Philippa Kaur

Abbott RJ, Brennan AC (2014) Altitudinal gradients, plant hybrid zones and evolutionary novelty. Philos Trans R Soc B Biol Sci 369:6–9Article 

Google Scholar 
Avise JC (2000) Phylogeography: the history and formation of species. Harvard University Press, Cambridge, MABook 

Google Scholar 
Baldassarre DT, White TA, Karubian J, Webster MS (2014) Genomic and morphological analysis of a semipermeable avian hybrid zone suggests asymmetrical introgression of a sexual signal. Evolution 68:2644–2657PubMed 
Article 
PubMed Central 

Google Scholar 
Barr KR, Dharmarajan G, Rhodes OE, Lance R, Leberg PL (2007) Novel microsatellite loci for the study of the black-capped vireo (Vireo atricapillus). Mol Ecol Notes 7:1067–1069CAS 
Article 

Google Scholar 
Barton NH, Gale KS (1993) Hybrid zones and the evolutionary process. In: Harrison RG (ed.) Hybrid Zones and the Evolutionary Process. Oxford University Press, New York, NY
Google Scholar 
Barton NH, Hewitt GM (1989) Adaption, speciation and hybrid zones. Nature 341:497–503CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Billerman SM, Murphy MA, Carling MD (2016) Changing climate mediates sapsucker (Aves: Sphyrapicus) hybrid zone movement. Ecol Evol 6:7976–7990PubMed 
PubMed Central 
Article 

Google Scholar 
Bell RC, Irian CG (2019) Phenotypic and genetic divergence in reed frogs across a mosaic hybrid zone on São Tomé Island. Biol J Linn Soc 128:672–680Article 

Google Scholar 
Bensch S, Price T, Kohn J (1997) Isolation and characterization of microsatellite loci in a Phylloscopus warbler. Mol Ecol 6:91–92CAS 
PubMed 
Article 

Google Scholar 
Bradbury IR, Bowman S, Borza T, Snelgrove PVR, Hutchings JA, Berg PR et al. (2014) Long distance linkage disequilibrium and limited hybridization suggest cryptic speciation in Atlantic cod. PLoS ONE 9:e106330Article 
CAS 

Google Scholar 
Brelsford A, Irwin DE (2009) Incipient speciation despite little assortative mating: the yellow-rumped warbler hybrid zone. Evolution 63:3050–3060PubMed 
Article 

Google Scholar 
Burg TM, Croxall JP (2004) Global population structure and taxonomy of the wandering albatross species complex. Mol Ecol 13:2345–2355CAS 
PubMed 
Article 

Google Scholar 
Carling MD, Zuckerberg B (2011) Spatio-temporal changes in the genetic structure of the Passerina bunting hybrid zone. Mol Ecol 20:1166–1175PubMed 
Article 

Google Scholar 
Carling MD, Thomassen HA (2012) The role of environmental heterogeneity in maintaining reproductive isolation between hybridizing Passerina (Aves: Cardinalidae) buntings. Int J Ecol 2012:295463Article 

Google Scholar 
Carpenter AM, Graham BA, Spellman GM, Klicka J, Burg TM (2021) Genetic, bioacoustic and morphological analyses reveal cryptic speciation in the warbling vireo complex (Vireo gilvus: Vireonidae: Passeriformes). Zool J Linn Soc zlab036 https://doi.org/10.1093/zoolinnean/zlab036Cicero C, Johnson NK (1998) Molecular phylogeny and ecological diversification in a clade of New World songbirds (genus Vireo). Mol Ecol 7:1359–1370CAS 
PubMed 
Article 

Google Scholar 
Chenuil A, Cahill AE, Délémontey N, Du Salliant du Luc E, Fanton H (2019) Problems and questions posed by cryptic species. A framework to guide future studies. Assessing to conserving biodiversity. History, philosophy and theory of the life sciences, Vol. 24. Springer. Daubenmire, Cham
Google Scholar 
Cheviron ZA, Brumfield RT (2012) Genomic insights into adaptation to high-altitude environments. Heredity 108:354–361CAS 
PubMed 
Article 

Google Scholar 
Coyne JA, Orr HA (2004) Speciation. Sinauer and Associates, Sunderland, Massachusetts
Google Scholar 
Culumber ZW, Shepard DB, Colemans SW, Rosenthal GG, Tobler M (2012) Physiological adaptation along environmental gradients and replicated hybrid zone structure in swordtails (Teleostei: Xiphophorus). J Evol Biol 25:1800–1814CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Dubay SG, Witt CC (2014) Differential high-altitude adaptation and restricted gene flow across a mid-elevation hybrid zone in Andean tit-tyrant flycatchers. Mol Ecol 23:3551–3565PubMed 
Article 
PubMed Central 

Google Scholar 
Garroway CJ, Bowman J, Cascaden TJ, Holloway GL, Mahan CG, Malcolm JR et al. (2010) Climate change induced hybridization in flying squirrels. Glob Chang Biol 16:113–121Article 

Google Scholar 
Grabenstein KC, Taylor SA (2018) Breaking barriers: Causes, consequences, and experimental utility of human-mediated hybridization. Trends Ecol Evol 33:198–212PubMed 
Article 

Google Scholar 
Graham BA, Cicero C, Strickland D, Woods JG, Coneybeare H, Dohms KM et al. (2021) Cryptic genetic diversity and cytonuclear discordance characterize contact among Canada jay (Perisoreus canadensis) morphotypes in western North America. Biol J Linn Soc 132:725–740Article 

Google Scholar 
Hammer Ø, Harper DA, Ryan PD (2001) Paleontological statistics software package for education and data analysis. Palaeontol Electron 4:9Haselhorst MSH, Parchman TL, Buerkle CA (2019) Genetic evidence for species cohesion, substructure and hybrids in spruce. Mol Ecol 28:2029–2045PubMed 
Article 

Google Scholar 
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978Article 

Google Scholar 
Hawley DM (2005) Isolation and characterization of eight microsatellite loci from the house finch (Carpodactus mexicanus). Mol Ecol Notes 5:443–445CAS 
Article 

Google Scholar 
Hebert PDN, Stoeckle MY, Zemlak TS, Francis CM (2004) Identification of birds through DNA barcodes. PLoS Biol 2:e312PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hewitt GM (1988) Hybrid zones-natural laboratories for evolutionary studies. Trends Ecol Evol 3:158–167CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hewitt GM (2001) Speciation, hybrid zones and phylogeography—or seeing genes in space and time. Mol Ecol 10:537–549CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978Article 

Google Scholar 
Hindley JA, Graham BA, Pulgarin-R PC, Burg TM (2018) The influence of latitude, geographic distance, and habitat discontinuities on genetic variation in a high latitude montane species. Sci Rep. 8:11846CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hinojosa JC, Koubínová D, Szenteczki MA, Pitteloud C, Dincă V, Alvarez N et al. (2019) A mirage of cryptic species: Genomics uncover striking mitonuclear discordance in the butterfly Thymelicus sylvestris. Mol Ecol 28:3857–3868PubMed 
Article 
PubMed Central 

Google Scholar 
Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Res 9:1322–1332Article 

Google Scholar 
Irwin DE (2020) Assortative mating in hybrid zones is remarkably ineffective in promoting speciation. Evolution 195:E150–E167
Google Scholar 
Johnson NK (1995) Speciation in vireos. I. Macrogeographic patterns of allozymic variation in the Vireo solitarius complex in the contiguous United States. Condor 97:903–919Article 

Google Scholar 
Johnson NK, Cicero C (2004) New mitochondrial DNA data affirm the importance of Pleistocene speciation in North American birds. Evolution 58:1122–1130PubMed 
Article 
PubMed Central 

Google Scholar 
Larson EL, Tinghitella RM, Taylor SA (2019) Insect hybridization and climate change. Front Ecol Evol 7:348Article 

Google Scholar 
Legendre P, Fortin M-J (2010) Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Mol Ecol Resour 10:831–844PubMed 
Article 

Google Scholar 
Lovell SF, Lein MR, Rogers SM (2021) Cryptic speciation in the warbling vireo (Vireo gilvus). Ornithology 138:ukaa071Article 

Google Scholar 
MacDonald ZG, Dupuis JR, Davis CS, Acorn JH, Nielsen SE, Sperling FAH (2020) Gene flow and climate-associated genetic variation in a vagile habitat specialist. Mol Ecol 29:3889–3906PubMed 
Article 

Google Scholar 
Manthey JD, Klicka J, Spellman GM (2011) Cryptic diversity in a widespread North American songbird: phylogeography of the brown creeper (Certhia americana). Mol Phylogenet Evol 58:502–512PubMed 
Article 

Google Scholar 
Marchetti K, Price T, Richman A (1995) Correlates of wing morphology with foraging behaviour and migration distance in the genus Phylloscopus. J Av Biol 26:177–181Article 

Google Scholar 
Martin H, Touzet P, Dufay M, Gode C, Schmitt E, Lahiani E et al. (2017) Lineages of Silene nutans developed rapid, strong, asymmetric postzygotic reproductive isolation in allopatry. Evolution 71:1519–1531CAS 
PubMed 
Article 

Google Scholar 
Martinez JG, Soler JJ, Soler M, Moller AP, Burke T (1999) Comparative population structure and gene flow of a brood parasite, the great spotted cuckoo (Clamator glandarius) and its primary host, the magpie (Pica pica). Evolution 53:269–278CAS 
PubMed 
PubMed Central 

Google Scholar 
Mettler RD, Spellman GM (2009) A hybrid zone revisited: Molecular and morphological analysis of the maintenance, movement, and evolution of a Great Plains avian (Cardinalidae: Pheucticus) hybrid zone. Mol Ecol 18:3256–3267CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meirmans PG, Van Tienderen PH (2004) GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes 4:792–794Article 

Google Scholar 
Nowakowski JK, Szulc J, Remisiewicz M (2014) The further the flight, the longer the wing: relationship between wing length and migratory distance in Old World reed and bush warblers (Acrocephalidae and Locustellidae). Ornis Fennica 91:178–186
Google Scholar 
Pavolova A, Amos JN, Joseph L, Loynes K, Austin JJ, Keogh JS et al. (2013) Perched at the mito-nuclear crossroads: divergent mitochondrial lineages correlate with environment in the face of ongoing nuclear gene flow in an Australian bird. Evol 67:3412–3428Article 
CAS 

Google Scholar 
Piertney SB, Marquiss M, Summers R (1998) Characterization of tetranucleotide microsatellite markers in the Scottish crossbill (Loxia scotica). Mol Ecol 7:1261–1263CAS 
PubMed 
Article 

Google Scholar 
Porras-Hurtado L, Ruiz Y, Santos C, Phillips C, Carracedo A, Lareu MV (2013) An overview of STRUCTURE: Applications, parameter settings, and supporting software. Front Genet 4:98PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pritchard J, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reding DM, Castañeda-Rico S, Shirazi S, Hofman CA, Cancellare IA, Lance SL et al. (2021) Mitochondrial genomes of the United States distribution of gray fox (Urocyon cinereoargenteus) reveal a major phylogeographic break at the Great Plains suture zone. Front Ecol Evol. https://doi.org/10.3389/fevo.2021.666800.Richardson DS, Jury FL, Dawson DA, Salgueiro P, Komdeur J, Burke T (2003) Fifty Seychelles warbler (Acrocephalus sechellensis) microsatellite loci polymorphic in Sylviidae species and their cross-species amplification in other passerine birds. Mol Ecol 9:2225–2230Article 

Google Scholar 
Riordan EC, Gugger PF, Ortego J, Smith C, Gaddis K, Thompson P et al. (2016) Association of genetic and phenotypic variability with geography and climate in three southern California oaks. Am J Bot 103:73–85PubMed 
Article 

Google Scholar 
Rush AC, Cannings RJ, Irwin DE (2009) Analysis of multilocus DNA reveals hybridization in a contact zone between Empidonax flycatchers. J Avian Biol 40:614–624Article 

Google Scholar 
Sartor CC, Cushman SA, Wan HY, Kretschmer R, Pereira JA, Bou N et al. (2021) The role of the environment in the spatial dynamics of an extensive hybrid zone between two neotropical cats. J Evol Biol 34:614–627PubMed 
Article 

Google Scholar 
Schukman JM, Lira-Noriega A, Townsend Peterson A (2011) Multiscalar ecological characterization of Say’s and eastern phoebes and their zone of contact in the Great Plains. Condor 113:372–384Article 

Google Scholar 
Seehausen O, Takimoto G, Roy D, Jokela J (2008) Speciation reversal and biodiversity dynamics with hybridization in changing environments. Mol Ecol 17:30–44PubMed 
Article 
PubMed Central 

Google Scholar 
Semenchuk GP (1992) The Atlas of Breeding Birds of Alberta. Fed. of Alberta Naturalists, Edmonton, p 243
Google Scholar 
Peakall R, Smouse PE (2012) GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics 28:2537–2539CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sorenson MD, Ast JC, Dimcheff DE, Yuri T, Mindell DP (1999) Primers for a PCR-based approach to mitochondrial genome sequencing in birds and other vertebrates. Mol Phylogent Evol 12:105–114CAS 
Article 

Google Scholar 
Spellman GM, Klicka J (2007) Phylogeography of the white-breasted nuthatch (Sitta carolinensis): diversification in North American pine and oak woodlands. Mol Ecol 16:1729–1740CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Stenzler LM, Fitzpatrick JW (2002) Isolation of microsatellite loci in the Florida scrub jay Aphelocoma coerulescens. Mol Ecol Notes 2:547–550CAS 
Article 

Google Scholar 
Swenson NG (2006) GIS-based niche models reveal unifying climatic mechanisms that maintain location of avian hybrid zones in a North America suture zone. J Evol Biol. 19:717–725CAS 
PubMed 
Article 

Google Scholar 
Swenson NG, Howard DJ (2005) Clustering of contact zones, hybrid zones, and phylogeographic breaks in North America. Am Nat 166:581–591PubMed 
Article 

Google Scholar 
Tarr CL, Fleischer RC (1998) Primers for polymorphic GT microsatellites isolated from the Mariana crow, Corvus kubaryi. Mol Ecol 7:253–255CAS 
PubMed 
Article 

Google Scholar 
Tarroso P, Pereira RJ, Martínez-Freiría F, Godinho R, Brito JC (2014) Hybridization at an ecotone: Ecological and genetic barriers between three Iberian vipers. Mol Ecol 23:1108–1123CAS 
PubMed 
Article 

Google Scholar 
Taylor SA, Larson EL, Harrison RG (2015) Hybrid zones: windows on climate change. Trends Ecol Evol 30:398–406PubMed 
PubMed Central 
Article 

Google Scholar 
Toews DPL, Mandic M, Richards JG, Irwin DE (2014) Migration, mitochondria and the yellow-rumped warbler. Evolution 68:241–255CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Toews DPL, Campagna L, Taylor SA, Balakrishnan CN, Baldassarre DT, Deane-Coe PE et al. (2016) Genomic approaches to understanding population divergence and speciation in birds. Auk 133:13–30Article 

Google Scholar 
Toews DPL, Irwin DE (2008) Cryptic speciation in a Holarctic passerine revealed by genetic and bioacoustic analyses. Mol Ecol 17:2691–2705CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
van Els P, Cicero C, Klicka J (2012) High latitudes and high genetic diversity: Phylogeography of a widespread boreal bird, the gray jay (Perisoreus canadensis). Mol Phylogenet Evol 63:456–465PubMed 
Article 
PubMed Central 

Google Scholar 
Voelker G, Rohwer S (1998) Contrasts in scheduling of molt and migration in eastern and western warbling vireos. Auk 155:142–155Article 

Google Scholar 
Walsh J, Billerman SM, Rohwer VG, Butcher BG, Lovette IJ (2020) Genomic and plumage variation across the controversial Baltimore and Bullock’s oriole hybrid zone. Auk 137:1–15Article 

Google Scholar 
Walsh J, Rowe RJ, Olsen BJ, Shriver WG, Kovach AI (2016) Genotype-environment associations support a mosaic hybrid zone between two tidal marsh birds. Ecol Evol 6:279–294PubMed 
Article 
PubMed Central 

Google Scholar 
Walsh P, Metzger D, Higuchi R (1991) Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10:506–513CAS 
PubMed 
PubMed Central 

Google Scholar 
Weir JT, Schluter D (2004) Ice sheets promote speciation in boreal birds. Proc R Soc B 271:1881–1887PubMed 
PubMed Central 
Article 

Google Scholar 
Williams JW (2003) Variations in tree cover in North America since the last glacial maximum. Glob Planet Change 35:1–23Article 

Google Scholar 
Williams DA, Berg EC, Hale AM, Hughes CR (2004) Characterization of microsatellites for parentage studies of white-throated magpie-jays (Calocitta formosa) and brown jays (Cyanocorax morio). Mol Ecol Notes 4:509–511CAS 
Article 

Google Scholar 
Zwartjes PW (2001) Genetic structuring among migratory populations of the black-whiskered vireo, with a comparison to the red-eyed vireo. Condor 103:439–448Article 

Google Scholar  More

Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamo, M. First Report of Outbreaks of the Fall Armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a New Alien Invasive Pest in West and Central Africa. PLoS ONE 11, e0165632 (2016).PubMed 
PubMed Central 

Google Scholar 
Cock, M. J. W., Beseh, P. K., Buddie, A. G., Cafa, G. & Crozier, J. Molecular methods to detect Spodoptera frugiperda in Ghana, and implications for monitoring the spread of invasive species in developing countries. Sci. Rep. 7, 4103 (2017).PubMed 
PubMed Central 

Google Scholar 
Nagoshi, R. N. et al. Comparative molecular analyses of invasive fall armyworm in Togo reveal strong similarities to populations from the eastern United States and the Greater Antilles. PLoS ONE 12, e0181982 (2017).PubMed 
PubMed Central 

Google Scholar 
Jacobs, A., van Vuuren, A. & Rong, I. H. Characterisation of the fall armyworm (Spodoptera frugiperda JE Smith) (Lepidoptera: Noctuidae) from South Africa. Afr. Entomol. 26, 45–49 (2018).
Google Scholar 
Otim, M. H. et al. Detection of sister-species in invasive populations of the fall armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae) from Uganda. PLoS ONE 13, e0194571 (2018).PubMed 
PubMed Central 

Google Scholar 
FAO. Briefing note on FAO actions on fall armyworm in Africa, (2018).FAO. Briefing note on FAO actions on fall armyworm, (2019).Ganiger, P. C. et al. Occurrence of the new invasive pest, fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), in the maize fields of Karnataka, India. Curr. Sci. India 115, 621–623 (2018).CAS 

Google Scholar 
Sharanabasappa, D. et al. First report of the fall Armyworm, Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae) an Alien invasive pest on Maize in India. Pest Manag. Horticultural Ecosyst. 24, 23–29 (2018).
Google Scholar 
FAO. in FAO Regional Conference for Asia and the Pacific, 35th Session 7 (Thimphu, Bhutan, 2019).EPPO. First report of Spodoptera frugiperda in Thailand. (2019).Tay, W. T. & Gordon, K. H. J. Going global – genomic insights into insect invasions. Curr. Opin. Insect Sci. 31, 123–130 (2019).PubMed 

Google Scholar 
Zhang, L. et al. Molecular identification of invasive fall armyworm Spodoptera frugiperda in Yunnan Province. Plant Prot. 45, 19–24 (2019).
Google Scholar 
Wu, Q., Jian, Y. & K, W. Analysis of migration routes of the fall armyworm Spodoptera frugiperda (J. E. Smith) form Myanmar to China. Plant Prot. 45, 1–6 (2019).CAS 

Google Scholar 
USDA. Fall armyworm damages corn and threatens other crops in Vietnam. United States Department of Agriculture, Foreign Agricultural Service, Report Number: VM2019-0017 (2019).FAO. Report of first detection of fall armyworm (FAW) in the Republic of the Philippines. Report No. PHL-02/1, (Food and Agriculture Organization of the United Nations, International Plant Protection Convention, 2019).Navasero, M. V. et al. Detection of the fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) using larval mrophological characters, and observations on its current local distribution in the Philippines. Philipp. Ent 33, 171–184 (2019).
Google Scholar 
Vennila, S. et al. in International Workshop on Facilitating International Research Collaboration on Transboundary Plant Pests. (Ministry of Agriculture, Forestry and Fisheries, Tsukuba, Ibaraki, Japan, 2019).FAO. First detection of fall armyworm in China. (Food and Agriculture Organization of the United Nations, International Plant Protection Convention, 2019).Silver, A. Caterpillar’s devastating march across China spurs hunt for native predator. Nature 570, 286–287 (2019).CAS 
PubMed 

Google Scholar 
Song, X. P. et al. Intrusion of Fall Armyworm (Spodoptera frugiperda) in Sugarcane and Its Control by Drone in China. Sugar Tech. 22, 734–737 (2020).
Google Scholar 
Czepak, C. et al. Especial Spodoptera: Migração acelerada. Cultivar Gd. Culturas 244, 26–29 (2019).
Google Scholar 
FAO. First detection of Fall armyworm in Torres Strait of Australia. (Food and Agriculture Organization of the United Nations, International Plant Protection Convention, 2020).Queensland Government, D. o. A. a. F. First mainland detection of fall armyworm, accessed 13 March 2020 (2020).Wild, S. Invasive pest hits Africa. Nature 543, 13–14 (2017).CAS 
PubMed 

Google Scholar 
Porter, J. E. & Hughes, J. H. Insect eggs transported on the outer surface of airplanes. J. Economic Entomol. 43, 555–557 (1950).
Google Scholar 
Jeger, M. et al. Pest categorisation of Spodoptera frugiperda. Efsa J. https://doi.org/10.2903/j.efsa.2017.4927 (2017).Early, R., Gonzalez-Moreno, P., Murphy, S. T. & Day, R. Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. Neobiota https://doi.org/10.3897/neobiota.40.28165 (2018).FAO. Fall armyworm likely to spread from India to other parts of Asia with South East Asia and South China most at risk. (Food and Agriculture Organization of the United Nation, 2018).Gouin, A. et al. Two genomes of highly polyphagous lepidopteran pests (Spodoptera frugiperda, Noctuidae) with different host-plant ranges. Sci. Rep. 7, 11816 (2017).PubMed 
PubMed Central 

Google Scholar 
Zhang, L. et al. Genetic structure and insecticide resistance characteristics of fall armyworm populations invading China. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13219 (2020).Westbrook, J., Fleischer, S., Jairam, S., Meagher, R. & Nagoshi, R. Multigenerational migration of fall armyworm, a pest insect. Ecosphere 10, e02919 (2019).
Google Scholar 
du Plessis, H., van den Berg, J., Ota, N. & Kriticos, D. J. Spodoptera frugiperda (Fall Armyworm). in CSIRO-InSTePP Pest Geography. June, 2018 (2018).FAO. First detection report of the fall armyworm Spodoptera frugiperda (Lepdioptera: Noctuidae) on maize in Myanmar. (Food and Agriculture Organization of the United Nations, International Plant Protection Convention, 2019).Sun, X.-X. et al. Case study on the first immigration of fall armyworm Spodoptera frugiperda invading into China. J. Integr. Agriculture 18, 2–10 (2019).
Google Scholar 
Day, R. et al. Fall armyworm: impacts and implications for Africa. Outlooks Pest Manag. 28, 196–201 (2017).
Google Scholar 
Assefa, F. & Ayalew, D. Status and control measures of fall armyworm (Spodoptera frugiperda) infestations in maize fields in Ethiopia: a review. Cogent Food Agr. 5, 1641902 (2019).
Google Scholar 
Hurska, A. J. Fall armyworm (Spodoptera frugiperda) management by smallholders. CAB Rev. 14, 11 (2019).
Google Scholar 
Firake, D. M. & Behere, G. T. Natural mortality of invasive fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) in maize agroecosystems of northeast India. Biol. Control 148, 104303 (2020).CAS 

Google Scholar 
Guan, F. et al. Whole-genome sequencing to detect mutations associated with resistance to insecticides and Bt proteins in Spodoptera frugiperda. Insect Sci. https://doi.org/10.1111/1744-7917.12838 (2020).Dumas, P. et al. Phylogenetic molecular species delimitations unravel potential new species in the pest genus Spodoptera Guenee, 1852 (Lepidoptera, Noctuidae). PLoS ONE 10, e0122407 (2015).PubMed 
PubMed Central 

Google Scholar 
Dumas, P. et al. Spodoptera frugiperda (Lepidoptera: Noctuidae) host-plant variants: two host strains or two distinct species? Genetica 143, 305–316 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagoshi, R. N. et al. Genetic characterization of fall armyworm (Spodoptera frugiperda) in Ecuador and comparisons with regional populations identify likely migratory relationships. PLoS ONE 14, e0222332 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jing, D. P. et al. Initial detections and spread of invasive Spodoptera frugiperda in China and comparisons with other noctuid larvae in cornfields using molecular techniques. Insect Sci. 27, 780–790 (2020).CAS 
PubMed 

Google Scholar 
Nagoshi, R. N. et al. Southeastern Asia fall armyworms are closely related to populations in Africa and India, consistent with common origin and recent migration. Sci. Rep. 10, 1421 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mahadeva, S. H. M. et al. Prevalence of “R” strain and molecular diversity of fall army worm Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) in India. Indian J. Entomol. 80, 544–553 (2018).
Google Scholar 
Murua, M. G. et al. Demonstration using field collections that Argentina fall armyworm populations exhibit strain-specific host plant preferences. J. Econ. Entomol. 108, 2305–2315 (2015).PubMed 

Google Scholar 
Nagoshi, R. N. The fall armyworm triose phosphate isomerase (Tpi) gene as a marker of strain identity and interstrain mating. Ann. Entomol. Soc. Am. 103, 283–292 (2010).CAS 

Google Scholar 
Nagoshi, R. N., Goergen, G., Plessis, H. D., van den Berg, J. & Meagher, R. Jr. Genetic comparisons of fall armyworm populations from 11 countries spanning sub-Saharan Africa provide insights into strain composition and migratory behaviors. Sci. Rep. 9, 8311 (2019).PubMed 
PubMed Central 

Google Scholar 
Czepak, C., Albernaz, C., Vivan, L. M., Guimarães, H. O. & Carvalhais, T. First reported occurrence of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) in Brazil. Pesq. Agropec. Trop., Goia.̂nia 43, 110–113 (2013).
Google Scholar 
Arnemann, J. A. et al. Multiple incursion pathways for Helicoverpa armigera in Brazil show its genetic diversity spreading in a connected world. Sci. Rep. 9, 19380 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tay, W. T. et al. A brave new world for an old world pest: Helicoverpa armigera (Lepidoptera: Noctuidae) in Brazil. PLoS ONE 8, e80134 (2013).Tay, W. T. et al. Mitochondrial DNA and trade data support multiple origins of Helicoverpa armigera (Lepidoptera, Noctuidae) in Brazil. Sci. Rep. 7, 45302 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Behere, G. T. et al. Mitochondrial DNA analysis of field populations of Helicoverpa armigera (Lepidoptera: Noctuidae) and of its relationship to H. zea. BMC Evol. Biol. 7, 117 (2007).PubMed 
PubMed Central 

Google Scholar 
Pearce, S. L. et al. Erratum to: Genomic innovations, transcriptional plasticity and gene loss underlying the evolution and divergence of two highly polyphagous and invasive Helicoverpa pest species. BMC Biol. 15, 69 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pearce, S. L. et al. Genomic innovations, transcriptional plasticity and gene loss underlying the evolution and divergence of two highly polyphagous and invasive Helicoverpa pest species. BMC Biol. 15, 63 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Guillemaud, T., Ciosi, M., Lombaert, E. & Estoup, A. Biological invasions in agricultural settings: insights from evolutionary biology and population genetics. Cr Biol. 334, 237–246 (2011).
Google Scholar 
Elfekih, S. et al. Genome-wide analyses of the Bemisia tabaci species complex reveal contrasting patterns of admixture and complex demographic histories. PLoS ONE 13, e0190555 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, C. J., Tay, W. T., McGaughran, A., Gordon, K. & Walsh, T. K. Population structure and gene flow in the global pest, Helicoverpa armigera. Mol. Ecol. 25, 5296–5311 (2016).CAS 
PubMed 

Google Scholar 
Anderson, C. J. et al. Hybridization and gene flow in the mega-pest lineage of moth, Helicoverpa. Proc. Natl Acad. Sci. USA 115, 5034–5039 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagoshi, R. N., Meagher, R. L. & Hay-Roe, M. Inferring the annual migration patterns of fall armyworm (Lepidoptera: Noctuidae) in the United States from mitochondrial haplotypes. Ecol. Evol. 2, 1458–1467 (2012).PubMed 
PubMed Central 

Google Scholar 
Wright, S. The interpretation of population-structure by F-statistics with special regard to systems of mating. Evolution 19, 395–420 (1965).
Google Scholar 
Luikart, G. & Cornuet, J. M. Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv. Biol. 12, 228–237 (1998).
Google Scholar 
Nagoshi, R. N. et al. Genetic characterization of fall armyworm infesting South Africa and India indicate recent introduction from a common source population. PLoS ONE 14, e0217755 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagoshi, R. N. et al. Analysis of strain distribution, migratory potential, and invasion history of fall armyworm populations in northern Sub-Saharan Africa. Sci. Rep.-Uk 8, 3710 (2018).
Google Scholar 
Arias, O. et al. Population genetic structure and demographic history of Spodoptera frugiperda (Lepidoptera: Noctuidae): implications for insect resistance management programs. Pest Manag. Sci. 75, 2948–2957 (2019).CAS 
PubMed 

Google Scholar 
Nguyen, T. K. O. & Vu, T. P. Checklist of turfgrass insect pests, morphology, biology and population fluctuation of Herpetograma phaeopteralis (Guenee) (Lepidopera: Pyralidae) in Ha Noi, in Spring-Summer 2008. in The 3rd National Conference of Ecology and Natural Resources, Ha Noi. 1490–1498.Pham, V. L. On time to recognise first potential Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae) in Vietnam and its Vietnamese name. in Plant Protection Magazine No. 4/2019 (Plant Protection Research Institute of Vietnam, July, 2019).Vu, T. P. Insect pests of turf grass, biology, ecology and the control of Herpetogramma phaeoptralis (Guenée) in Hà Nội in Spring Summer 2008 MSc. Thesis, Hà Nội Agriculture University, Vietnam (2008).Nguyen, V. D., Ha, Q. H. & Nguyen, T. T. C. in Vietnam Insects and Pests. (ed. V. L. Pham) (2012).Gilligan, T. M. & Passoa, S. C. LepIntercept, An identification resource for intercepted Lepidoptera larvae. Identification Technology Program (ITP), (2014).Gui, F. R. et al. Genomic and transcriptomic analysis unveils population evolution and development of pesticide resistance in fall armyworm Spodoptera frugiperda. Protein Cell https://doi.org/10.1007/s13238-020-00795-7 (2020).Stokstad, E. FOOD SECURITY New crop pest takes Africa at lightning speed. Science 356, 473–474 (2017).CAS 
PubMed 

Google Scholar 
Baloch, M. N., Fan, J. Y., Haseeb, M. & Zhang, R. Z. Mapping potential distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in Central Asia. Insects 11, 172 (2020).PubMed Central 

Google Scholar 
Juarez, M. L. et al. Population structure of Spodoptera frugiperda maize and rice host forms in South America: are they host strains? Entomol. Exp. Appl. 152, 182–199 (2014).CAS 

Google Scholar 
Groot, A. T. et al. Evolution of reproductive isolation of Spodoptera frugiperda. Pheromone Communication in Moths: Evolution, Behavior, and Application, 291–300 (2016).Nagoshi, R. N., Meagher, R. L., Nuessly, G. & Hall, D. G. Effects of fall armyworm (Lepidoptera: Noctuidae) interstrain mating in wild populations. Environ. Entomol. 35, 561–568 (2006).
Google Scholar 
Haenniger, S. et al. Sexual communication of Spodoptera frugiperda from West Africa: adaptation of an invasive species and implications for pest management. Sci. Rep. 10, 2892 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Orsucci, M. et al. Transcriptional plasticity evolution in two strains of Spodoptera frugiperda (Lepidoptera: Noctuidae) feeding on alternative host-plants. Preprint at bioRxiv https://doi.org/10.1101/263186 (2018).Lopes-da-Silva, M., Sanches, M. M., Stancioli, A. R., Alves, G. & Sugayama, R. The role of natural and human-mediated pathways for invasive agricultural pests: a historical analysis of cases from Brazil. Agric. Sci. 5, 634–646 (2014).
Google Scholar 
Nagoshi, R. N. et al. Haplotype profile comparisons between Spodoptera frugiperda (Lepidoptera: Noctuidae) populations from Mexico with those from Puerto Rico, South America, and the United States and their implications to migratory behavior. J. Economic Entomol. 108, 135–144 (2015).CAS 

Google Scholar 
Tembrock, L. R., Timm, A. E., Zink, F. A. & Gilligan, T. M. Phylogeography of the recent expansion of Helicoverpa armigera (Lepidoptera: Noctuidae) in South America and the Caribbean basin. Ann. Entomol. Soc. Am. 112, 388–401 (2019).CAS 

Google Scholar 
Lombaert, E. et al. Bridgehead effect in the worldwide invasion of the biocontrol harlequin ladybird. PLoS ONE 5, e9743 (2010).PubMed 
PubMed Central 

Google Scholar 
Desneux, N., Luna, M. G., Guillemaud, T. & Urbaneja, A. The invasive South American tomato pinworm, Tuta absoluta, continues to spread in Afro-Eurasia and beyond: the new threat to tomato world production. J. Pest Sci. 84, 403–408 (2011).
Google Scholar 
Valencia-Montoya, W. A. et al. Adaptive introgression across semipermeable species boundaries between local Helicoverpa zea and invasive Helicoverpa armigera moths. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msaa108 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Walsh, T. K. et al. Multiple recombination events between two cytochrome P450 loci contribute to global pyrethroid resistance in Helicoverpa armigera. PLoS ONE 13, e0197760 (2018).PubMed 
PubMed Central 

Google Scholar 
Liu, X. et al. Risks of biological invasion on the belt and road. Curr. Biol. 29, 499–505.e494 (2019).CAS 
PubMed 

Google Scholar 
Gimenez, S. et al. Adaptation by copy number variation increases insecticide resistance in the fall armyworm. Preprint at Commun Biol. 664, https://doi.org/10.1038/s42003-020-01382-6 (2020).Yainna, S. et al. Genomic balancing selection is key to the invasive success of the fall armyworm. Preprint at bioRxiv https://doi.org/10.1101/2020.06.17.154880 (2020).Tay, W. T. et al. Novel molecular approach to define pest species status and tritrophic interactions from historical Bemisia specimens. Sci. Rep.-Uk 7, ARTN 429 (2017).
Google Scholar 
Walsh, T. K. et al. Mitochondrial DNA genomes of five major Helicoverpa pest species from the Old and New Worlds (Lepidoptera: Noctuidae). Ecol. Evol. 9, 2933–2944 (2019).PubMed 
PubMed Central 

Google Scholar 
Bernt, M. et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).PubMed 

Google Scholar 
Villesen, P. FaBox: an online toolbox for FASTA sequences. Mol. Ecol. Notes 7, 965–968 (2007).CAS 

Google Scholar 
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nam, K. et al. Divergent selection causes whole genome differentiation without physical linkage among the targets in Spodoptera frugiperda (Noctuidae). Preprint at bioRxiv https://doi.org/10.1101/452870 (2018).Liu, H. et al. Chromosome level draft genomes of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), an alien invasive pest in China. Preprint at bioRxiv https://doi.org/10.1101/671560 (2019).Xiao, H. et al. The genetic adaptations of fall armyworm Spodoptera frugiperda facilitated its rapid global dispersal and invasion. Mol. Ecol. Resour. 20, 1050–1068 (2020).CAS 
PubMed 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner. (Lawrence Berkeley National Laboratory. 2014).Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Trifinopoulos, J., Nguyen, L. T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lewis, P. O. A likelihood approach to estimating phylogeny from discrete morphological character data. Syst. Biol. 50, 913–925 (2001).CAS 
PubMed 

Google Scholar 
Minh, B. Q., Nguyen, M. A. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Huson, D. H. & Scornavacca, C. Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks. Syst. Biol. 61, 1061–1067 (2012).PubMed 

Google Scholar 
Hellenthal, G. et al. A genetic atlas of human admixture history. Science 343, 747–751 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
PubMed Central 

Google Scholar 
Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).PubMed 
PubMed Central 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fu, Y. X. & Li, W. H. Statistical tests of neutrality of mutations. Genetics 133, 693–709 (1993).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfeifer, B., Wittelsburger, U., Ramos-Onsins, S. E. & Lercher, M. J. PopGenome: an efficient Swiss army knife for population genomic analyses in R. Mol. Biol. Evol. 31, 1929–1936 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright, S. The genetical structure of populations. Ann. Eugen. 15, 323–354 (1951).CAS 
PubMed 

Google Scholar 
Raymond, M. & Rousset, F. Genepop (Version-1.2) – population-genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).
Google Scholar 
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Neuditschko, M., Khatkar, M. S. & Raadsma, H. W. NetView: a high-definition network-visualization approach to detect fine-scale population structures from genome-wide patterns of variation. PLoS ONE 7, e48375 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Steinig, E. J., Neuditschko, M., Khatkar, M. S., Raadsma, H. W. & Zenger, K. R. netview p: a network visualization tool to unravel complex population structure using genome-wide SNPs. Mol. Ecol. Resour. 16, 216–227 (2016).CAS 
PubMed 

Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 

Google Scholar 
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodohl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Google Scholar 
Sundqvist, L., Keenan, K., Zackrisson, M., Prodohl, P. & Kleinhans, D. Directional genetic differentiation and relative migration. Ecol. Evol. 6, 3461–3475 (2016).PubMed 
PubMed Central 

Google Scholar 
Bastian, M., Heymann, S. & Jacomy, M. in International AAAI Conference on Weblogs and Social Media (2009).Tay, T. et al. Global FAW population genomic signature supports complex introduction events across the Old World. v1. CSIRO. Data Collection. https://doi.org/10.25919/y3nd-2903 (2021).Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl Acad. Sci. USA 70, 3321–3323 (1973).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nei, M. & Chesser, R. K. Estimation of fixation indices and gene diversities. Ann. Hum. Genet. 47, 253–259 (1983).CAS 
PubMed 

Google Scholar  More

Protected Planet: The World Database on Protected Areas (UNEP-WCMC and IUCN, accessed 2021); www.protectedplanet.netVenter, O. et al. Bias in protected-area location and its effects on long-term aspirations of biodiversity conventions. Conserv. Biol. 32, 127–134 (2018).Article 

Google Scholar 
Ward, M. et al. Just ten percent of the global terrestrial protected area network is structurally connected via intact land. Nat. Commun. 11, 4563 (2020).CAS 
Article 

Google Scholar 
Adams, W. M. Against Extinction: The Story of Conservation (Earthscan, 2004).Watson, J. E. M. Dudley, Segan, N. & Hockings, D. B. The performance and potential of protected areas. Nature 515, 67–73 (2014).CAS 
Article 

Google Scholar 
Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
Stolton, S. et al. The Futures of Privately Protected Areas (IUCN, 2014).Protected Planet: The World Database on Protected Areas (UNEP-WCMC and IUCN, accessed November 2018); www.protectedplanet.netBingham, H. et al. Privately protected areas: advances and challenges in guidance, policy and documentation. Parks 23, 13–28 (2017).Article 

Google Scholar 
Gallo, J., Pasquini, L., Reyers, B. & Cowling, R. M. The role of private conservation areas in biodiversity representation and target achievement within the Little Karoo region, South Africa. Biol. Conserv. 142, 446–454 (2009).Article 

Google Scholar 
Schutz, J. Creating an integrated protected area network in Chile: a GIS assessment of ecoregion representation and the role of private protected areas. Environ. Conserv. 45, 269–277 (2018).Article 

Google Scholar 
Ielyzaveta, I. M. & Cook, C. N. The role of privately protected areas in achieving biodiversity representation within a national protected area network. Conserv. Sci. Pract. 2, e307 (2020).
Google Scholar 
Graves, R. A., Williamson, M. A., Belote, R. T. & Brandt, J. S. Quantifying the contribution of conservation easements to large‐landscape conservation. Biol. Conserv. 232, 83–96 (2019).Article 

Google Scholar 
De Vos, A. & Cumming, G. S. The contribution of land tenure diversity to the spatial resilience of protected area networks. People Nat. 1, 331–346 (2019).Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
Article 

Google Scholar 
Borrini-Feyerabend, G. et al. Governance of Protected Areas: From Understanding to Action (IUCN, 2013).Lee, A. & Schultz, K. A. Comparing British and French colonial legacies: a discontinuity analysis of Cameroon. Q. J. Polit. Sci. 7, 365–410 (2012).Article 

Google Scholar 
Acemoglu, D., Johnson, S. & Robinson, J. A. The colonial origins of comparative development: an empirical investigation. Am. Econ. Rev. 91, 1369–1401 (2001).Article 

Google Scholar 
De Vos, A., Clements, H. S., Biggs, D. & Cumming, G. S. The dynamics of proclaimed privately protected areas in South Africa over 83 years. Conserv. Lett. 12, e12644 (2019).
Google Scholar 
Conservation Programs (USDA, accessed 21 October 2021); https://www.ers.usda.gov/topics/natural-resources-environment/conservation-programs/Zimmer, H. C., Mavromihalis, J., Turner, V. B., Moxham, C. & Liu, C. Native grasslands in the PlainsTender incentive scheme: conservation value, management and monitoring. Rangel. J. 32, 205–214 (2010).Article 

Google Scholar 
A Global Standard for the Identification of Key Biodiversity Area (IUCN, 2021); https://portals.iucn.org/library/sites/library/files/documents/Rep-2016-005.pdfVenter, O. et al. Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release (SEDAC, 2021); https://doi.org/10.7927/H46T0JQ4Hoekstra, J. M., Boucher, T. M., Ricketts, T. H. & Roberts, C. Confronting a biome crisis: global disparities of habitat loss and protection. Ecol. Lett. 8, 23–29 (2005).Article 

Google Scholar 
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).CAS 
Article 

Google Scholar 
Bengtsson, J. et al. Grasslands—more important for ecosystem services than you might think. Ecosphere 10, e02582 (2019).Article 

Google Scholar 
Working Together for Grasslands. How Ranchers and the WWF Help Protect the Northern Great Plains (WWF, 2021); https://www.worldwildlife.org/stories/working-together-for-grasslandsHenderson, K. A. et al. Landowner perceptions of the value of natural forest and natural grassland in a mosaic ecosystem in southern Brazil. Sustain. Sci. 11, 321–330 (2016).Article 

Google Scholar 
Kamal, S., Grodzinska-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 
Williamson, M. A., Schwartz, M. W. & Lubell, M. N. Spatially explicit analytical models for social–ecological systems. BioScience 68, 885–895 (2018).
Google Scholar 
Watson, J. E. M. et al. Persistent disparities between recent rates of habitat conversion and protection and implications for future global conservation targets. Conserv. Lett. 9, 413–421 (2016).Article 

Google Scholar 
Di Marco, M. et al. Quantifying the relative irreplaceability of important bird and biodiversity areas. Conserv. Biol. 30, 392–402 (2015).Article 

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

Google Scholar 
Sanderson, E. W. et al. The human footprint and the last of the wild: the human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience 52, 891–904 (2002).Article 

Google Scholar 
Clements, H. S., Kerley, G. I. H., Cumming, G. S., De Vos, A. & Cook, C. N. Privately protected areas provide key opportunities for the regional persistence of large‐ and medium‐sized mammals. J. Appl. Ecol. 56, 537–546 (2018).Article 

Google Scholar 
Song, P., Kim, G., Mayer, A., He, R. & Tian, G. Assessing the ecosystem services of various types of urban green spaces based on i-Tree Eco. Sustainability 12, 1630 (2020).CAS 
Article 

Google Scholar 
Trzyna, T. Urban Protected Areas: Profiles and Best Practice Guidelines (IUCN, 2014).Li, E. et al. (2019) An urban biodiversity assessment framework that combines an urban habitat classification scheme and citizen science data. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00277 (2019).Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
Rissman, A. R. & Merenlender, A. M. The conservation contributions of conservation easements: analysis of the San Francisco Bay Area protected lands spatial database. Ecol. Soc. 13, 25 (2008).Article 

Google Scholar 
Strategic Plan for Biodiversity 2011–2020, Including Aichi Biodiversity Targets (CBD, 2011); https://www.cbd.int/sp/Saura, S., Bastin, L., Battistella, L., Mandrici, A. & Dubois, G. Protected areas in the world’s ecoregions: how well connected are they? Ecol. Indic. 76, 144–158 (2017).Article 

Google Scholar 
World Database of Key Biodiversity Areas (BirdLife International, accessed September 2020); http://www.keybiodiversityareas.org/site/requestgisSaura, S. & Torné, J. Conefor Sensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environ. Model. Softw. 24, 135–139 (2009).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014). http://www.R-Project.org/Milam, A. et al. in Protected Areas: Are They Safeguarding Biodiversity? (eds Joppa, L. et al.) 81–101 (Wiley-Blackwell, 2016).Mason, C. et al. Telemetry reveals existing marine protected areas are worse than random for protecting the foraging habitat of threatened shy albatross. Divers. Distrib. 24, 1744–1755 (2018).Article 

Google Scholar 
Lewis, E. et al. Dynamics in the global protected-area estate since 2004. Conserv. Biol. 33, 570–579 (2017).Article 

Google Scholar 
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).CAS 
Article 

Google Scholar 
Schleicher, J., Peres, C. A., Amano, T., Llactayo, W. & Leader-Williams, N. Conservation performance of different conservation governance regimes in the Peruvian Amazon. Nature 7, 113–118 (2017).
Google Scholar 
Shumba, T. et al. Effectiveness of private land conservation areas in maintaining natural land cover and biodiversity intactness. Glob. Ecol. Conserv. 22, e00935 (2020).Article 

Google Scholar  More

Zhang, N. et al. Members of the Fusarium solani species complex that cause infections in both humans and plants are common in the environment. J. Clin. Microbiol. 44, 2186–2190 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Donnell, K. et al. Molecular Phylogenetic Diversity, Multilocus Haplotype Nomenclature, and In Vitro antifungal resistance within the Fusarium solani species complex. J. Clin. Microbiol. 46, 2477–2490 (2008).PubMed 
PubMed Central 

Google Scholar 
Schroers, H. J. et al. Epitypification of Fusisporium (Fusarium) solani and its assignment to a common phylogenetic species in the Fusarium solani species complex. Mycologia 108, 806–819 (2016).CAS 
PubMed 

Google Scholar 
O’Donnell, K. Molecular phylogeny of the Nectria haematococca-Fusarium solani species complex. Mycologia 92, 919–938 (2000).
Google Scholar 
Gleason, F., Allerstorfer, M. & Lilje, O. Newly emerging diseases of marine turtles, especially sea turtle egg fusariosis (SEFT), caused by species in the Fusarium solani complex (FSSC). Mycology 11, 184–194 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fernando, N. et al. Fatal Fusarium solani species complex infections in elasmobranchs: the first case report for black spotted stingray (Taeniura melanopsila) and a literature review. Mycoses 58, 422–431 (2015).PubMed 

Google Scholar 
Sarmiento-Ramírez, J. M. et al. Global distribution of two fungal pathogens threatening endangered Sea Turtles. PLoS ONE 9, e85853 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mayayo, E., Pujol, I. & Guarro, J. Experimental pathogenicity of four opportunist Fusarium species in a murine model. J. Med. Microbiol. 48, 363–366 (1999).CAS 
PubMed 

Google Scholar 
Muhvich, A. G., Reimschuessel, R., Lipsky, M. M. & Bennett, R. O. Fusarium solani isolated from newborn bonnethead sharks, Sphyrna tiburo (L.). J. Fish Dis. 12, 57–62 (1989).
Google Scholar 
Crow, G. L., Brock, J. A. & Kaiser, S. Fusarium solani fungal infection of the lateral line canal system in captive scalloped hammerhead sharks (Sphyrna lewini) in Hawaii. J. Wildl. Dis. 31, 562–565 (1995).CAS 
PubMed 

Google Scholar 
Cabañes, F. J. et al. Cutaneous hyalohyphomycosis caused by Fusarium solani in a loggerhead sea turtle (Caretta caretta L.). J. Clin. Microbiol. 35, 3343–3345 (1997).PubMed 
PubMed Central 

Google Scholar 
Cafarchia, C. et al. Fusarium spp. in Loggerhead Sea Turtles (Caretta caretta): From Colonization to Infection. Vet. Pathol. 57, 139–146 (2019).PubMed 

Google Scholar 
Garcia-Hartmann, M., Hennequin, C., Catteau, S., Béatini, C. & Blanc, V. Cas groupés d’infection à Fusarium solani chez de jeunes tortues marines Caretta caretta nées en captivité. J. Mycol. Med. 28, 113–118 (2017).
Google Scholar 
Orós, J., Delgado, C., Fernández, L. & Jensen, H. E. Pulmonary hyalohyphomycosis caused by Fusarium spp in a Kemp’s ridley sea turtle (Lepidochelys kempi): An immunohistochemical study. N. Z. Vet. J. 52, 150–152 (2004).PubMed 

Google Scholar 
Candan, A. Y., Katılmış, Y. & Ergin, Ç. First report of Fusarium species occurrence in loggerhead sea turtle (Caretta caretta) nests and hatchling success in Iztuzu Beach, Turkey. Biologia (Bratisl). https://doi.org/10.2478/s11756-020-00553-4 (2020).Article 

Google Scholar 
Sarmiento-Ramirez, J. M., van der Voort, M., Raaijmakers, J. M. & Diéguez-Uribeondo, J. Unravelling the Microbiome of eggs of the endangered Sea Turtle Eretmochelys imbricata identifies bacteria with activity against the emerging pathogen Fusarium falciforme. PLoS ONE 9, e95206 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sarmiento-Ramírez, J. M. et al. Fusarium solani is responsible for mass mortalities in nests of loggerhead sea turtle, Caretta caretta, in Boavista, Cape Verde. FEMS Microbiol. Lett. 312, 192–200 (2010).PubMed 

Google Scholar 
Sarmiento-Ramirez, J. M., Sim, J., Van West, P. & Dieguez-Uribeondo, J. Isolation of fungal pathogens from eggs of the endangered sea turtle species Chelonia mydas in Ascension Island. J. Mar. Biol. Assoc. United Kingdom 97, 661–667 (2017).CAS 

Google Scholar 
Hoh, D., Lin, Y., Liu, W., Sidique, S. & Tsai, I. Nest microbiota and pathogen abundance in sea turtle hatcheries. Fungal Ecol. 47, 100964 (2020).
Google Scholar 
Güçlü, Ö., Bıyık, H. & Şahiner, A. Mycoflora identified from loggerhead turtle (Caretta caretta) egg shells and nest sand at Fethiye beach, Turkey. Afr. J. Microbiol. Res. 4, 408–413 (2010).
Google Scholar 
Gambino, D. et al. First data on microflora of loggerhead sea turtle (Caretta caretta) nests from the coastlines of Sicily. Biol. Open 9, bio045252 (2020).PubMed 
PubMed Central 

Google Scholar 
Bailey, J. B., Lamb, M., Walker, M., Weed, C. & Craven, K. S. Detection of potential fungal pathogens Fusarium falciforme and F. keratoplasticum in unhatched loggerhead turtle eggs using a molecular approach. Endanger. Species Res. 36, 111–119 (2018).
Google Scholar 
Summerbell, R. C. & Schroers, H.-J. Analysis of Phylogenetic Relationship of Cylindrocarpon lichenicola and Acremonium falciforme to the Fusarium solani Species Complex and a Review of similarities in the spectrum of opportunistic infections caused by these fungi. J. Clin. Microbiol. 40, 2866–2875 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nel, R., Punt, A. E. & Hughes, G. R. Are coastal protected areas always effective in achieving population recovery for nesting sea turtles?. PLoS ONE 8, e63525 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Branch, G. & Branch, M. Living Shores. (Pippa Parker, 2018).Fuller, M. S., Fowles, B. E. & Mclaughlin, D. J. Isolation and pure culture study of marine phycomycetes. Mycologia 56, 745–756 (1964).
Google Scholar 
Greeff, M. R., Christison, K. W. & Macey, B. M. Development and preliminary evaluation of a real-time PCR assay for Halioticida noduliformans in abalone tissues. Dis. Aquat. Organ. 99, 103–117 (2012).CAS 
PubMed 

Google Scholar 
Sandoval-Denis, M., Lombard, L. & Crous, P. W. Back to the roots: a reappraisal of Neocosmospora. Persoonia Mol. Phylogeny Evol. Fungi 43, 90–185 (2019).CAS 

Google Scholar 
O’Donnell, K., Cigelnik, E. & Nirenberg, H. I. Molecular systematics and phylogeography of the Gibberella fujikuroi species complex. Mycologia 90, 465–493 (1998).
Google Scholar 
Geiser, D. M. et al. FUSARIUM-ID v. 1. 0: A DNA sequence database for identifying Fusarium. Eur. J. Plant Pathol. 110, 473–479 (2004).ADS 
CAS 

Google Scholar 
O’Donnell, K. et al. Phylogenetic diversity of insecticolous fusaria inferred from multilocus DNA sequence data and their molecular identification via FUSARIUM-ID and FUSARIUM MLST. Mycologia 104, 427–445 (2012).PubMed 

Google Scholar 
Chehri, K., Salleh, B. & Zakaria, L. Morphological and phylogenetic analysis of Fusarium solani species complex in Malaysia. Microb. Ecol. 69, 457–471 (2015).PubMed 

Google Scholar 
Lanfear, R., Frandsen, P., Wright, A., Senfeld, T. & Calcott, B. PartionFinder 2: new methods for selecting partioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. https://doi.org/10.1093/molbev/msw260 (2016).Article 

Google Scholar 
Ronquist, F. et al. Efficient Bayesian phylogenetic inference and model selection across a large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
PubMed Central 

Google Scholar 
Leslie, J. F. & Summerell, B. A. The Fusarium Laboratory manual (Blackwell Publishing, Hoboken, 2006).
Google Scholar 
Fisher, N. L., Burgess, L. W., Toussoun, T. A. & Nelson, P. E. Carnation leaves as a substrate and for preserving cultures of Fusarium species. Phytopathology 72, 151 (1982).
Google Scholar 
Smyth, C. W. et al. Unraveling the ecology and epidemiology of an emerging fungal disease, sea turtle egg fusariosis (STEF). PLOS Pathog. 15, e1007682 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rachowicz, L. J. et al. The novel and endemic pathogen hypotheses: Competing explanations for the origin of emerging infectious diseases of wildlife. Conserv. Biol. 19, 1441–1448 (2005).
Google Scholar 
Lombard, L., Sandoval-Denis, M., Cai, L. & Crous, P. W. Changing the game: resolving systematic issues in key Fusarium species complexes. Persoonia Mol. Phylogeny Evol. Fungi 43, i–ii (2019).CAS 

Google Scholar 
Short, D. P. G., Donnell, K. O., Zhang, N., Juba, J. H. & Geiser, D. M. Widespread occurrence of diverse human pathogenic types of the fungus Fusarium detected in plumbing drains. J. Clin. Microbiol. 49, 4264–4272 (2011).PubMed 
PubMed Central 

Google Scholar 
White, T. J., Burns, T., Lee, S. & Taylor, J. Amplification and direct identification 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, San Diego, 1990).
Google Scholar 
Sekimoto, S., Hatai, K. & Honda, D. Molecular phylogeny of an unidentified Haliphthoros-like marine oomycete and Haliphthoros milfordensis inferred from nuclear-encoded small- and large-subunit rRNA genes and mitochondrial-encoded cox2 gene. Mycoscience 48, 212–221 (2007).CAS 

Google Scholar 
Petersen, A. B. & Rosendahl, S. Ø. Phylogeny of the Peronosporomycetes (Oomycota) based on partial sequences of the large ribosomal subunit (LSU rDNA). Mycol. Res. 104, 1295–1303 (2000).CAS 

Google Scholar 
O’Donnell, K. et al. Phylogenetic diversity and microsphere array-based genotyping of human pathogenic fusaria, including isolates from the multistate contact lens-associated U.S. keratitis outbreaks of 2005 and 2006. J. Clin. Microbiol. 45, 2235–2248 (2007).PubMed 
PubMed Central 

Google Scholar 
Migheli, Q. et al. Molecular Phylogenetic diversity of dermatologic and other human pathogenic fusarial isolates from hospitals in Northern and Central Italy. J. Clin. Microbiol. 48, 1076–1084 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar  More

The MiDAS global consortium was established in 2018 to coordinate the sampling and collection of metadata from WWTPs across the globe (Supplementary Data 1). Samples were obtained in duplicates from 740 WWTPs in 425 cities, 31 countries on six continents (Fig. 1a). The majority of the WWTPs were configured with the activated sludge process (69.7%) (Fig. 1b), and these were the main focus of the subsequent analyses. Nevertheless, WWTPs based on biofilters, moving bed bioreactors (MBBR), membrane bioreactors (MBR), and granular sludge were also sampled to cover the microbial diversity in other types of WWTPs. The activated sludge plants were designed for carbon removal only (C; 22.1%), carbon removal with nitrification (C,N; 9.5%), carbon removal with nitrification and denitrification (C,N,DN; 40.9%), and carbon removal with nitrogen removal and enhanced biological phosphorus removal, EBPR (C,N,DN,P; 21.7%) (Fig. 1c). The first type represents the simplest design whereas the latter represents the most advanced process type with varying oxic and anoxic stages or compartments.Fig. 1: Sampling of WWTPs across the world.a Geographical distribution of WWTPs included in the study and their process configuration. b Distribution of plant types. MBBR moving bed bioreactor, MBR membrane bioreactor. c Distribution of process types for the activated sludge plants. C carbon removal, C,N carbon removal with nitrification, C,N,DN carbon removal with nitrification and denitrification, C,N,DN,P carbon removal with nitrogen removal and enhanced biological phosphorus removal (EBPR). The values next to the bars are the number of WWTPs in each group.Full size imageMiDAS 4: a global 16S rRNA gene catalogue and taxonomy for WWTPsMicrobial community profiling at high taxonomic resolution (genus- and species-level) using 16S rRNA gene amplicon sequencing requires a reference database with high-identity reference sequences (≥99% sequence identity) for the majority of the bacteria in the samples and a complete seven-rank taxonomy (domain to species) for all reference sequences16,20. To create such a database for bacteria in WWTPs globally, we applied synthetic long-read full-length 16S rRNA gene sequencing20,21 on samples from all WWTPs included in this study.More than 5.2 million full-length 16S rRNA gene sequences were obtained after quality filtering and primer trimming. The sequences were processed with AutoTax20 to yield 80,557 full-length 16S rRNA gene amplicon sequence variant (FL-ASVs). These reference sequences were added to our previous MiDAS 3 database16, providing a combined database (MiDAS 4) with a total of 90,164 unique, chimera-free FL-ASV reference sequences. The absence of detectable chimeric sequences is a unique feature of the database and is achieved due to the attachment of unique molecular identifiers (UMIs) to each end of the original template molecules before any PCR amplification steps21. This allows filtering of true biological sequences from chimera already in the synthetic long-read assembly20,21. The novelty of the FL-ASVs were determined based on the percent identity shared with their closest relatives in the SILVA 138 SSURef NR99 database and the threshold for each taxonomic rank proposed by Yarza et al.22. Out of all FL-ASVs, 88% had relatives above the genus-level threshold (≥94.5% identity) and 56% above the species-level threshold (≥98.7% identity) (Fig. 2 and Table 1).Fig. 2: Novel sequences and de novo taxa defined in the MiDAS 4 reference database.The phylogenetic trees are based on a multiple alignment of all MiDAS 4 reference sequences, which were first aligned against the global SILVA 138 alignment using the SINA aligner, and subsequently pruned according to the ssuref:bacteria positional variability by parsimony filter in ARB to remove hypervariable regions. The eight phyla with most FL-ASVs are highlighted in different colours. Sequence novelty was determined by the percent identity between each FL-ASV and their closest relative in the SILVA_138_SSURef_Nr99 database according to Usearch mapping and the taxonomic thresholds proposed by Yarza et al.22 shown in Table 1. Taxonomy novelty was defined based on the assignment of de novo taxa by AutoTax20.Full size imageTable 1 Novel sequences and de novo taxa observed in the MiDAS 4 reference database.Full size tableMiDAS 4 provides placeholder names for many environmental taxaAlthough only a small percentage of the reference sequences in MiDAS 4 represented new putative taxa at higher ranks (phylum, class, or order) according to the sequence identity thresholds proposed by Yarza et al.22, a large number of sequences lacked lower-rank taxonomic classifications and was assigned de novo placeholder names by AutoTax20 (Fig. 2 and Table 1). In total, de novo taxonomic names were generated by AutoTax for 26 phyla (30.6% of observed), 83 classes (37.2% of observed), 297 orders (46.8% of observed), and more than 8000 genera (86.3% of observed). Without the de novo taxonomy we would not be able to discuss these taxa across studies to unveil their potential role in wastewater treatment systems.Phylum-specific phylogenetic trees were created to determine if the FL-ASV reference sequences that were assigned to de novo phyla were actual phyla or simply artifacts related to the naive sequence identity-based assignment of de novo placeholder taxonomies (Supplementary Fig. 1a). The majority (65 FL-ASVs) created deep branches from within the Alphaproteobacteria together with 16S rRNA gene sequences from mitochondria, suggesting they represented divergent mitochondrial genes rather than true novel phyla. We also observed several FL-ASVs assigned to de novo phyla that branched from the classes Parcubacteria (3 FL-ASVs) and Microgenomatis (22 FL-ASVs) within the Patescibacteria phylum. These two classes were originally proposed as superphyla due to an unusually high rate of evolution of their 16S rRNA genes23,24. It is, therefore, likely that these de novo phyla are also artefacts due to the simple taxonomy assignment approach, which does not take different evolutionary rates into account20. Most of the class- and order-level novelty was found within the Patescibacteria, Proteobacteria, Firmicutes, Planctomycetota and Verrucomicrobiota. (Supplementary Fig. 1b). At the family- and genus-level, we also observed many de novo taxa affiliated to Bacteroidota, Bdellovibrionota and Chloroflexi.MiDAS 4 provides a common taxonomy for the fieldThe performance of the MiDAS 4 database was evaluated based on an independent amplicon dataset from the Global Water Microbiome Consortium (GWMC) project2, which covers ~1200 samples from 269 WWTPs. The raw GWMC amplicon data of the 16S rRNA gene V4 region was resolved into ASVs, and the percent identity to their best hits in MiDAS 4 and other reference databases was calculated (Fig. 3). The MiDAS 4 database had high-identity hits (≥99% identity) for 72.0 ± 9.5% (mean ± SD) of GWMC ASVs with ≥0.01% relative abundance, compared to 57.9 ± 8.5% for the SILVA 138 SSURef NR99 database, which was the best of the universal reference databases (Fig. 3). The relative abundance cutoff selects taxa that likely have a quantitative impact on the ecosystem while filtering out the rare biosphere which includes many bacteria introduced with the influent wastewaters25. Similar analyses of ASVs obtained from the samples included in this study showed, not surprisingly, even better performance with high-identity hits for 90.7 ± 7.9% of V1–V3 ASVs and 90.0 ± 6.6% of V4 ASVs with ≥0.01% relative abundance, compared to 60.6 ± 11.9% and 73.9 ± 10.3% for SILVA (Supplementary Fig. 2a). Although the sampling of WWTPs was focused towards activated sludge plants, the MiDAS 4 database also includes high-identity references for most ASVs in other plant types (granules, biofilters, etc.) (Supplementary Fig. 2b). This suggests that most taxa were shared across plant types, although often present in other relative abundances.Fig. 3: Database evaluation based on amplicon data from the Global Water Microbiome Consortium project.Raw amplicon data from the Global Water Microbiome Consortium project2 was processed to resolve ASVs of the 16S rRNA gene V4 region. The ASVs for each of the samples were filtered based on their relative abundance (only ASVs with ≥0.01% relative abundance were kept) before the analyses. The percentage of the microbial community represented by the remaining ASVs after the filtering was 88.35 ± 2.98% (mean ± SD) across samples. High-identity (≥99%) hits were determined by the stringent mapping of ASVs to each reference database. Classification of ASVs was done using the SINTAX classifier. The violin and box plots represent the distribution of percent of ASVs with high-identity hits or genus/species-level classifications for each database across n = 1165 biologically independent samples. Box plots indicate median (middle line), 25th, 75th percentile (box) and the min and max values after removing outliers based on 1.5x interquartile range (whiskers). Outliers have been removed from the box plots to ease visualisation. Different colours are used to distinguish the different databases.Full size imageUsing MiDAS 4 with the SINTAX classifier, it was possible to obtain genus-level classifications for 75.0 ± 6.9% of the GWMC ASVs with ≥0.01% relative abundance (Fig. 3). In comparison, SILVA 138 SSURef NR99, which was the best of the universal reference databases, could only classify 31.4 ± 4.2% of the ASVs to genus-level. When MiDAS 4 was used to classify amplicons from this study, we obtained genus-level classification for 92.0 ± 4.0% of V1–V3 ASVs and 84.8 ± 3.6% of V4 ASVs (Supplementary Fig. 2a). This is close to the theoretical limit set by the phylogenetic signal provided by each amplicon region analyzed20. Improved classifications were also observed for archaeal V4 ASVs (93.3 ± 10.6% for MiDAS 4 vs 69.3 ± 21.3% for SILVA), although no additional archaeal reference sequences were added to the MiDAS database in this study.MiDAS 4 was also able to assign species-level classifications to 40.8 ± 7.1% of the GWMC ASVs. In contrast, the 16S rRNA gene reference database obtained from GTDB SSU r89, which is the only universal reference database that contains a comprehensive species-level taxonomy, only classified 9.9 ± 2.0% of the ASVs (Fig. 3). For the ASVs created in this study, MiDAS 4 provided a species-level classification for 68.4 ± 6.1% of the V1–V3 and 48.5 ± 6.0% of the V4 ASVs (Supplementary Fig. 2a).Based on the large number of WWTPs sampled, their diversity, and the independent evaluation based on the GWMC dataset2, we expect that the MiDAS 4 reference database essentially covers the large majority of bacteria in WWTPs worldwide. Therefore, the MiDAS 4 taxonomy should act as a shared vocabulary for wastewater treatment microbiologists, providing opportunities for cross-study comparisons and ecological studies at high taxonomic resolution.Comparison of the V1–V3 and V4 primer sets for community profiling of WWTPsBefore investigating what factors shape the activated sludge microbiota, we compared short-read amplicon data created for all activated sludge samples belonging to the four main process types (C; C,N; C,N,DN and C,N,DN,P) collected in the Global MiDAS project using two commonly used primer sets that target the V1–V3 or V4 variable region of the 16S rRNA gene. The V1–V3 primers were chosen because the corresponding region of the 16S rRNA gene provides the highest taxonomic resolution of common short-read amplicons20,26, and these primers have previously shown great correspondence with metagenomic data and quantitative fluorescence in situ hybridisation (FISH) results for wastewater treatment systems17. The V4 region has a lower phylogenetic signal, but the primers used for amplification have better theoretical coverage of the bacterial diversity in the SILVA database20,26.The majority of genera (62%) showed less than twofold difference in relative abundances between the two primer sets, and the rest were preferentially detected with either the V1–V3 or the V4 primer (19% for both) (Fig. 4). We observed that several genera of known importance detected in high abundance by V1–V3 were hardly observed by V4, including Acidovorax, Rhodoferax, Ca. Villigracilis, Sphaerotilus and Leptothrix. Similarly, we observed genera abundant with V4 but strongly underestimated by V1–V3, such as Acinetobacter and Prosthecobacter. A complete list of differentially detected genera (Supplementary Data 2) serves as a valuable tool in combination with in silico primer evaluation for deciding which primer pair to use for targeted studies of specific taxa.Fig. 4: Comparison of relative genus abundance based on V1–V3 and V4 region 16S rRNA gene amplicon data.a Mean relative abundance was calculated based on 709 activated sludge samples. Genera present at ≥0.001% relative abundance in V1–V3 and/or V4 datasets are considered. Genera with less than twofold difference in relative abundance between the two primer sets are shown with gray circles, and those that are overrepresented by at least twofold with one of the primer sets are shown in red (V4) and blue (V1–V3). The twofold difference is an arbitrary choice; however, it relates to the uncertainty we usually encounter in amplicon data. Genus names are shown for all taxa present at a minimum of 0.1% mean relative abundance (excluding those with de novo names). b Heatmaps of the most abundant genera with more than twofold relative abundance difference between the two primer sets.Full size imageBecause the V1–V3 primers provide better classification rates at the genus- and species-level (Supplementary Fig. 2a), we primarily focused on this dataset for the following analyses. It should be noted that the V1–V3 primer set performs poorly on anammox bacteria27,28 and does not target archaea at all. To determine the importance of these groups, we estimated their relative read abundance using the V4 amplicon data. Ca. Brocadia and Ca. Anammoximicrobium were the only anammox genera detected, and the latter was never more than 0.6% abundant. Ca. Brocadia was observed in MBBR reactors and granular sludge in anammox reactors with relative read abundances reaching 29%, but it was below 0.1% relative abundance in all but two of the activated sludge samples investigated. For archaea, the relative read abundance was generally low (median = 0.18%), but for a few WWTPs high (up to 11.7%), so archaea should not be neglected in these cases.Process and environmental factors affecting the activated sludge microbiotaAlpha diversity analysis revealed that the rarefied (10,000 read per sample) richness and diversity in activated sludge plants were most strongly affected by process type, industrial load and continent (Supplementary Fig. 3 and Supplementary Note 1). The richness and diversity increased with the complexity of the treatment process, as found in other studies, reflecting the increased number of niches29. In contrast, it decreased with high industrial loads, presumably because industrial wastewater often is less complex and therefore promotes the growth of fewer specialised species7. The effect of continents is presumably caused by the necessary unbalanced sampling of WWTPs and confounded by the effects of plant types and industrial loads.Distance decay relationship (DDR) analyses were used to determine the effect of geographic distance on the microbial community similarity of activated sludge plants with the four main process types (Supplementary Fig. 4 and Supplementary Note 2). We found that distance decay was only effective within shorter geographical distances (2500 km) at the ASV-level, but higher similarities with OTUs clustered at 97% and even more at the genus-level. This suggests that many ASVs are geographically restricted and functionally redundant in the activated sludge microbiota, so different strains or species from the same genus across the world may provide similar functions.To gain a deeper understanding of the factors that shape the activated sludge microbiota, we examined the genus-level taxonomic beta-diversity using principal coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) analyses (Fig. 5 and Supplementary Note 3). We have chosen taxonomic diversity instead of phylogenetic diversity (UniFrac) because many of the important traits are categorical (yes/no) and only conserved at lower taxonomic ranks (genus/species). The analysis was made at the genus-level due to the high classification rate achieved with MiDAS 4 and because genera were less affected by DDR compared to ASVs. We found that the overall microbial community was most strongly affected by continent and temperature in the WWTPs. However, process type, industrial load and the climate zone also had significant impacts. The percentage of total variation explained by each parameter was generally low, indicating that the global WWTPs microbiota represents a continuous distribution rather than distinct states, as observed for the human gut microbiota30.Fig. 5: Effects of process and environmental factors on the activated sludge microbial community structure. Principal coordinate analyses of Bray–Curtis and Soerensen beta-diversity for genera based on V1–V3 amplicon data. Samples are coloured based on metadata.The fraction of variation in the microbial community explained by each variable in isolation was determined by PERMANOVA (Adonis R2-values). Exact P values 0.1% relative abundance in 80% (strict core), 50% (general core) and 20% (loose core) of all activated sludge plants (Fig. 6a).Fig. 6: Identification of core and conditionally rare or abundant taxa based on V1–V3 amplicon data.a Identification of strict, general and loose core genera based on how often a given genus was observed at a relative abundance above 0.1% in WWTPs. b Identification of conditionally rare or abundant (CRAT) genera based on whether a given genus was observed at a relative abundance above 1% in at least one WWTP. The cumulative genus abundance is based on all ASVs classified at the genus-level. All core genera were removed before identification of the CRAT genera. c, d Number of genera and species, respectively, and their abundance in different process types across the global WWTPs. Values for genera and species are divided into strict core, general core, loose core, CRAT, other taxa and unclassified ASVs. The relative abundance of different groups was calculated based on the mean relative abundance of individual genera or species across samples. C carbon removal, C,N carbon removal with nitrification, C,N,DN carbon removal with nitrification and denitrification, C,N,DN,P carbon removal with nitrogen removal and enhanced biological phosphorus removal (EBPR).Full size imageIn addition to the core taxa, we also identified conditionally rare or abundant taxa (CRAT)32 (Fig. 6b). These are taxa typically present in low abundance but occasionally become prevalent, including taxa related to process disturbances, such as bacteria causing activated sludge foaming or those associated with the degradation of specific residues in industrial wastewater. CRAT have only been studied in a single WWTP treating brewery wastewater, despite their potential effect on performance32,33. CRAT are here defined as taxa which are not part of the core, but present in at least one WWTP with a relative abundance above 1%.Core taxa and CRAT were identified for both the V1–V3 and V4 amplicon data to ensure that critical taxa were not missed due to primer bias. We identified 250 core genera (15 strict, 65 general and 170 loose) and 715 CRAT genera (Supplementary Data 4). The strict core genera (Fig. 7) mainly contained genera with versatile metabolisms found in several environments, including Flavobacterium, Novosphingobium and Haliangium. The general core (Fig. 7) included many known bacteria associated with nitrification (Nitrosomonas and Nitrospira), polyphosphate accumulation (Tetrasphaera, Ca. Accumulibacter) and glycogen accumulation (Ca. Competibacter). The loose core contained well-known filamentous bacteria (Ca. Microthrix, Ca. Promineofilum, Ca. Sarcinithrix, Gordonia, Kouleothrix and Thiothrix), but also Nitrotoga, a less common nitrifier in WWTPs.Fig. 7: Percent relative abundance of strict and general core taxa across process types.The taxonomy for the core genera indicates phylum and genus. For general core species, genus names are also provided. De novo taxa in the core are highlighted in red. C carbon removal, C,N carbon removal with nitrification, C,N,DN carbon removal with nitrification and denitrification, C,N,DN,P carbon removal with nitrogen removal and enhanced biological phosphorus removal (EBPR).Full size imageBecause MiDAS 4 allowed for species-level classification, we also identified core and CRAT species based on the same criteria as for genera (Supplementary Fig. 7 and Supplementary Data 4). This revealed 113 core species (0 strict, 9 general and 104 loose). The general core species (Fig. 7) included Nitrospira defluvii and Tetrasphaera midas_s_5, a common nitrifier and PAO, respectively. Arcobacter midas_s_2255, a potential pathogen commonly abundant in the influent wastewater, was also part of the general core34. The loose core contained additional species associated with nitrification (Nitrosomonas midas_s_139 and Nitrospira nitrosa), polyphosphate accumulation (Ca. Accumulibacter phosphatis, Dechloromonas midas_s_173, Tetrasphaera midas_s_45), as well as known filamentous species (Ca. Microthrix parvicella and midas_s_2 (recently named Ca. M. subdominans35), Ca. Villigracilis midas_s_471 and midas_s_9223, Leptothrix midas_s_884). In addition to the core species, we identified 1417 CRAT species. As CRAT taxa are generally found in low abundance and the current study does not include time series or influent data, we cannot say anything conclusive about their general implications for the ecosystem. However, they may be present due to short-term mass immigration25 or specific operational conditions36 and in both cases, potentially affect the plant operation. They should therefore be considered important target for further investigations together with the core taxa.Many core taxa and CRAT can only be identified with MiDAS 4The core taxa and CRAT included a large proportion of MiDAS 4 de novo taxa. At the genus-level, 106/250 (42%) of the core genera and 500/715 (70%) of the CRAT genera had MiDAS placeholder names. At the species-level, the proportion was even higher. Here placeholder names were assigned to 101/113 (89%) of the core species and 1352/1417 (95%) CRAT species. This highlights the importance of a comprehensive taxonomy that includes the uncultured environmental taxa.The core and CRAT taxa cover the majority of the global activated sludge microbiotaAlthough the core taxa and CRAT represent a small fraction of the total diversity observed in the MiDAS 4 reference database, they accounted for the majority of the observed global activated sludge microbiota (Fig. 6c, d). Accumulated read abundance estimates ranged from 57–68% for the core genera and 11–13% for the CRAT, and combined they accounted for 68–79% of total read abundance in the WWTPs depending on process types. The core taxa represented a larger proportion of the activated sludge microbiota for the more advanced process types, which likely reflects the requirement of more versatile bacteria associated with the alternating redox conditions in these types of WWTPs. The remaining fraction, 21–32%, consisted of 6–8% unclassified genera and genera present in very low abundance, presumably with minor importance for the plant performance. The species-level core taxa and CRAT represented 11–24% and 24–33% accumulated read abundance, respectively. Combined, they accounted for almost 50% of the observed microbiota.Global diversity within important functional guildsThe general change from simple to advanced WWTPs with nutrient removal and the transition to water resource recovery facilities (WRRFs) requires increased knowledge about the bacteria responsible for the removal and recovery of nutrients, so we examined the global diversity of well-described nitrifiers, denitrifiers, PAOs and GAOs (Fig. 8). GAOs were included because they may compete with the PAOs for nutrients and thereby interfere with the biological recovery of phosphorus37. Because MiDAS 4 provided species-level resolution for a large proportion of activated sludge microbiota, we also investigated the species-level diversity within genera affiliated with the functional guilds. A complete overview of species in all genera detected in this global study is provided in the MiDAS field guide (https://www.midasfieldguide.org/guide).Fig. 8: Global diversity of genera belonging to major functional groups.The percent relative abundance represents the mean abundance for each country considering only WWTPs with the relevant process types. Countries are grouped based on continent (shifting colour).Full size imageNitrosomonas and potential comammox Nitrospira were the only abundant (≥0.1% average relative abundance) genera found among ammonia-oxidising bacteria (AOBs), whereas both Nitrospira and Nitrotoga were abundant among the nitrite oxidisers (NOBs), with Nitrospira being the most abundant across all countries (Fig. 8). Nitrobacter was not detected, and Nitrosospira was detected in only a few plants in very low abundance (≤0.01% average relative abundance). At the species-level, each genus had 2–5 abundant species (Supplementary Fig. 8). The most abundant and widespread Nitrosomonas species was midas_s_139. However, midas_s_11707 and midas_s_11733 were dominating in a few countries. For Nitrospira, the most abundant species in nearly all countries was N. defluvii. ASVs classified as the comammox N. nitrosa38,39 was also common in many countries across the world. However, because the comammox trait is not phylogenetically conserved at the 16S rRNA gene level38,39, we cannot conclude that these ASVs represent true comammox bacteria. For Nitrotoga, only two species were detected with notable abundance, midas_s_181 and midas_s_9575. Ammonia-oxidising archaea (AOAs) were not detected with MiDAS 4 due to the lack of reference sequences, and because AOAs are not targeted by the V1–V3 primer pair. However, analyses of our V4 amplicon dataset classified with the SILVA database revealed a considerable relative read abundance of AOAs in Malaysia and the Philippines, but absence or low abundance of AOAs in other countries (Supplementary Fig. 9). Other studies have occasionally found AOAs across the world, but generally in lower abundance than AOBs40,41,42. To ensure detection of AOAs with MiDAS 4, we anticipate adding external reference sequences for AOAs in a future release of the database.Denitrifying bacteria are very common in advanced activated sludge plants, but are generally poorly described. Among the known genera, Rhodoferax, Zoogloea and Thauera were most abundant (Fig. 8). Zoogloea and Thauera are well-known floc formers, sometimes causing unwanted slime formation43. Rhodoferax was the most common denitrifier in Europe, whereas Thauera dominated in Asia. Many denitrifiers could not be classified at the species-level (Supplementary Fig. 10), likely due to highly conserved 16S rRNA genes. An exception was Zoogloea, where midas_s_1080 and Z. caeni and were the most abundant species worldwide.EBPR is performed by PAOs, with three genera recognised as important in full-scale WWTPs: Tetrasphaera, Dechloromonas and Ca. Accumulibacter13. According to relative read abundance, all three were found in EBPR plants globally, with Tetrasphaera as the most prevalent (Fig. 8). Dechloromonas was also abundant in nitrifying and denitrifying plants without EBPR, indicating a more diverse ecology. Four recognised GAOs were found globally: Ca. Competibacter, Defluviicoccus, Propionivibrio and Micropruina, with Ca. Competibacter being the most abundant (Fig. 8). Only a few species (2–6 species) in each genus were dominant across the world for both PAOs (Supplementary Fig. 11) and GAOs (Supplementary Fig. 12), except for Ca. Competibacter, which covered ~20 abundant but country-specific species. Among PAOs, the abundant species were Tetrasphaera midas_s_5, Dechloromonas midas_s_173, (recently named D. phosphorivorans) Ca. Accumulibacter midas_s_315, Ca. A. phosphatis and Ca. A. aalborgensis. Interestingly, some of the most abundant PAOs and GAOs were also abundant in the simple process design with C-removal, indicating more versatile metabolisms.Global diversity of filamentous bacteriaFilamentous bacteria are essential for creating strong activated sludge flocs. However, in large numbers, they can also lead to loose flocs and poor settling properties. This is known as bulking, a major operational problem in many WWTPs. Many can also form foam on top of process tanks due to hydrophobic surfaces. Presently, approximately 20 genera are known to contain filamentous species44, and among those, the most abundant are Ca. Microthrix, Leptothrix, Ca. Villigracilis, Trichococcus and Sphaerotilus (Fig. 9). They are all well-known from studies on mitigation of poor settling properties in WWTPs. Interestingly, Leptothrix, Sphaerotilus and Ca. Villigracilis belong to the genera where abundance-estimation depended strongly on primers, with V4 underestimating their abundance (Fig. 3). Ca. Microthrix and Leptothrix were strongly associated with continents, most common in Europe and less in Asia and North America (Fig. 9).Fig. 9: Global diversity of known filamentous organisms.The percent relative abundance represents the mean abundance for each country across all process types. Countries are grouped based on the continent (shifting colour).Full size imageMany of the filamentous bacteria were linked to specific process types (Supplementary Fig. 13), e.g. Ca. Microthrix were not observed in WWTPs with carbon removal only, and Ca. Amarolinea were only abundant in plants with nutrient removal. The number of abundant species within the genera were generally low, with one species in Trichococcus, two in Ca. Microthrix and approximately five in Leptothrix and Ca. Villigracilis (Supplementary Fig. 14). Only five abundant species were observed for Sphaerotilus. However, a substantial fraction of unclassified ASVs was also observed, demonstrating that certain species within this genus are poorly resolved based on the 16S rRNA gene. Ca. Promineofilum was also poorly resolved at the species-level (Supplementary Fig. 15).Conclusion and perspectivesWe present a worldwide collaborative effort to produce MiDAS 4, an ASV-resolved full-length 16S rRNA gene reference database, which covers more than 31,000 species and enables genus- to species-level resolution in microbial community profiling studies. MiDAS 4 covers the vast majority of WWTP bacteria globally and provides a strongly needed common taxonomy for the field, which provides the foundation for comprehensive linking of microbial taxa in the ecosystem with their functional traits. Presently, hundreds of studies are undertaken to combine engineering and microbial aspects of full-scale WWTPs. However, most ASVs or OTUs in these studies are classified at poor taxonomic resolution (family-level or above) due to the use of incomplete universal reference databases. Because many important functional traits are only conserved at high taxonomic resolution (genus- or species-level), this strongly hampers our ability to transfer taxa-specific knowledge from one study to another. This will change with MiDAS 4, and we expect that reprocessing of data from earlier studies may reveal new perspectives into wastewater treatment microbiology. Our online Global MiDAS Field Guide presents the data generated in this study and summarises present knowledge about all taxa. We encourage researchers within the field to contribute new knowledge to MiDAS using the contact link in the MiDAS website (https://www.midasfieldguide.org/guide/contact).The global microbiota of activated sludge plants has been predicted to harbour a massive diversity with up to one billion species2. However, most of these occur at very low abundance and are of little importance for the treatment process. By focusing only on the abundant taxa, we can see that this number is much smaller, i.e., ~1000 genera and 1500 species. We consider these taxa functionally the most important globally, representing a “most wanted list” for future studies. Some taxa are abundant in most WWTPs (core taxa), and others are occasionally abundant in fewer plants (CRAT). The CRAT have received little attention in the field of wastewater treatment, but they can be of profound importance for WWTP performance. Both groups have a high fraction of poorly characterised species. The high taxonomic resolution provided by MiDAS 4 enables us to identify samples where these important core taxa occur in high abundance. This provides an ideal starting point for obtaining high-quality metagenome-assembled genomes (MAGs), isolation of pure cultures, in addition to targeted culture-independent studies to uncover their physiological and ecological roles.Among the known functional guilds, such as nitrifiers or polyphosphate-accumulating organisms, the same genera were found worldwide, with only a few abundant species in each genus. There were differences in the community structure, and the abundance of dominant species was mainly shaped by process type, temperature, and in some cases, continent. This discovery sends an important message to the field: relatively few species are abundant worldwide, so research or operational results can reliably be transferred from one geographical region to another, stimulating the transition from WWTPs to more sustainable WRRFs.The relatively low number of uncharacterised abundant species also shows that it is within our reach to describe them all in terms of identity, physiology, ecology and dynamics, providing the necessary knowledge for informed process optimisation and management. The number of poorly described genera (i.e. those with only a MiDAS placeholder genus name) was 88 among the 250 core genera (35%) and more than 89% at the species-level, so there is still some work to do to link their identities and function. An important step in this direction is the visualisation of the populations. With the comprehensive set of FL-ASVs, it is possible to design highly specific FISH probes, and to critically evaluate the old probes. In the Danish WWTPs, we have successfully done this for groups in the Acidobacteriota42 based on the MiDAS 3 database18. Our recent retrieval of more than 1000 high-quality MAGs from Danish WWTPs with advanced process design is also an important step to link identity to function43. The HQ-MAGs can be linked directly to MiDAS 4 as they contain complete 16S rRNA genes. They cover 62% (156/250) of the core genera and 61% (69/113) of the core species identified in this study. These MAGs may also form the basis for further studies to link identity and function, e.g. by applying metatranscriptomics44 and other in situ techniques such as FISH combined with Raman45,46, guided by the “most wanted” list provided in this study. We expect that MiDAS 4 will have significant implications for future microbial ecology studies in wastewater treatment systems. More

IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Sindelar, A. J. et al. Winter oilseed production for biofuel in the US Corn Belt: opportunities and limitations. GCB Bioenergy 9, 508–524 (2017).CAS 

Google Scholar 
Stöckle, C. O. et al. Evaluating opportunities for an increased role of winter crops as adaptation to climate change in dryland cropping systems of the U.S. Inland Pacific Northwest. Clim. Change 146, 247–261 (2018).
Google Scholar 
Williams, C. M., Henry, H. A. L. & Sinclair, B. J. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biol. Rev. 90, 214–235 (2015).
Google Scholar 
Seifert, C. A., Azzari, G. & Lobell, D. B. Satellite detection of cover crops and their effects on crop yield in the Midwestern United States. Environ. Res. Lett. 13, 064033 (2018).
Google Scholar 
Marcillo, G. S. & Miguez, F. E. Corn yield response to winter cover crops: an updated meta-analysis. J. Soil Water Conserv. 72, 226–239 (2017).
Google Scholar 
Zhu, L., Ives, A. R., Zhang, C., Guo, Y. & Radeloff, V. C. Climate change causes functionally colder winters for snow cover-dependent organisms. Nat. Clim. Change 9, 886–893 (2019).
Google Scholar 
Mankin, J. S. & Diffenbaugh, N. S. Influence of temperature and precipitation variability on near-term snow trends. Clim. Dynam. 45, 1099–1116 (2015).
Google Scholar 
Zhu, L., Radeloff, V. C. & Ives, A. R. Characterizing global patterns of frozen ground with and without snow cover using microwave and MODIS satellite data products. Remote Sens. Environ. 191, 168–178 (2017).
Google Scholar 
Huning, L. S. & AghaKouchak, A. Global snow drought hot spots and characteristics. Proc. Natl Acad. Sci. USA 117, 19753–19759 (2020).CAS 

Google Scholar 
Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).
Google Scholar 
Trnka, M. et al. Adverse weather conditions for European wheat production will become more frequent with climate change. Nat. Clim. Change 4, 637–643 (2014).
Google Scholar 
Li, D., Wrzesien, M. L., Durand, M., Adam, J. & Lettenmaier, D. P. How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett. 44, 6163–6172 (2017).
Google Scholar 
Biemans, H. et al. Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nat. Sustain. 2, 594–601 (2019).
Google Scholar 
Acevedo, E., Silva, P. & Silva, H. in Bread Wheat: Improvement and Production (eds Curtis, B. C. et al.) 39–70 (FAO Plant Production and Protection, 2002).Baker, J. T., Pinter, P. J., Reginato, R. J. & Kanemasu, E. T. Effects of temperature on leaf appearance in spring and winter wheat cultivars. Agron. J. 78, 605–613 (1986).
Google Scholar 
Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl Acad. Sci. USA 112, 6931–6936 (2015).CAS 

Google Scholar 
Müller, C. et al. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).
Google Scholar 
Talukder, A. S. M. H. M., McDonald, G. K. & Gill, G. S. Effect of short-term heat stress prior to flowering and early grain set on the grain yield of wheat. Field Crops Res. 160, 54–63 (2014).
Google Scholar 
Farooq, M., Bramley, H., Palta, J. A. & Siddique, K. H. M. Heat stress in wheat during reproductive and grain-filling phases. Crit. Rev. Plant Sci. 30, 491–507 (2011).Cuadra, S. V., Kimball, B. A., Boote, K. J., Suyker, A. E. & Pickering, N. Energy balance in the DSSAT-CSM-CROPGRO model. Agric. For. Meteorol. 297, 108241 (2021).
Google Scholar 
Harder, P., Helgason, W. D. & Pomeroy, J. W. Modeling the snowpack energy balance during melt under exposed crop stubble. J. Hydrometeorol. 19, 1191–1214 (2018).
Google Scholar 
Barlow, K. M., Christy, B. P., O’Leary, G. J., Riffkin, P. A. & Nuttall, J. G. Simulating the impact of extreme heat and frost events on wheat crop production: a review. Field Crops Res. 171, 109–119 (2015).
Google Scholar 
Wang, W. et al. Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region. Cryosphere 10, 1721–1737 (2016).
Google Scholar 
Seifert, C. A. & Lobell, D. B. Response of double cropping suitability to climate change in the United States. Environ. Res. Lett. 10, 024002 (2015).
Google Scholar 
Pullens, J. W. M. et al. Risk factors for European winter oilseed rape production under climate change. Agric. For. Meteorol. 272–273, 30–39 (2019).
Google Scholar 
Chopra, R. et al. Identification and stacking of crucial traits required for the domestication of pennycress. Nat. Food 1, 84–91 (2020).
Google Scholar 
Crews, T. E., Carton, W. & Olsson, L. Is the future of agriculture perennial? Imperatives and opportunities to reinvent agriculture by shifting from annual monocultures to perennial polycultures. Glob. Sustain. 1, e11 (2018).Harkness, C. et al. Adverse weather conditions for UK wheat production under climate change. Agric. Meteorol. 282–283, 107862 (2020).
Google Scholar 
Schierhorn, F., Hofmann, M., Gagalyuk, T., Ostapchuk, I. & Müller, D. Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages. Clim. Change 169, 39 (2021).Michel, S. et al. Improving and maintaining winter hardiness and frost tolerance in bread wheat by genomic selection. Front. Plant Sci. 10, 1195 (2019).
Google Scholar 
Mahfoozi, S., Limin, A. E. & Fowler, D. B. Influence of vernalization and photoperiod responses on cold hardiness in winter cereals. Crop Sci. 41, 1006–1011 (2001).
Google Scholar 
Dutra, E. et al. An improved snow scheme for the ECMWF land surface model: description and offline validation. J. Hydrometeorol. 11, 899–916 (2010).
Google Scholar 
Ge, Y. & Gong, G. Land surface insulation response to snow depth variability. J. Geophys. Res. Atmos. 115, 8107 (2010).
Google Scholar 
Hunt, J. R. et al. Early sowing systems can boost Australian wheat yields despite recent climate change. Nat. Clim. Change 9, 244–247 (2019).
Google Scholar 
Sloat, L. L. et al. Climate adaptation by crop migration. Nat. Commun. 11, 1243 (2020) .Ainsworth, E. A. & Long, S. P. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation? Glob. Change Biol. 27, 27–49 (2021).
Google Scholar 
Shimoda, S. et al. Effects of snow compaction ‘yuki-fumi’ on soil frost depth and volunteer potato control in potato–wheat rotation system in Hokkaido. Plant Prod. Sci. 24, 186–197 (2021).CAS 

Google Scholar 
Luojus, K. et al. GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset. Sci. Data 8, 163 (2021)..IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions Version 1 (NSIDC, 2008).Jing, Q. et al. Assessing the options to improve regional wheat yield in Eastern Canada using the CSM–CERES–wheat model. Agron. J. 109, 510–523 (2017).
Google Scholar 
Vogel, F. A. & Bange, G. A. Understanding USDA Crop Forecasts (USDA, 1999).Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).
Google Scholar 
Brown, R. D. & Brasnett, B. Daily Snow Depth Analysis Data Version 1 (Canadian Meteorological Centre, 2010).Brasnett, B. A global analysis of snow depth for numerical weather prediction. J. Appl. Meteorol. Climatol. 38, 726–740 (1999).
Google Scholar 
Toure, A. M., Reichle, R. H., Forman, B. A., Getirana, A. & De Lannoy, G. J. M. Assimilation of MODIS snow cover fraction observations into the NASA catchment land surface model. Remote Sens. 10, 316 (2018).
Google Scholar 
Snauffer, A. M., Hsieh, W. W. & Cannon, A. J. Comparison of gridded snow water equivalent products with in situ measurements in British Columbia, Canada. J. Hydrol. 541, 714–726 (2016).
Google Scholar 
Census of Agriculture (USDA National Agricultural Statistics Service, 2017).Skinner, D. Z. & Mackey, B. Freezing tolerance of winter wheat plants frozen in saturated soil. Field Crops Res. 113, 335–341 (2009).
Google Scholar 
Lollato, R. P. et al. Climate-risk assessment for winter wheat using long-term weather data. Agron. J. 112, 2132–2151 (2020).
Google Scholar 
Siebers, M. H. et al. Heat waves imposed during early pod development in soybean (Glycine max) cause significant yield loss despite a rapid recovery from oxidative stress. Glob. Change Biol. 21, 3114–3125 (2015).
Google Scholar 
Çakir, R. Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Res. 89, 1–16 (2004).
Google Scholar 
Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).
Google Scholar 
Chen, M., Griffis, T. J., Baker, J., Wood, J. D. & Xiao, K. Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes. J. Geophys. Res. Biogeosci. 120, 310–325 (2015).CAS 

Google Scholar 
Larson, K. M. & Small, E. E. Daily Snow Depth and SWE from GPS Signal-to-Noise Ratios Version 1 (NSIDC, 2017).Sturm, M. et al. Estimating snow water equivalent using snow depth data and climate classes. J. Hydrometeorol. 11, 1380–1394 (2010).
Google Scholar 
McCabe, G. J. & Wolock, D. M. Recent declines in western U.S. snowpack in the context of twentieth-century climate variability. Earth Interact. 13, 1–15 (2009).
Google Scholar 
Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2019).
Google Scholar 
Qiao, S. et al. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5, 014010 (2010).
Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).CAS 

Google Scholar 
Xie, Y., Gibbs, H. K. & Lark, T. J. Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017. Earth Syst. Sci. Data 13, 5689–5710 (2021).
Google Scholar 
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).CAS 

Google Scholar 
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar 
Elliott, J. et al. The global gridded crop model intercomparison: data and modeling protocols for phase 1 (v1.0). Geosci. Model Dev. 8, 261–277 (2015).
Google Scholar 
Li, X., Shen, Z., Harri, A. & Coble, K. H. Comparing survey-based and programme-based yield data: implications for the U.S. Agricultural Risk Coverage-County programme. Geneva Pap. Risk Insur. Issues Pract. 45, 184–202 (2020).
Google Scholar 
Hawkins, E., Osborne, T. M., Ho, C. K. & Challinor, A. J. Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agric. Meteorol. 170, 19–31 (2013).
Google Scholar 
Ho, C. K., Stephenson, D. B., Collins, M., Ferro, C. A. T. & Brown, S. J. Calibration strategies: a source of additional uncertainty in climate change projections. Bull. Am. Meteorol. Soc. 93, 21–26 (2012).
Google Scholar  More

Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
Schleuter, D., Daufresne, M., Massol, F. & Argillier, C. A user’s guide to functional diversity indices. Ecol. Monogr. 80, 469–484 (2010).Article 

Google Scholar 
Petchey, O. L. & Gaston, K. J. Extinction and the loss of functional diversity. Proc. R. Soc. B Biol. Sci. 269, 1721–1727 (2002).Article 

Google Scholar 
Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science (80-. ). 277, 1300–1302 (1997).Díaz, S. & Cabido, M. Vive la différence: Plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

Google Scholar 
Tilman, D. Functional diversity. in Encyclopedia of Biodiversity, Volume 3 (ed. Levin, S. A.) 109–120 (Academic Press, 2001).McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).PubMed 
Article 

Google Scholar 
Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: Functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).Article 

Google Scholar 
Petchey, O. L., Hector, A. & Gaston, K. J. How do different measures of functional diversity perform?. Ecology 85, 847–857 (2004).Article 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
Article 
ADS 

Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity (FD), species richness and community composition. Ecol. Lett. 5, 402–411 (2002).Article 

Google Scholar 
Halpern, B. S. & Floeter, S. R. Functional diversity responses to changing species richness in reef fish communities. Mar. Ecol. Prog. Ser. 364, 147–156 (2008).Article 
ADS 

Google Scholar 
Seymour, C. L., Simmons, R. E., Joseph, G. S. & Slingsby, J. A. On bird functional diversity: Species richness and functional differentiation show contrasting responses to rainfall and vegetation structure in an arid landscape. Ecosystems 18, 971–984 (2015).Article 

Google Scholar 
Müller, J., Jarzabek-Müller, A., Bussler, H. & Gossner, M. M. Hollow beech trees identified as keystone structures for saproxylic beetles by analyses of functional and phylogenetic diversity. Anim. Conserv. 17, 154–162 (2014).Article 

Google Scholar 
Ulrich, W. et al. Species assortment or habitat filtering: A case study of spider communities on lake islands. Ecol. Res. 25, 375–381 (2010).Article 

Google Scholar 
Mouillot, D., Dumay, O. & Tomasini, J. A. Limiting similarity, niche filtering and functional diversity in coastal lagoon fish communities. Estuar. Coast. Shelf Sci. 71, 443–456 (2007).Article 
ADS 

Google Scholar 
Cadotte, M. W. & Tucker, C. M. Should environmental filtering be abandoned?. Trends Ecol. Evol. 32, 429–437 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
Rader, R., Bartomeus, I., Tylianakis, J. M. & Laliberté, E. The winners and losers of land use intensification: Pollinator community disassembly is non-random and alters functional diversity. Divers. Distrib. 20, 908–917 (2014).Article 

Google Scholar 
Sol, D. et al. The worldwide impact of urbanisation on avian functional diversity. Ecol. Lett. 23, 962–972 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
Bihn, J. H., Gebauer, G. & Brandl, R. Loss of functional diversity of ant assemblages in secondary tropical forests. Ecology 91, 782–792 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
Balestrieri, R. et al. A guild-based approach to assessing the influence of beech forest structure on bird communities. For. Ecol. Manage. 356, 216–223 (2015).Article 

Google Scholar 
Basile, M., Mikusiński, G. & Storch, I. Bird guilds show different responses to tree retention levels: A meta-analysis. Glob. Ecol. Conserv. 18, e00615 (2019).Article 

Google Scholar 
Czeszczewik, D. et al. Effects of forest management on bird assemblages in the Bialowieza Forest, Poland. iForest – Biogeosciences For. 8, 377–385 (2015).Article 

Google Scholar 
Wesołowski, T. Primeval conditions—What can we learn from them? Ibis (Lond. 1859). 149, 64–77 (2007).Paillet, Y. et al. Biodiversity differences between managed and unmanaged forests: meta-analysis of species richness in europe. Conserv. Biol. 24, 101–112 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
Götzenberger, L. et al. Ecological assembly rules in plant communities-approaches, patterns and prospects. Biol. Rev. 87, 111–127 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
Fox, J. W. & Kerr, B. Analyzing the effects of species gain and loss on ecosystem function using the extended Price equation partition. Oikos 121, 290–298 (2012).Article 

Google Scholar 
Fox, J. W. Using the Price Equations to partition the effects of biodiversity loss on ecosystem function. Ecology 87, 2687–2696 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
Winfree, R. W., Fox, J., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).PubMed 
Article 

Google Scholar 
Storch, I. et al. Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary, multi‐scale approach. Ecol. Evol. ece3.6003 (2020) https://doi.org/10.1002/ece3.6003.Pommerening, A. & Murphy, S. T. A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking. Forestry 77, 27–44 (2004).Article 

Google Scholar 
Bauhus, J., Puettmannn, K. J. & Kühne, C. Close-to-nature forest management in Europe: does it support complexity and adaptability of forest ecosystems? in Managing Forests as Complex Adaptive Systems: Building Resilience to the Challenge of Global Change 187–213 (Routledge/The Earthscan Forest Library, 2013). https://doi.org/10.4324/9780203122808.Bauhus, J., Puettmannn, K. J. & Kühne, C. Is Close-to-Nature Forest Management in Europe Compatible with Managing Forests as Complex Adaptive Forest Ecosystems? in Managing Forests as Complex Adaptive Systems: Building Resilience to the Challenge of Global Change (eds. Messier, C., Puettmannn, K. J. & Coates, K. D.) 187–213 (Routledge/The Earthscan Forest Library, 2013).Balestrieri, R., Basile, M., Posillico, M., Altea, T. & Matteucci, G. Survey effort requirements for bird community assessment in forest habitats. Acta Ornithol. 52, 1–9 (2017).Article 

Google Scholar 
Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

Google Scholar 
Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
Article 

Google Scholar 
Gower, J. C. A general coefficient of similarity and some of its properties. Biometrics 27, 857 (1971).Article 

Google Scholar 
Kahl, T. & Bauhus, J. An index of forest management intensity based on assessment of harvested tree volume, tree species composition and dead wood origin. Nat. Conserv. 7, 15–27 (2014).Article 

Google Scholar 
Paillet, Y. et al. Quantifying the recovery of old-growth attributes in forest reserves: A first reference for France. For. Ecol. Manage. 346, 51–64 (2015).Article 

Google Scholar 
Burrascano, S., Lombardi, F. & Marchetti, M. Old-growth forest structure and deadwood: Are they indicators of plant species composition? A case study from central Italy. Plant Biosyst. 142, 313–323 (2008).Article 

Google Scholar 
Van Wagner, C. E. Practical aspects of the line intersect method. (Minister of Supply and Services Canada, 1982).Larrieu, L. et al. Tree related microhabitats in temperate and Mediterranean European forests: A hierarchical typology for inventory standardization. Ecol. Indic. 84, 194–207 (2018).Article 

Google Scholar 
Asbeck, T., Pyttel, P., Frey, J. & Bauhus, J. Predicting abundance and diversity of tree-related microhabitats in Central European montane forests from common forest attributes. For. Ecol. Manage. 432, 400–408 (2019).Article 

Google Scholar 
Paillet, Y. et al. The indicator side of tree microhabitats: A multi-taxon approach based on bats, birds and saproxylic beetles. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13181 (2018).Article 

Google Scholar 
Basile, M. et al. What do tree-related microhabitats tell us about the abundance of forest-dwelling bats, birds, and insects?. J. Environ. Manage. 264, 110401 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
Wang, Q., Adiku, S., Tenhunen, J. & Granier, A. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens. Environ. 94, 244–255 (2005).Article 
ADS 

Google Scholar 
Rafique, R., Zhao, F., De Jong, R., Zeng, N. & Asrar, G. R. Global and regional variability and change in terrestrial ecosystems net primary production and NDVI: A model-data comparison. Remote Sens. 8, 1–16 (2016).Article 

Google Scholar 
Bates, D. et al. Package ‘lme4’. R Found. Stat. Comput. Vienna 12, (2014).Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. (Springer, New York, 2009). https://doi.org/10.1007/978-0-387-87458-6.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. (2019).R Core Team. R: A language and environment for statistical computing. (2021).Mayfield, M. M. et al. What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land-use change. Glob. Ecol. Biogeogr. 19, 423–431 (2010).
Google Scholar 
Pavoine, S. & Bonsall, M. B. Measuring biodiversity to explain community assembly: a unified approach. Biol. Rev. 86, 792–812 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Mayfield, M. M., Boni, M. F., Daily, G. C. & Ackerly, D. Species and functional diversity of natie and human-dominated plant communities. Ecology 86, 2365–2372 (2005).Article 

Google Scholar 
Holdaway, R. J. & Sparrow, A. D. Assembly rules operating along a primary riverbed-grassland successional sequence. J. Ecol. 94, 1092–1102 (2006).Article 

Google Scholar 
Matuoka, M. A., Benchimol, M., de Almeida-Rocha, J. M. & Morante-Filho, J. C. Effects of anthropogenic disturbances on bird functional diversity: A global meta-analysis. Ecol. Indic. 116, 106471 (2020).Article 

Google Scholar 
Leaver, J., Mulvaney, J., Ehlers-Smith, D. A., Ehlers-Smith, Y. C. & Cherry, M. I. Response of bird functional diversity to forest product harvesting in the Eastern Cape, South Africa. For. Ecol. Manage. 445, 82–95 (2019).Article 

Google Scholar 
Poos, M. S., Walker, S. C. & Jackson, D. A. Functional-diversity indices can be driven by methodological choices and species richness. Ecology 90, 341–347 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
Mayfield, M. M., Boni, M. F., Daily, G. C. & Ackerly, D. Species and functional diversity of native and human-dominated plant communities. Ecology 86, 2365–2372 (2005).Article 

Google Scholar 
Tsianou, M. A. & Kallimanis, A. S. Different species traits produce diverse spatial functional diversity patterns of amphibians. Biodivers. Conserv. 25, 117–132 (2016).Article 

Google Scholar 
Gregory, R. D., Skorpilova, J., Vorisek, P. & Butler, S. An analysis of trends, uncertainty and species selection shows contrasting trends of widespread forest and farmland birds in Europe. Ecol. Indic. 103, 676–687 (2019).Article 

Google Scholar 
Peña, R. et al. Biodiversity components mediate the response to forest loss and the effect on ecological processes of plant–frugivore assemblages. Funct. Ecol. 34, 1257–1267 (2020).Article 

Google Scholar 
Chase, J. M., Blowes, S. A., Knight, T. M., Gerstner, K. & May, F. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584, 238–243 (2020).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
Fedrowitz, K. et al. Can retention forestry help conserve biodiversity? A meta-analysis. J. Appl. Ecol. 51, 1669–1679 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Horák, J. et al. Green desert?: Biodiversity patterns in forest plantations. For. Ecol. Manage. 433, 343–348 (2019).Article 

Google Scholar 
Ameztegui, A. et al. Bird community response in mountain pine forests of the Pyrenees managed under a shelterwood system. For. Ecol. Manage. 407, 95–105 (2017).Article 

Google Scholar 
Basile, M., Balestrieri, R., de Groot, M., Flajšman, K. & Posillico, M. Conservation of birds as a function of forestry. Ital. J. Agron. 11, 42–48 (2016).
Google Scholar 
Uezu, A. & Metzger, J. P. Vanishing bird species in the Atlantic Forest: Relative importance of landscape configuration, forest structure and species characteristics. Biodivers. Conserv. 20, 3627–3643 (2011).Article 

Google Scholar 
Endenburg, S. et al. The homogenizing influence of agriculture on forest bird communities at landscape scales. Landsc. Ecol. 34, 1–15 (2019).Article 

Google Scholar 
Reif, J. et al. Changes in bird community composition in the Czech Republic from 1982 to 2004: Increasing biotic homogenization, impacts of warming climate, but no trend in species richness. J. Ornithol. 154, 359–370 (2013).Article 

Google Scholar 
Morelli, F. et al. Evidence of evolutionary homogenization of bird communities in urban environments across Europe. Glob. Ecol. Biogeogr. 25, 1284–1293 (2016).Article 

Google Scholar 
Devictor, V., Julliard, R., Couvet, D., Lee, A. & Jiguet, F. Functional homogenization effect of urbanization on bird communities. Conserv. Biol. 21, 741–751 (2007).PubMed 
Article 

Google Scholar 
Doxa, A., Paracchini, M. L., Pointereau, P., Devictor, V. & Jiguet, F. Preventing biotic homogenization of farmland bird communities: The role of High Nature Value farmland. Agric. Ecosyst. Environ. 148, 83–88 (2012).Article 

Google Scholar 
Van Turnhout, C. A. M., Foppen, R. P. B., Leuven, R. S. E. W., Siepel, H. & Esselink, H. Scale-dependent homogenization: Changes in breeding bird diversity in the Netherlands over a 25-year period. Biol. Conserv. 134, 505–516 (2007).Article 

Google Scholar 
Clavero, M. & Brotons, L. Functional homogenization of bird communities along habitat gradients: Accounting for niche multidimensionality. Glob. Ecol. Biogeogr. 19, 684–696 (2010).
Google Scholar 
Gustafsson, L. et al. Retention as an integrated biodiversity conservation approach for continuous-cover forestry in Europe. Ambio 49, 85–97 (2020).PubMed 
Article 

Google Scholar 
Lelli, C. et al. Biodiversity response to forest structure and management: Comparing species richness, conservation relevant species and functional diversity as metrics in forest conservation. For. Ecol. Manage. 432, 707–717 (2019).Article 

Google Scholar 
Aquilué, N., Messier, C., Martins, K. T., Dumais-Lalonde, V. & Mina, M. A simple-to-use management approach to boost adaptive capacity of forests to global uncertainty. For. Ecol. Manage. 481, (2021).Manes, F., Ricotta, C., Salvatori, E., Bajocco, S. & Blasi, C. A multiscale analysis of canopy structure in Fagus sylvatica L. and Quercus cerris L. old-growth forests in the Cilento and Vallo di Diano National Park. Plant Biosyst. 144, 202–210 (2010).Article 

Google Scholar 
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes – eight hypotheses. Biol. Rev. 87, 661–685 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
Kirsch, J.-J. et al. The use of water-filled tree holes by vertebrates in temperate forests. Wildlife Biol. 2021, wlb.00786
(2021). More

Ellis, E. C., Beusen, A. H. W. & Goldewijk, K. K. Anthropogenic Biomes: 10,000 BCE to 2015 CE. Land. 9(5), 129 (2020).Article 

Google Scholar 
Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. PNAS. 109, 16083–8 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Doherty, T. S., Hays, G. C. & Driscoll, D. A. Human disturbance causes widespread disruption of animal movement. Nat. Ecol. Evol. 5, 513–519 (2021).PubMed 
Article 

Google Scholar 
Frid, A. & Dill, L. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6, 11 (2002).
Google Scholar 
Rodgers, J. A. & Schwikert, S. T. Buffer-zone distances to protect foraging and loafing waterbirds from disturbance by personal watercraft and outboard-powered boats. Conserv. Bio. 16, 216–224 (2002).Article 

Google Scholar 
Constantine, R., Brunton, D. H. & Dennis, T. Dolphin-watching tour boats change bottlenose dolphin (Tursiops truncates) behaviour. Biol. Conserv. 117, 299–307 (2004).Article 

Google Scholar 
Gill, J. A., Sutherland, W. J. & Watkinson, A. R. A method to quantify the effects of human disturbance on animal populations. J. Appl. Ecol. 33, 786–792 (1996).Article 

Google Scholar 
King, J. M. & Heinen, J. T. An assessment of the behaviors of overwintering manatees as influenced by interactions with tourists at two sites in central Florida. Biol. Conserv 117, 227–234 (2004).Article 

Google Scholar 
Stockwell, C. A., Bateman, G. C. & Berger, J. Conflicts in national parks: A case study of helicopters and bighorn sheep time budgets at the Grand Canyon. Biol. Conserv 56, 317–328 (1991).Article 

Google Scholar 
Diamond, J. M. The design of a nature reserve system for Indone-Asian New Guinea. In Conservation Biology: The Science of Scarcity and Cliversity (ed. Soule, M.) 485–503 (Sinauer, Sunderland, Massachusetts, 1986).Ceballos, G., García, A. & Ehrlich, P. R. The sixth extinction crisis loss of animal populations and species. J. Cosmol. 8, 1821–1831 (2010).
Google Scholar 
Kerr, J. T. & Deguise, I. Habitat loss and the limits to endangered species recovery. Ecol. Lett. 7, 1163–1169 (2004).Article 

Google Scholar 
Mbora, D. N. M. & McPeek, M. A. Host density and human activities mediate increased parasite prevalence and richness in primates threatened by habitat loss and fragmentation. J. Anim. Ecol. 78, 210–218 (2009).PubMed 
Article 

Google Scholar 
Low, T. The New Nature (Penguin Books Limited, 2003).
Google Scholar 
Baxter-Gilbert, J., Riley, J. L. & Measey, J. Fortune favors the bold toad: Urban-derived behavioral traits may provide advantages for invasive amphibian populations. Behav. Ecol. Sociobiol. 75, 130 (2021).Article 

Google Scholar 
Coleman, J. L. & Barclay, R. M. R. Prey availability and foraging activity of grassland bats in relation to urbanization. J. Mammal. 94, 1111–1122 (2013).Article 

Google Scholar 
Castellano, M. J. & Valone, T. J. Effects of livestock removal and perennial grass recovery on the lizards of a desertified arid grassland. J. Arid Environ. 66, 87e95 (2006).Article 

Google Scholar 
Huey, R. B. Temperature, physiology, and the ecology of reptiles. In Biology of the Reptilia (eds. Gans, C., & Pough, F.H.) Vol. 12. (Academic Press, London, 1982).White, D. et al. Assessing risks to biodiversity from future landscape change. Conserv. Biol. 11, 349360 (1997).Article 

Google Scholar 
Carpio, A. J., Oteros, J., Tortosa, F. S. & Guerrero-Casado, J. Land use and biodiversity patterns of the herpetofauna: The role of olive groves. Acta Oecol. 70, 103–111 (2016).Article 

Google Scholar 
Geyle, H. M., Tingley, R., Amey, A. P. & Chapple, D. G. Reptiles on the brink: Identifying the Australian terrestrial snake and lizard species most at risk of extinction. Pac. Conserv. Biol. 27, 3–12 (2021).Article 

Google Scholar 
Doherty, T. S. et al. Reptile responses to anthropogenic habitat modification: A global meta-analysis. Glob. Ecol. Biogeogr. 29(7), 1265–1279 (2020).Article 

Google Scholar 
Hu, Y., Doherty, T. S. & Jessop, T. S. How influential are squamate reptile traits in explaining population responses to environmental disturbances?. Wildl. Res. 47(3), 249–259 (2020).Article 

Google Scholar 
Poole, G. & Berman, C. An ecological perspective on in-stream temperature: natural heat dynamics and mechanisms of human-caused thermal degradation. Environ. Manag. 27, 787–802 (2001).CAS 
Article 

Google Scholar 
Tang, X. et al. Human activities enhance radiation forcing through surface albedo associated with vegetation in beijing. Remote Sens. 12(5), 837 (2020).Article 

Google Scholar 
Barna, A., Masum, A. K. M., Hossain, M. E., Bahadur, E.H. & Alam, M. S. A study on human activity recognition using gyroscope, accelerometer, temperature and humidity data. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6 (2019).Moore, M. & Seigel, R. A. No place to nest or bask: Effects of human disturbance on the nesting and basking habits of yellow-blotched map turtles (Graptemys flavimaculata). J. Biol. Conserv. 130(3), 386–393 (2006).Article 

Google Scholar 
Bonnet, X., Naulleau, G. & Shine, R. The dangers of leaving home: Dispersal and mortality in snakes. Biol. Conserv. 89(1), 39–50 (1999).Article 

Google Scholar 
Haxton, T. Road mortality of Snapping Turtles, Chelydra serpentina, in central Ontario during their nesting period. Can. Field-Nat. 114(1), 106–110 (2000).
Google Scholar 
Koenig, J., Shine, R. & Shea, G. L. The ecology of an Australian reptile icon: How do blue-tongued lizards (Tiliqua scincoides) survive in suburbia?. Wildl. Res. 28(3), 214–227 (2001).Article 

Google Scholar 
Uetz, P. How many Reptile species?. Herpetol. Rev. 31, 13–15 (2000).
Google Scholar 
Todd, R. L., Steven, P., Rowland, G. & Oldham, G. Herpetological observations from field expeditions to North Karnataka and Southwest Maharashtra, India. Herpetol. Bull. 112, 17–37 (2010).
Google Scholar 
Sathish Kumar, V. M. The conservation of Indian Reptiles: An approach with molecular aspects. Reptile Rap. 14, 2–8 (2012).
Google Scholar 
Berryman, A. A. & Hawkins, B. A. The refuge as an integrating concept in ecology and evolution. Oikos. 115, 92–196 (2006).Article 

Google Scholar 
Webb, J. K., Pringle, R. M. & Shine, R. How do nocturnal snakes select diurnal retreat sites?. Copeia 2004, 919–925 (2004).Article 

Google Scholar 
Skinner, M. & Miller, N. Aggregation and social interaction in garter snakes (Thamnophis sirtalis sirtalis). Behav. Ecol. Sociobiol. 74, 51 (2020).Article 

Google Scholar 
Aubret, F. & Shine, R. Causes and consequences of aggregation by neonatal tiger snakes (Notechis scutatus, Elapidae). Austral Ecol. 34(2), 210–217 (2009).Article 

Google Scholar 
Myres, B. & Eells, M. Thermal aggregation in Boa constrictor. Herpetologica 24(1), 61–66 (1968).
Google Scholar 
Parrish, J. K. & Edelstein-keshet, L. Coinplexity, pattern, and evolutionary trade-offs in animal aggregation. Science 284, 99–101 (1999).CAS 
PubMed 
Article 

Google Scholar 
Trevesa, A. Theory and method in studies of vigilance and aggregation. Anim. Behav. 60, 711–722 (2000).Article 

Google Scholar 
Greene, H. W. Snakes (University of California Press, 1997).Book 

Google Scholar 
Huey, R. B., Peterson, C. R., Arnold, S. J. & Porter, W. P. Hot rocks and not-so-hot rocks: Retreat-site selection by garter snakes and its thermal consequences. Ecology 70, 931–944 (1989).Article 

Google Scholar 
Christian, K. & Weavers, B. Analysis of activity and energetics of the lizard Varanus rosenbergi. Copeia 1994, 289–295 (1994).Article 

Google Scholar 
Autumn, K. & de Nardo, D. F. Behavioural thermoregulation increases growth rate in nocturnal lizard. J. Herpetol. 29, 157–162 (1995).Article 

Google Scholar 
Milne, T., Bull, C. M. & Hutchinson, M. N. Use of burrows by the endangered pygmy blue-tongue lizard, Tiliqua adelaidensis (Scincidae). Wildl. Res. 30, 523–528 (2003).Article 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. PNAS. 111, 5610–5615 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer ‘“cold-blooded”’ animals against climate warming. PNAS 106, 3835–3840 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stevenson, D. J., Dyer, K. J. & Willis-Stevenson, B. A. Survey and monitoring of the eastern indigo snake in georgia. Southeast. Nat. 2(3), 393–408 (2003).Article 

Google Scholar 
Zappalorti, R. T. & Reinert, H. K. Artificial refugia as a habitat-improvement strategy for snake conservation. Contrib. Herpetol. 11, 369–375 (1994).
Google Scholar 
Griffith, B., Scott, J. M., Carpenter, J. W. & Reed, C. Translocation as a species conservation tool: Status and strategy. Science 245, 477–480 (1989).CAS 
PubMed 
Article 

Google Scholar 
Mullin, S. J. Snakes Ecology and Conservation (eds. Stephen, J. M. & Richard, A. S.). (Cornell University Press, 2011).Lei, J., Booth, D. T. & Dwyer, R. G. Spatial ecology of yellow-spotted goannas adjacent to a sea turtle nesting beach. Aust. J. Zool. 65, 77–86 (2017).Article 

Google Scholar 
Ermi, Z. Snakes of China. (Anhui Science and Technology Press, 2006).Schulz, K. D. A Monograph of the Colubrid Snakes of the Genus Elaphe Fitzinger (Czech Republic, Koeltz Scientific Books, 1996).
Google Scholar 
Pallas, P. S. Reise durch verschiedene Provinzen des Russischen Reiches, Vol. 2. 744 (Kaiserl. Akad. Wiss., St. Petersburg, 1773).Auffenberg, W., Arian, Q. N. & Kurshid, N. Preferred habitat, home range and movement patterns of Varanus bengalensis in southern Pakistan. Mertensiella 2, 7–28 (1991).
Google Scholar 
McDiarmid, R. W. Reptile Biodiversity: Standard Methods for Inventory and Monitoring. (University of California Press, 2002).Riley, J. L., Baxter-gilbert, J. H. & Litzgus, J. D. A comparison of three external transmitter attachment methods for snakes. Wildl. Soc. Bull. 41(1), 132–139 (2017).Article 

Google Scholar 
Meine, C., & Archibald, G. The Cranes: Status Survey and Conservation Action Plan (IUCN, 1996).Mori, A. & Toda, M. Body temperature of subtropical snakes at night: How cold is their blood?. Curr. Herpetol. 37(2), 151–157 (2018).Article 

Google Scholar 
Crane, M., Silva, I., Marshall, B. M. & Strine, C. T. Lots of movement, little progress: A review of reptile home range literature. PeerJ 9, e11742 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7, 1124–1132 (2016).Article 

Google Scholar 
Fleming, C. H. & Calabrese, J. M. A new kernel density estimator for accurate home-range and species-range area estimation. Methods Ecol. Evol. 8, 571–579 (2017).Article 

Google Scholar 
Fleming, C. H. et al. From fine-scale foraging to home ranges: A semivariance approach to identifying movement modes across spatiotemporal scales. Am. Nat. 183, 154–167 (2014).Article 

Google Scholar 
Fleming, C. H., Noonan, M. J., Medici, E. P. & Calabrese, J. M. Overcoming the challenge of small effective sample sizes in home-range estimation. Methods Ecol. Evol. 10, 1679–1689 (2019).Article 

Google Scholar 
Uhlenbeck, G. E. & Ornstein, L. S. On the theory of the Brownian motion. Phys. Rev. 36, 823 (1930).CAS 
MATH 
Article 

Google Scholar 
Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
Bhattacharyya, A. On a measure of divergence between two statistical populations defined by their probability distributions. News Bull. Calcutta Math. Soc. 35, 99–109 (1943).MathSciNet 
MATH 

Google Scholar 
Winner, K. et al. Statistical inference for home range overlap. Methods Ecol. Evol. 9, 1679–1691 (2018).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2016). http://www.R-project.org. Accessed September 2022.Calenge, A. The package ‘“adehabitat”’ for the R software: Tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

Google Scholar 
Manley, B. F. J., McDonald, L. L. & Thomas, D. L. Resource Selection by Animals: Statistical Design and Analysis for Field Studies (Chapman and Hall, 1993).Book 

Google Scholar  More