Philippa Kaur

Lawrence, H. In City Trees: A Historical Geography from the Renaissance through to the Nineteenth Century (Charlottesville and London: University of Virginia Press, 2006, Lewis Mumford. The City in History: Its Origins, Its Transformations and Its Prospects (San Diego: Harvest Book Harcourt, 1961).Frawley, J. Campaigning for street trees, Sydney botanic gardens, 1890s–1920s. Environ. Hist. 15(3), 303–322. https://doi.org/10.3197/096734009X12474738199953 (2009).Article 

Google Scholar 
Seburanga, J. L., Kaplin, B. A., Zhang, Q.-X. & Gatesire, T. Amenity trees and green space structure in urban settlements of Kigali, Rwanda. Urban. For. Urban Green. 13(84–9313), 84–93. https://doi.org/10.1016/j.ufug.2013.08.001 (2014).Article 

Google Scholar 
Wilson, E. H. Northern trees in southern lands. J. Arnold Arbor. 4(2), 61–90 (1923).Article 

Google Scholar 
Gwedla, N. & Shackleton, C. M. Population size and development history determine street tree distribution and composition within and between Eastern Cape towns, South Africa. Urban. For. Urban. Gree. 25, 11–18. https://doi.org/10.1016/j.ufug.2017.04.014 (2017).Article 

Google Scholar 
Jacobs, A. B., Macdonald, E. & Rofé, Y. In The Boulevard Book: History, Evolution, Design of Multiway Boulevards (MIT Press, Cambridge, MA 2002), Robinson, W. The Parks and Gardens of Paris Considered in Relation to the Wants of Other Cities and of Private and Public Gardens (McMillan and Co., London , 1878).Akbari, A. H., Pomerantz, M. & Taha, H. Cool surfaces and shade trees to reduce energy use and improve air quality in urban. Sol. Energy. 70(3), 295–310 (2001).ADS 
Article 

Google Scholar 
Roy, S., Byrne, J. & Pickering, C. A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban For. Urban Green. 11, 351–363. https://doi.org/10.1016/j.ufug.2012.06.006 (2012).Article 

Google Scholar 
Schäffler, A. & Swilling, M. Valuing green infrastructure in an urban environment under pressure—The Johannesburg case. Ecol. Econ. 86, 246–257. https://doi.org/10.1016/j.ecolecon.2012.05.008 (2013).Article 

Google Scholar 
Santamour, F. S. Trees for urban planting: Diversity, uniformity and common sense. In Proceedings of the 7th Conference of the Metropolitan Tree Improvement Alliance (METRIA), vol. 7, 57–65 (1990).Shams, Z. I. Changes in diversity and composition of flora along a corridor of different land uses in Karachi over 20 years: caUses and implications. Urban. For. Urban Green. 17, 71–79. https://doi.org/10.1016/j.ufug.2016.03.002 (2016).Article 

Google Scholar 
Kambites, C. & Owen, S. Renewed prospects for green infrastructure planning in the UK. Plan. Prac. Res. 21(94), 483–496. https://doi.org/10.1080/02697450601173413 (2006).Article 

Google Scholar 
Cho, M. A., Malahlelac, O. & Ramoeloa, A. Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study. Int. J. Appl. Earth. Obs. 38, 349–357. https://doi.org/10.1016/j.jag.2015.01.015 (2015).Article 

Google Scholar 
Niculescu, S., Lardeux, C., Grigoras, I., Hanganu, J. & David, L. Synergy between LiDAR, RADARSAT-2, and spot-5 images for the detection and mapping of wetland vegetation in the Danube Delta. IEEE J Sel. Top. Appl. Earth. Obs. Remote Sens. 9, 3651–3666 (2016).ADS 
Article 

Google Scholar 
Lefebvre, A., Picand, P.-A. & Sannier, C. Mapping tree cover in European cities: Comparison of classification algorithms for an operational production framework. In 2015 Joint Urban Remote Sensing Event (JURSE), IEEE, 1–4 (2015) https://doi.org/10.1109/JURSE.2015.7120511.Wyndham, C. H., Strydom, N. B., Van Rensburg, A. J. & Rogers, G. G. Effects on maximal oxygen intake of acute changes in altitude in a deep mine. J. Appl. Physiol. 29(5), 552–555 (1970).CAS 
Article 

Google Scholar 
Hegnauer, R. Chemotaxonomie der Pflanzen, vol. 3, 268–281 (Birkhäuser Verlag, Basel, 1964).Mabberley, D. J. The Plant-Book, 2nd edn. 87, 368–369 (Cambridge University Press, Cambridge, 1997).Gachet, M. S. & Schühly, W. Jacaranda—An ethnopharmacological and phytochemical review. J. Ethnopharmacol. 121, 14–27. https://doi.org/10.1016/j.jep.2008.10.015 (2009).CAS 
Article 
PubMed 

Google Scholar 
Gilman, E. F. & Watson, D. G. Jacaranda mimosifolia. Fact Sheet ST-317, Environmental Horticulture Department, Florida Cooperative Extension Service, University of Florida, Gainesville, http://www.ci.milpitas.ca.gov/_pdfs/council/2016/021616/item_04.pdf Accessed 6 June 2020 (1993).Dineva, S. B. Comparative studies of the leaf morphology and structure of white ash Fraxinus americana L. and London plane tree Platanus acerifolia Willd growing in polluted area. Dendrobiology 52, 3–8 (2004).
Google Scholar 
Liu, G., Li, Z. & Bao, M. Colchicine-induced chromosome doubling in Platanus acerifolia and its effect on plant morphology. Euphytica 157, 145–154. https://doi.org/10.1007/s10681-007-9406-6 (2007).Article 

Google Scholar 
Henry, A. & Flood, M. G. The history of the London plane, Platanus acerifolia, with notes on the Genus Platanus. Proc. R. Irish Acad Sect. B Biol. Geol. Chem. Sci. 35, 9–28 (1919).
Google Scholar 
Chavez, P. S. Image-based atmospheric corrections revisited and improved. Photogram. Eng. Rem. S. 62, 1025–1036 (1996).
Google Scholar 
Riano, D., Chuvieco, E., Salas, J. & Aguado, I. Assessment of different topographic corrections in Landsat-T. M. data for mapping vegetation types. IEEE Trans. Geosci. Remote Sens. 41, 1056–1061. https://doi.org/10.1109/TGRS.2003.811693 (2003).ADS 
Article 

Google Scholar 
Rouse J. W., Haas, R. H., Schell, J. A. & Deering, D. W. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, USA: NASASP-351; 1974. Monitoring vegetation system in the great plains with ERTS, 3010–3017 (1974).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2021).Du, Y. et al. New hyperspectral discrimination measure for spectral characterization. Opt. Eng. 43(8), 1777–1786 (2004).ADS 
Article 

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

Google Scholar 
Bruzzone, L., Roli, F. & Serpico, S. B. An extension to multiclass cases of the Jefferys-Matusita distance. IEEE Trans. Pattern. Anal. Mach. Intell. 33, 1318–1321 (1995).
Google Scholar 
Kaufman, Y. & Remer, L. Detection of forests using mid-IR reflectance: An application for aerosol studies. IEEE Trans. Geosci. Remote Sens. 32(3), 672–683 (1994).ADS 
Article 

Google Scholar 
Padma, S. & Sanjeevi, S. Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis. Int. J. Appl. Earth. Obs. 32, 138–151. https://doi.org/10.1016/j.jag.2014.04.001 (2014).Article 

Google Scholar 
Kavzoglu, T. & Mather, P. M.. The use of feature selection techniques in the context of artificial neural networks. In Proceedings of the 26th Annual Conference of the Remote Sensing Society (CD-ROM), 12–14 September (Leicester, UK, 2000).Gunal, S. & Edizkan, R. Subspace based feature selection for pattern recognition. Info. Sci. 178, 3716–3726. https://doi.org/10.1016/j.ins.2008.06.001 (2008).Article 

Google Scholar 
Tolpekin, V. A. & Stein, A. Quantification of the effects of land-cover-class spectral separability on the accuracy of markov-random-field-based superresolution mapping. IEEE Trans. Geosci. Remote Sens. 47(9), 3283–3297. https://doi.org/10.1109/TGRS.2009.2019126 (2009).ADS 
Article 

Google Scholar 
Paterson, M., Lucas, R. M. & Chisholm, L. Differentiation of selected Australian woodland species using CASI data. In Proceedings IEEE International Geoscience and Remote Sensing Symposium, 643–645 (University of New South Wales, Australia, 2001).Richards, J. A. & Jai, X. Remote Sensing Digital Analysis: An Introduction, 4th edition (Springer, Berlin, 1999).Veraverbeke, S., Harris, S. & Hook, S. Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sens. Environ. 115, 2702–2709. https://doi.org/10.1016/j.rse.2011.06.010 (2011).ADS 
Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
Georganos, S. et al. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto Int. https://doi.org/10.1080/10106049.2019.1595177 (2019).Article 

Google Scholar 
Mellor, A., Haywood, A., Stone, C. & Jones, S. The performance of random forests in an operational setting for large area sclerophyll forest classification. Remote Sens. 5, 2838–2856. https://doi.org/10.3390/rs5062838 (2013).ADS 
Article 

Google Scholar 
Congalton, R. G. Accuracy assessment and validation of remotely sensed and other spatial information. Int. J. Wildland. Fire. 10, 321–328 (2001).Article 

Google Scholar 
Thomas, I. L., Ching, N. P., Benning, V. M. & D’aguanno, J. A. Review Article A review of multi-channel indices of class separability. Int. J. Remote Sens. 8(3), 331–350. https://doi.org/10.1080/01431168708948645 (1987).Article 

Google Scholar 
Mausel, P. W., Kramber, W. J. & Lee, J. K. Optimum band selection for supervised classification of multispectral data. Photogramm. Eng. Remote. Sens. 56(1), 55–60 (1990).
Google Scholar 
Singh, A. Some clarifications about the pairwise divergence measure in remote sensing. Int. J. Remote Sens. 5(3), 623–627. https://doi.org/10.1080/01431168408948845 (1984).Article 

Google Scholar 
Kumar, P. et al. A statistical significance of differences in classification accuracy of crop types using different classification algorithms. Geocarto Int. 32(2), 206–224. https://doi.org/10.1080/10106049.2015.1132483 (2017).Article 

Google Scholar 
McPherson, E. G., Simpson, J. R., Peper, P. J., Xiao, Q. & Wu, C. Los Angeles 1-Million Tree Canopy Cover Assessment. General Technical Report PSW-GTR-207. U.S. Department of Agriculture Forest Service Pacific Southwest Research Station. Albany, CA, 1–64 (2008).Rahimizadeh, N., Kafaky, S. B., Sahebi, M. R. & Mataji, A. Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods. Environ. Monit. Assess. 192, 43. https://doi.org/10.1007/s10661-019-8015-x (2020).Article 

Google Scholar 
McRoberts, R. E. Satellite image-based maps: Scientific inference or pretty pictures?. Remote. Sens. Environ. 115, 715–724. https://doi.org/10.1016/j.rse.2010.10.013 (2011).ADS 
Article 

Google Scholar 
McRoberts, R. E. Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data. Remote. Sens. Environ. 114, 1017–1025. https://doi.org/10.1016/j.rse.2009.12.013 (2010).ADS 
Article 

Google Scholar 
Kokubu, Y., Hara, S. & Tani, A. Mapping seasonal tree canopy cover and leaf area using worldview-2/3 satellite imagery: A megacity-scale case study in Tokyo urban area. Remote. Sens. 12(9), 1505. https://doi.org/10.3390/rs12091505 (2020).Article 

Google Scholar 
Johannesburg City Parks and Zoo. 2018. The city that’s a rain forest. http://www.jhbcityparks.com/index.php/street-trees-contents-29. Accessed 14 June 2020.Tesfamichael, S. G., Newete, S. W., Adam, E. & Dubula, B. Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants. GIsci. Remote Sens. 55(3), 417–436. https://doi.org/10.1080/15481603.2017.1396658 (2018).Article 

Google Scholar 
McPherson, E. G., Simpsona, J. R., Xiao, Q. & Wu, C. Million trees Los Angeles canopy cover and benefit assessment. Landsc. Urban. Plan. 99, 40–50 (2011).Article 

Google Scholar 
Baines, O., Wilkes, P. & Disney, M. Quantifying urban forest structure with open-access remote sensing data sets. Urban For. Urban Green. 50, 126653. https://doi.org/10.1016/j.ufug.2020.126653 (2020).Article 

Google Scholar 
Nowak, D. J. et al. Measuring and analyzing urban tree cover. Landsc. Urban Plan. 36, 49–57 (1996).Article 

Google Scholar 
Estoque, R. C. et al. Remotely sensed tree canopy cover-based indicators for monitoring global sustainability and environmental initiatives. Environ. Res. Lett. 16, 044047. https://doi.org/10.1088/1748-9326/abe5d9 (2021).ADS 
CAS 
Article 

Google Scholar 
Paap, T., de Beer, W., Migliorini, D., Nel, W. J. & Wingfield, M. J. The polyphagous shot hole borer (PSHB) and its fungal symbiont Fusarium euwallaceae: A new invasion in South Africa Trudy. Aust. Plant. Pathol. 47, 231–237. https://doi.org/10.1007/s13313-018-0545-0 (2018).Article 

Google Scholar  More

Cox, P. A., Banack, S. A. & Murch, S. J. Biomagnification of cyanobacterial neurotoxins and neurodegenerative disease among the Chamorro people of Guam. Proc. Natl. Acad. Sci. U.S.A. 100, 13380–13383 (2003).ADS 
CAS 
Article 

Google Scholar 
Brand, L. E., Pablo, J., Compton, A., Hammerschlag, N. & Mash, D. C. Cyanobacterial blooms and the occurrence of the neurotoxin, beta-N-methylamino-L-alanine (BMAA), in south Florida aquatic food webs. Harmful Algae 9, 620–635 (2010).CAS 
Article 

Google Scholar 
Metcalf, J. S., Banack, S. A., Richer, R. & Cox, P. A. Neurotoxic amino acids and their isomers in desert environments. J. Arid Environ. 112, 140–144 (2015).ADS 
Article 

Google Scholar 
Violi, J. P., Mitrovic, S. M., Colville, A., Main, B. J. & Rodgers, K. J. Prevalence of (beta)-methylamino-L-alanine (BMAA) and its isomers in freshwater cyanobacteria isolated from eastern Australia. Ecotoxicol. Environ. Saf. 172, 72–81 (2019).CAS 
Article 

Google Scholar 
Jonasson, S. et al. Transfer of a cyanobacterial neurotoxin within a temperate aquatic ecosystem suggests pathways for human exposure. Proc. Natl. Acad. Sci. 107, 9252–9257 (2010).ADS 
CAS 
Article 

Google Scholar 
Metcalf, J. et al. Toxin analysis of freshwater cyanobacterial and marine harmful algal blooms on the west coast of Florida and implications for estuarine environments. Neurotox. Res. 39, 27–35 (2021).CAS 
Article 

Google Scholar 
Cox, P. A. et al. Cyanobacteria and BMAA exposure from desert dust: a possible link to sporadic ALS among Gulf War veterans. Amyotroph. Lateral Scler. 10, 109–117 (2009).CAS 
Article 

Google Scholar 
Charlton, T. S., Marini, A. M., Markey, S. P., Norstog, K. & Duncan, M. W. Quantification of the neurotoxin 2-amino-3-(methylamino)-propanoic acid (BMAA) in Cycadales. Phytochemistry 31, 3429–3432 (1992).CAS 
Article 

Google Scholar 
Whiting, M. G. Toxicity of cycads. Econ. Bot. 17, 270–302 (1963).Article 

Google Scholar 
Cox, P. A., Davis, D. A., Mash, D. C., Metcalf, J. S. & Banack, S. A. Dietary exposure to an environmental toxin triggers neurofibrillary tangles and amyloid deposits in the brain. Proc. R. Soc. B: Biol. Sci. 283, 20152397 (2016).Article 

Google Scholar 
Scott, L. L. & Downing, T. G. A single neonatal exposure to BMAA in a rat model produces neuropathology consistent with neurodegenerative diseases. Toxins 10, 22 (2018).Article 

Google Scholar 
Roy, U. et al. Metabolic profiling of zebrafish (Danio rerio) embryos by NMR spectroscopy reveals multifaceted toxicity of (beta)-methylamino-L-alanine (BMAA). Sci. Rep. 7, 1–12 (2017).ADS 
Article 

Google Scholar 
Purdie, E. L., Metcalf, J. S., Kashmiri, S. & Codd, G. A. Toxicity of the cyanobacterial neurotoxin (beta)-N-methylamino-L-alanine to three aquatic animal species. Amyotroph. Lateral Scler. 10, 67–70 (2009).CAS 
Article 

Google Scholar 
Brenner, E. D. et al. Arabidopsis mutants resistant to s (+)-(beta)-methyl-(alpha), (beta)-diaminopropionic acid, a cycad-derived glutamate receptor agonist. Plant Physiol. 124, 1615–1624 (2000).CAS 
Article 

Google Scholar 
Schneider, D., Wink, M., Sporer, F. & Lounibos, P. Cycads: Their evolution, toxins, herbivores and insect pollinators. Naturwissenschaften 89, 281–294 (2002).ADS 
CAS 
Article 

Google Scholar 
Koi, S. & Daniels, J. Life history variations and seasonal polyphenism in Eumaeus atala (Lepidoptera: Lycaenidae). Florida Entomol. 100, 219–229 (2017).Article 

Google Scholar 
Koi, S. A butterfly picks its poison: Cycads (Cycadaceae), integrated pest management and Eumaeus atala Poey (Lepidoptera: Lycaenidae). Entomol. Ornithol. Herpetol. 6 (2017).Brenner, E. D., Stevenson, D. W. & Twigg, R. W. Cycads: Evolutionary innovations and the role of plant-derived neurotoxins. Trends Plant Sci. 8, 446–452 (2003).CAS 
Article 

Google Scholar 
Prado, A. The cycad herbivores. Bull. Soc. D’entomol. Quebec 18, 3–6 (2011).
Google Scholar 
Popova, A. & Koksharova, O. Neurotoxic non-proteinogenic amino acid (beta)-N-methylamino-L-alanine and its role in biological systems. Biochem. Mosc. 81, 794–805 (2016).CAS 
Article 

Google Scholar 
Salzman, S., Whitaker, M. R. L. & Pierce, N. E. Cycad-feeding insects share a core gut microbiome. Biol. J. Lin. Soc. 123, 728–738 (2018).Article 

Google Scholar 
Whitaker, M. R. & Salzman, S. Ecology and evolution of cycad-feeding Lepidoptera. Ecol. Lett. 23, 1862–1877 (2020).Article 

Google Scholar 
Zhou, X., Escala, W., Papapetropoulos, S., Bradley, W. G. & Zhai, R. G. BMAA neurotoxicity in Drosophila. Amyotroph. Lateral Scler. 10, 61–66 (2009).CAS 
Article 

Google Scholar 
Zhou, X., Escala, W., Papapetropoulos, S. & Zhai, R. G. (beta)-N-methylamino-L-alanine induces neurological deficits and shortened life span in Drosophila. Toxins 2, 2663–2679 (2010).CAS 
Article 

Google Scholar 
Mekdara, N. T. et al. A novel lenticular arena to quantify locomotor competence in walking fruit flies. J. Exp. Zool. A Ecol. Genet. Physiol. 317, 382–394 (2012).Article 

Google Scholar 
Goto, J. J., Koenig, J. H. & Ikeda, K. The physiological effect of ingested (beta)-N-methylamino-L-alanine on a glutamatergic synapse in an in vivo preparation. Comp. Biochem. Physiol. Part C: Toxicol. Pharmacol. 156, 171–177 (2012).CAS 

Google Scholar 
Okle, O., Rath, L., Galizia, C. G. & Dietrich, D. R. The cyanobacterial neurotoxin (beta)-N-methylamino-L-alanine (BMAA) induces neuronal and behavioral changes in honeybees. Toxicol. Appl. Pharmacol. 270, 9–15 (2013).CAS 
Article 

Google Scholar 
Spencer, P. S. et al. Guam amyotrophis lateral sclerosis-parkinsonism-dementia linked to a plant excitant neurotoxin. Science 237, 517–522 (1987).ADS 
CAS 
Article 

Google Scholar 
Bernays, E. A. & Chapman, R. F. Host-plant selection by phytophagous insects. In Host-Plant Selection by Phytophagous Insects. Contemporary Topics in Entomology, vol. 2, 201–213 (Springer, Boston, MA, 1994).Zandt, P. A. V. Plant defense, growth, and habitat: A comparative assessment of constitutive and induced resistance. Ecology 88, 1984–1993 (2007).Article 

Google Scholar 
Duncan, M. W. Role of the cycad neurotoxin BMAA in the amyotrophic lateral sclerosi-parkisonism dementia complex of the Western Pacific. Adv. Neurol. 56, 301–310 (1991).CAS 
PubMed 

Google Scholar 
Banack, S. A. & Cox, P. A. Distribution of the neurotoxic nonprotein amino acid BMAA in Cycas micronesica. Bot. J. Linn. Soc. 143, 165–168 (2003).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Therneau, T. M. A Package for Survival Analysis in R. R package version 3.2-11 (2021).Kassambara, A., Kosinski, M. & Biecek, P. survminer: Drawing Survival Curves using ’ggplot2’. R package version 0.4.9 (2021).Pennington, Z. T. et al. eztrack: An open-source video analysis pipeline for the investigation of animal behavior. Sci. Rep. 9, 1–11 (2019).Article 

Google Scholar 
Pérez, F. & Granger, B. E. IPython: A system for interactive scientific computing. Comput. Sci. Eng. 9, 21–29 (2007).Article 

Google Scholar 
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Natl. Acad. Sci. 114, 9641–9646 (2017).CAS 
Article 

Google Scholar 
Karlsson, O., Roman, E. & Brittebo, E. B. Long-term cognitive impairments in adult rats treated neonatally with (beta)-N-methylamino-L-alanine. Toxicol. Sci. 112, 185–195 (2009).CAS 
Article 

Google Scholar 
Whitaker, M. R. L., Salzman, S., Gratacos, X. & Tucker Lima, J. Localized overabundance of an otherwise rare butterfly threatens endangered cycads. Florida Entomol. 103, 519–522 (2021).Article 

Google Scholar 
Backmann, P. et al. Delayed chemical defense: Timely expulsion of herbivores can reduce competition with neighboring plants. Am. Nat. 193, 125–139 (2019).Article 

Google Scholar 
Yáñez-Espinosa, L. & Sosa-Sosa, F. Population structure of Dioon purpusii rose in Oaxaca, Mexico. Neotrop. Biol. Conserv. 2, 46–54 (2007).
Google Scholar 
Robbins, R. K. et al. A switch to feeding on cycads generates parallel accelerated evolution of toxin tolerance in two clades of Eumaeus caterpillars (Lepidoptera: Lycaenidae). Proc. Natl. Acad. Sci.118 (2021).Grunseich, J. M., Thompson, M. N., Aguirre, N. M. & Helms, A. M. The role of plant-associated microbes in mediating host-plant selection by insect herbivores. Plants 9, 6 (2020).CAS 
Article 

Google Scholar 
Zhang, Y. & Whalen, J. K. Production of the neurotoxin beta-N-methylamino-L-alanine may be triggered by agricultural nutrients: An emerging public health issue. Water Res. 170, 115335 (2020).CAS 
Article 

Google Scholar  More

Experimental design and crop managementThe study was conducted during 2019 in a field experiment on an arable soil (classified as Luvisols) in the deep root experimental facility at the University of Copenhagen, Denmark (Supplementary Table S4). The experiment was conducted with two diverse perennial deep-rooted species: the tap-rooted forage legume lucerne (Medicago sativa L. (cv. Creno); Family: Fabaceae) with the capacity to fix N2 and the intermediate wheatgrass (Thinopyrum intermedium; Family: Poaceae) kernza developed by the Land Institute (Salina, Kansas, USA). Kernza was initially sown on April 11th, 2015 and lucerne on September 9th, 2016 with a seeding density of 20 kg seeds ha−1. Every year, kernza was fertilized with NPK fertilizer (21:7:3; NH4:NO3 = 1.28) as a single dose in early spring (before the onset of plant growth). Kernza was harvested every year in August using a combine harvester and lucerne three times per year in June, August, and October. Plants were rainfed with a subsurface drain installed at both 1 and 2 m depth running between the plots.For each species, fixed frames of 0.75 m2 were inserted in the soil (ca. 5 cm) within each field plot. Specifically, three field plots of lucerne (with observable root nodulation) and kernza were used where each of the three kernza field plots contained two subplots of N fertilized kernza at 100 kg N ha−1 (K100) (i.e., the standard fertilization within this field) and N fertilized kernza at 200 kg N ha−1 (K200) (i.e., within the range of standard fertilization practices for kernza). Before the onset of plant growth, all plots received 15N (as 15NH4Cl; 98 atom%) in trace amounts (corresponding to 1 kg N ha−1) to trace N allocation from the surface to deeper layers.
13C/14C-CO2-labelingWithin each fixed frame, the 13C/14C-CO2-labeling was conducted using an atmospheric labeling chamber41. Labeling with C-tracers was done with multiple-pulse labeling (three times per week) over two months until first harvest (May 2nd to June 20th 2019). Glass beakers containing 13C labeled bicarbonate (0.1 g mL−1 labeling solution; 99 atom%), and 14C labeled bicarbonate (11 kBq mL−1 labeling solution) within a solution of NaOH (1 M) were added within each of the labeling chambers. Once chambers were sealed, hydrochloric acid (HCl; 2 M) was added to the labeling solution (in equivalent amounts) via a syringe promoting 14CO2/13CO2 evolution. Chambers remained sealed for one to three hours (between 9 am and 12 pm) depending on weather conditions (i.e., the duration and intensity of sunshine). The amount of added labeling solution sequentially increased with increasing plant growth (i.e., 5 mL per 20 cm increase in plant height) reaching a plant height of 100–120 cm at the termination of the labeling.Shoot, root, and soil samplingThe labeling plots (0.75 m2) were harvested on June 20th, 2019 to obtain the aboveground biomass of lucerne and kernza (K100 and K200). The aboveground biomass in addition to samples obtained from unlabeled parts of the field was directly stored at − 20 °C until drying at 105 °C for two days. For each plot and unlabeled samples, the plant biomass was homogenized and ball-milled for subsequent isotopic analyses.Soil cores to 1.5 m depth were taken inside all labeling plots, and cores were subdivided into four depth intervals: 0–25, 25–50, 50–100, and 100–150 cm. The soil coring was conducted in 25 cm intervals using a soil auger (6 cm inner diameter). Specifically, per depth three soil samples were taken and stored at 4–5 °C (ca. two days) and then immediately processed and stored at -20 °C until analyses. Roots, bulk soil and rhizosphere soil (adhering to the roots), were separated by sequential sieving of the soil with finer mesh sizes to 1 mm as described by Peixoto, et al.26. A subsample of the bulk soil (ca. 150 g) from each depth in all labeling plots was washed on a 250 µm sieve to recover root fragments for subsequent isotopic determination in unrecovered root fragments. Soil samples (and associated roots) from unlabeled parts of the larger field plots were used to determine natural abundance of 13C/14C/15N with depth. The collection of plant material complied with relevant institutional guidelines and seeds were gifted by University of Copenhagen.Determination of 13C/14C/15N enrichment, and C and N quantityFor each defined depth, samples of roots and soil were homogenized, freeze-dried (except PLFA samples that were stored at − 20 °C), and ground in a ball-mill for the determination of total C and N, 13C, 15N, and 14C activity. Total C, N, 13C, and 15N were measured with a FLASH 2000 CHNS/O Elemental Analyzer (Thermo Fisher Scientific, Cambridge, UK) combined to a Delta V Advantage isotope ratio mass spectrometer via a ConFlo III interface (Thermo Fisher Scientific, Bremen, Germany) at the Centre for Stable Isotope Research and Analysis (Georg August University Göttingen, Göttingen, Germany).All δ13C values are standardized to the Vienna PeeDee Belemnite international isotope standard and δ15N values standardized to the δ15N values of atmospheric N2. 13C and 15N enrichment is expressed as atom% excess as calculated by the atom% difference between the respective labeled and unlabeled samples. The 14C activity was determined by combustion in a Hidex 600 OX Oxidizer (Hidex, Turku, Finland) and counted on a liquid scintillation counter (Tri-Carb 3180TR/SL, PerkinElmer, Waltham, MA, USA). 14C enrichment is determined by the difference in the 14C activity (Bq g−1) between the respective labeled and unlabeled samples.Calculation of root C and net rhizodepositionThe amount of root C (mg C kg−1 soil) was calculated based on the root dry matter and C concentration divided by the quantity of soil sampled38. For the determination of net rhizodeposition, 14C was used due to lower detection limits in deeper soil layers42. A modified tracer mass balance approach described by Rasmussen, et al.43 with adjusted unrecovered root fragments41 was used to determine the net rhizodeposition based on the following equations where the %ClvR is the relative proportion of rhizodeposition expressed as the percent C lost via rhizodeposition:$${text{%ClvR}} = frac{{^{{{14}}} {text{C Soil (rhizosphere + adjusted bulk)}}}}{{^{{{14}}} {text{C bulk soil }} + ,^{{{14}}} {text{C rhizosphere soil}} + ,^{{{14}}} {text{C Root}}}} times 100.$$$${text{Net rhizodeposition}} = frac{{{text{%ClvR }} times {text{ root C content}}}}{{left( {100 – % {text{ClvR}}} right)}}$$The 14C soil content was the sum of the adjusted bulk soil 14C and rhizosphere 14C content for each soil sample. The 14C rhizosphere and bulk soil content for each soil sample were determined by multiplying the total quantity of C by the 14C enrichment of the soil. The adjusted bulk soil 14C content was calculated as the difference between the bulk 14C soil content by the 14C root washed content as determined by the multiplication of 14C enrichment in root fragments recovered from a subsample of soil by the total C content within the entire soil volume sampled. The 14C root content was determined by multiplying the total quantify of C in roots by the 14C enrichment. Similar equations were used to calculate the net rhizodeposition of N based on 15N enrichment within the soil and roots.Biomarker analysesPhospholipid fatty acid (PLFA)The analysis of PLFAs was done according to a modified protocol by Frostegård, et al.44 with a detailed description of the modifications provided by Gunina, et al.45. In brief, 25 μL of 1,2-Dinonadecanoyl-sn-Glycero-3-Phosphatidylcholine (C19:0) (1 mg mL–1) were added to each of the samples and used in the quantification of recovery of the phospholipids. The lipid fraction from 5–6 g of rhizosphere soil was extracted twice using a one-phase Bligh-Dyer extractant46 of chloroform, methanol (MeOH), and citrate buffer (pH 4) (1:2:0.8, v/v/v). To isolate the phospholipid fraction, a solid-phase extraction with activated silica gel and methanol elution was conducted. The derivatization into fatty acid methyl esters occurred via a sequential hydrolyzation with 0.5 mL sodium hydroxide (NaOH) (0.5 M) in MeOH for 10 min at 100 °C and methylation with 0.75 mL of boron trifluoride (BF3) (1.3 M) in MeOH for 15 min at 80 °C. An external standard stock solution containing 28 individual fatty acids (ca. 1 mg mL–1 per fatty acid) used in the quantification of PLFA content was simultaneously derivatized with the samples. The residues were dissolved in 185 μL of toluene, and 15 μL of the internal standard 2, tridecanoic acid methyl ester (C13:0) (1 mg mL–1) were added to each sample prior to measurement using an Agilent 7820A GC coupled to an Agilent 5977 quadrupole mass spectrometer (Agilent, Waldbronn, Germany). The sum of all PLFAs was used as a proxy of the living microbial biomass based on the direct relation between PLFAs and microbial biomass.Amino sugars (AS)Amino sugars were extracted according to a modified protocol by Zhang and Amelung47 with a detailed description of the procedure by Peixoto, et al.26. In brief, 0.8–1.5 g of freeze-dried rhizosphere soil were hydrolyzed with the addition of 11 mL of 6 M HCl for 8 h at 105 °C. Following hydrolysis, soil samples were filtered and HCl was removed via rotary evaporation at 45 °C to dry the filtrate. Prior to derivatization both iron precipitates and salts were removed from the filtrate and 25 μL of the internal standard 1, methylglucamine (MeGlcN) (1 mg mL–1) was added and used for quantification of recovery. The derivatization into aldononitrile acetates was conducted as described by Zhang and Amelung47. For the quantification of AS, an external standard stock solution containing the AS: N-acetylglucosamine (GlcN) (2 mg mL–1), N-acetylgalactosamine (GalN) (2 mg mL–1), N-acetylmuramic acid (MurN) (1 mg mL–1), mannosamine (ManN) (2 mg mL–1), and MeGlcN (1 mg mL–1) was derivatized and analyzed with the samples. The residues were dissolved in 185 μL of ethyl acetate-hexane (1:1, v/v), and 15 μL of the internal standard 2, tridecanoic acid methyl ester (1 mg mL–1), were added to the samples for measurement using an Agilent 7890A GC coupled to Agilent 7000A triple quadrupole mass spectrometer (Agilent, Waldbronn, Germany). Total amino sugars content was calculated as the summation of the four detected amino sugars: GlcN, MurN, GalN, and ManN.Amino acids (AA)Amino acids were extracted from both freeze-dried rhizosphere soil and root samples according to the protocol by Enggrob, et al.48. In brief, 0.8–3 g of rhizosphere soil and 0.02 g of root were hydrolyzed with the addition of 2 mL of 6 M HCl for 20 h at 110 °C to break the peptide bonds. Samples were subsequently purified via the removal of lipophilic and solid compounds by the addition of 4 mL n-hexane/dichloromethane (6:5, v/v) to the soil and root samples. Following centrifugation, the aqueous phase was filtered through glass wool and rinsed with 2 × 0.5 mL 0.1 M HCl into new glass tubes with the addition of 300 μL of the internal standard, norleucine (2.5 mM). The samples were freeze-dried and the residues dissolved in 1 mL 0.01 M HCl prior to the separation of amino acids and amino sugars (i.e., N containing compounds) on a polypropylene column with a cation exchange resin. The amino acids were eluted with a 2.5 M ammonium hydroxide solution and freeze-dried prior to derivatization of the amino acids as described by Enggrob, et al.48. For the quantification of AA, an external standard stock solution containing 14 AA was derivatized and analyzed with the samples. The amino acids were measured using a trace GC Ultra mounted with a TriPlus autosampler (Thermo Scientific, Hvidovre, Denmark) coupled via a combustion reactor (GC IsoLink, Thermo Scientific) to an isotope ratio mass spectrometer (Delta V Plus IRMS, Thermo Scientific). The total AA content of the rhizosphere soil and roots was based on the summation of the AA: alanine, Asx (asparagine and aspartate), Glx (glutamine and glutamate), glycine, isoleucine, lysine, phenylalanine, Pro/Thr (proline and threonine), serine, tyrosine, and valine.Compound-specific stable isotope probingTo determine the 13C enrichment of biomarkers, all raw δ13C were measured individually for AS and PLFA using a Delta V Advantage isotope ratio mass spectrometer via a ConFlo III interface (Thermo Fisher Scientific, Bremen, Germany). For AA, all raw δ13C were measured using a trace GC Ultra mounted with a TriPlus autosampler (Thermo Scientific, Hvidovre, Denmark) coupled via a combustion reactor (GC IsoLink, Thermo Scientific) to an isotope ratio mass spectrometer (Delta V Plus IRMS, Thermo Scientific). For each sample, chromatogram peaks identified based on retention times specific for the measured amino sugars, PLFA, and AA were integrated using Isodat v. 3.0 (Thermo Fisher Scientific). All raw δ13C values were corrected for dilution by additional C atoms added during the derivatization, amount dependence, offset, and drift (for PLFA samples)49,50,51. To determine the 13C incorporation into each biomarker, the 13C excess for each biomarker as determined by the difference between the 13C of the labeled and unlabeled biomarker was multiplied by the C content of the specific biomarker.Relative microbial stabilization (RMS)The relative microbial stabilization is based on the relation of rhizodeposited 13C in the PLFA and amino sugar pools as described in detail by Peixoto, et al.26. The underlying assumption is that 13C incorporation into the amino sugar pool indicates the transformation of rhizodeposited C into necromass52,53, and the 13C incorporation into the PLFA pool (i.e., the living microbial biomass) represents a temporary C pool as PLFAs are immediately exposed to degradation following cell lysis54. The relative microbial stabilization (RMS) is calculated as follows:$${text{Relative microbial stabilization}} = {text{log}}frac{{{text{Average weighted atom% }},^{{{13}}} {text{C excess AS}}}}{{{text{Average weighted atom% }},^{{{13}}} {text{C excess PLFA}}}}$$where the average weighted atom% 13C excess is determined by the total 13C incorporation divided by the total C content of the respective PLFA or amino sugar pools. Accordingly RMS  0 is indicative of higher stabilization of C based on the dominant entry of C into the microbial necromass. However, the RMS indicator does not imply the absolute stability of rhizodeposited C, but rather signifies the potential for microbial stabilization among contrasting experimental variables (i.e., depth and plant species).Molecular analysisDNA extractionFrom each sample, 0.5 g of freeze-dried rhizosphere soil was used for DNA extraction using the Fast DNA Spin kit for Soil (MP Biomedicals, Solon, OH, USA) according to the manufacturer’s protocol with a single modification. Following, the addition of Binding Matrix, the suspension was washed with 5.5 M Guanidine Thiocyanate (protocol from MP Biomedicals) to remove humic acids that could inhibit preceding polymerase chain reaction (PCR) steps. The DNA was eluted in DNase free water and purified using the NucleoSpin gDNA Clean-up kit following the manufacturer’s protocol (Macherey–Nagel, Düren, Germany). The purity and concentration of DNA were checked on Nanodrop and Qubit, respectively.Amplicon sequencingExtracted DNA was sent to Novogene Europe (Cambridge, United Kingdom) for library preparation and amplicon sequencing. For 16S rRNA gene amplicon sequencing of the V3-V4 regions, the primer pair 341 F and 806 R were used (Supplementary Table S5). To identify the fungal communities, we targeted the Internal Transcribed Spacer (ITS) Region 1, using the primer pair ITS1 and ITS2 (Supplementary Table S5). The constructed libraries were sequenced using a Novaseq 6000 platform producing 2 × 250 bp paired-end reads. Raw sequences were deposited in the NCBI Sequence Read Archive (Bioproject number PRJNA736561).Quantitative PCRCopy numbers of the 16S rRNA gene were determined by quantitative PCR (qPCR) using the primers 341F and 805R (Supplementary Table S5) on an AriaMX Real-Time PCR System (Agilent Technologies, Santa Clara, CA, USA). An external plasmid standard curve was made based on the pCR 2.1 TOPO vector (Thermo Fisher Scientific, Waltham, MA, USA) with a 16S rRNA gene insert amplified from bulk soil. The PCR reaction was performed in 20 µl reactions containing: 1 × Brilliant III Ultra-Fast SYBR green low ROX qPCR Master Mix (Agilent Technologies, Santa Clara, CA, USA), 0.05 µg/µl BSA (New England Biolabs Inc., Ipswich, MA, USA), 0.4 µM of each primer and 2 μl of template DNA. The thermal cycling conditions were 3 min at 95 °C followed by 40 cycles of 20 s at 95 °C and 30 s at 58 °C, and a final extension for 1 min at 95 °C. A melting curve was included according to the default settings of the AriaMx qPCR software (Agilent Technologies). The reaction efficiencies were between 97 and 102%. Fungal quantification was done by qPCR amplification of the Internal Transcribed Spacer 1 (ITS1) using the primers ITS1-F and ITS2 (Supplementary Table S5). A plasmid standard curve was made using the pCR 2.1 TOPO vector containing an ITS1 region from Penicillium aculeatum. Reaction mixture and cycling conditions were as described above for the 16S rRNA gene (Supplementary Table S5). The reaction efficiency was 84%.Quantification of functional genes involved in N cyclingThe five bacterial genes amoA, nirK, nirS, nosZ, and nifH coding for enzymes involved in N-cycling were quantified by qPCR on an AriaMx Real-Time PCR System (Agilent Technologies). Reaction mixtures and cycling conditions were as described above for the 16S rRNA gene (Supplementary Table S5). The standard curves were prepared as described in Garcia-Lemos, et al.55. The reaction efficiencies were in the range 87%-105%.Sequence processingRaw reads were treated using DADA2 version 1.14.156. In brief, reads were quality checked and primers were removed using Cutadapt v. 1.1557. We followed the protocol DADA2 using default parameters, with a few modifications. For 16S rRNA sequences, the forward and reverse reads were trimmed to 222 and 219 bp, respectively, while the maxEE was set to 2 and 5 for forward and reverse reads, respectively. Detection of amplicon sequence variants (ASVs) was done using the pseudo-pool option and forward and reverse reads were merged with a minimum overlap of 10 bp. Merged reads in the range of 395–439 bp were kept, as reads outside this range are considered too long or too short for the sequenced region. Taxonomy was assigned using the Ribosomal Database Project (RDP) classifier58 with the Silva database v.13859. For ITS region 1, quality filtered reads shorter than 50 bp were removed prior to merging the forward and the reverse reads, with maxEE set to two for both forward and reverse reads. During merging, the minimum overlap was set to 20 (default). Taxonomy was assigned with the RDP classifier using the Unite v. 8.2 database60 after removal of chimeras.As ITS region 1 has a variable length, reads can be lost during merging. Hence, to validate our dataset we ran only the forward reads through the DADA2 pipeline and compared the overall community structure with the dataset from the merging using a Mantel test. No significant changes were observed in the community structures between the two datasets (r = 0.99; p = 0.0001). To obtain the highest taxonomic resolution, the dataset based on the merged reads was used. Further analysis was done using the phyloseq v. 1.30.0 R package61.Statistical analysisAnalyses of variance (ANOVA) were conducted to examine the effects of N fertilized kernza at 100 kg N ha−1 (K100) and kernza at 200 kg N ha−1 (K200) as well as to test the effect of the deep-rooted plant species: kernza and lucerne, and soil depth on each of the dependent variables. An average across the two subplots within each of the three kernza field plots was used when measured variables did not significantly differ between K100 and K200. Subsequent pairwise comparisons of the means was conducted using the TukeyHSD post-hoc test. Homogeneity of variance and normality were confirmed (data log-transformed when required) for all comparisons using the Fligner-Killeen test of homogeneity of variances62 and the Shapiro–Wilk test of normality63. A permutational multivariate analysis of variance (PERMANOVA) using the Bray–Curtis dissimilarity matrix with the adonis function in the vegan R package was used to test the effect of K100 and K200, lucerne across both K100 and K200, and depth on the bacterial and fungal communities. The multivariate homogeneity of group dispersions or variances were confirmed for all comparisons using the function betadisper in vegan. The bacterial and fungal communities in response to the ascribed variables were visually represented as ordination plots with a Principle Coordinates Analysis (PCoA). Unique ASVs were defined for each depth and between K100, K200, and lucerne as ASVs only present in those samples belonging to a specific depth and treatment. Significance testing was conducted at p  More

Pollard W, Omelon C, Andersen D, McKay C. Perennial spring occurrence in the Expedition Fiord area of western Axel Heiberg Island, Canadian High Arctic. Can J Earth Sci. 1999;36:105–20.CAS 
Article 

Google Scholar 
Andersen DT. Cold springs in permafrost on Earth and Mars. J Geophys Res. 2002;107:4–1-4-7.
Google Scholar 
Niederberger TD, Perreault NN, Tille S, Lollar BS, Lacrampe-Couloume G, Andersen D, et al. Microbial characterization of a subzero, hypersaline methane seep in the Canadian High Arctic. ISME J. 2010;4:1326–39.CAS 
PubMed 
Article 

Google Scholar 
Goordial J, Lamarche-Gagnon G, Lay CY, Whyte L. Left out in the cold: life in cryoenvironments. In: Seckbach J, Oren A, Stan-Lotter H, editors. Polyextremophiles. New York: Springer; 2013. p. 335–64.Gilichinsky D, Rivkina E, Bakermans C, Shcherbakova V, Petrovskaya L, Ozerskaya S, et al. Biodiversity of cryopegs in permafrost. FEMS Microbiol Ecol. 2005;53:117–28.CAS 
PubMed 
Article 

Google Scholar 
Rivkina EM, Friedmann EI, McKay CP, Gilichinsky DA. Metabolic activity of permafrost bacteria below the freezing point. Appl Environ Microbiol. 2000;66:3230–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brown MV, Bowman JP. A molecular phylogenetic survey of sea-ice microbial communities (SIMCO). FEMS Microbiol Ecol. 2001;35:267–75.CAS 
PubMed 
Article 

Google Scholar 
Murray AE, Kenig F, Fritsen CH, McKay CP, Cawley KM, Edwards R, et al. Microbial life at -13 degrees C in the brine of an ice-sealed Antarctic lake. Proc Natl Acad Sci USA. 2012;109:20626–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Orosei R, Lauro SE, Pettinelli E, Cicchetti A, Coradini M, Cosciotti B, et al. Radar evidence of subglacial liquid water on Mars. Science. 2018;361:490–3.CAS 
PubMed 
Article 

Google Scholar 
Lauro SE, Pettinelli E, Caprarelli G, Guallini L, Pio Rossi A, Mattei E, et al. Multiple subglacial water bodies below the south pole of Mars unveiled by new MARSIS data. Nat Astron. 2021;5:63–70.Article 

Google Scholar 
Bishop JL, Yesilbas M, Hinman NW, Burton ZFM, Englert PAJ, Toner JD, et al. Martian subsurface cryosalt expansion and collapse as trigger for landslides. Sci Adv. 2021;7:1–13.
Google Scholar 
Allen CC, Oehler DZ. A case for ancient springs in Arabia Terra, Mars. Astrobiology. 2008;8:1093–112.CAS 
PubMed 
Article 

Google Scholar 
Battler MM, Osinski GR, Banerjee NR. Mineralogy of saline perennial cold springs on Axel Heiberg Island, Nunavut, Canada and implications for spring deposits on Mars. Icarus. 2013;224:364–81.CAS 
Article 

Google Scholar 
Leask EK, Ehlmann BL. Evidence for deposition of chloride on Mars from small‐volume surface water events into the Late Hesperian‐Early Amazonian. AGU Adv. 2022;3:1–19.Article 

Google Scholar 
Howell SM, Pappalardo RT. NASA’s Europa Clipper-a mission to a potentially habitable ocean world. Nat Commun. 2020;11:1–4.Article 

Google Scholar 
Farley KA, Williford KH, Stack KM, Bhartia R, Chen A, de la Torre M, et al. Mars 2020 mission overview. Space Sci Rev. 2020;216:1–41.Article 

Google Scholar 
Kargel JS, Kaye JZ, Head JW, Marion GM, Sassen R, Crowley JK, et al. Europa’s crust and ocean: origin, composition, and the prospects for life. Icarus. 2000;148:226–65.CAS 
Article 

Google Scholar 
Taubner RS, Pappenreiter P, Zwicker J, Smrzka D, Pruckner C, Kolar P, et al. Biological methane production under putative Enceladus-like conditions. Nat Commun. 2018;9:1–11.CAS 
Article 

Google Scholar 
Lamarche-Gagnon G, Comery R, Greer CW, Whyte LG. Evidence of in situ microbial activity and sulphidogenesis in perennially sub-0 degrees C and hypersaline sediments of a high Arctic permafrost spring. Extremophiles. 2015;19:1–15.CAS 
PubMed 
Article 

Google Scholar 
Lay CY, Mykytczuk NC, Yergeau E, Lamarche-Gagnon G, Greer CW, Whyte LG. Defining the functional potential and active community members of a sediment microbial community in a high-arctic hypersaline subzero spring. Appl Environ Microbiol. 2013;79:3637–48.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:1–9.Article 

Google Scholar 
Gruber-Vodicka HR, Seah BKB, Pruesse E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems. 2020;5:1–16.Article 

Google Scholar 
Li D, Liu CM, Luo R, Sadakane K, Lam TW. 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 
Article 

Google Scholar 
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:1–15.Article 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen IA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 2020;49:D751–D63.PubMed Central 
Article 

Google Scholar 
Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Sundaramurthi JC, Lee J, et al. Genomes OnLine Database (GOLD) v.8: overview and updates. Nucleic Acids Res. 2020;49:D723–D733.PubMed Central 
Article 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.PubMed Central 

Google Scholar 
Schmieder R, Edwards R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE. 2011;6:1–11.Article 

Google Scholar 
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.CAS 
PubMed 
Article 

Google Scholar 
Royo-Llonch M, Sanchez P, Ruiz-Gonzalez C, Salazar G, Pedros-Alio C, Sebastian M, et al. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nat Microbiol. 2021;6:1561–74.CAS 
PubMed 
Article 

Google Scholar 
Ghosh W, Dam B. Biochemistry and molecular biology of lithotrophic sulfur oxidation by taxonomically and ecologically diverse bacteria and archaea. FEMS Microbiol Rev. 2009;33:999–1043.CAS 
PubMed 
Article 

Google Scholar 
Boden R. Reclassification of Halothiobacillus hydrothermalis and Halothiobacillus halophilus to Guyparkeria gen. nov. in the Thioalkalibacteraceae fam. nov., with emended descriptions of the genus Halothiobacillus and family Halothiobacillaceae. Int J Syst Evol Microbiol. 2017;67:3919–28.CAS 
PubMed 
Article 

Google Scholar 
Sorokin DY, Abbas B, van Zessen E, Muyzer G. Isolation and characterization of an obligately chemolithoautotrophic Halothiobacillus strain capable of growth on thiocyanate as an energy source. FEMS Microbiol Lett. 2014;354:69–74.CAS 
PubMed 
Article 

Google Scholar 
Meier DV, Pjevac P, Bach W, Hourdez S, Girguis PR, Vidoudez C, et al. Niche partitioning of diverse sulfur-oxidizing bacteria at hydrothermal vents. ISME J. 2017;11:1545–58.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Headd B, Engel AS. Evidence for niche partitioning revealed by the distribution of sulfur oxidation genes collected from areas of a terrestrial sulfidic spring with differing geochemical conditions. Appl Environ Microbiol. 2013;79:1171–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Preisig O, Zufferey R, Thoney-Meyer L, Appleby CA, Hennecke H. A high-affinity cbb3-type cytochrome oxidase terminates the symbiosis-specific respiratory chain of Bradyrhizobium japonicum. J Bacteriol. 1996;178:1532–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mikucki JA, Pearson A, Johnston DT, Turchyn AV, Farquhar J, Schrag DP, et al. A contemporary microbially maintained subglacial ferrous “ocean”. Science. 2009;324:397–400.CAS 
PubMed 
Article 

Google Scholar 
Ruff SE, Biddle JF, Teske AP, Knittel K, Boetius A, Ramette A. Global dispersion and local diversification of the methane seep microbiome. Proc Natl Acad Sci USA. 2015;112:4015–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lloyd KG, Lapham L, Teske A. An anaerobic methane-oxidizing community of ANME-1b archaea in hypersaline Gulf of Mexico sediments. Appl Environ Microbiol. 2006;72:7218–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maignien L, Parkes RJ, Cragg B, Niemann H, Knittel K, Coulon S, et al. Anaerobic oxidation of methane in hypersaline cold seep sediments. FEMS Microbiol Ecol. 2013;83:214–31.CAS 
PubMed 
Article 

Google Scholar 
Campen R, Kowalski J, Lyons WB, Tulaczyk S, Dachwald B, Pettit E, et al. Microbial diversity of an Antarctic subglacial community and high-resolution replicate sampling inform hydrological connectivity in a polar desert. Environ Microbiol. 2019;21:2290–306.PubMed 
Article 

Google Scholar 
Cooper ZS, Rapp JZ, Carpenter SD, Iwahana G, Eicken H, Deming JW. Distinctive microbial communities in subzero hypersaline brines from Arctic coastal sea ice and rarely sampled cryopegs. FEMS Microbiol Ecol. 2019;95:1–15.Article 

Google Scholar 
Winkel M, Mitzscherling J, Overduin PP, Horn F, Winterfeld M, Rijkers R, et al. Anaerobic methanotrophic communities thrive in deep submarine permafrost. Sci Rep. 2018;8:1–13.CAS 

Google Scholar 
Lay CY, Mykytczuk NC, Niederberger TD, Martineau C, Greer CW, Whyte LG. Microbial diversity and activity in hypersaline high Arctic spring channels. Extremophiles. 2012;16:177–91.CAS 
PubMed 
Article 

Google Scholar 
Bhattarai S, Cassarini C, Lens PNL. Physiology and distribution of archaeal methanotrophs that couple anaerobic oxidation of methane with sulfate reduction. Microbiol Mol Biol Rev. 2019;83:1–31.Article 

Google Scholar 
Kleindienst S, Ramette A, Amann R, Knittel K. Distribution and in situ abundance of sulfate-reducing bacteria in diverse marine hydrocarbon seep sediments. Environ Microbiol. 2012;14:2689–710.CAS 
PubMed 
Article 

Google Scholar 
Timmers PH, Welte CU, Koehorst JJ, Plugge CM, Jetten MS, Stams AJ. Reverse methanogenesis and respiration in methanotrophic archaea. Archaea. 2017;2017:1–22.Article 

Google Scholar 
Leu AO, Cai C, McIlroy SJ, Southam G, Orphan VJ, Yuan Z, et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 2020;14:1030–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haroon MF, Hu S, Shi Y, Imelfort M, Keller J, Hugenholtz P, et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature. 2013;500:567–70.CAS 
PubMed 
Article 

Google Scholar 
Cai C, Leu AO, Xie GJ, Guo J, Feng Y, Zhao JX, et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 2018;12:1929–39.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oshkin IY, Wegner CE, Luke C, Glagolev MV, Filippov IV, Pimenov NV, et al. Gammaproteobacterial methanotrophs dominate cold methane seeps in floodplains of West Siberian rivers. Appl Environ Microbiol. 2014;80:5944–54.PubMed 
PubMed Central 
Article 

Google Scholar 
Cabrol L, Thalasso F, Gandois L, Sepulveda-Jauregui A, Martinez-Cruz K, Teisserenc R, et al. Anaerobic oxidation of methane and associated microbiome in anoxic water of Northwestern Siberian lakes. Sci Total Environ. 2020;736:1–16.Article 

Google Scholar 
Orcutt B, Boetius A, Elvert M, Samarkin V, Joye SB. Molecular biogeochemistry of sulfate reduction, methanogenesis and the anaerobic oxidation of methane at Gulf of Mexico cold seeps. Geochim Cosmochim Acta. 2005;69:4267–81.CAS 
Article 

Google Scholar 
Knittel K, Losekann T, Boetius A, Kort R, Amann R. Diversity and distribution of methanotrophic archaea at cold seeps. Appl Environ Microbiol. 2005;71:467–79.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schubert CJ, Coolen MJ, Neretin LN, Schippers A, Abbas B, Durisch-Kaiser E, et al. Aerobic and anaerobic methanotrophs in the Black Sea water column. Environ Microbiol. 2006;8:1844–56.CAS 
PubMed 
Article 

Google Scholar 
Wang J, Hua M, Cai C, Hu J, Wang J, Yang H, et al. Spatial-temporal pattern of sulfate-dependent anaerobic methane oxidation in an intertidal zone of the East China Sea. Appl Environ Microbiol. 2019;85:1–15.
Google Scholar 
Dyksma S, Bischof K, Fuchs BM, Hoffmann K, Meier D, Meyerdierks A, et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J. 2016;10:1939–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Perreault NN, Greer CW, Andersen DT, Tille S, Lacrampe-Couloume G, Lollar BS, et al. Heterotrophic and autotrophic microbial populations in cold perennial springs of the high Arctic. Appl Environ Microbiol. 2008;74:6898–907.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cordero PRF, Bayly K, Man Leung P, Huang C, Islam ZF, Schittenhelm RB, et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 2019;13:2868–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nigro LM, Elling FJ, Hinrichs KU, Joye SB, Teske A. Microbial ecology and biogeochemistry of hypersaline sediments in Orca Basin. PLoS ONE. 2020;15:1–25.Article 

Google Scholar 
Rath KM, Fierer N, Murphy DV, Rousk J. Linking bacterial community composition to soil salinity along environmental gradients. ISME J. 2019;13:836–46.CAS 
PubMed 
Article 

Google Scholar 
Yoon JH, Lee MH, Kang SJ, Oh TK. Salegentibacter salinarum sp. nov., isolated from a marine solar saltern. Int J Syst Evol Microbiol. 2008;58:365–9.CAS 
PubMed 
Article 

Google Scholar 
Sangwan N, Xia F, Gilbert JA. Recovering complete and draft population genomes from metagenome datasets. Microbiome. 2016;4:1–11.Article 

Google Scholar 
Goordial J, Raymond-Bouchard I, Zolotarov Y, de Bethencourt L, Ronholm J, Shapiro N, et al. Cold adaptive traits revealed by comparative genomic analysis of the eurypsychrophile Rhodococcus sp. JG3 isolated from high elevation McMurdo Dry Valley permafrost, Antarctica. FEMS Microbiol Ecol. 2016;92:1–11.
Google Scholar 
Laso-Perez R, Wegener G, Knittel K, Widdel F, Harding KJ, Krukenberg V, et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature. 2016;539:396–401.CAS 
PubMed 
Article 

Google Scholar 
Dombrowski N, Teske AP, Baker BJ. Expansive microbial metabolic versatility and biodiversity in dynamic Guaymas Basin hydrothermal sediments. Nat Commun. 2018;9:1–13.CAS 
Article 

Google Scholar 
Oren A. Thermodynamic limits to microbial life at high salt concentrations. Environ Microbiol. 2011;13:1908–23.CAS 
PubMed 
Article 

Google Scholar 
Gunde-Cimerman N, Plemenitas A, Oren A. Strategies of adaptation of microorganisms of the three domains of life to high salt concentrations. FEMS Microbiol Rev. 2018;42:353–75.CAS 
PubMed 
Article 

Google Scholar 
Hechler T, Pfeifer F. Anaerobiosis inhibits gas vesicle formation in halophilic. Archaea Mol Microbiol. 2009;71:132–45.CAS 
PubMed 
Article 

Google Scholar 
Stokke R, Roalkvam I, Lanzen A, Haflidason H, Steen IH. Integrated metagenomic and metaproteomic analyses of an ANME-1-dominated community in marine cold seep sediments. Environ Microbiol. 2012;14:1333–46.CAS 
PubMed 
Article 

Google Scholar 
Wegener G, Krukenberg V, Riedel D, Tegetmeyer HE, Boetius A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature. 2015;526:587–90.CAS 
PubMed 
Article 

Google Scholar 
Skennerton CT, Chourey K, Iyer R, Hettich RL, Tyson GW, Orphan VJ. Methane-fueled syntrophy through extracellular electron transfer: uncovering the genomic traits conserved within diverse bacterial partners of anaerobic methanotrophic archaea. mBio. 2017;8:1–14.Article 

Google Scholar 
Krukenberg V, Riedel D, Gruber-Vodicka HR, Buttigieg PL, Tegetmeyer HE, Boetius A, et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ Microbiol. 2018;20:1651–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Youssef NH, Rinke C, Stepanauskas R, Farag I, Woyke T, Elshahed MS. Insights into the metabolism, lifestyle and putative evolutionary history of the novel archaeal phylum ‘Diapherotrites’. ISME J. 2015;9:447–60.CAS 
PubMed 
Article 

Google Scholar 
Castelle CJ, Brown CT, Anantharaman K, Probst AJ, Huang RH, Banfield JF. Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations. Nat Rev Microbiol. 2018;16:629–45.CAS 
PubMed 
Article 

Google Scholar 
Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng JF, et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013;499:431–7.CAS 
PubMed 
Article 

Google Scholar 
Dombrowski N, Lee JH, Williams TA, Offre P, Spang A. Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett. 2019;366:1–12.Article 

Google Scholar 
Wong HL, MacLeod FI, White RA 3rd, Visscher PT, Burns BP. Microbial dark matter filling the niche in hypersaline microbial mats. Microbiome. 2020;8:1–14.Article 

Google Scholar 
Schut GJ, Nixon WJ, Lipscomb GL, Scott RA, Adams MW. Mutational analyses of the enzymes involved in the metabolism of hydrogen by the hyperthermophilic archaeon Pyrococcus furiosus. Front Microbiol. 2012;3:1–6.Article 

Google Scholar 
Ruuskanen MO, Colby G, St. Pierre KA, St. Louis VL, Aris‐Brosou S, Poulain AJ. Microbial genomes retrieved from High Arctic lake sediments encode for adaptation to cold and oligotrophic environments. Limnol Oceanogr. 2020;65:S233–S247.CAS 
Article 

Google Scholar 
Vigneron A, Cruaud P, Lovejoy C, Vincent WF. Genomic evidence of functional diversity in DPANN archaea, from oxic species to anoxic vampiristic consortia. ISME Commun. 2022;2:1–10.Article 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
PubMed 
Article 

Google Scholar 
Meheust R, Castelle CJ, Matheus Carnevali PB, Farag IF, He C, Chen LX, et al. Groundwater Elusimicrobia are metabolically diverse compared to gut microbiome Elusimicrobia and some have a novel nitrogenase paralog. ISME J. 2020;14:2907–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hahn CR, Farag IF, Murphy CL, Podar M, Elshahed MS, Youssef NH. Microbial diversity and sulfur cycling in an early earth analogue: from ancient novelty to modern commonality. mBio. https://doi.org/10.1128/mbio.00016-22. (in press).Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods. 2015;12:7–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rummel JD, Beaty DW, Jones MA, Bakermans C, Barlow NG, Boston PJ, et al. A new analysis of Mars “Special Regions”: findings of the second MEPAG Special Regions Science Analysis Group (SR-SAG2). Astrobiology. 2014;14:887–968.PubMed 
Article 

Google Scholar 
Harris RL, Schuerger AC, Wang W, Tamama Y, Garvin ZK, Onstott TC. Transcriptional response to prolonged perchlorate exposure in the methanogen Methanosarcina barkeri and implications for Martian habitability. Sci Rep. 2021;11:1–16.Article 

Google Scholar 
Webster CR, Mahaffy PR, Atreya SK, Moores JE, Flesch GJ, Malespin C, et al. Background levels of methane in Mars’ atmosphere show strong seasonal variations. Science. 2018;360:1093–6.CAS 
PubMed 
Article 

Google Scholar 
Oehler DZ, Etiope G. Methane seepage on Mars: where to look and why. Astrobiology. 2017;17:1233–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marlow JJ, Larowe DE, Ehlmann BL, Amend JP, Orphan VJ. The potential for biologically catalyzed anaerobic methane oxidation on ancient Mars. Astrobiology. 2014;14:292–307.CAS 
PubMed 
Article 

Google Scholar 
Ji M, Greening C, Vanwonterghem I, Carere CR, Bay SK, Steen JA, et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature. 2017;552:400–3.CAS 
PubMed 
Article 

Google Scholar 
Berg JS, Ahmerkamp S, Pjevac P, Hausmann B, Milucka J, Kuypers MMM. How low can they go? Aerobic respiration by microorganisms under apparent anoxia. FEMS Microbiol Rev. 2022;fuac006. https://doi.org/10.1093/femsre/fuac006.Berg JS, Pjevac P, Sommer T, Buckner CRT, Philippi M, Hach PF, et al. Dark aerobic sulfide oxidation by anoxygenic phototrophs in anoxic waters. Environ Microbiol. 2019;21:1611–26.CAS 
PubMed 
Article 

Google Scholar 
Stamenković V, Ward LM, Mischna M, Fischer WW. O2 solubility in Martian near-surface environments and implications for aerobic life. Nat Geosci. 2018;11:905–9.Article 

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

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

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

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