Ecology

FennelThe main effect of fertilization (F) significantly impacted all measured parameters of fennel. Intercropping (I) pattern affected all parameters except plant height and 1000-seed weight. Significant I × F interactions occurred for umbel number, seed yield, essential oil content (EO), EO yield, oil content, and oil yield (Table 2).Table 2 Analysis of variance for the effect of cropping pattern and fertilization on evaluated traits in fennel.Full size tablePlant heightThe tallest fennel plants (125.8 cm) occurred in the sole cropping (Fs), while the shortest plants (98.6 cm) occurred in 1F:2FG. Across intercropping patterns, the Fs treatment had 28%, 14%, 11% taller fennel plants than 1F:2FG, 2F:2FG, and 2F:4FG, respectively (Fig. 1A). Compared to the unfertilized control, HA and BFS increased fennel plant height by 10% and 13%, respectively (Fig. 1B).Figure 1Means comparison for the main effects of cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on plant height (A), and fertilization [C (control), HA (humic acid), BFS (biofertilizers)] on plant height (B) and 1000-seed weight (C) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image1000-seed weightCompared to the unfertilized control (3.9 g per 1000 seeds), BFS and HA increased the 1000-seed weight of fennel by 24.1% and 14.5% (4.9 and 4.5 g per 1000 seeds), respectively (Fig. 1C).Umbel numberThe fennel sole cropping fertilized with HA produced the most umbels of fennel (51.5), while 2F:4FG without fertilization produced the least (32). Averaged across fertilizer types within each intercropping system, 1F:2FG, 2F:2FG, and 2F:4FG had 21.1%, 16.3%, and 26.7% fewer umbels than fennel sole cropping, respectively. Across intercropping systems, HA and BFS increased the umbel number by 17.8% and 16.5% compared with the unfertilized control, respectively (Fig. 2A).Figure 2Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on umbel number (A) and seed yield (B) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageSeed yieldThe different intercropping patterns had lower fennel seed yields than fennel sole cropping. Sole cropping fertilized with BFS and HA produced the highest fennel seed yields (2233 and 2209 kg ha–1, respectively), followed by unfertilized sole cropping (1960 kg ha–1). The lowest seed yields occurred in the unfertilized controls in 1F:2FG (933 kg ha–1) and 2F:4FG (1033 kg ha–1). Averaged across fertilization treatments, fennel seed yield in 1F:2FG, 2F:2FG, and 2F:4FG decreased by 41.7, 26.8, and 36.3%, respectively, compared to fennel sole cropping (Fs). Averaged across intercropping patterns, HA and BFS increased fennel seed yield by 33.3% and 39.5% compared with the unfertilized control, respectively (Fig. 2B).Essential oil content and yieldThe different intercropping patterns produced higher EO contents of fennel than fennel sole cropping. The highest absolute EO content of fennel (4.22%) occurred in 2F:2FG fertilized with BFS, although this did not statistically differ from the 2F:2FG fertilized with HA (4.04%) or 2F:4FG fertilized with HA or BFS (3.8% and 4.00%, respectively) (Fig. 3A). The lowest EO contents occurred in the unfertilized control (2.38%), HA (2.55%), and BFS (2.57%) in the Fs system. Averaged across fertilization treatments, the EO content of fennel in 1F:2FG, 2F:2FG, and 2F:4FG increased by 36%, 52%, and 44% compared to fennel sole cropping, respectively. Within each intercropping pattern, and with the exception of Fs, the HA and BFS treatments had higher EO contents of fennel, none of which significantly differed, increasing by 25% and 29%, respectively (Fig. 3A).Figure 3Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on essential oil content (A), essential oil yield (B), oil content (C), and oil yield (D) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageMaximum EO yields of fennel occurred with HA or BFS applied in 2F:2FG (65.2 and 66.6 kg ha–1) and 2F:4FG (60.7 and 65.5 kg ha–1), respectively, while the lowest EO yields occurred in the unfertilized control in 1F:2FG (27.2 kg ha–1), 2F:2FG (33.2 kg ha–1), and 2F:4FG (32.1 kg ha–1). Averaged across intercropping patterns, the EO yield of fennel increased by 66.1% and 74.7% with HA and BFS, respectively (Fig. 3B).Fennel essential oil compositionGC–FID and GC–MS analyses identified 14 components in the fennel EO (representing 97.4–99.9% of the total composition) (Table 3), with the main constituents being trans-anethole (78.3–84.85%), estragole (3.02–7.17%), fenchone (4.14–7.52%), and limonene (3.15–4.88%). The highest percentage of (E)-anethole, estragole, and fenchone occurred in 2F:2FG with BFS. The highest limonene content occurred in 2F:4 FG with HA. The relative contents of trans-anethole, fenchone, and limonene increased by 3.9%, 16.6%, and 8.4% compared with fennel sole cropping. Notably, the contents of most compounds increased with HA and BFS. Compared to the unfertilized control, trans-anethole, fenchone, and limonene contents increased by 2.9%, 21.5%, and 7.9% with BFS and 2.3%, 22.4%, and 11.9% with HA, respectively (Table 3).Table 3 Proportion of fennel essential oil constituents under different cropping patterns and fertilization.Full size tableFennel oil content and yieldAmong the studied treatments, the highest fennel oil content occurred with HA or BFS application in 1F:2FG (16.3% and 16.6%) and 2F:2FG (16.3% and 17.4%), respectively. The lowest fennel oil contents occurred in the unfertilized control, HA, and BFS treatments (12.5%, 12.8%, and 12.9%, respectively) under fennel sole cropping, and the unfertilized control in 2F:4FG (12.6%). Averaged across fertilizer treatments, fennel oil content in 1F:2FG, 2F:2FG, and 2F:4FG increased by 22.8%, 26.0%, and 12.6% compared with fennel sole cropping, respectively. Across intercropping patterns, HA and BFS increased fennel oil content by 13.5% and 16.5%, respectively (Fig. 3C).The maximum oil yield of fennel (318.6 kg ha–1) occurred in 2F:2FG fertilized with BFS, while the lowest oil yield (129.3 kg ha–1) occurred in 1F:2FG without fertilization. Across intercropping patterns, HA and BFS increased fennel oil yield by 50.8% and 62.6%, respectively (Fig. 3D).Oil compoundsGC–FID and GC–MS analyses identified nine constituents that represented 94.3–97.9% of the total fennel oil composition. The main oil constituents were oleic acid (39.2–48.3%), linoleic acid (17.1–24.8%), stearic acid (10.9–15.4%), lauric acid (10.1–14.00%), and arachidic acid (2.2–3.4%). The highest oleic and linoleic acid contents occurred in 2F:4FG and 2F:2FG fertilized with BFS, respectively. Across fertilizer treatments, oleic and linoleic acid contents increased by 6% and 21%, respectively, under different intercropping patterns compared with fennel sole cropping. Across systems, HA and BFS enhanced oleic acid content by 1.8% and 8% and linoleic acid by 7.9% and 8.2%, respectively, compared with the unfertilized control. The highest percentage of stearic and lauric acids occurred in the unfertilized control of fennel sole cropping. Conversely, the lowest stearic and lauric acid contents occurred in 2F:2FG and 2F:4FG fertilized with BFS, 16.1% and 14.2% higher than fennel sole cropping, respectively. Finally, HA and BFS decreased stearic acid content by an average of 5.4% and 7.2%, respectively (Table 4).Table 4 Proportion of fennel oil constituents under different cropping patterns and fertilization.Full size tablePhenolic compoundsThe main phenolic compounds of fennel were chlorogenic acid (10.4–15.3 ppm), quercetin (7.0–17.2 ppm), and cinnamic acid (4.1–8.9 ppm). The highest chlorogenic acid and quercetin contents occurred in 2F:2FG fertilized with BFS and HA, respectively, while the lowest contents occurred in the fennel sole cropping system without fertilizer. Averaged across the three intercropping patterns, the chlorogenic acid and quercetin contents were 18.5% and 80.1% higher than the fennel sole cropping system. The chlorogenic acid and quercetin contents increased by 13% and 17% with BFS and 22% and 15% with HA, respectively (Table 5).Table 5 Concentration of phenolic compounds in fennel under different cropping patterns and fertilization.Full size tableFenugreekThe main effects of intercropping (I) pattern (C) and fertilizer (F) were significant for all parameters analyzed in fenugreek. Significant I × F interactions occurred for plant height, pod number per plant, seed yield, oil content, and oil yield of fenugreek (Table 6).Table 6 Analysis of variance for the effects of cropping patterns and fertilization on evaluated traits in fenugreek.Full size tablePlant heightThe 2F:2FG intercropping system fertilized with BFS produced the tallest fenugreek plants (63 cm), followed by 1F:2FG with BFS (53.3 cm) and 2F:4FG with BFS (56 cm), and 2F:2FG with HA (55 cm). The unfertilized control produced the shortest fenugreek plants (42 cm) in the sole cropping. Most fertilizer treatments across different intercropping patterns produced taller fenugreek plants than their sole cropping counterparts. Across fertilizer treatments, 1F:2FG, 2F:2FG, and 2F:4FG produced 16.2%, 26.8%, and 14.6% taller fenugreek plants than sole cropping, respectively. Across cropping patterns, BFS and HA increased fenugreek plant height by 5.7% and 15.2% compared with the unfertilized control, respectively (Fig. 4A).Figure 4Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on plant height (A) and pod number per plant (B) of fenugreek. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imagePod number per plantThe fenugreek sole cropping with BFS and HA and 2F:4FG with BFS produced the most pods per plant (21.3, 20.3, and 20, respectively), while the unfertilized controls in 1F:2FG, 2F:2FG, and 2F:4FG produced the least (11.6, 12, and 13.3, respectively). Across fertilization treatments, 1F:2FG, 2F:2FG, and 2F:4FG had 30.1%, 25.6%, and 14.3% fewer pods per plant, respectively, than the fenugreek sole cropping system. Across cropping systems, HA and BFS increased pod number per plant in fenugreek by 25% and 33%, respectively, relative to the corresponding sole cropping (Fig. 4B).Seed number per podAcross fertilization treatments, fenugreek sole cropping produced the most seeds per pod (7.09), followed by 2F:4FG (6.02), 2F:2FG (4.93), and 1F:2FG (4.41) (Fig. 5A). In relative terms, sole cropping produced 60.5%, 43.9%, and 17.6% more seeds per pod than 1F:2FG, 2F:2FG, and 2F:4FG (Fig. 5A). Across cropping patterns, BFS and HA increased seed number per pod by 8.1% and 17.4% compared with the unfertilized control, respectively (Fig. 5B).Figure 5Means comparison for the main effects of cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on seed number per pod (A) and 1000-seed weight (C), and fertilization [C (control), HA (humic acid), BFS (biofertilizers)] on seed number per pod (B) and 1000-seed weight (D) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image1000-seed weightAmong different cropping patterns, sole cropping and 1F:2FG produced the highest (10.45 g) and lowest (8.34 g) fenugreek seed weights, respectively. In relative terms, fenugreek sole cropping produced 25.3%, 21.8%, and 12.4% higher seed weights than 1F:2FG, 2F:2FG, and 2F:4FG, respectively (Fig. 5C). Across cropping patterns, BFS and HA increased fenugreek seed weight by 3.7% and 5.7% compared with the control, respectively (Fig. 5D).Seed yieldMeans comparisons showed that sole cropping produced higher fenugreek seed yields than intercropping patterns. Sole cropping with BFS (1240 kg ha–1) and HA (1217 kg ha–1) produced the highest seed yields followed by the unfertilized control (Fig. 6A). The unfertilized control in 1F:2FG (437 kg ha–1) and 2F:2FG (467 kg ha–1) produced the lowest fenugreek seed yields. In all cases, and within each cropping pattern, BFS and HS produced higher fenugreek seed yields than the unfertilized control. As a result, BFS and HA increased fenugreek seed yield by 25.2% and 31.5% compared with the unfertilized control, respectively (Fig. 6A).Figure 6Means comparison for the interaction effects of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on seed yield (A), oil content (B), and oil yield (C) of fenugreek. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageOil content and yieldThe 2F:2FG cropping pattern with BFS produced the highest fenugreek oil content (8.3%), while the unfertilized control in sole cropping produced the lowest (5.9%). Across fertilizer treatments, 1F:2FG, 2F:2 FG, and 2F:4 FG produced 11.7%, 18.5%, and 15.7% higher fenugreek oil contents than sole cropping, respectively. In the 2F:2FG and 2F:4FG cropping patterns, BFS produced higher oil content (%) than HA. As a result, across cropping patterns, HA and BFS increased fenugreek oil content by 12.3% and 19.4%, respectively (Fig. 6B).Sole cropping with HA and BFS and 2F:2FG with BFS produced the highest fenugreek oil yields (77.1, 80.0, and 74.4 kg ha–1, respectively), while the unfertilized controls in 1F:2FG and 2F:4FG produced the lowest (27.51 and 29.8 kg ha–1, respectively). The 1F:2FG, 2F:2FG, and 2F:4FG cropping patterns produced 45.9%, 20.7%, and 41.5% lower fenugreek oil yields than fenugreek sole cropping, respectively. Moreover, except for sole cropping, BFS produced the highest fenugreek oil yield, followed by HA and the unfertilized control (Fig. 6C).Oil compoundsGC–FID and GC–MS analyses identified seven constituents (representing 91.09–99.27% of the total composition) in fenugreek oil. The main oil constituents were linoleic acid (26.1–37.1%), linolenic acid (16.9–22.4%), oleic acid (15.1–21.2%), palmitic acid (11.2–17.1%), lauric acid (5.0–12.3%), and myristic acid (3.1–6.4%). The highest linoleic and oleic acid percentages occurred in 1F:2FG and 2F:4FG with BFS. The 1F:2FG cropping pattern with BFS also had the highest linolenic acid percentage. The fenugreek sole cropping system without fertilization (control) had the lowest content of these three compounds. The intercropping patterns had 17%, 18.2%, and 17.1% higher oleic, linoleic, and linolenic acid contents than fenugreek sole cropping. In addition, HA and BFS increased oleic acid content by 15.6% and 8.8%, linoleic acid content by 12.8% and 7%, and linolenic acid content by 7.5% and 12.9%, respectively. Fenugreek sole cropping without fertilization produced the highest lauric acid and palmitic contents, 29.33% and 22.81% higher than the intercropping patterns (Table 7).Table 7 Proportion of fenugreek oil constituents under different cropping patterns and fertilization.Full size tablePhenolic compoundsThe main phenolic compounds in fenugreek were chlorogenic acid (2.01–5.49 ppm), caffeic acid (2.42–4.93 ppm), quercetin (1.98–4.45 ppm), comaric (1.09–2.43 ppm), apigenin (1.97–2.99 ppm), and gallic acid (1.76–2.92 ppm). The 2F:2FG cropping pattern with HA produced the highest quercetin and gallic acid contents, and 2F:4FG with HA produced the highest chlorogenic and caffeic acid contents. The 2F:2FG and 2F:4FG cropping patterns with BFS produced the highest comaric and apigenin contents, respectively. In contrast, fenugreek sole cropping without fertilization produced the lowest contents of the abovementioned compounds (Table 8).Table 8 Proportion of fenugreek concentration of phenolic compounds under different cropping patterns and fertilization.Full size tableLand equivalent ratio (LER)The 2F:4FG and 2F:2FG intercropping patterns treated with BFS had the highest partial LERs for fennel (0.82) and fenugreek (0.72), respectively. In addition, 2F:2FG with BFS and 1F:2FG without fertilization produced the highest (1.42) and lowest (0.86) total LERs, respectively (Fig. 7).Figure 7Partial and total land equivalent ratio (LER) for seed yields of different fennel and fenugreek intercropping patterns [1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] and fertilization [C (Control), HA (humic acid), BFS (biofertilizers)]. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image More

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

Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.CAS 
PubMed 

Google Scholar 
Anderson PK, Cunningham AA, Patel NG, Morales FJ, Epstein PR, Daszak P. Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol Evol. 2004;19:535–44.PubMed 

Google Scholar 
Taylor LH, Latham SM, Woolhouse MEJ. Risk factors for human disease emergence. Philos Trans R Soc B Biol Sci. 2001;356:983–9.CAS 

Google Scholar 
White R, Murray S, Rohweder M. Pilot analysis of global ecosystems: grassland ecosystems. 2000 World Resources Institute. Washington, DC.Zhao Y, Liu Z, Wu J. Grassland ecosystem services: a systematic review of research advances and future directions. Landsc Ecol. 2020;35:793–814.
Google Scholar 
Trubl G, Jang HBin, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems. 2018;3:e00076–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HBin, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zablocki O, Adriaenssens EM, Frossard A, Seely M, Ramond J-B, Cowan D. Metaviromes of extracellular soil viruses along a Namib desert aridity gradient. Genome Announc. 2017;5:e01470–16.PubMed 
PubMed Central 

Google Scholar 
Jin M, Guo X, Zhang R, Qu W, Gao B, Zeng R. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome. 2019;7:58.PubMed 
PubMed Central 

Google Scholar 
Adriaenssens EM, Kramer R, Van Goethem MW, Makhalanyane TP, Hogg I, Cowan DA. Environmental drivers of viral community composition in Antarctic soils identified by viromics. Microbiome. 2017;5:83.PubMed 
PubMed Central 

Google Scholar 
Williamson KE, Fuhrmann JJ, Wommack KE, Radosevich M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu Rev Virol. 2017;4:201–19.CAS 
PubMed 

Google Scholar 
Starr EP, Nuccio EE, Pett-Ridge J, Banfield JF, Firestone MK. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc Natl Acad Sci. 2019;116:25900–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu R, Davison MR, Gao Y, Nicora CD, Mcdermott JE, Burnum-Johnson KE, et al. Moisture modulates soil reservoirs of active DNA and RNA viruses. Commun Biol. 2021;4:1–11.
Google Scholar 
Hurwitz BL, Sullivan MB. The Pacific Ocean Virome (POV): a marine viral metagenomic dataset and associated protein clusters for quantitative viral ecology. PLoS One. 2013;8:e57355.CAS 
PubMed 
PubMed Central 

Google Scholar 
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
PubMed 

Google Scholar 
Wolf YI, Kazlauskas D, Iranzo J, Lucía-Sanz A, Kuhn JH, Krupovic M, et al. Origins and evolution of the Global RNA virome. MBio. 2018;9:e02329–18.PubMed 
PubMed Central 

Google Scholar 
Shi M, Lin XD, Tian JH, Chen LJ, Chen X, Li CX, et al. Redefining the invertebrate RNA virosphere. Nature. 2016;540:539–43.CAS 

Google Scholar 
Callanan J, Stockdale SR, Shkoporov A, Draper LA, Ross RP, Hill C. Expansion of known ssRNA phage genomes: from tens to over a thousand. Sci Adv. 2020;6:eaay5981.CAS 
PubMed 
PubMed Central 

Google Scholar 
Koonin EV, Dolja VV, Krupovic M, Varsani A, Wolf YI, Yutin N, et al. Global organization and proposed megataxonomy of the virus world. Microbiol Mol Biol Rev. 2020;84:e00061-19.PubMed 
PubMed Central 

Google Scholar 
Cobbin JC, Charon J, Harvey E, Holmes EC, Mahar JE. Current challenges to virus discovery by meta-transcriptomics. Curr Opin Virol. 2021;51:48–55.CAS 
PubMed 

Google Scholar 
Trubl G, Hyman P, Roux S, Abedon ST. Coming-of-age characterization of soil viruses: a user’s guide to virus isolation, detection within metagenomes, and viromics. Soil Syst. 2020;4:1–34. MDPI AG.
Google Scholar 
Santos-Medellin C, Zinke LA, ter Horst AM, Gelardi DL, Parikh SJ, Emerson JB. Viromes outperform total metagenomes in revealing the spatiotemporal patterns of agricultural soil viral communities. ISME J. 2021;15:1–15.
Google Scholar 
Adriaenssens EM, Farkas K, Harrison C, Jones DL, Allison HE, McCarthy AJ. Viromic analysis of wastewater input to a river catchment reveals a diverse assemblage of RNA viruses. mSystems. 2018;3:e00025–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bibby K, Peccia J. Identification of viral pathogen diversity in sewage sludge by metagenome analysis. Environ Sci Technol. 2013;47:1945–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
Culley A. New insight into the RNA aquatic virosphere via viromics. Virus Res. 2018;244:84–89.CAS 
PubMed 

Google Scholar 
Withers E, Hill PW, Chadwick DR, Jones DL. Use of untargeted metabolomics for assessing soil quality and microbial function. Soil Biol Biochem. 2020;143:107758.CAS 

Google Scholar 
Trubl G, Solonenko N, Chittick L, Solonenko SA, Rich VI, Sullivan MB. Optimization of viral resuspension methods for carbon-rich soils along a permafrost thaw gradient. PeerJ. 2016;4:e1999.PubMed 
PubMed Central 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 2011;17:10.
Google Scholar 
Joshi N, Fass J. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files. 2011.Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
PubMed 

Google Scholar 
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.CAS 
PubMed 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60. Nature Publishing Group.PubMed 

Google Scholar 
Huson DH, Beier S, Flade I, Górska A, El-Hadidi M, Mitra S. et al.MEGAN Community Edition – interactive exploration and analysis of large-scale microbiome sequencing data.PLOS Comput Biol. 2016;12:e1004957PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Google Scholar 
Mistry J, Finn RD, Eddy SR, Bateman A, Punta M. Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Res. 2013;41:e121–e121.CAS 
PubMed 
PubMed Central 

Google Scholar 
Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum information about an uncultivated virus genome (MIUViG). Nat Biotechnol. 2018;37:29–37.PubMed 
PubMed Central 

Google Scholar 
Germain P-L, Vitriolo A, Adamo A, Laise P, Das V, Testa G. RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods. Nucleic Acids Res. 2016;44:5054–67.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2019.Wickham H. ggplot2: elegant graphics for data analysis. 2016. Springer-Verlag New York.Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33:2938–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
PubMed 
PubMed Central 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.PubMed 
PubMed Central 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–W259.CAS 
PubMed 
PubMed Central 

Google Scholar 
Roux S, Emerson JB, Eloe-Fadrosh EA, Sullivan MB. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ. 2017;5:e3817.PubMed 
PubMed Central 

Google Scholar 
Ayllón MA, Turina M, Xie J, Nerva L, Marzano SYL, Donaire L, et al. ICTV virus taxonomy profile: botourmiaviridae. J Gen Virol. 2020;101:454–5.PubMed 
PubMed Central 

Google Scholar 
Krishnamurthy SR, Janowski AB, Zhao G, Barouch D, Wang D. Hyperexpansion of RNA bacteriophage diversity. PLOS Biol. 2016;14:e1002409.PubMed 
PubMed Central 

Google Scholar 
Hillman BI, Cai G. The family Narnaviridae. Simplest of RNA viruses. Adv Virus Res. 2013;86:149–76.
Google Scholar 
Obbard DJ, Shi M, Roberts KE, Longdon B, Dennis AB. A new lineage of segmented RNA viruses infecting animals. Virus Evol. 2020;6:61.
Google Scholar 
Xu X, Bei J, Xuan Y, Chen J, Chen D, Barker SC, et al. Full-length genome sequence of segmented RNA virus from ticks was obtained using small RNA sequencing data. BMC Genom. 2020;21:1–8.
Google Scholar 
Roossinck MJ. The good viruses: viral mutualistic symbioses. Nat Rev Microbiol. 2011;9:99–108. Nature Publishing Group.CAS 
PubMed 

Google Scholar 
Milgroom MG, Cortesi P. Biological control of chestnut blight with hypovirulence: a critical analysis. Annu Rev Phytopathol. 2004;42:311–38. Annual ReviewsCAS 
PubMed 

Google Scholar 
Zell R, Delwart E, Gorbalenya AE, Hovi T, King AMQ, Knowles NJ, et al. ICTV virus taxonomy profile: Picornaviridae. J Gen Virol. 2017;98:2421–2.CAS 
PubMed 
PubMed Central 

Google Scholar 
Valles SM, Chen Y, Firth AE, Guérin DMA, Hashimoto Y, Herrero S, et al. ICTV virus taxonomy profile: Dicistroviridae. J Gen Virol. 2017;98:355–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Barrios E. Soil biota, ecosystem services and land productivity. Ecol Econ. 2007;64:269–85.
Google Scholar 
Vainio EJ, Chiba S, Ghabrial SA, Maiss E, Roossinck M, Sabanadzovic S, et al. ICTV virus taxonomy profile: Partitiviridae. J Gen Virol. 2018;99:17–18.CAS 
PubMed 

Google Scholar 
Yong CY, Yeap SK, Omar AR, Tan WS. Advances in the study of nodavirus. PeerJ. 2017;2017:e3841.
Google Scholar 
Schmitt AP, Lamb RA. Escaping from the cell: assembly and budding of negative-strand RNA viruses. In: Kawaoka Y (ed). Biology of negative-strand RNA viruses: the power of reverse genetics. 2004. (Springer Berlin Heidelberg, Berlin, Heidelberg, pp 145–96.Käfer S, Paraskevopoulou S, Zirkel F, Wieseke N, Donath A, Petersen M, et al. Re-assessing the diversity of negative-strand RNA viruses in insects. PLoS Pathog. 2019;15:e1008224.PubMed 
PubMed Central 

Google Scholar 
Bejerman N, Debat H, Dietzgen, RG. The plant negative-sense RNA virosphere: virus discovery through new eyes. Front. Microbiol. 2020;11:588427.PubMed 
PubMed Central 

Google Scholar 
Wolf YI, Silas S, Wang Y, Wu S, Bocek M, Kazlauskas D, et al. Doubling of the known set of RNA viruses by metagenomic analysis of an aquatic virome. Nat Microbiol. 2020;5:1262–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
Adriaenssens EM, Kramer R, van Goethem MW, Makhalanyane TP, Hogg I, Cowan DA. Environmental drivers of viral community composition in Antarctic soils identified by viromics. Microbiome. 2017;5:1–14.
Google Scholar 
Mahmoud H, Jose L. Phage and nucleocytoplasmic large viral sequences dominate coral viromes from the Arabian Gulf. Front Microbiol. 2017;8:2063.PubMed 
PubMed Central 

Google Scholar 
Koyama A, Steinweg JM, Haddix ML, Dukes JS, Wallenstein MD. Soil bacterial community responses to altered precipitation and temperature regimes in an old field grassland are mediated by plants. FEMS Microbiol Ecol. 2018;94:fix156.
Google Scholar 
Hurwitz BL, Hallam SJ, Sullivan MB. Metabolic reprogramming by viruses in the sunlit and dark ocean. Genome Biol. 2013;14:R123.PubMed 
PubMed Central 

Google Scholar  More

Seymour, C. L. et al. Caught on camera: The impacts of urban domestic cats on wild prey in an African city and neighbouring protected areas. Glob. Ecol. Conserv. 23, e01198 (2020).Article 

Google Scholar 
Mori, E. et al. License to Kill? Domestic Cats Affect a Wide Range of Native Fauna in a Highly Biodiverse Mediterranean Country. Front. Ecol. Evol. 7, 477 (2019).Kays, R. et al. The small home ranges and large local ecological impacts of pet cats. Anim. Conserv. 23, 516–523 (2020).Loss, S. R., Will, T. & Marra, P. P. The impact of free-ranging domestic cats on wildlife of the United States. Nat. Commun. 4, 1396 (2013).ADS 
Article 

Google Scholar 
Van Heezik, Y., Smyth, A., Adams, A. & Gordon, J. Do domestic cats impose an unsustain386 able harvest on urban bird populations?. Biol. Conserv. 143, 121–130 (2010).Article 

Google Scholar 
Woods, M., McDonald, R. A. & Harris, S. Predation of wildlife by domestic cats Felis catus in Great Britain. Mammal Rev. 33, 174–188 (2003).Article 

Google Scholar 
Li, Y. et al. Estimates of wildlife killed by free-ranging cats in China. Biol. Conserv. 253, 108929 (2021).Article 

Google Scholar 
Barratt, D. G. Home range size, habitat utilisation and movement patterns of suburban and farm cats Felis catus. Ecography 20, 271–280 (1997).Article 

Google Scholar 
Moseby, K. E., Stott, J. & Crisp, H. Movement patterns of feral predators in an arid environment–implications for control through poison baiting. English. Wildl. Res. 36, 422–435 (2009).Article 

Google Scholar 
Hall, C. M. et al. Factors determining the home ranges of pet cats: A meta-analysis. Biol. Conserv. 203, 313–320 (2016).Article 

Google Scholar 
Castañeda, I. et al. Trophic patterns and home-range size of two generalist urban carnivores: A review. J. Zool. 307, 79–92 (2019).Article 

Google Scholar 
Hebblewhite, M. & Haydon, D. T. Distinguishing technology from biology: A critical review of the use of GPS telemetry data in ecology. Philos. Trans. R. Soc. B Biol. Sci. 365, 2303–2312 (2010).Article 

Google Scholar 
Allen, A. M. et al. Scaling up movements: From individual space use to population patterns. Ecosphere 7, e01524 (2016).
Google Scholar 
Trouwborst, A., McCormack, P. C. & Martínez Camacho, E. Domestic cats and their impacts on biodiversity: A blind spot in the application of nature conservation law. People Nat. 2, 235–250 (2020).Article 

Google Scholar 
Sims, V., Evans, K. L., Newson, S. E., Tratalos, J. A. & Gaston, K. J. Avian assemblage structure and domestic cat densities in urban environments. Divers. Distrib. 14, 387–399 (2008).Article 

Google Scholar 
Lepczyk, C. A., Mertig, A. G. & Liu, J. Landowners and cat predation across rural-to-urban landscapes. Biol. Conserv. 115, 191–201 (2004).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing (Vienna, Austria, 2021).Heggøy, O. & Shimmings, P. Huskattens predasjon på fugler i Norge. En vurdering basert på en litteraturgjennomgang tech. rep. 36 (2018).Morgan, S. et al. Urban cat (Felis catus) movement and predation activity associated with a wetland reserve in New Zealand. Wildl. Res. 36, 574–580 (2009).Calver, M., Grayson, J., Lilith, M. & Dickman, C. Applying the precautionary principle to the issue of impacts by pet cats on urban wildlife. Biol. Conserv. 144, 1895–1901 (2011).Article 

Google Scholar 
Crowley, S., Cecchetti, M. & Mcdonald, R. Diverse perspectives of cat owners indicate bar riers to and opportunities for managing cat predation of wildlife. Front. Ecol. Environ. 18, 544–549 (2020).Treves, A., Krofel, M., Ohrens, O. & van Eeden, L. M. Predator control needs a standard of unbiased randomized experiments with cross-over design. Front. Ecol. Evol. 7, 462 (2019).Ferreira, G. A., Machado, J. C., Nakano-Oliveira, E., Andriolo, A. & Genaro, G. The effect of castration on home range size and activity patterns of domestic cats living in a natural area in a protected area on a Brazilian island. Appl. Anim. Behav. Sci. 230, 105049 (2020).Bengsen, A. J. et al. Feral cat home-range size varies predictably with landscape productivity and population density. J. Zool. 298, 112–120 (2016).Article 

Google Scholar 
López-Jara, M. J. et al. Free-roaming domestic cats near conservation areas in Chile: Spatial movements, human care and risks for wildlife. Perspect. Ecol. Conserv. 19, 387–398 (2021).Gillies, C. & Clout, M. The prey of domestic cats (Felis catus) in two suburbs of Auckland City, New Zealand. J. Zool. 259, 309–315 (2003).Article 

Google Scholar 
Pirie, T. J., Thomas, R. L. & Fellowes, M. D. E. Pet cats (Felis catus) from urban boundaries use different habitats, have larger home ranges and kill more prey than cats from the suburbs. Landsc. Urban Plan. 220, 104338 (2022).Article 

Google Scholar 
Vucetich, J. A., Hebblewhite, M., Smith, D. W. & Peterson, R. O. Predicting prey population dynamics from kill rate, predation rate and predator-prey ratios in three wolf-ungulate systems. J. Anim. Ecol. 80, 1236–1245 (2011).Article 

Google Scholar 
Kennedy, M., Phillips, B. E. N. L., Legge, S., Murphy, S. A. & Faulkner, R. A. Do dingoes suppress the activity of feral cats in northern Australia?. Austral Ecol. 37, 134–139 (2012).Article 

Google Scholar 
Crooks, K. R. & Soule, M. E. Mesopredator release and avifaunal extinctions in a fragmented system. English. Nature 400, 563–566 (1999).ADS 
CAS 
Article 

Google Scholar 
Ferreira, J. P., Leita, O. I., Santos-Reis, M. & Revilla, E. Human-related factors regulate the spatial ecology of domestic cats in sensitive areas for conservation. PLOS ONE 6, e25970 (2011).ADS 
CAS 
Article 

Google Scholar 
Brook, L. A., Johnson, C. N. & Ritchie, E. G. Effects of predator control on behaviour of an apex predator and indirect consequences for mesopredator suppression. J. Appl. Ecol. 49, 1278–1286 (2012).Article 

Google Scholar 
Laundre, J. W., Hernandez, L. & Altendorf, K. B. Wolves, elk, and bison: Reestablishing the “landscape of fear’’ in Yellowstone National Park, USA. English. Can. J. Zool. 79, 1401–1409 (2001).Article 

Google Scholar 
Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. English. Ecol. Lett. 12, 9820–998 (2009).Article 

Google Scholar 
Milleret, C. et al. GPS collars have an apparent positive effect on the survival of a large carnivore. Biol. Lett. 17, 20210128 (2021).Cecchetti, M., Crowley, S. L., Goodwin, C. E. D. & McDonald, R. A. Provision of high meat content food and object play reduce predation of wild animals by domestic cats Felis catus. Curr. Biol. 31, 1107-1111.e5 (2021).CAS 
Article 

Google Scholar 
Linklater, W., Farnworth, M., van Heezik, Y., Stafford, K. & Macdonald, E. Prioritizing cat owner behaviors for a campaign to reduce wildlife depredation. Conserv. Sci. Pract. 1, 1:e29 (2019).Selinske, M. J. et al. Identifying and prioritizing human behaviors that benefit biodiversity. Conserv. Sci. Pract. 2, e249 (2020).
Google Scholar 
McDonald, J. L., Maclean, M., Evans, M. R. & Hodgson, D. J. Reconciling actual and perceived rates of predation by domestic cats. Ecol. Evol. 5, 2745–2753 (2015).Article 

Google Scholar 
Bischof, R. et al. Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring. Proc. Natl. Acad. Sci. 117, 30531–30538 (2020).CAS 
Article 

Google Scholar 
Bischof, R., Gjevestad, J. G. O., Ordiz, A., Eldegard, K. & Milleret, C. High frequency GPS bursts and path-level analysis reveal linear feature tracking by red foxes. Sci. Rep. 9, 8849 (2019).ADS 
Article 

Google Scholar 
Gupte, P. R. et al. A guide to pre-processing high-throughput animal tracking data. J. Anim. Ecol. 91, 287–307 (2022).Article 

Google Scholar 
Morris, G. & Conner, L. Assessment of accuracy, fix success rate, and use of estimated horizontal position error (EHPE) to filter inaccurate data collected by a common commercially available GPS logger. PLoS ONE 12, e0189020 (2017).Article 

Google Scholar 
Clapp, J. G., Holbrook, J. D. & Thompson, D. J. GPSeqClus: An R package for sequential clustering of animal location data for model building, model application and field site investigations. Methods Ecol. Evol. 12, 787–793 (2021).Article 

Google Scholar 
Nielson, M., R., Sawyer, H. & McDonald, T. L. BBMM: Brownian Bridge Movement Model R Package Version 3.0 (2013).Horne, J. S., Garton, E. O., Krone, S. M. & Lewis, J. S. Analyzing animal movements using Brownian bridges. Ecology 88, 2354–2363 (2007).Article 

Google Scholar 
Sawyer, H., Kauffman, M. J., Nielson, R. M. & Horne, J. S. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecol. Appl. 19, 2016–2025 (2009).Article 

Google Scholar 
Fischer, J. W., Walter, W. D. & Avery, M. L. Brownian bridge movement models to characterize birds’ home ranges. Condor 115, 298–305 (2013).Article 

Google Scholar 
Seidler, R., Long, R., Berger, J., Bergen, S. & Beckmann, J. Identifying impediments to long-distance mammal migrations. Conserv. Biol. 29 (2014).Collins, G. Seasonal distribution and routes of pronghorn in the Northern Great Basin. West. N. Am. Nat. 76, 101–112 (2016).Article 

Google Scholar  More

Phylogenetic analysis with multiple markersThe final alignment of 5670 bp length contained 843 variable sites (14.7%). Missing data accounted for 53.5% of the alignment cells and the relative GC content was 39.5%. Our phylogeny suggests that the investigated Holarctic taxa of the niger clade sensu Ref.34 are divided into two major clades with strong statistical support (Fig. 1). The first major clade (L. niger group) consists exclusively of Palearctic species (L. niger, L. platythorax, L. japonicus, L. emarginatus, L. balearicus, L. grandis, L. cinereus, the L. alienus-complex, L. sakagamii, L. productus and L. hayashi), with the exception of an unnamed Nearctic subclade recovered as sister to the rest of the group. This unnamed subclade we describe as a new species below (L. ponderosae sp. nov.). Lasius ponderosae sp. nov. corresponds to what was previously known as the Nearctic form of “L. niger” sensu ref.17, but includes some western Nearctic populations formerly assigned to “L. alienus”17,52 as well. Monophyly of L. ponderosae sp. nov. was fully supported by Bayesian inference (pp = 1) and moderately supported by maximum likelihood (66% bootstrap support, Fig. 1). Lasius ponderosae sp. nov. is distantly related to L. niger; and L.niger is a close relative of L. japonicus and L. platythorax, as well as other Palearctic taxa. The second major clade (L. brunneus group) within the investigated Holarctic members of the L. niger clade contains both Nearctic and Palearctic species not closely related to the taxa of interest (Fig. 1).Figure 1Molecular phylogeny of 26 Holarctic ant taxa belonging to the subgenus Lasius sensu Wilson (1955) and two outgroup taxa (L. pallitarsis and L. mixtus). The phylogeny was calculated under the coalescent model and incorporates data from 9 genes (mtDNA: COI, COII, 16S, nuDNA: Defensin, H3, LR, Wg, Top1 & 28S). Names of species native to the Nearctic are shown in red and those of species native to the Palearctic in blue. Node labels show posterior probability (Bayesian inference) followed by bootstrap support (Maximum likelihood). The scale bar indicates the length of 0.01 substitutions/site.Full size imageDNA-barcodingThe native North American species L. ponderosae sp. nov. contains at least 15 COI-mitotypes (n = 28 sequenced specimens) belonging to four distinct deep lineages, with divergences of up to 5.9%. Haplotype diversity was 0.899 and nucleotide diversity was 0.012. None of the mitotypes of this species was found to be widespread or particularly abundant. In striking contrast, low genetic diversity was found in L. niger across its entire distribution (Fig. 2). No more than 7 different COI-mitotypes were detected in samples from distant localities representing most of the known range (n = 70 specimens from 12 countries), from Spain in the West to the Siberian Baikal-region in the East (Fig. 2). Their maximum pairwise divergence was only 0.6%, with a haplotype diversity of 0.682 and a nucleotide diversity below 0.001. One mitotype of L. niger is highly dominant within the native range, occurring from Western Europe to Central Siberia (mitotype h2 in Fig. 2).Figure 2Mitotype tree and distribution maps for 98 DNA-barcodes belonging to 7 mitotypes of the ant Lasius niger (blue, n = 70) and 15 mitotypes of L. ponderosae sp. nov. (red, n = 28). The red dashed line delimits the expected natural range of L. ponderosae sp. nov.53 Maps have been created using the free R-package “ggmap” v3.0.0 (https://github.com/dkahle/ggmap) in R v4.1.1. Map tiles by Stamen Design, under CC BY 3.0.Full size imageRecent Palearctic L. niger introduction to CanadaPalearctic Lasius niger was introduced to several localities in coastal Canada in recent times, where at least 11 populations were found in two metropolitan areas (Vancouver and Halifax areas, see Table S2 for details). Those populations consist of the most dominant Palearctic mitotype of L. niger (h2). However, in 3 localities in the Vancouver area, 3 specimens with a second mitotype were found (mitotype h4, Fig. 2, Table S2) in syntopy with those carrying the most common mitotype h2. This second Canadian COI-mitotype (h4) was not found among our samples from the Old World, although it only differs by a single nucleotide substitution from mitotypes found there. A review of BOLD data revealed that the Canadian barcoded specimens of L. niger were mostly collected in anthropogenic habitats such as schoolyards (Supplementary Table S2).Description of Lasius ponderosae sp. novLasius ponderosae Schär, Talavera, Rana, Espadaler, Cover, Shattuck and Vila. ZooBank LSID: urn:lsid:zoobank.org:act:22E2743A-2F1C-4870-B318-A1F2DF2B464C Etymology: ponderosae alludes to the ponderosa pine tree (Pinus ponderosa) that is at the centre of occurrence in the ponderosa pine—gambel oak communities in the western Rocky Mountains and northern Arizona.Type material: located at the Museum of Comparative Zoology, Cambridge, USA. Two paratype workers each will be deposited at the collections of University of California Davis (UCDC), the University of Utah (JTLC) and the Natural History Museum of Los Angeles County (LACM).Holotype: worker, Fig. 3a–c. Type locality: USA, Utah: Uintah Co., Uintah Mtns., 2408 m. 18.6 mi N. Jct. Rt. 40 on Rt. 191, 40.66378°N, − 109.47918°E, leg. 15.VII.2013, S. P. Cover; J. D. Rana, collection code SPC 8571. Measurements [mm]: HL: 0.899, HW: 0.823, SL: 0.821, EL: 0.239, EW: 0.189, ProW: 0.56, ML: 1.069, HTL: 0.863, CI: 92, SI: 100.Figure 3Frontal, lateral and dorsal view of the holotype worker (a–c), a paratype gyne (d–f) and a paratype male of Lasius ponderosae sp. nov. (g–i).Full size imageParatypes: 15 workers, two gynes (Fig. 3d–f), two males (Fig. 3g–i) from the same series as the holotype, morphometric data is given in the Appendix, Table S5 and Table S6. CO1 mitotype h17: Genbank Accession no. LT977508.Description of the worker caste: A member of a complex of cryptic species resembling L. niger. Intermediate in overall body size, antennal scape length and eye size and comparable to related species (Table 1). Terminal segment of maxillary palps and torulo-clypeal distance relative to head size shorter than in related Palearctic species (Table 1). Mandibles with 8 or rarely 7 or 9 regular denticles and lacking offset teeth at their basal angle. Penultimate and terminal basal mandibular teeth of subequal size, and the gap in between with subequal area than the basal tooth. Anterior margin of clypeus evenly rounded. Dorsofrontal profile of pronotum slightly angular (Fig. 4a). Propodeal dome short and flat, usually lower than mesonotum (Fig. 4a). Body with abundant and long pilosity, especially lateral propodeum, genae, hind margin and underside of head. Pilosity of tibiae and antennal scapes variable, ranging from almost no setae (“L. alienus”-like phenotype) to very hairy (“L. niger”-like phenotype). Microscopic pubescent hairs on forehead between frontal carinae long and fine. Clypeus typically with only few scattered pubescent hairs (Figs. 3, 4c). Coloration of body dark brown, occasionally yellowish- or reddish-brown or slightly bicolored with head and thorax lighter than abdomen. Femora and antennal scapes brown. Mandibles and distal parts of legs yellowish to dark brown. Specimens of all 3 castes are shown in Fig. 3a–i and morphometric data are summarized in Table 1 and raw measurements are available in Table S5 and S6.Table 1 Morphometric data of Lasius ponderosae sp. nov. and comparison to morphologically similar Palearctic species.Full size tableFigure 4Average thorax profile of Lasius ponderosae sp. nov. (a) and members of the Palearctic L. niger-complex (b). Figures were created by image averaging (L. ponderosae sp. nov n = 35; Palearctic L. niger-complex n = 30 specimens). Frontal view of head and detail of clypeus of the Holotype worker of L. ponderosae sp. nov. (c) and a non-type worker of L. niger (d).Full size imageDiagnosis: Lasius ponderosae sp. nov. workers key out to “L. niger” using Wilson’s 1955 key to the Nearctic Lasius species. However, some populations with reduced pilosity may also be identified as “L. alienus” using this key. Lasius alienus is a Eurasian species not known from North America33. The Nearctic “L. alienus” sensu Wilson (1955) includes both, L. americanus as well as populations of L. ponderosae sp. nov. with sparse setae counts on tibia and/or scapes. Lasius ponderosae sp. nov. can be distinguished from L. americanus by the presence of abundant, long setae surpassing the sides of the head in full face view (nGen  > 5 and nOcc  > 10 vs. nGen  0.8 across models and runs). The strongest predictors were: Annual Mean Temperature (mean variable importance = 0.32), Mean Temperature of Coldest Quarter (0.23), Temperature Annual Range (0.23) and Temperature Seasonality (0.24). The contribution of land cover was low (0.02). The model predicted high probabilities of occurrence of L. niger in the eastern United States and southeastern Canada, including the island of Newfoundland, and small areas of suitable habitat in southwestern Canada and the Aleutians (Fig. 6). The area with high predicted occurrence probability of L. niger in the New World includes the two sites where populations have actually established (which were not used in the modeling): Nova Scotia and Vancouver. Further areas with high occurrence probabilities are New England, Southern Ontario, the Great Lakes-region and the Northern Appalachians. Low occurrence probabilities were found for the central North American prairies as well as arctic, boreal, arid, subtropical and tropical regions (Fig. 6). Considering the highest occurrence probability range (0.8–1 on a 0–1 probability scale), the area of suitable habitats for L. niger is 4,547,537 km2 in Europe and 1,308,920 km2 in North America. For an intermediate to high occurrence probability range (0.5–1) we estimated 5,371,055 km2 in Europe and 3,054,283 km2 in North America, and for the widest probability range (0.2–1) we estimated 6,155,643 km2 of suitable areas in Europe and 6,889,745 km2 in North America (Fig. 6).Figure 6Projected occurrence probability from ecological niche modeling for the Palearctic ant Lasius niger which has been introduced to Canada, based on 19 climatic and one land use variable. The intensity of blue colour indicates the probability of occurrence on a 0–1 scale based on 180 presences (black circles) and 182 absences (white circles) in the native range in the Old World (a). The model was then projected to North America to estimate areas of suitable habitat for this introduced species (b). These maps have been created using the free R-package “ggplot2” v3.3.5 (https://ggplot2.tidyverse.org) in R v4.1.1.Full size image 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

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

ColoniesColony setup occurred prior to initiation of the study, between March and May 2017, in Mississippi, USA. Using established methods, queenless colony divisions, obtained from a large commercial beekeeping operation, were equalised to an average calculated population size of ~ 7000 workers112, and housed in 10-frame Langstroth hives (Table S1). After acclimatisation for 24–48 h, they each received an imminently emerging queen cell, containing a queen from one of two stocks, added to the same worker baseline. The stocks used consisted of an Italian ‘Commercial’ stock, propagated from collaborator established breeder queens, and thus representative of the industry standard, and the Varroa-resistant ‘Pol-line’ stock54. To ensure consistency, all queens were reared in the same ‘cell builder’ colonies, based at the USDA Honey Bee Breeding, Genetics and Physiology Laboratory, in Baton Rouge, Louisiana, USA. Colonies from each stock were held in independent apiaries, 80 km apart to maintain physical isolation; and to control genetic fidelity, virgin queens were open mated to drones of the same stock via drone saturation. Fourteen days after queen emergence, colonies were inspected, and mated queens were marked with paint on the thorax, to assist with identification, with white corresponding to Commercial, and blue to Pol-line. Colonies were allowed to acclimatise for six weeks before sampling began, and those that failed to achieve mating success, or had unacceptably high [≥ 3.0 ‘mites per hundred bees’ (MPHB)] Varroa levels, were removed, normalising the average between-stock Varroa difference to  More