration can reduce extinction rates in fragmented forests. Proc. Natl. Acad. Sci. USA. 114, 9635–9640 (2017).Article 
ADS 
CAS 

Google Scholar 
Diamond, J. M. Biogeographic kinetics: Estimation of relaxation times for avifaunas of southwest Pacific islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).Article 
ADS 
CAS 

Google Scholar 
Terborgh, J. Preservation of natural diversity: The problem of extinction prone species. Bioscience 24, 715–722 (1974).Article 

Google Scholar 
Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt revisited. Nature 371, 65–66 (1994).Article 
ADS 

Google Scholar 
Halley, J. M., Monokrousos, N., Mazaris, A. D., Newmark, W. D. & Vokou, D. Dynamics of extinction debt across five taxonomic groups. Nat. Commun. 7, 1–6 (2016).Article 

Google Scholar 
Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian amazon. Science 337, 228–232 (2012).Article 
ADS 
CAS 

Google Scholar 
Hanski, I. Extinction debt and species credit in boreal forests: Modelling the consequences of different approaches to conservation. Ann. Zool. Fennici 37, 271–280 (2000).
Google Scholar 
LaBarbera, M. Analyzing body size as a factor in ecology and evolution. Annu. Rev. Ecol. Syst. 20, 97–117 (1989).Article 

Google Scholar 
Oakleaf, J. K. et al. Habitat selection by recolonizing wolves in the northern Rocky mountains of the United States. J. Wildl. Manage. 70, 554–563 (2006).Article 

Google Scholar 
Cushman, S. A., McKelvey, K. S. & Schwartz, M. K. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23, 368–376 (2009).Article 

Google Scholar 
Schwartz, M. K. et al. Wolverine gene flow across a narrow climatic niche. Ecology 90, 3222–3232 (2014).Article 

Google Scholar 
McKelvey, K. S. et al. Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors. Ecol. Appl. 21, 2882–2897 (2011).Article 

Google Scholar 
Carroll, C., Mcrae, B. H. & Brookes, A. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conserv. Biol. 26, 78–87 (2012).Article 

Google Scholar 
Parks, S. A., McKelvey, K. S. & Schwartz, M. K. Effects of weighting schemes on the identification of wildlife corridors generated with least-cost methods. Conserv. Biol. 27, 145–154 (2013).Article 

Google Scholar 
Peck, C. P. et al. Potential paths for male-mediated gene flow to and from an isolated grizzly bear population. Ecosphere 8, e01969 (2017).Article 

Google Scholar 
Wild Migrations: Atlas of Wyoming’s Ungulates. (Oregon State University, 2018).Singleton, P. H., Gaines, W. L. & Lehmkuhl, J. F. Landscape permeability for large carnivores in Washington: A geographic information system weighted-distance and least-cost corridor assessment. (2002).Long, R. A. et al. The Cascades carnivore connectivity project: A landscape genetic assessment of connectivity in Washington’s north Cascades ecosystem. Final report for the Seattle City Light Wildlife Research Program (2013).Diamond, J. M. The island dilemma: Lessons of modern biogeographic studies for the design of natural reserves. Biol. Conserv. 7, 129–146 (1975).Article 

Google Scholar 
Wilson, E. O. & Willis, E. O. Applied biogeography. In Ecological structure of ecological communities (eds. Cody, M. L, & Diamond, J. M.) 522–534 (Harvard University Press, 1975)
Google Scholar 
Halley, J. M. & Iwasa, Y. Neutral theory as a predictor of avifaunal extinctions after habitat loss. Proc. Natl. Acad. Sci. USA 108, 2316–2321 (2011).Article 
ADS 
CAS 

Google Scholar 
Cushman, S. A., Lewis, J. S. & Landguth, E. L. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov. Ecol. 1, 1–11 (2013).Article 

Google Scholar 
Singleton, P. H. & Lehmkuhl, J. F. I-90 Snoqualmie pass wildlife habitat linkage assessment. Final Report. USDA, Pacific Northwest Research Station. (2000).Craighead, L., Craighead, A., Oeschslia, L. & Kociolek, A. Bozeman pass post-fencing wildlife monitoring. Final Report. FHWA/MT-10-006/8173 (2011).Andis, A. Z., Huijser, M. P. & Broberg, L. Performance of arch-style road crossing structures from relative movement rates of large mammals. Front. Ecol. Evol. 5, 1–13 (2017).Article 

Google Scholar 
Millward, L. Small mammal microhabitat use and species composition at a wildlife crossing structure compared with nearby forest (Central Washington University, 2018).
Google Scholar 
Bischof, R., Steyaert, S. M. J. G. & Kindberg, J. Caught in the mesh: Roads and their network-scale impediment to animal movement. Ecography 40, 1369–1380 (2017).Article 

Google Scholar 
Balkenhol, N. & Waits, L. P. Molecular road ecology: Exploring the potential of genetics for investigating transportation impacts on wildlife. Mol. Ecol. 18, 4151–4164 (2009).Article 

Google Scholar 
Clevenger, A. P. & Wierzchowski, J. Maintaining and restoring connectivity in landscapes fragmented by roads. In Connectivity Conservation, (eds. Crooks, K. R. & Sanjayan, M.) 502–535 (Cambridge University Press, 2010.)
Google Scholar 
Sawaya, M. A., Kalinowski, S. T. & Clevenger, A. P. Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proc. R. Soc. B Biol. Sci. 281, 20131705 (2014).Article 

Google Scholar 
Sawaya, M. A., Clevenger, A. P. & Schwartz, M. K. Demographic fragmentation of a protected wolverine population bisected by a major transportation corridor. Biol. Conserv. 236, 616–625 (2019).Article 

Google Scholar 
Kamal, S., Grodzińska-Jurczak, M. & Brown, G. Conservation on private land: A review of global strategies with a proposed classification system. J. Environ. Plan. Manag. 58, 576–597 (2015).Article 

Google Scholar 
Wasserman, T. N., Cushman, S. A., Littell, J. S., Shirk, A. J. & Landguth, E. L. Population connectivity and genetic diversity of American marten (Martes americana) in the United States northern Rocky Mountains in a climate change context. Conserv. Genet. 14, 529–541 (2013).Article 

Google Scholar 
Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).Article 

Google Scholar 
Cushman, S. A., Landguth, E. L. & Flather, C. H. Evaluating the sufficiency of protected lands for maintaining wildlife population connectivity in the U.S. northern Rocky Mountains. Divers. Distrib. 18, 873–884 (2012).Article 

Google Scholar 
Beier, P., Spencer, W., Baldwin, R. F. & Mcrae, B. H. Toward best practices for developing regional connectivity maps. Conserv. Biol. 25, 879–892 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2020). More

Ethics statementAnimals’ care was in accordance with institutional guidelines. Ethical permit was issued by Malmö-Lund djurförsöksetiska nämnd 5.8.18-00848/2018.Field workWe carried out the field work in Sweden during four breeding seasons (2018–2021). Adult male willow warblers were captured in their breeding territories using mist nets and playback of a song. From each bird, we collected the innermost primary feather from the right wing. From the birds that returned with a logger we also collected ~20 μl of blood from the brachial wing vein. The blood was stored in SET buffer (0.015 M NaCl, 0.05 M Tris, 0.001 M of EDTA, pH 8.0) at room temperature until deposited for permanent storage at −20 °C. We deployed Migrate Technology Ltd geolocators (Intigeo-W30Z11-DIP 12 × 5 × 4 mm, 0.32 g) and used a nylon string to mount them on birds with the “leg-loop” harness method as outlined in our previous work24. The mass of the logger relative to that of the bird was on average 3.3% (range 2.7–3.8%).The tagged birds were ringed with a numbered aluminum ring, and two, colored plastic rings for later identification in the field. In total, we tagged 466 males (349 in 2018 and 117 in 2020) at breeding territories. During the first tagging season (2018), birds were trapped at 17 locations (average 22 birds per site; range 7–30) distributed across Sweden (Fig. S1). Three of the sites were in southern Sweden to document migration routes of allopatric trochilus and three sites were located above the Arctic circle to record migratory routes of allopatric acredula, whereas the remaining (239) loggers were spread over 11 sites located in the migratory divide. Given the observed densities and distribution of hybrids after analyzing returning birds in 2019, we deployed 117 more loggers at one single site (63.439°N, 14.831°E) in 2020. We successfully retrieved tracks from 57 birds tagged in 2019 and 16 from birds tagged in 2021. In search for birds with loggers, we checked circa 3000 willow warbler males and covered an area of at least 0.5 km radius around each site the year after tagging.Geolocator data treatmentThe R package GeoLight (version 2.0)25 was used to extract and analyze locations from raw geolocator data. All twilight events were obtained with light threshold of 3 lux. The most extreme outliers were trimmed with “loessFilter” function and a K value of 3. We used GeoLight’s function “getElevation” for estimating the sun elevation angle for the breeding period: these sets of locations were used to infer the positions for autumn departure direction. In addition, we carried out a “Hill-Ekström” calibration for the longest stationary winter site during the period before the spring equinox. Winter calibration produced location sets that better reflected the winter coordinates of the main winter site in sub-Saharan Africa26. We reduced some of the inherent geolocation “noise” by applying cantered 5-day rolling means to the coordinates. The equinox periods were visually identified by inspecting standard deviations in latitude. Latitudes from equinox periods were omitted (on average autumn equinox obscured data for 45 days (range 25–68). For the main winter site, we used the longest period at which bird stayed stationary and from which in all cases begun the spring migration (mean = 118, SD = 23 days). Timing of autumn departure was estimated by manual inspection of longitudes and latitudes plotted in time series. To estimate at which longitude the birds crossed the Mediterranean, we extracted the longitude when birds crossed latitude 35 N° (Mediterranean crossing longitude). For 29 birds, it was possible to directly extract the longitude at crossing latitude 35 N°. For the rest of the cases, the birds had not reached latitude 35 N° before the latitude was obscured by the equinox, we calculated the mean longitude of 10 days from the onset of fall equinox as a measure of the Mediterranean crossing. This measurement correlated highly with the winter longitude (r = 0.78, p = 2.8 × 10−16). To control for the birds relative breeding site longitude, we extracted the departure direction (1°–360°) relative from the tagging site to the location where the birds crossed the Mediterranean (departure direction). The departure data was of circular type (measured in 360°), however the variance did not span more than 180° degrees (range 151°–224°). Therefore, we proceeded with analyses using linear statistics. Geographic distances and departure direction were calculated using R package “geosphere” (version 1.5-10). Complete set of positions of each individual bird with equinoxes excluded is presented in Supplementary Data 1.Laboratory work and molecular data extractionWe extracted DNA from blood samples following the ammonium acetate protocol16. Genotyping for divergent regions on chromosome 1 (InvP-Ch1) and chromosome 5 (InvP-Ch5) was done using a qPCR SNP assay16, which is based on one informative SNP per region (SNP 65 for chromosome 1 and SNP 285 for chromosome 5). Probes and primers were produced by Thermo Fisher Scientific and were designed using the online Custom TaqMan® Assay Design tool (Table S4). We used Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) and the universal Fast-two-steps protocol: 95 °C, 15 min—40*(95 °C, 10 s–60 °C, 30 s, plate read. Both regions contain inversion polymorphisms that restrict recombination between subspecies-specific haplotypes and contain nearly all the SNPs separating the two subspecies13. For each region, we scored genotypes as either “Tro” (homozygous for trochilus haplotypes), “Acr” (homozygous for acredula haplotypes) or “Het” (heterozygous). The method that we used to assess the presence of MARB-a is based on a qPCR assay that quantifies the copy number of a novel TE (previously known as AFLP-WW212) that has expanded in acredula. The quantification of repeats by this method has been shown to be highly repeatable (R2 = 0.88) when comparing estimates obtained from DNA in blood and feathers15. We used the forward (5′-CCTTGCATACTTCTATTTCTCCC-3′) and reverse (5′-CATAGGACAGACATTGTTGAGG-3′) primers developed by Caballero-López et al.15 to amplify the TE motif. For reference of a single copy region we used the primers SFRS3F and SFRS3R27. We diluted DNA to 1 ng/μl−1 and used a Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) with SYBR-green-based detection. Total reaction volume was 25 μl of which 4 μl of DNA, 12.5 μl of SuperMix, 0.1 μl ROX, 1 μl of primer (forward and reverse), and 6.4 μl of double distilled H2O. We ran quantifications of the single copy gene and the TE variant found on MARB-a on separate plates with the following settings: 50 °C for 2 min as initial incubation, 95 °C for 2 min X 43 (94 °C for 30 s [55.3 °C SFRS3 and 55.5 °C for TE, 30 s] and 72 °C for 45 s). Each sample was run in duplicate and together with a two-fold serial standard dilution (2.5–7.8 × 10−2 ng). Allopatric trochilus have 0–6 copies whereas allopatric acredula have 8–45 copies15; a bimodal distribution was also confirmed in this new data set (Fig. S2). Accordingly, for the present analyses, we split the data in two groups: birds with ≤6 TE copies and birds with >7, translating into absence or presence of MARB-a, with the former assumed to be homozygous for the absence of MARB-a and the latter heterozygous or homozygous for the presence of MARB-a. Data from two investigated willow warbler families suggest a Mendelian inheritance pattern and provide support for our interpretation of how TE copy numbers reflect the three genotypes (Table S5). Moreover, the TE copy numbers within the hybrid swarm have a distribution similar to a combination of allopatric trochilus and acredula, further supporting that the copies are inherited as intact blocks (haplotypes). However, a precise distinction between heterozygotes and homozygotes on MARB-a is still not possible15.Statistical analysisWe used linear models with departure direction, winter longitude, migration distance and departure timing as response variables and the three genetic markers: MARB-a (a factor with two levels), InvP-Ch1 (a factor with three levels) and InvP-Ch5 (a factor with three levels) as explanatory variables. Models were constructed with R base package “stats”. We reported Type II ANOVA for models with more than one explanatory variable and no interactions and type III ANOVA results for models with interaction term by using R package “Car” (version 3.0-12)28. We initially constructed mixed effect models with timing of departure and tagging year as random factors however, this delivered singular fits due to insufficient sample sizes across categories. Normality of residuals was checked with a Shapiro–Wilk test. For carrying out circular statistics on autumn migration direction we used the R package “circular” (version 0.4-93). Watson’s U2 pairwise comparisons of different groups delivered the same results as linear models (Table S2 and Fig. S5). Circular means were identical to conventional linear means in our data set, which we take as another evidence that linear models are appropriate for the analysis of our data (Table S3 and Fig. S5). Maps in Figs. 1 and 2b and S1, S3 and S4 were created with R package “ggplot2” (version 3.3.6) using continent contours from Natural Earth, naturalearthdata.com/. Heat gradient over the maps in Fig. 1a–d were created with R package “gstat” (version 2.0-8) and the inverse distance weighting power of 3.0. Circular plots were created with ORIANA (version 4.02). All analyses were carried out with R version 4.1.1 (R Core Team 2021).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Chase, J. M. et al. The interaction between predation and competition: A review and synthesis. Ecol. Lett. 5, 302–315. https://doi.org/10.1046/j.1461-0248.2002.00315.x (2002).Article 

Google Scholar 
Droge, E., Creel, S., Becker, M. S. & M’Soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128. https://doi.org/10.1038/s41559-017-0220-9 (2017).Article 

Google Scholar 
Allesina, S. & Levine Jonathan, M. A competitive network theory of species diversity. Proc. Natl. Acad. Sci. U.S.A. 108, 5638–5642. https://doi.org/10.1073/pnas.1014428108 (2011).Article 
ADS 

Google Scholar 
Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285. https://doi.org/10.1038/ncomms12285 (2016).Article 
ADS 
CAS 

Google Scholar 
Letten, A. D. & Stouffer, D. B. The mechanistic basis for higher-order interactions and non-additivity in competitive communities. Ecol. Lett. 22, 423–436. https://doi.org/10.1111/ele.13211 (2019).Article 

Google Scholar 
Lotka, A. J. Elements of physical biology. Sci. Prog. Twent. Century (1919–1933) 21, 341–343 (1926).
Google Scholar 
Volterra, V. Variazioni e Fluttuazioni del Numero d’Individui in Specie Animali Conviventi. (Società Anonima Tipografica “Leonardo da Vinci”, 1926).Schmitz, O. J. Top predator control of plant biodiversity and productivity in an old-field ecosystem. Ecol. Lett. 6, 156–163. https://doi.org/10.1046/j.1461-0248.2003.00412.x (2003).Article 

Google Scholar 
Fey, K., Banks, P. B., Oksanen, L. & Korpimäki, E. Does removal of an alien predator from small islands in the Baltic Sea induce a trophic cascade?. Ecography 32, 546–552. https://doi.org/10.1111/j.1600-0587.2008.05637.x (2009).Article 

Google Scholar 
Terborgh John, W. Toward a trophic theory of species diversity. Proc. Natl. Acad. Sci. U.S.A. 112, 11415–11422. https://doi.org/10.1073/pnas.1501070112 (2015).Article 
ADS 
CAS 

Google Scholar 
Pringle, R. M. et al. Predator-induced collapse of niche structure and species coexistence. Nature 570, 58–64. https://doi.org/10.1038/s41586-019-1264-6 (2019).Article 
ADS 
CAS 

Google Scholar 
Sandom, C. et al. Mammal predator and prey species richness are strongly linked at macroscales. Ecology 94, 1112–1122. https://doi.org/10.1890/12-1342.1 (2013).Article 

Google Scholar 
Louette, G. & De Meester, L. Predation and priority effects in experimental zooplankton communities. Oikos 116, 419–426. https://doi.org/10.1111/j.2006.0030-1299.15381.x (2007).Article 

Google Scholar 
Johnston, N. K., Pu, Z. & Jiang, L. Predator identity influences metacommunity assembly. J. Anim. Ecol. 85, 1161–1170. https://doi.org/10.1111/1365-2656.12551 (2016).Article 

Google Scholar 
Karakoc, C., Radchuk, V., Harms, H. & Chatzinotas, A. Interactions between predation and disturbances shape prey communities. Sci. Rep. 8, 2968. https://doi.org/10.1038/s41598-018-21219-x (2018).Article 
ADS 
CAS 

Google Scholar 
Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) (Princeton University Press, 2011).Book 

Google Scholar 
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 2001).Book 

Google Scholar 
Daniel, J., Gleason, J. E., Cottenie, K. & Rooney, R. C. Stochastic and deterministic processes drive wetland community assembly across a gradient of environmental filtering. Oikos 128, 1158–1169. https://doi.org/10.1111/oik.05987 (2019).Article 

Google Scholar 
Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028 (2004).Article 
ADS 

Google Scholar 
Chase, J. M., Biro, E. G., Ryberg, W. A. & Smith, K. G. Predators temper the relative importance of stochastic processes in the assembly of prey metacommunities. Ecol. Lett. 12, 1210–1218. https://doi.org/10.1111/j.1461-0248.2009.01362.x (2009).Article 

Google Scholar 
Werner, E. E. & Peacor, S. D. A review of trait-mediated indirect interactions in ecological communities. Ecology 84, 1083–1100. https://doi.org/10.1890/0012-9658(2003)084[1083:AROTII]2.0.CO;2 (2003).Article 

Google Scholar 
Pearson, D. E., Ortega, Y. K., Eren, Ö. & Hierro, J. L. Community assembly theory as a framework for biological invasions. Trends Ecol. Evol. 33, 313–325. https://doi.org/10.1016/j.tree.2018.03.002 (2018).Article 

Google Scholar 
Duchesne, É. et al. Variable strength of predator-mediated effects on species occurrence in an arctic terrestrial vertebrate community. Ecography 44, 1236–1248. https://doi.org/10.1111/ecog.05760 (2021).Article 

Google Scholar 
Ryberg, W. A., Smith, K. G. & Chase, J. M. Predators alter the scaling of diversity in prey metacommunities. Oikos 121, 1995–2000. https://doi.org/10.1111/j.1600-0706.2012.19620.x (2012).Article 

Google Scholar 
Carrete Vega, G. & Wiens, J. J. Why are there so few fish in the sea?. Proc. R. Soc. B 279, 2323–2329. https://doi.org/10.1098/rspb.2012.0075 (2012).Article 

Google Scholar 
Barrett, M. et al. Living planet report 2018: Aiming higher. (2018).Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873. https://doi.org/10.1111/brv.12480 (2019).Article 

Google Scholar 
Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42. https://doi.org/10.1016/j.gecco.2017.01.008 (2017).Article 

Google Scholar 
Hammerschlag, N. et al. Ecosystem function and services of aquatic predators in the anthropocene. Trends Ecol. Evol. 34, 369–383. https://doi.org/10.1016/j.tree.2019.01.005 (2019).Article 

Google Scholar 
Wang, T. et al. Amur tigers and leopards returning to China: direct evidence and a landscape conservation plan. Landsc Ecol 31, 491–503. https://doi.org/10.1007/s10980-015-0278-1 (2016).Article 

Google Scholar 
Hong, S. et al. Stream health, topography, and land use influences on the distribution of the Eurasian otter Lutra lutra in the Nakdong River basin, South Korea. Ecol. Indic. 88, 241–249. https://doi.org/10.1016/j.ecolind.2018.01.004 (2018).Article 

Google Scholar 
Guter, A., Dolev, A., Saltz, D. & Kronfeld-Schor, N. Using videotaping to validate the use of spraints as an index of Eurasian otter (Lutra lutra) activity. Ecol. Indic. 8, 462–465. https://doi.org/10.1016/j.ecolind.2007.04.009 (2008).Article 

Google Scholar 
Sittenthaler, M., Bayerl, H., Unfer, G., Kuehn, R. & Parz-Gollner, R. Impact of fish stocking on Eurasian otter (Lutra lutra) densities: A case study on two salmonid streams. Mamm. Biol. 80, 106–113. https://doi.org/10.1016/j.mambio.2015.01.004 (2015).Article 

Google Scholar 
Zheng, B., Huang, H., Zhang, Y. & Dai, D. The Fishes of Tumen River (Jilin People’s Publishing House, 1980).
Google Scholar 
Fleishman, E., Murphy, D. D. & Brussard, P. F. A new method for selection of umbrella species for conservation planning. Ecol Appl 10, 569–579. https://doi.org/10.1890/1051-0761(2000)010[0569:ANMFSO]2.0.CO;2 (2000).Article 

Google Scholar 
Roberge, J.-M. & Angelstam, P. E. R. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18, 76–85. https://doi.org/10.1111/j.1523-1739.2004.00450.x (2004).Article 

Google Scholar 
McGowan, J. et al. Conservation prioritization can resolve the flagship species conundrum. Nat. Commun. 11, 994. https://doi.org/10.1038/s41467-020-14554-z (2020).Article 
ADS 
CAS 

Google Scholar 
Katano, I., Doi, H., Eriksson, B. K. & Hillebrand, H. A cross-system meta-analysis reveals coupled predation effects on prey biomass and diversity. Oikos 124, 1427–1435. https://doi.org/10.1111/oik.02430 (2015).Article 

Google Scholar 
Leibold, M. A. A graphical model of keystone predators in food webs: Trophic regulation of abundance, incidence, and diversity patterns in communities. Am. Nat. 147, 784–812. https://doi.org/10.1086/285879 (1996).Article 

Google Scholar 
McPeek, M. A. The consequences of changing the top predator in a food web: A comparative experimental approach. Ecol. Monogr. 68, 1–23. https://doi.org/10.1890/0012-9615(1998)068[0001:TCOCTT]2.0.CO;2 (1998).Article 

Google Scholar 
Chase, J. M. & Leibold, M. A. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, 2003).Book 

Google Scholar 
Gravel, D., Canham, C. D., Beaudet, M. & Messier, C. Reconciling niche and neutrality: The continuum hypothesis. Ecol. Lett. 9, 399–409. https://doi.org/10.1111/j.1461-0248.2006.00884.x (2006).Article 

Google Scholar 
Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306. https://doi.org/10.1038/nature01767 (2003).Article 
ADS 
CAS 

Google Scholar 
Yin, X., Wang, J., Yin, H. & Ruan, Y. Does inducible defense mitigate physiological stress responses of prey to predation risk?. Hydrobiologia 843, 173–181. https://doi.org/10.1007/s10750-019-04046-7 (2019).Article 

Google Scholar 
Chalcraft, D. R. & Resetarits, W. J. Jr. Predator identity and ecological impacts: Functional redundancy or functional diversity?. Ecology 84, 2407–2418. https://doi.org/10.1890/02-0550 (2003).Article 

Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758. https://doi.org/10.1111/j.1461-0248.2006.00924.x (2006).Article 

Google Scholar 
Burner, R. C. et al. Functional structure of European forest beetle communities is enhanced by rare species. Biol. Conserv. 267, 109491. https://doi.org/10.1016/j.biocon.2022.109491 (2022).Article 

Google Scholar  More

Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, ChinaQian Zhao, Yao Zhang & Shilong PiaoSchool of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengKey Laboratory of Earth Surface System and Human—Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengDepartment of Earth and Environment, Boston University, Boston, MA, USARanga B. MyneniCSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Catalonia, SpainJosep PeñuelasCREAF, Barcelona, Catalonia, SpainJosep PeñuelasState Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaShilong Piao More