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

In this study we analysed how tree and arborescent palm species richness was related to aboveground carbon stock, commercially relevant timber stock, and commercially relevant NTFP abundance in tropical forests, and how these relationships were influenced by environmental stratification at different spatial scales. We found that species richness showed significant relationships with all three ecosystem services stock components, but its relationships were strongly influenced by variation across forest types and biogeographical strata. This is further explained below.Across the Guiana Shield, species richness showed a positive relationship with carbon stock and timber, but not with NTFP abundance. Although relationships only differed in significance among the biogeographical subregions, they differed in direction between terra firme forests and white sand forests. Species richness was positively related to carbon stock and timber stock in terra firme forests, whereas it was negatively related to NTFP abundance in white sand forests. The positive species-carbon relationship across forests of the Guiana Shield is in line with the effects described by hypotheses such as the ‘niche complementarity’ and ‘selection effect’10 and is in line with previous findings at regional spatial scales6,21. To our knowledge, the relationship between species richness and timber stock has not been previously analysed for tropical forests. Interestingly, the observed positive species-timber relationship in terra firme forests of the Guiana Shield contrasts with the negative species-timber relationship found for subtropical forests in both the U.S.A. and Spain20, although this may be explained by the difference in ecosystems. The non-significant species-NTFP abundance relationship across the Guiana Shield and the negative relationship within white sand forests seems to contradict previous findings. Steur et al.24 found a negative species-NTFP abundance relationship for tropical forests in Suriname. However, this negative relationship was found across multiple forest types, including flooded forests that had low species richness and high NTFP abundance. These flooded forests most likely influenced the species-NTFP abundance relationship across all forest types.In contrast to the relationship between species richness and carbon stock, no mechanism has been proposed for how species richness would influence commercial timber stock and NTFP abundance. Although our results suggest that species richness had a positive relationship with timber, the relationship was not found within multiple biogeographical subregions. For NTFP abundance, species richness did not contribute to explaining variation when variation across biogeographical subregions was accounted for (i.e. was included as an explanatory variable). We here tentatively propose that both commercial relevant timber stock and NTFP abundance are driven by variation in species floristic composition, rather than by species richness. For services such as commercial timber and NTFP provisioning, only a subset of all species is relevant (in this study, 9.4% of all morphospecies for timber and 3.8% for NTFPs), and such subsets are likely not random selections. For example, for Suriname, it was found that variation in commercially relevant NTFP abundance was driven by a particularly small selection of NTFP producing species with high abundances (referred to as ‘NTFP oligarchs’)24, and for commercial relevant timber stock, it is commonly known that selections tend to include more abundant than rare species. Additionally, as the relative abundance of species tends to vary across floristic regions in Amazonia, where, for example, certain species are dominant in particular forest types and biogeographical regions31,32, it can be expected that commercial timber stock and NTFP abundance are determined by floristic composition. In support, for NTFP abundance in Suriname tropical forests, Steur et al.24 found that floristic composition was a stronger predictor of NTFP abundance than species richness.Across all of Amazonia, species richness had a positive relationship with carbon stock, but only when variation among biogeographical regions was accounted for. The positive species-carbon relationship across Amazonia partly contrasts with previous findings at continental spatial scales11,13. When variation across climatic and/or edaphic variables was accounted for, Sullivan et al.13 found no significant species-carbon relationship across South-America, while Poorter et al.33 did find a positive relationship across Meso- and South-America. Here, we propose that accounting for differences among biogeographical regions can explain the previously found contrasts at continental spatial scales. In our dataset, for individual regions, we found either a positive relationship or a non-significant, but weakly positive, relationship between carbon stock and species richness (Fig. 2). However, when the data were aggregated across all regions, this resulted in a non-significant, and weakly negative, relationship. This reflects a known statistical phenomenon referred to as a ‘Simpson’s paradox’34, in which a relationship appears in multiple distinct groups but disappears or reverses when the groups are combined. Additional post-hoc tests of leaving one region out at a time showed that this pattern was not dependent of any particular biogeographical region. This is the first time that an analysis based on empirical data provides evidence for a Simpson’s paradox in species-ecosystem service relationships.It is likely that the observed differences in carbon stock across the biogeographical regions of Amazonia are influenced by multiple factors. For example, the biogeographical regions used in our analyses were recognised according to differences in substrate history, geological age and floristic composition, which could all contribute to variation in carbon stock. The substrate history and geological age of the biogeographical regions have been related to differences in soil fertility35, while multiple spatial gradients in floristic composition identified across the Amazon coincide with a spatial gradient in wood density28. However, further analysis is needed to obtain better insight into the relative contributions of these and other variables to explain the observed variation in carbon stock across the biogeographical regions. This requires data on multiple environmental variables, including floristic composition, climatic variables such as the length of the dry period, soil conditions, and intensity of disturbance.In our analyses, terra firme forests determined the relationship of species richness with the carbon stock, timber stock, and NTFP abundance across the datasets. Although this is most likely the effect of unequal sample sizes, with terra firme forests being the dominant forest type in terms of sample size (n = 130 vs. n = 21 for the Guiana Shield dataset; n = 257 vs. n = 26 for the Amazonia dataset), we expect that the observed relationships reflect the general pattern. Terra firme forests are the most dominant forest type in terms of geographical area32 and were representatively sampled. Regardless, the analyses per forest type had added value. The significant relationship between species richness and NTFP abundance in white sand forests across the Guiana Shield would otherwise have been overlooked.Due to the known scarcity of reliable and adequate information on which timber and NTFP species are being commercially traded36,37,38,39, we used a fixed set of timber and NTFP species to apply across the Guiana Shield plots. However, in reality, timber and NTFP species can be expected to vary according to socio-economic factors, such as culture, access, and harvest costs, which may change over space and time. Therefore, estimates of timber stock and NTFP abundance can be expected to differ across spatial gradients, and thus, their possible relationships with species richness cannot be easily generalised. To circumvent this, timber stock and NTFP abundance would have to be estimated on the basis of ‘flexible’ species selections that can change according to local socio-economic contexts. To this end, detailed information on both commercially relevant timber and NTFP species is urgently needed. Yet, for our study area, we did not observe major differences in selected species, and we included broad selections of species, which should make timber stock and NTFP abundance robust against small deviations in species selection. It must be noted that our approach of quantifying commercial relevant timber stock and NTFP abundance does not consider the value of timber and NTFPs for subsistence use. In addition, NTFPs can also be derived from other growth forms, such as lianas, shrubs and herbs. Last, because NTFP production data was not available we used NTFP abundance as a proxy for NTFP stock, following similar assessments of NTFP stock 24,40. A limitation of this approach is that each NTFP species individual has an equal contribution to NTFP stock, whereas it can be expected that large individuals may have a larger contribution than smaller individuals and that production volumes can differ for different types of NTFPs, for example barks vs. seeds.Our findings illustrate the importance of considering environmental stratification and spatial scale when analysing relationships between biodiversity and ecosystem services. First, environmental stratification can help detect relationships that are otherwise obscured by environmental heterogeneity. For example, although the association between species richness and carbon stock across Amazonia was relatively weak (explaining ~ 3% of total variation vs. ~ 15% in the Guiana Shield) and was obscured by variation in carbon stock across biogeographical strata, by using environmental stratification the positive relationship remained detectable. Second, environmental heterogeneity tends to vary with spatial scale; therefore, its importance needs to be checked according to spatial scale. For example, at the regional scale of the Guiana Shield, biogeographical subregions explained a moderate amount of variation in carbon stock (~ 20%), while at the spatial scale of Amazonia, biogeographical regions explained more than half of total variation in carbon stock (~ 55%). Such an increase and ultimate importance of variation across biogeographical strata might also explain the absence of a significant relationship between species richness and carbon stock across African and/or Asian tropical forests as reported by Sullivan et al.13.In our analyses, we found evidence of a positive relationship between species richness and carbon stock across and within Amazonia. This supports the notion that win–win scenarios are possible in conservation approaches, where, for example, REDD+ can be expected to help conserve tropical forests that contain large amounts of carbon stock and high concentrations of species9. However, we conclude that species richness is not always a strong predictor of biomass-based ecosystem services. In our analyses, NTFP abundance was not driven by species richness, and we ultimately expect the same for timber stock. We expect that differences in floristic composition, linked to differences across forest types and biogeographical strata, will be more relevant than species richness in explaining variation in timber stock and NTFP abundance. This would mean that conserving timber and NTFP related ecosystem services requires the development of additional region-specific strategies that account for differences in floristic composition. For example, areas with high concentrations of timber or NTFPs could be considered in the designation of multiple use protected areas41, such as the extractive reserves in Brazil, or be included as ‘high conservation value areas’ (HCVAs) in sustainable forest management certification42. More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

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

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