Ecology

1.Van Hees, P. A. W., Jones, D. L., Finlay, R., Godbold, D. L. & Lundström, U. S. The carbon we do not see – The impact of low molecular weight compounds on carbon dynamics and respiration in forest soils: a review. Soil Biol. Biochem. 37, 1–13 (2005).Article 
CAS 

Google Scholar 
2.Shindo, H., Ohta, S. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IX. Distribution of phenolic acids in soils of paddy fields and forests. Soil Sci. Plant Nutr. 24, 233–243 (1978).CAS 
Article 

Google Scholar 
3.Katase, T. Distribution of different forms of p-hydroxybenzoic, vanillic, p-coumaric and ferulic acids in forest soil. Soil Sci. Plant Nutr. 27, 365–371 (1981).CAS 
Article 

Google Scholar 
4.Muscolo, A. & Sidari, M. Seasonal fluctuations in soil phenolics of a coniferous forest: effects on seed germination of different coniferous species. Plant Soil. 284, 305–318 (2006).CAS 
Article 

Google Scholar 
5.Whitehead, D. C., Dibb, H. & Hartley, R. D. Bound phenolic compounds in water extracts of soils, plant roots and leaf litter. Soil Biol. Biochem. 15, 133–136 (1983).CAS 
Article 

Google Scholar 
6.Kuiters, A. T. & Sarink, H. M. Leaching of phenolic compounds from leaf and needle litter of several deciduous and coniferous trees. Soil Biol. Biochem. 18, 475–480 (1986).CAS 
Article 

Google Scholar 
7.Gallet, C. & Pellissier, F. Phenolic compounds in natural solutions of a coniferous forest. J. Chem. Ecol. 23, 2401–2412 (1997).CAS 
Article 

Google Scholar 
8.Schofield, J. A., Hagerman, A. E. & Harold, A. Loss of tannins and other phenolics from willow leaf litter. J. Chem. Ecol. 24, 1409–1421 (1998).CAS 
Article 

Google Scholar 
9.Kaiser, K., Guggenberger, G., Haumaier, L. & Zech, W. Seasonal variations in the chemical composition of dissolved organic matter in organic forest floor layer leachates of old-growth Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) stands in northeastern Bavaria, German. Biogeochemistry. 55, 103–143 (2001).CAS 
Article 

Google Scholar 
10.Li H. et al. Forest gaps alter the total phenol dynamics in decomposing litter in an alpine fir forest. PLoS ONE. 11, e0148426 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Blagodatskaya, E. & Kuzyakov, Y. Mechanisms of real and apparent priming effects and their dependence on soil microbial biomass and community structure: critical review. Biol. Fertil. Soils. 45, 115–131 (2008).Article 

Google Scholar 
12.Nottingham, A. T., Turner, B. L., Chamberlain, P. M., Stott, A. W. & Tanner, E. V. J. Priming and microbial nutrient limitation in lowland tropical forest soils of contrasting fertility. Biogeochemistry. 111, 219–237 (2012).CAS 
Article 

Google Scholar 
13.Stewart, C. E., Moturi, P., Follett, R. F. & Halvorson, A. D. Lignin biochemistry and soil N determine crop residue decomposition and soil priming. Biogeochemistry. 124, 335–351 (2015).CAS 
Article 

Google Scholar 
14.Lonardo, D. P. Di et al. Priming of soil organic matter: chemical structure of added compounds is more important than the energy content. Soil Biol. Biochem. 108, 41–54 (2017).Article 
CAS 

Google Scholar 
15.Zwetsloot, M. J. et al. Prevalent root-derived phenolics drive shifts in microbial community composition and prime decomposition in forest soil. Soil Biol. Biochem. 145, 530–541 (2020).Article 
CAS 

Google Scholar 
16.Tao, X. et al. Winter warming in Alaska accelerates lignin decomposition contributed by Proteobacteria. Microbiome 8, 84 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Wutzler, T. & Reichstein, M. Priming and substrate quality interactions in soil organic matter models. Biogeosciences. 10, 2089–2103 (2013).Article 

Google Scholar 
18.Guenet, B. et al. Impact of priming on global soil carbon stocks. Glob Chang Biol. 24, 1873–1883 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Schöning, I. & Kögel-Knabner, I. Chemical composition of young and old carbon pools throughout Cambisol and Luvisol profiles under forests. Soil Biol. Biochem. 38, 2411–2424 (2006).Article 
CAS 

Google Scholar 
20.Kleber, M. et al. Old and stable soil organic matter is not necessarily chemically recalcitrant: implications for modeling concepts and temperature sensitivity. Glob Chang Biol. 17, 1097–1107 (2011).Article 

Google Scholar 
21.Northup, R. R., Dahlgren, R. A. & Yu, Z. Intraspecific variation of conifer phenolic concentration on a marine terrace soil acidity gradient; a new interpretation. Plant Soil. 171, 255–262 (1995).CAS 
Article 

Google Scholar 
22.Sanger, L. J., Cox, P., Splatt, P., Whelan, M. J. & Anderson, J. M. Variability in the quality of Pinus sylvestris needles and litter from sites with different soil characteristics: Lignin and phenylpropanoid signature. Soil Biol. Biochem. 28, 829–835 (1996).CAS 
Article 

Google Scholar 
23.Thevenot, M., Dignac, M. F. & Rumpel, C. Fate of lignins in soils: a review. Soil Biol. Biochem. 42, 1200–1211 (2010).CAS 
Article 

Google Scholar 
24.Zwetsloot, M. J. & Bauerle, T. L. Phenolic root exudate and tissue compounds vary widely among temperate forest tree species and have contrasting effects on soil microbial respiration. New Phytol. 218, 530–541 (2018).CAS 
PubMed 
Article 

Google Scholar 
25.Burges, N., Hurst, H. & Walkden, B. The phenolic constituents of humic acid and their relation to the lignin of the plant cover. Geochim. Cosmochim. Acta. 28, 1547–1554 (1964).CAS 
Article 

Google Scholar 
26.Kuiters, A. T. & Denneman, C. A. J. Water-soluble phenolic substances in soils under several coniferous and deciduous tree species. Soil Biol. Biochem. 19, 765–769 (1987).CAS 
Article 

Google Scholar 
27.Jalal, M. A. F. & Read, D. J. The organic acid composition of Calluna heathland soil with special reference to phyto- and fungitoxicity. Plant Soil. 70, 273–286 (1983).CAS 
Article 

Google Scholar 
28.Shindo, H., Ohta, S. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IX. Distribution of phenolic acids in soils of paddy fields and forests. Soil Sci. Plant Nutr. 24, 233–243 (1978).CAS 
Article 

Google Scholar 
29.Whitehead, D. C., Dibb, H. & Hartley, R. D. Extractant pH and the release of phenolic compounds from soils, plant roots and leaf litter. Soil Biol. Biochem. 13, 343–348 (1981).CAS 
Article 

Google Scholar 
30.Ed, V., Boyd, S. & Mokma, D. Extraction of phenolic compounds from a spodsol profile. Soil Sci. 140, 412–420 (1985).Article 

Google Scholar 
31.Wang, Y. et al. Environmental behaviors of phenolic acids dominated their rhizodeposition in boreal poplar plantation forest soils. J. Soils Sediments. 16, 1858–1870 (2016).CAS 
Article 

Google Scholar 
32.Phillips, R. P. et al. Tree species and mycorrhizal associations influence the magnitude of rhizosphere effects. Ecology. 87, 1302–1313 (2006).PubMed 
Article 

Google Scholar 
33.Blum, U. & Shafer, S. R. Microbial populations and phenolic acids in soil. Soil Biol. Biochem. 20, 793–800 (1988).CAS 
Article 

Google Scholar 
34.Shafer, S. R. & Blum, U. Influence of Phenolic acids on microbial populations in the rhizosphere of cucumber. J. Chem. Ecol. 17, 369–389 (1991).CAS 
PubMed 
Article 

Google Scholar 
35.Eilers, K. G., Lauber, C. L., Knight, R. & Fierer, N. Shifts in bacterial community structure associated with inputs of low molecular weight carbon compounds to soil. Soil Biol. Biochem. 42, 896–903 (2010).CAS 
Article 

Google Scholar 
36.Morrissey, E. M. et al. Phylogenetic organization of bacterial activity. ISME J. 10, 2336–2340 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Huang P., Wang T., Wang M., Wu M., Hsu N. Retention of phenolic acids by noncrystalline hydroxy-aluminum and-iron compounds and clay minerals of soils. Soil Sci. 123, 213–219 (1977).CAS 
Article 

Google Scholar 
38.Cecchi, A. M., Koskinen, W. C., Cheng, H. H. & Haider, K. Sorption-desorption of phenolic acids as affected by soil properties. Biol. Fertil. Soils. 39, 235–242 (2004).CAS 
Article 

Google Scholar 
39.Shindo, H. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IV adsorption and movement of phenolic acids in soils. Soil Sci. Plant Nutr. 22, 23–33 (1976).CAS 
Article 

Google Scholar 
40.DeAngelis K. M. et al. Characterization of trapped lignin-degrading microbes in tropical forest soil. PLoS ONE. 6, e19306 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Pold G., Melillo J. M., DeAngelis K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front Microbiol. 6, 480 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 13, 413–429 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Folman, L. B., Klein Gunnewiek, P. J. A., Boddy, L., De & Boer, W. Impact of white-rot fungi on numbers and community composition of bacteria colonizing beech wood from forest soil. FEMS Microbiol. Ecol. 63, 181–191 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Valášková, V. et al. Phylogenetic composition and properties of bacteria coexisting with the fungus Hypholoma fasciculare in decaying wood. ISME J. 3, 1218–1221 (2009).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Mandal, S. M., Chakraborty, D. & Dey, S. Phenolic acids act as signaling molecules in plant-microbe symbioses. Plant Signal Behav. 5, 359–368 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Munoz Aguilar, M. et al. Chemotaxis of rhizobium leguminosarum biovar phaseoli towards flavonoid inducers of the symbiotic nodulation genes. J. Gen. Microbiol. 134, 2741–2746 (1988).CAS 

Google Scholar 
47.Morrissey, E. M. et al. Bacterial carbon use plasticity, phylogenetic diversity and the priming of soil organic matter. ISME J. 11, 1890–1899 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Fontaine, S., Mariotti, A. & Abbadie, L. The priming effect of organic matter: a question of microbial competition? Soil Biol. Biochem. 35, 837–843 (2003).CAS 
Article 

Google Scholar 
49.Liu, X. J. A. et al. The soil priming effect: consistent across ecosystems, elusive mechanisms. Soil Biol Biochem. 140, 107617 (2020).CAS 
Article 

Google Scholar 
50.Fanin, N., Alavoine, G. & Bertrand, I. Temporal dynamics of litter quality, soil properties and microbial strategies as main drivers of the priming effect. Geoderma [Internet]. 377, 114576 (2020).CAS 
Article 

Google Scholar 
51.Fuchs, G., Boll, M. & Heider, J. Microbial degradation of aromatic compounds – from one strategy to four. Nat. Rev. Microbiol. 9, 803–816 (2011).CAS 
PubMed 
Article 

Google Scholar 
52.Yang, Z. H. & Ji, G. D. Quantitative response relationships between degradation rates and functional genes during the degradation of beta-cypermethrin in soil. J. Hazard Mater. 299, 719–724 (2015).CAS 
PubMed 
Article 

Google Scholar 
53.Nishiyama, E., Ohtsubo, Y., Nagata, Y. & Tsuda, M. Identification of Burkholderia multivorans ATCC 17616 genes induced in soil environment by in vivo expression technology. Environ. Microbiol. 12, 2539–2558 (2010).CAS 
PubMed 
Article 

Google Scholar 
54.Wilhelm et al. Paraburkholderia madseniana sp. nov., a phenolic acid-degrading bacterium isolated from acidic forest soil. Int. J. Syst. Evol. Microbiol. 70, (2020). https://doi.org/10.1099/ijsem.0.004029.55.Pallant, E. & Riha, S. J. Surface soil acidification under red pine and Norway spruce. Soil Sci. Soc. Am. J. 54, 1124–1130 (1990).CAS 
Article 

Google Scholar 
56.Fahey T. J. et al. Earthworm effects on the incorporation of litter C and N into soil organic matter in a sugar maple forest. Ecol. Appl. 23, 1185–1201 (2013).PubMed 
Article 

Google Scholar 
57.Melvin, A. M. & Goodale, C. L. Tree species and earthworm effects on soil nutrient distribution and turnover in a northeastern United States common garden. Can. J. For. Res. 43, 180–187 (2013).CAS 
Article 

Google Scholar 
58.Suarez E. Invasion of Northern Hardwood Forests by Exotic Earthworm Communities in South-Central New York. Cornell; 2004.59.Greweling T., Peech M. Chemical soil tests. Ithaca; 1960.60.Griffiths, R. I., Whiteley, A. S., O’Donnell, A. G. & Bailey, M. J. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl. Environ. Microbiol. 66, 5488–5491 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Neufeld, J. D. et al. DNA stable-isotope probing. Nat. Protoc. 2, 860–866 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Wilhelm, R., Szeitz, A., Klassen, T. L. & Mohn, W. W. Sensitive, efficient quantitation of 13C-enriched nucleic acids via ultrahigh-performance liquid chromatography-tandem mass spectrometry for applications in stable isotope probing. Appl. Environ. Microbiol. 80, 7206–7211 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the miseq illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article 
CAS 

Google Scholar 
65.De Caceres, M. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology, Ecology. 90, 3566–3574 (2009). http://sites.google.com/site/miqueldecaceres/.PubMed 
Article 

Google Scholar 
66.Wilhelm, R. C., Niederberger, T. D., Greer, C. & Whyte, L. G. Microbial diversity of active layer and permafrost in an acidic wetland from the Canadian High Arctic. Can. J. Microbiol. 57, 303–315 (2011).CAS 
PubMed 
Article 

Google Scholar 
67.Ye, J. et al. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 13, 134 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.López-Gutiérrez, J. C. et al. Quantification of a novel group of nitrate-reducing bacteria in the environment by real-time PCR. J. Microbiol. Methods. 57, 399–407 (2004).PubMed 
Article 
CAS 

Google Scholar 
69.Markowitz, V. M. et al. IMG/M: A data management and analysis system for metagenomes. Nucleic Acids Res. 36(Suppl. 1), 534–538 (2008).
Google Scholar 
70.Martiny, A. C., Treseder, K. & Pusch, G. Phylogenetic conservatism of functional traits in microorganisms. ISME J. 7, 830–838 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41(D1), 590–596 (2013).Article 
CAS 

Google Scholar 
74.Wickham, H. Elegant graphics for data analysis. Media. 35, 211 (2009).
Google Scholar 
75.McMurdie P. J., Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Oksanen J. et al. Vegan: community ecology package. R Packag. 2015;77.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Price M. N., Dehal P. S., Arkin A. P. FastTree 2 – Approximately maximum-likelihood trees for large alignments. PLoS ONE. 5, e9490 (2010).Article 
CAS 

Google Scholar 
79.Wilhelm R. C. et al. Paraburkholderia solitsugae sp. nov. and Paraburkholderia elongata sp. nov., phenolic acid-degrading bacteria isolated from forest soil and emended description of Paraburkholderia madseniana. Int. J. Syst. Evol. Microbiol. 70, 5093–5105 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Chain, P. S. G. et al. Burkholderia xenovorans LB400 harbors a multi-replicon, 9.73-Mbp genome shaped for versatility. PNAS. 103, 15280–15287 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
81.Mason-Jones, K. & Kuzyakov, Y. “Non-metabolizable” glucose analogue shines new light on priming mechanisms: Triggering of microbial metabolism. Soil Biol. Biochem. 107, 68–76 (2017).CAS 
Article 

Google Scholar 
82.Yuan, Y. et al. Exudate components exert different influences on microbially mediated C losses in simulated rhizosphere soils of a spruce plantation. Plant Soil. 419, 127–140 (2017).CAS 
Article 

Google Scholar 
83.Liu, X. J. A. et al. Labile carbon input determines the direction and magnitude of the priming effect. Appl. Soil Ecol. 109, 7–13 (2017).Article 

Google Scholar 
84.Sugai, S. F. & Schimel, J. P. Decomposition and biomass incorporation of 14C-labeled glucose and phenolics in taiga forest floor: effect of substrate quality, successional state, and season. Soil Biol. Biochem. 25, 1379–1389 (1993).CAS 
Article 

Google Scholar 
85.Fontaine, S. et al. Fungi mediate long term sequestration of carbon and nitrogen in soil through their priming effect. Soil Biol. Biochem. 43, 86–96 (2011).CAS 
Article 

Google Scholar 
86.Zhu, Z. et al. Microbial stoichiometric flexibility regulates rice straw mineralization and its priming effect in paddy soil. Soil Biol. Biochem. 121, 67–76 (2018).CAS 
Article 

Google Scholar 
87.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 1–7 (2016).Article 
CAS 

Google Scholar 
88.Smirnova, G. V. & Oktyabrsky, O. N. Relationship between Escherichia coli growth rate and bacterial susceptibility to ciprofloxacin. FEMS Microbiol. Lett. 365, 1–6 (2018).Article 
CAS 

Google Scholar 
89.Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. rRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microbiol. 66, 1328–1333 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
90.Méndez V., Agulló L., González M., Seeger M. The homogentisate and homoprotocatechuate central pathways are involved in 3- and 4-hydroxyphenylacetate degradation by Burkholderia xenovorans LB400. PLoS ONE. 6, e17583 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
91.Johnson, G. R. & Olsen, R. H. Multiple pathways for toluene degradation in Burkholderia sp. strain JS150. Appl. Environ. Microbiol. 63, 4047–4052 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Andreolli, M. et al. Endophytic Burkholderia fungorum DBT1 can improve phytoremediation efficiency of polycyclic aromatic hydrocarbons. Chemosphere. 92, 688–694 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Somtrakoon, K. et al. Phenanthrene stimulates the degradation of pyrene and fluoranthene by Burkholderia sp. VUN10013. World J. Microbiol. Biotechnol. 24, 523–531 (2008).CAS 
Article 

Google Scholar 
94.Chain, P. S. G. et al. Burkholderia xenovorans LB400 harbors a multi-replicon, 9.73-Mbp genome shaped for versatility. Proc. Natl. Acad. Sci. 103, 15280–15287 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
95.Raj, A., Krishna Reddy, M. M. & Chandra, R. Identification of low molecular weight aromatic compounds by gas chromatography-mass spectrometry (GC-MS) from kraft lignin degradation by three Bacillus sp. Int. Biodeterior Biodegrad. 59, 292–296 (2007).CAS 
Article 

Google Scholar 
96.Shi, Y. et al. Biochemical investigation of kraft lignin degradation by Pandoraea sp. B-6 isolated from bamboo slips. Bioprocess Biosyst. Eng. 36, 1957–1965 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Moraes, E. C. et al. Lignolytic-consortium omics analyses reveal novel genomes and pathways involved in lignin modification and valorization. Biotechnol. Biofuels. 11, 1–16 (2018).CAS 
Article 

Google Scholar 
98.Coenye, T. et al. Burkholderia fungorum sp. nov. and Burkholderia caledonica sp. nov., two new species isolated from the environment, animals and human clinical samples. Int. J. Syst. Evol. Microbiol. 51, 1099–1107 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Lim, Y. W., Baik, K. S., Han, S. K., Kim, S. B. & Bae, K. S. Burkholderia sordidicola sp. nov., isolated from the white-rot fungus Phanerochaete sordida. Int. J. Syst. Evol. Microbiol. 53, 1631–1636 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Herzog C. et al. Microbial succession on decomposing root litter in a drought-prone Scots pine forest. ISME J. 13, 2346–2362 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Yeoh Y. K. et al. Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence. Nat. Commun. 8, 215 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
102.Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 1–11 (2018).Article 
CAS 

Google Scholar 
103.Badri, D. V., Chaparro, J. M., Zhang, R., Shen, Q. & Vivanco, J. M. Application of natural blends of phytochemicals derived from the root exudates of arabidopsis to the soil reveal that phenolic-related compounds predominantly modulate the soil microbiome. J. Biol. Chem. 288, 4502–4512 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
104.Sinsabaugh, R. L. Phenol oxidase, peroxidase and organic matter dynamics of soil. Soil Biol. Biochem. 42, 391–404 (2010).CAS 
Article 

Google Scholar 
105.Henning, J. A. et al. Root bacterial endophytes alter plant phenotype, but not physiology. PeerJ. 2016, 1–20 (2016).
Google Scholar 
106.Caballero-Mellado, J., Martínez-Aguilar, L., Paredes-Valdez, G., & Estrada-de los Santos, P. Burkholderia unamae sp. nov., an N2-fixing rhizospheric and endophytic species. Int. J. Syst. Evol. Microbiol. 54, 1165–1172 (2004).CAS 
PubMed 
Article 

Google Scholar 
107.Martínez-Aguilar, L. et al. Burkholderia caballeronis sp. nov., a nitrogen fixing species isolated from tomato (Lycopersicon esculentum) with the ability to effectively nodulate Phaseolus vulgaris. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 104, 1063–1071 (2013).
Google Scholar 
108.De Meyer, S. E. et al. Symbiotic and non-symbiotic Paraburkholderia isolated from South African Lebeckia ambigua root nodules and the description of Paraburkholderia fynbosensis sp. Nov. Int. J. Syst. Evol. Microbiol. 68, 2607–2614 (2018).PubMed 
Article 
CAS 

Google Scholar 
109.Peeters, C. et al. Phylogenomic study of Burkholderia glathei-like organisms, proposal of 13 novel Burkholderia species and emended descriptions of Burkholderia sordidicola, Burkholderia zhejiangensis, and Burkholderia grimmiae. Front Microbiol. 7, 1–19 (2016).
Google Scholar 
110.Vandamme, P. et al. Burkholderia humi sp. nov., Burkholderia choica sp. nov., Burkholderia telluris sp. nov., Burkholderia terrestris sp. nov. and Burkholderia udeis sp. nov.: Burkholderia glathei-like bacteria from soil and rhizosph. Int. J. Syst. Evol. Microbiol. 63(PART 12), 4707–4718 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
111.Shiraishi, A., Matsushita, N. & Hougetsu, T. Nodulation in black locust by the Gammaproteobacteria Pseudomonas sp. and the Betaproteobacteria Burkholderia sp. Syst. Appl. Microbiol. 33, 269–274 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
112.Thijs, S. et al. Exploring the rhizospheric and endophytic bacterial communities of Acer pseudoplatanus growing on a TNT-contaminated soil: towards the development of a rhizocompetent TNT-detoxifying plant growth promoting consortium. Plant Soil. 385, 15–36 (2014).CAS 
Article 

Google Scholar 
113.Mavengere, N. R., Ellis, A. G. & Le Roux, J. J. Burkholderia aspalathi sp. nov., isolated from root nodules of the South African legume Aspalathus abietina Thunb. Int. J. Syst. Evol. Microbiol. 64(PART 6), 1906–1912 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
114.Blair, P. M. et al. Exploration of the biosynthetic potential of the populus microbiome. mSystems. 3, 1–17 (2018).Article 

Google Scholar 
115.Peters, N. K. & Verma, D. P. S. Phenolic compounds as regulators of gene expression in plant-microbe interactions. Mol. Plant-Microbe Interact. 3, 4–8 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
116.Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 
Article 

Google Scholar  More

1.Higuchi, R. et al. DNA sequences from the quagga, an extinct member of the horse family. Nature 312, 282–284 (1984).CAS 
PubMed 
Article 
ADS 

Google Scholar 
2.Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. U. S. A. 110, 15758–15763 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
3.Hansen, H. B. et al. Comparing ancient DNA preservation in petrous bone and tooth cementum. PLoS ONE 12, e0170940 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Hagan, R. W. et al. Comparison of extraction methods for recovering ancient microbial DNA from paleofeces. Am. J. Phys. Anthropol. 171, 275–284 (2020).PubMed 
Article 

Google Scholar 
5.Epp, L. S., Zimmermann, H. H. & Stoof-Leichsenring, K. R. Sampling and extraction of ancient DNA from sediments. In Ancient DNA. Methods in Molecular Biology (eds Shapiro, B. et al.) 31–44 (Humana Press, 2019).
Google Scholar 
6.Modi, A. et al. Combined methodologies for gaining much information from ancient dental calculus: testing experimental strategies for simultaneously analysing DNA and food residues. Archaeol. Anthropol. Sci. 12, 10 (2020).Article 

Google Scholar 
7.Campos, P. F. & Gilbert, M. T. P. DNA extraction from keratin and chitin. In Ancient DNA. Methods in Molecular Biology (eds Shapiro, B. et al.) 57–63 (Humana Press, 2019).
Google Scholar 
8.Adler, C. J. et al. Sequencing ancient calcified dental plaque shows changes in oral microbiota with dietary shifts of the neolithic and industrial revolutions. Nat. Genet. 45, 450-455e1 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Warinner, C. et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 46, 336–344 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Weyrich, L. S. et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357–361 (2017).CAS 
PubMed 
Article 
ADS 

Google Scholar 
11.Slon, V. et al. Neandertal and Denisovan DNA from Pleistocene sediments. Science 356, 605–608 (2017).CAS 
PubMed 
Article 
ADS 

Google Scholar 
12.Teasdale, M. D. et al. The York Gospels: a 1000-year biological palimpsest. R. Soc. Open. Sci. 4, 170988 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
13.Boast, A. et al. Coprolites reveal ecological interactions lost with the extinction of New Zealand birds. Proc. Natl. Acad. Sci. U. S. A. 115, 1546–1551 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Zarrillo, S. et al. The use and domestication of Theobroma cacao during the mid-Holocene in the upper Amazon. Nat. Ecol. Evol. 2, 1879–1888 (2018).PubMed 
Article 

Google Scholar 
15.Cano, R. J. et al. Amplification and sequencing of DNA from a 120–135 million-year-old weevil. Nature 363, 536–538 (1993).CAS 
PubMed 
Article 
ADS 

Google Scholar 
16.DeSalle, R. et al. DNA sequences from a fossil termite in Oligo-Miocene amber and their phylogenetic implications. Science 257, 1933–1936 (1992).CAS 
PubMed 
Article 
ADS 

Google Scholar 
17.Sherratt, E. et al. Amber fossils demonstrate deep-time stability of Caribbean lizard communities. Proc. Natl. Acad. Sci. U. S. A. 112, 9961–9966 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
18.Sadowski, E. M. et al. Carnivorous leaves from Baltic amber. Proc. Natl. Acad. Sci. U. S. A. 112, 190–195 (2015).CAS 
PubMed 
Article 
ADS 

Google Scholar 
19.Xing, L. et al. A feathered dinosaur tail with primitive plumage trapped in mid-Cretaceous amber. Curr. Biol. 26, 3352–3360 (2016).CAS 
PubMed 
Article 

Google Scholar 
20.Rikkinen, J., Grimaldi, D. A. & Schmidt, A. R. Morphological stasis in the first myxomycete from the Mesozoic, and the likely role of cryptobiosis. Sci. Rep. 9, 19730 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
21.Peñalver, E. et al. Thrips pollination of Mesozoic gymnosperms. Proc. Natl. Acad. Sci. U. S. A. 109, 8623–8628 (2012).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
22.Cai, C. et al. Beetle pollination of cycads in the mesozoic. Curr. Biol. 28, 2806-2812.e1 (2018).CAS 
PubMed 
Article 

Google Scholar 
23.Bao, T., Wang, B., Li, J. & Dilcher, D. Pollination of Cretaceous flowers. Proc. Natl. Acad. Sci. U. S. A. 116, 24707–24711 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Labandeira, C. Amber. Paleont. Soc. Pap. 20, 163–215 (2014).Article 

Google Scholar 
25.Solórzano-Kraemer, M. M. et al. A revised definition for copal and its significance for palaeontological and Anthropocene biodiversity-loss studies. Sci. Rep. 10, 19904 (2020).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
26.Clifford, D. J. & Hatcher, P. G. Structural transformations of polylabdanoid resinites during maturation. Org. Geochem. 23, 407–418 (1995).CAS 
Article 

Google Scholar 
27.Lambert, J. B., Santiago-Blay, J. A., Wu, Y. & Levy, A. J. Examination of amber and 490 related materials by NMR spectroscopy. Magn. Reson. Chem. 53, 2–8 (2015).CAS 
PubMed 
Article 

Google Scholar 
28.Stankiewicz, B. A. et al. Chemical preservation of plants and insects in natural resins. Proc. Biol. Sci. 265, 641–647 (1998).CAS 
PubMed Central 
Article 

Google Scholar 
29.McCoy, V. E. et al. Ancient amino acids from fossil feathers in amber. Sci. Rep. 9, 6420 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
30.Bada, J. L. et al. Amino acid racemization in amber-entombed insects: implications for DNA preservation. Geochim. Cosmochim. Acta. 58, 3131–3135 (1994).CAS 
PubMed 
Article 
ADS 

Google Scholar 
31.Collins, M. J. et al. Is amino acid racemization a useful tool for screening for ancient DNA in bone?. Proc. R. Soc. B 276, 2971–2977 (2009).CAS 
PubMed 
Article 

Google Scholar 
32.Allentoft, M. E. et al. The half-life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proc. R. Soc. B 279, 4724–4733 (2012).CAS 
PubMed 
Article 

Google Scholar 
33.Kistler, L. et al. A new model for ancient DNA decay based on paleogenomic meta-analysis. Nucleic Acids Res. 45, 6310–6320 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
34.DeSalle, R., Barcia, M. & Wray, C. PCR jumping in clones of 30-million-year-old DNA fragments from amber preserved termites (Mastotermes electrodominicus). Experientia 49, 906–909 (1993).CAS 
PubMed 
Article 

Google Scholar 
35.Poinar, G. O., Poinar, H. N. & Cano, R. J. DNA from amber inclusions. In Ancient DNA (eds Herrmann, B. & Hummel, S.) 92–103 (Springer, 1994).
Google Scholar 
36.Austin, J. J. et al. Problems of reproducibility: does geologically ancient DNA survive in amber-preserved insects?. Proc. Roy. Soc. Lond. Ser. B 264, 467–474 (1997).CAS 
Article 
ADS 

Google Scholar 
37.Penney, D. et al. Absence of ancient DNA in sub-fossil insect inclusions preserved in ‘Anthropocene’ Colombian copal. PLoS ONE 8, e73150 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
38.Peris, D. et al. DNA from resin-embedded organisms: past, present and future. PLoS ONE 15, e0239521 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Reich, D. et al. Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468, 1053–1060 (2010).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
40.Meyer, M. et al. A high-coverage genome sequence from an archaic Denisovan individual. Science 338, 222–226 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
41.Prüfer, K. et al. The complete genome sequence of a Neandertal from the Altai Mountains. Nature 505, 43–49 (2014).PubMed 
Article 
ADS 
CAS 

Google Scholar 
42.Prüfer, K. et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science 358, 655–658 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
43.Meyer, M. et al. Nuclear DNA sequences from the Middle Pleistocene Sima de los Huesos hominins. Nature 531, 504–507 (2016).CAS 
PubMed 
Article 
ADS 

Google Scholar 
44.Gilbert, M. T., Bandelt, H. J., Hofreiter, M. & Barnes, I. Assessing ancient DNA studies. Trends Ecol. Evol. 20, 541–544 (2005).PubMed 
Article 

Google Scholar 
45.Willerslev, E. & Cooper, A. Ancient DNA. Proc. Biol. Sci. 272, 3–16 (2005).CAS 
PubMed 

Google Scholar 
46.Penney, D., Wadsworth, C. & Green, D. I. Extraction of inclusions from (sub)fossil resins, with description of a new species of stingless bee (Hymenoptera: Apidae: Meliponini), in quaternary Colombian copal. Paleontol. Contrib. 2013, 7:1–6 (2013).
Google Scholar 
47.Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 6, pdb.prot5448 (2010).Article 

Google Scholar 
48.Modi, A. et al. Complete mitochondrial sequences from Mesolithic Sardinia. Sci. Rep. 7, 42869 (2017).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
49.Peltzer, A. et al. EAGER: efficient ancient genome reconstruction. Genome Biol. 17, 60 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Andrews, S. FastQC: a quality control tool for high throughput sequence data (2010). Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc51.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Schubert, M. et al. Improving ancient DNA read mapping against modern reference genomes. BMC Genomics 13, 178 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Li, H. et al. The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Altschul, S. et al. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Jackman, S. D. et al. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter. Genome Res. 27, 768–777 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Huson, D. H. et al. MEGAN community edition: interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Hofreiter, M., Jaenicke, V., Serre, D., Haeseler, A. & Pääbo, S. DNA sequences from multiple amplifications reveal artifacts induced by cytosine deamination in ancient DNA. Nucleic Acids Res. 9, 4793–4799 (2011).
Google Scholar 
58.Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl. Acad. Sci. U. S. A. 104, 14616–14621 (2007).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
59.Sawyer, S., Krause, J., Guschanski, K., Savolainen, V. & Pääbo, S. Temporal patterns of nucleotide misincorporations and DNA fragmentation in ancient DNA. PLoS ONE 7, e34131 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
60.Jonsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Kumar, S. et al. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Noonan, J. P. et al. Genomic sequencing of pleistocene cave bears. Science 309, 597–599 (2005).CAS 
PubMed 
Article 
ADS 

Google Scholar 
64.Green, R. E. et al. Analysis of one million base pairs of Neanderthal DNA. Nature 444, 330–336 (2006).CAS 
PubMed 
Article 
ADS 

Google Scholar 
65.Garcia-Garcera, M. et al. Fragmentation of contaminant and endogenous DNA in ancient samples determined by shotgun sequencing; prospects for human palaeogenomics. PLoS ONE 6, e24161 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
66.Llamas, B. et al. From the field to the laboratory: Controlling DNA contamination in human ancient DNA research in the high-throughput sequencing era. STAR 3, 1–14 (2017).Article 

Google Scholar 
67.Wintertona, S. L., Brian, M. W. & Evert, I. S. Phylogeny and Bayesian divergence time estimations of small-headed Xies (Diptera: Acroceridae) using multiple molecular markers. Mol. Phylogenet. Evol. 43, 808–832 (2007).Article 
CAS 

Google Scholar 
68.Gillung, J. P. & Wintertona, S. L. Evolution of fossil and living spider flies based onmorphological and molecular data (Diptera, Acroceridae). Syst. Entomol. 44, 820–841 (2019).Article 

Google Scholar 
69.Klasson, L. et al. The mosaic genome structure of the Wolbachia wRi strain infecting Drosophila simulans. Proc. Natl. Acad. Sci. U. S. A. 106, 5725–5730 (2009).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
70.Daley, T. & Smith, A. D. Predicting the molecular complexity of sequencing libraries. Nat. Methods 10, 325–327 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Spotted wing drosophila captures within the United StatesComparison between the raspberry field and wooded area, during pre-harvest and harvest periods to account for presence of developing and fully ripened fruit, SWD captures and selectivity per QB dry sticky trap is found in Fig. 1A,B. No difference was found in average capture per trap between either area during the pre-harvest period, nor was there a difference between these and the field during the harvest period. The wooded area during the harvest period captured the greatest amount of SWD/trap (F1,209 = 7.335, P = 0.007) (Fig. 1A). Dry sticky traps baited with QB had a significantly higher selectivity during the pre-harvest period in the raspberry field than in the wooded area but was not significantly different from the trap selectivity in the wooded area during the harvest period. The pre-harvest wooded area trap selectivity was not different from the harvest field trap selectivity. While the harvest field trap selectivity was lower than that of the wooded area trap selectivity during the same period (F1,203 = 23.6, P  More

1.Root, T. L. et al. Nature 421, 57–60 (2003).CAS 
Article 

Google Scholar 
2.Morelli, T. L. et al. Front. Ecol. Environ. 18, 228–234 (2020).Article 

Google Scholar 
3.Armstrong, J. B. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-021-00994-y (2021).4.Northcote, T. G. N. Am. J. Fish. Manage. 17, 1029–1045 (1997).Article 

Google Scholar 
5.Xu, C., Letcher, B. H. & Nislow, K. H. Freshwater Biol. 55, 2253–2264 (2010).Article 

Google Scholar 
6.Schlosser, I. J. BioScience 41, 704–712 (1991).Article 

Google Scholar 
7.Al-Chokhachy, R., Alder, J., Hostetler, S., Gresswell, R. & Shepard, B. Glob. Change Biol. 19, 3069–3081 (2013).Article 

Google Scholar 
8.Fausch, K. D., Torgersen, C. E., Baxter, C. V. & Li, H. W. BioScience 52, 483–498 (2002).Article 

Google Scholar 
9.Muhlfeld, C. C. et al. Science 360, 866–867 (2018).CAS 

Google Scholar 
10.Kovach, R. P. et al. Rev. Fish Biol. Fisher. 26, 135–151 (2016).Article 

Google Scholar 
11.Isaak, D. J., Young, M. K., Nagel, D. E., Horan, D. L. & Groce, M. C. Glob. Change Biol. 21, 2540–2553 (2015).Article 

Google Scholar 
12.Isaak, D. J. et al. Water Resour. Res. 53, 9181–9205 (2017).Article 

Google Scholar 
13.Small-Lorenz, S. L., Culp, L. A., Ryder, T. B., Will, T. C. & Marra, P. P. Nat. Clim. Change 3, 91–93 (2013).Article 

Google Scholar 
14.Rieman, B. E. & Dunham, J. B. Ecol. Freshw. Fish 9, 51–64 (2000).Article 

Google Scholar 
15.Guzzo, M. M., Blanchfield, P. J. & Rennie, M. D. Proc. Natl Acad. Sci. USA 114, 9912–9917 (2017).CAS 
Article 

Google Scholar 
16.Brennan, S. R. et al. Science 364, 783–786 (2019).CAS 
Article 

Google Scholar 
17.Hauer, F. R. et al. Sci. Adv. 2, e1600026 (2016).Article 

Google Scholar  More

Traits are attributes that speak to biophysical limitations, pressure on species, ecological functionality, and interactions. They have found their way to the forefront of many discussions and debates about ecosystem dynamics and, with a slight time lag, social-ecological systems1,2,3. The promise is that a traits framework can further our understanding of patterns, dynamics, interactions, and tipping points within and across complex social-ecological systems. But what will it take to make good on this promise, in particular for our cities, where change is fast and—being the places where the majority of humans live—human perceptions are particularly diverse? What kind of framing, what research, would allow traits—classically understood as a different representation and interpretation of well-established and known properties of the social-ecological system―to fully work as “mediators” for understanding the behavior, functions, and needs of urban systems under pressure?This perspective aims to contribute to the current wide-ranging discussion about traits in both theoretical and applied ecology, and parallel work on better understanding human connections to nature. To this end, we explore the potential of using an expanded conceptualization of traits as a platform for integrated approaches to understanding the different facets of people-in-nature relationships and dynamics4,5.Expanding from the original “characteristics which have demonstrable links to the organism’s function”6, we see traits as a nexus where different theories and conceptualisations about social-ecological systems can connect, intertwine and comprehensively allow us to assess the current state of a system—and even more importantly, evaluate the implications of change (Box 1 and Fig. 1). To make it an integrative and useful framework for urban studies and policy/practice, traits need to be easy to recognise and relevant to decision makers across scales and in different contexts. In addition, information on trait profiles—generic as well as site specific—need to be easily available through monitoring or in databases.Fig. 1: Traits within social ecological systems.Theoretical flow chart linking the entities of a social-ecological system to its traits, demonstrating how a traits framework—as outlined in this article—might be positioned to support the analysis, interpretation and governance of urban systems.Full size imageOur argument is threefold: The first dimension focuses on how to assess and anticipate change by establishing chains of interconnected traits that describe and causally connect sensitivity and response to different urban pressures such as heat, soil compaction, environmental toxicants, and stormwater runoff, understood through “response” traits7,8,9,10 to their functional consequences11, mediated by “effect traits”. The second dimension is grounded in human perceptions and appraisal of diversity to highlight the different cues and characteristics people use to detect change or articulate value narratives, and it is linked to the role of traits in ecological literacy. Here, we propose traits be viewed as boundary objects, i.e., features that carry meaning across society (although the meanings might be diverse and sometimes conflicting), and that this second dimension is essential for understanding the role of society and humans in a traits framework. The third dimension outlines how the first two dimensions connect to inform and support decision making and management at different scales, for example in different, multilayer, and multiactor governance processes12 (Fig. 2).Fig. 2: A traits framework for scientific study and practical application.The three dimensions of a social-ecological traits framework for understanding and governing urban systems. The first dimension is represented by observable traits of the urban environment, e.g., features of humans and other co-inhabiting species and their differing responses to pressures and selection, leading to functional consequences and finally, altered characters of an urban social-ecological system. The second dimension is characterized by feedback loops between those effect outcomes and individual and collective perceptions and decision making. Lastly, the third dimension is represented by urban ecosystem planning and management embedded in governance processes and instruments. Through its ability to connect different spheres and discourses, an expanded traits framework can aim for effective and inclusive decision support that is responsive and place-adapted. By expanding and bridging these three dimensions, we can connect different insights and knowledge about ecosystem function and human perceptions, values and interactions with the environment. This will support the development of a (meta-) theoretically grounded, practically applicable traits framework to interrogate reciprocal feedback linkages and nature-human relationships. The figure includes resources from Freepik.com.Full size imageBox 1 definitionsFunctional trait: A feature of an organism which has demonstrable links to the organism’s function69, and, as such, “determines the organism’s response to pressures (response trait), and/or its effects on ecosystem processes or services (effect trait). In plants, functional traits include morphological, ecophysiological, biochemical and regeneration traits, including demographic traits (at population level). In animals, these traits are combined with life history and behavioral traits (e.g., guilds, organisms that use similar resources/habitats)”70, p. 2779.Boundary object: “[…] those […] objects which both inhabit several intersecting social worlds and satisfy the information requirements of each of them. Boundary objects are objects, which are both plastic enough to adapt to local needs and the constraints of the several parties involving them, yet robust enough to maintain a common identity across sites. […] They have different meanings in different social worlds [and across cultures] but their structure is common enough to more than one world to make them recognizable, a means of translation.”71 p. 393, see also72.Social-ecological traits (expanded definition): An ecologically or socially (inter)active and demonstrable feature of the environment at any level or scale. A social-ecological trait either mediates reactions to selective social-ecological filtering (response trait) or determines effects on ecosystem processes or services (effect trait), or both. The aggregate trait profile of a given entity should ideally speak both to ecological functioning and socio-cultural meaning.The first dimension: response and its effect outcomesTrait-based approaches have been used for descriptive purposes13 to enable broader global comparisons that transcend the constraints of regional taxonomic diversity (e.g., see refs. 6,14) and allow for the types of generalizations sought in ecology15,16. Traits offer a way of looking at causality and change, and trait profiles can indicate whether emergent communities are functionally different from historic communities. To this end, traits can be divided into those that determine an organism’s sensitivity and response to environmental factors, and those that relate to its effect on the environment4,17. When combined, the two categories of traits can be used to detect, identify and monitor the current state of ecosystems, and to anticipate the outcomes of change8,10,17,18,19.An environment described through traits: The urban bio-physical environment includes hydrology and soils, as well as biotic elements (flora and fauna), and understanding the relationships among those components is necessary to measure and anticipate the profound effects of urbanisation. Currently, knowledge of plant traits is most developed4,20, although there is work emerging on traits for animals or other taxonomic groups8,21 as well as for soil and geodiversity22. Animal studies so far tend to focus on habitat modelling for birds, insects, invertebrates and a few on mammals (e.g., see refs. 3,8,16,23). Many studies have looked at the impact of different community assemblages on ecological functions through effect traits and, in particular, how altered or dynamically changing communities will affect ecosystem process through changes in representation of effect traits (but e.g., see ref. 23). However, the link between traits and ecosystem functions has largely been inferred (ibid.), and is, according to Cadotte et al.24, rudimentary (see also25 and26). As we indicated with our definition of traits (Box 1), we see a value in including soil properties as traits and not to leave them as “environmental filters”, as this may offer a more dynamic way of understanding one of the major urban processes of change—soil sealing and compaction—and thus help guide urban development.Traits at different levels and scales: Traits at the species level are by far the best known and most explored, but there are also studies that use traits from other ecological levels—gene, community, ecosystem and landscape—as indicators for tracking response to stress27 and calculating functional “performance”. A common approach to scale is to aggregate species level information. For example, the average values of aggregations of plant species traits at the ecosystem level provide a basis for calculating overall sensitivity to pressures28. This in turn, and drawing on different sets of traits, allows for estimations of changes in ecosystem function (e.g., see ref. 29). However, there are other characteristics that could also be understood as traits. At the landscape level the mosaic of ecosystems and the location and combination of patches are used to assess flows and exchange across larger areas (e.g., see ref. 30). A good example is a city in a river valley, where water flows and exact location within the drainage basin affect urban green spaces and their aggregated matter production, CO2 absorption or carbon sub-section31. Aggregate, or higher-level traits, such as structural composition and functional diversity of vegetation, matter flows, or species migration, are the most common traits analysed through remote sensing in order to track trends25. More work needs to be done to explore relevant traits at different levels of organisation to match the scale and nature of disturbances and the spatial and temporal scale at which different functions are most relevant. Being explicit about scale, and ensuring traits at different levels are nested, allows for tracking of processes across scales.Individual traits, trait combinations, and interlinked suites of traits: A key promise of traits is to provide mechanistic explanations of observed structure, patterns and functionality, which is usually demonstrated through statistical correlations. Further developing suites of response and effect traits could provide valuable input and indicators for assessment and monitoring frameworks. For example, traits could inform DPSIR (drivers, pressures, state, impact, and response) models by anticipating or measuring response to a pressure and the direct and indirect impact this response could have. At a more fundamental level, traits explain whether impacts may be causing a change in the functional state of the system. Interlinked traits, from those determining sensitivity, to those mediating response elicited by sensitivity, could improve mechanistic understanding by supporting the development of stepwise response-effect pathways17. For example, land conversion—like the soil sealing and compaction typical in cities—fundamentally alters soil properties, which in turn affects vegetation. Soil properties influence the growth and composition of plant communities. This translates into trait-mediated effects like reduction of total leaf area, which leads to cascading effects of early leaf senescence and limitation of stomatal transpiration. This reduces water exchange capacity, which in turn is key for mediating air cooling or shading and other functions/services plants may offer to humans.For this first dimension, trait databases, classical field inventories, and experiments, remote sensing data, and GIS-based information are crucial15,32. We see valuable developments from the past two decades of research towards achieving a traits response-effect library in both the ecology and remote sensing communities33,34, even if recent advances from remote sensing studies still rarely find entrance into urban planners’ work and policy decision-making35. In particular, the development in the technical dimensions of detecting traits and trait variation20,34, and tracking these over time, has recently rapidly developed. The progress in application of high-resolution hyperspectral data, light detection, and ranging (LiDAR) or the possibility of mounting the recently developed sensors on unmanned aerial vehicles (UAVs) equip the researchers with addditional tools that can not only expand the range of measurable traits but also allow easy access to data. This provides a powerful support for urban planning and, ultimately, urban governance. Moreover, applications for tablets or smartphones offer alternative ways to directly involve citizens in ecosystem monitoring and further develop citizen science (e.g., see refs. 22,36).The second dimension: traits as an interdisciplinary bridgeThe literature explicitly using the term traits tends to focus on soil, geodiversity, plant, and community trait profiles as an outcome of social-ecological selection through environmental conditions, species interactions, human preferences, management regimes etc. (e.g., see refs. 4,37). This approach has started to address not just how people filter traits (e.g., see ref. 38), but the reason(s) behind either individual or group decisions that lead to filtering (e.g., see refs. 39,40). Here, we propose that the environment, described through traits, could be considered a boundary object (Box 1), allowing for a multiplicity of views, disciplinary connections, engagements, and perceptions, and that speaks to the complexity of social-ecological systems. This will expand the range of functions used to describe a system, and the types of traits required to capture them.Ecological functions relative to ecosystem services: The plant and animal traits that people respond to may not be the same ones that mediate responses to environmental change. For example, seed mass and specific leaf area are important plant functional traits41 but are less likely to influence people’s preferences for urban vegetation (e.g., see ref. 42). Indeed, some esthetic traits promoted by human decision-making and management, such as selection for leaf variation and predominantly deciduous plants, may also lead to the predominance of woody plants that are strongly affected by water stress, fungal attack or insect infestation or trimmed canopies, and thus promote reduced fitness of individual organisms and communities43. On the other hand, a successful reproductive strategy such as the emission of high quantities of pollen might limit the suitability to human-dominated environments (including cities) due to allergenic potential44. Do we need more, or different traits to link ecosystem dynamics more strongly to the lived reality of people? Are traits too simplistic proxies, or perhaps too specific features, to express and understand people–nature interactions? Introducing humans and human appraisal into our trait framework encourages a broader definition of what might be relevant traits. Traits used in this way provide a specific link to interactions and feedback mechanisms between human wellbeing and functional ecology (and respective proxies that serve multiple relational (feedback) purposes).Traits as relational features: Trait lists already include features which are easy to understand and readily detected by human sensory organs, and thus find traction in society or connect to existing ethno-biological narrations39. Traits such as flower colour, leaf shape, and canopy density, which may not necessarily be considered central functional traits, are important drivers of people’s preferences37,39,45,46. Both size and colour of the flowers are plant traits affecting people’s perception47 and can thus be an important factor for gaining societal approval for more urban greenery48. Seasonality is another relevant trait; for example, an extended flowering season49. At the same time, there is a growing interest in flowers and blooming meadows among gardeners worldwide also to support insects in urban landscapes to counteract global biodiversity decline37,39.In this vein, we argue that traits are a formative force influencing human wellbeing and world views, giving shape to ecological systems and linked human affordances (through, e.g., shade and sensory stimuli), and social systems by shaping the context of human activities and experiences. For example, we know that people recognize and value a wide range of plant traits, and that this has even been identified as a useful way to speak about the state of nature and large scale change50. There is evidently a role for traits and trait composition as language for more “functional” ecological literacy36,50. This position as a boundary object needs to be further explored and linked to the responses of social-ecological urban systems, which are subject to a multitude of pressures, including climate change and soil sealing.Traits as boundary objects and connectors between knowledge systems: What is needed to better position and connect the concept of traits to multiple different literatures and disciplines and enable traits to be used as a useful boundary object? Many disciplines outside the ecological and environmental sciences have an interest in understanding ecosystem function and biodiversity, and how people relate to these ideas. Traits, and deeper meanings of some traits, can be found within environmental psychology, ethno-botany/zoology and environmental anthropology. Trait-based approaches may also be well suited to engage with other ways of knowing, such as traditional ecological knowledge and religious systems. This disciplinary and trans-disciplinary knowledge is needed if traits are to connect social-ecological attributes to diverse human values and wellbeing dimensions, and to ensure we do not produce trivial and culturally biased conclusions51,52. Based on the diverse use and potential meanings of the word “traits”, we argue that a traits framework, and traits-focused interdisciplinary discussions and projects, could support a dual ontological stance where some connections are more universal, while others are inherently interpretational or simply individual. Hence, this may help to effectively connect the social and cultural dimensions of traits to a deep ecological understanding of change and its multiple consequences. This would be an important development that allows for critical engagement with concepts like tipping points and system states and what they actually mean in a complex social–ecological urban system.The third dimension: traits for decision supportThe major purpose of the traits concept, as we present it here, is to develop an ontologically inclusive traits framework capable of addressing both the resilience of ecological functions and the experiential and relational aspects of human interactions with nature. On the applied side, this would be relevant to a wide range of decision-making processes, not least urban planning. Clearly visible and easy-to-map traits are well-suited as indicators to describe the state of urban landscapes relevant for biodiversity and society alike. To this end, there are still many questions that need answers. For example, how can the understanding of trait profiles help improve species selection in times of climate change, to inform management priorities and strengthen cross-community stewardship, especially where the diversity of response traits may be low? And which traits are incompatible and how are they best kept separate, a question particularly relevant in the light of zoonosis like the COVID-19 pandemic in 2020? And finally, what traits could best serve as reasonable proxies or indicators to provide either cues or early signals of species responses to (fundamental) change in urban environments?Supporting holistic decisions: Already now we see increasing use of traits in modelling and decision support tools like CiTree and iTree53,54. As cities strive to adapt to climate change by, for example, revising tree species selection (e.g., see ref. 55), an improved understanding of the relationship between detectable functional traits and the provision of ecosystem services can help avoid maladaptation56. For example, replacing shade trees with fine-foliaged trees may improve adaptation to future climates but would not provide the same levels of climate mitigation57. From a decision-making point of view, key traits are those determining the response of ecosystems to human-induced pressures such as air pollution, soil sealing, or urban heat islands, as well as those mediating the effects of these changes on ecosystem services and related benefits as perceived by people8,58.A traits framework that uses our social-ecological definition of traits might support informed decisions on trade-offs. For example, invasive or non-native plants are often seen as ecologically problematic, but certain traits such as high leaf coverage or flower colour and shape make them socially desirable48. Traits connected to more social-ecological dimensions will allow for a more holistic assessment of options and the potential trade-off implications of different choices. While decisions are often grounded, implicitly or explicitly, in considerations of multiple traits (e.g., see ref. 53), we need to ensure that traits considered in the plant selection include both traits related to broad and diverse preferences and desires for ecosystem services and traits, that ensure a resilient response to drivers of change that may impact their ability to provide these services (see, e.g., the scoring system for urban vegetation species proposed by Tiwary et al.59).Urban planning informed by an expanded traits framework and spatial-temporal patterns of trait profiles has the promise to be adaptive in the best sense and thus, resilient. More city and regional comparisons are needed to make target setting and threshold discussions grounded and allow for global discussion. This requires a targeted effort at broader inclusion of cases and trait data from different climates, biomes, multiple ecological levels but also cultures, and would move traits studies towards a truly transdisciplinary venture with real impact on how we plan and manage our cities.Feasible and easy to use: Indicator traits need to be robust, easy to measure and low-cost to assess, and have a causal link to relevant social-ecological processes and patterns (such as ecosystem services for recreation, cooling or food4,60). The potential use in planning and decision-making at multiple levels again point to the need to discuss the scales and levels for traits studies to make sure trait levels are nested and logically commensurable. Higher-level, larger-scale properties such as landscape morphology and water availability, the profile of pest communities or potential invasions can be further informed by the development of more detailed traits frameworks. This makes traits frameworks highly relevant also from an economic, social and health perspective, especially in intensely managed environments like cities, where combinations of multiple stressors and external factors create small scale heterogeneity and fast temporal change in pressures61,62.Trait selection can play that important role for assisting in the planning and design and then evaluation of the functionality of high-biodiversity green spaces63, and for trait-informed assessment of “performance”, e.g., of ecologically protected areas. A relevant example to this point is the ongoing debate about how to evaluate ex-ante, and then monitor, the implementation of nature-based solutions62,64, which remains a challenge65. Could this be done using traits instead of commonly used area-based indicators? Could traits become the basis to design and assess the impacts of offsets and compensation measures, thus increasing their efficacy? From this perspective, we see in a traits framework the potential to support a shift towards more flexible and effective planning approaches, more suitable to address today’s urban challenges and to promote greater well-being, sustainability and resilience of present and future cities.Conclusion and looking aheadThrough their direct relation to ecosystem services such as cooling and fresh air, easy-to-understand traits can be an entry-point for nature awareness and, subsequently, ecological knowledge in decision-making both at the citizen and the societal level66. However, to make traits successful indicators of global, regional, or local environmental changes, it is vital that urban society is understood as diverse across characteristics such as cultural background, physical mobility, gender, age, degree of formal or informal education, access to information and communication, purchasing power, and political influence67. All these factors affect the needs, preferences, and values of individuals and groups, and the way each interpret human-nature relationships. Only by taking these factors into account, planning for spatial-temporal diversity in traits across an urban landscape will create more inclusive urban systems that foster multiple benefits for both people and biodiversity68.The expansion and implementation of a traits-based approach for urban systems is impeded by availability of traits data. For example, trait databases are usually a primary data source in studies on urban ecology, however, these data have mainly been collected in natural areas or controlled environments such as laboratories, where organisms may display different trait values than those in urban environments. Studies have also been concentrated in the global north, and there are major challenges with potentially transferring and adapting thinking mostly developed in the Global North to rapidly urbanising areas in Africa, Asia and South America.To enable a social-ecological traits framework for interdisciplinary discussion and for guiding urban planning and decision making, we suggest a three-pronged approach for building a social-ecological understanding of trait mediated interactions and their implications, and make this understanding useful to practice (Table 1). Large-scale monitoring needs to be coupled with in-depth understanding of response mechanisms and their impact on ecosystem functions as well as services, and a deeper connection between traits and human perception as well as sense-making of the world we live in. Application to human perception and sense-making requires more data, theory and empirical work, and especially the way people relate to traits will likely vary considerably across cities and contexts across the globe. All branches of investigation need to be embedded in an interdisciplinary discussion about the role that traits play for social-ecological interactions and mutual exchange. Drawing on this broad evidence base, synthesized knowledge will offer a more comprehensive support for urban decision making, not least in anticipation of future change.Table 1 Research agenda.Full size table More

Bioreactor operation and samplingA continuous-flow MBR made from Plexiglass with a working volume of 12 L was used for enrichment (Supplementary Fig. S1). The reactor was installed with a submerged hollow fiber ultrafiltration membrane module (0.02 μm pore size, Litree, China) with a total membrane surface area of 0.03 m2. A level control system was set up to prevent liquid overflowing. The reactor was fed with diluted real urine with Total Kjeldahl Nitrogen (TKN) concentration of 140–405 mg N L−1 (for detailed influent composition see Supplementary Table S1). Initially, the reactor was inoculated with activated sludge taken from the aeration tank of a municipal wastewater treatment plant (Tsinghua Campus Water Reuse). The pH was maintained at 6.0 ± 0.1 by adding 1 M NaOH to buffer acidification by ammonia oxidation. The airflow was controlled at 2 L min−1, leading to the dissolved oxygen (DO) concentration above 4 mg O2 L−1 as regularly measured by a DO probe (WTW Multi 3420). The airflow also served to wash the membrane and mix the liquid. The temperature was controlled at 22–25 °C. The initial hydraulic retention time (HRT) was 3 days and was decreased to 1.5 days on day 222. The sludge retention time (SRT) was infinite as no biomass was discharged.The MBR was operated for 490 days. During this period, influent and effluent samples (10 mL each) were collected 1–3 times per week and used to determine the concentrations of TKN, total nitrite nitrogen (TNN = NO2−-N + HNO2-N), and nitrate nitrogen, according to standard methods.19 Mixed liquid samples (25 mL) were also taken weekly to measure mixed-liquor suspended solids (MLSS) and mixed-liquor volatile suspended solids (MLVSS).19 Biomass samples (10 mL) were regularly taken for qPCR and microbial community analyses (see below).Batch testsIn order to test urea hydrolysis and subsequent nitrification in the enrichment culture, short-term incubations were performed in a cylindrical batch reactor (8 ×18.5 cm [d × h], made from Plexiglass). 150 mL biomass was sampled from the reactor and washed three times in 1 x PBS buffer to remove any remaining nitrogen source. Subsequently, the biomass was resuspended in a 400 mL growth medium, which contained urea (about 40 mg N L−1), NaHCO3 (120 mg L−1), and 2 mL Hunter’s trace elements stock. Dissolved oxygen was controlled above 4 mg O2 L−1. Biotic and abiotic controls were performed under identical conditions with NH4Cl (~40 mg N L−1) instead of urea. The pH in all batch assays was maintained at 6.0 ± 0.1 by adding 1 M HCl or NaOH. According to the microbial activities during long-term operation, each batch assay lasted 6 to 8 h, and samples (5 mL) were taken every 20 to 60 min. Biomass was removed by sterile syringe filter (0.45 μm pore size, JINTENG, China), and urea, ammonium, nitrite, and nitrate concentrations were determined as described above. All experiments were performed in triplicate.DNA extractionBiomass (2 mL) for DNA extraction was collected on days 0, 53, 98, 131, 161, 189, 210, 238, 266, 301, 321, 358, 378, 449, and 471. DNA was extracted using the FastDNA™ SPIN Kit for Soil (MP Biomedicals, CA, U.S.) according to the manufacturer’s protocols. DNA purity and concentration were examined using agarose gel electrophoresis and spectrophotometrically on a NanoDrop 2000 (ThermoFisher Scientific, Waltham, MA, USA).16S rRNA gene amplicon sequencing and data analysisThe V4-V5 region of the 16 S rRNA gene was amplified using the universal primers 515F (5′-barcode-GTGCCAGCMGCCGCGG-3′) and 907 R (5′-CCGTCAATTCMTTTRAGTTT-3′).20 PCR products were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions and quantified using the QuantiFluor™ -ST (Promega, USA). Amplicons were pooled in equimolar concentrations and sequenced using the Illumina MiSeq PE3000 sequencer as per the manufacturer’s protocol. Amplicon sequences were demultiplexed and quality filtered using QIIME (version 1.9.1).21 Reads 10 bp were assembled. UPARSE (version 7.0.1090 http://drive5.com/uparse/) was used to cluster operational units (OTUs) on a 97% similarity cut-off level, and UCHIME to identify and remove chimeric sequences. The taxonomy of each 16S rRNA gene sequence was assigned by the RDP Classifier algorithm (http://rdp.cme.msu.edu/) according to the SILVA (SSU132) 16S rRNA database using a confidence threshold of 70%.Quantification of various amoA by qPCRTo quantify the abundances of comammox Nitrospira, AOB and AOA in the bioreactor, qPCR targeting the functional marker gene amoA was performed on DNA extracted from the bioreactor at different time points. We used the specific primers Ntsp-amoA 162F/359R amplifying comammox Nitrospira clades A and clade B simultaneously,12 Arch-amoAF/amoAR targeting AOA amoA,22 and amoA-1F/amoA-2R for AOB amoA.23 Reactions were conducted on a Bori 9600plus fluorescence quantitative PCR instrument using previously reported thermal profiles (Supplementary Table S2). Triplicate PCR assays were performed the appropriately diluted samples (10–30 ng μL−1) and 10-fold serially diluted plasmid standards as described by Guo et al.24. Plasmid standards containing the different amoA variants were obtained by TA-cloning with subsequent plasmid DNA extraction using the Easy Pure Plasmid MiniPrep Kit (TransGen Biotech, China). Standard curves covered three to eight orders of magnitude with R2 greater than 0.999. The efficiency of qPCR was about 95%.Library construction and metagenomic sequencingThe extracted DNA was fragmented to an average size of about 400 bp using Covaris M220 (Gene Company Limited, China) for paired-end library construction. A paired-end library was constructed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt-end of fragments. Paired-end sequencing was performed on Illumina NovaSeq PE150 (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq Reagent Kits according to the manufacturer’s instructions (www.illumina.com).Metagenomic assembly and genome binningRaw metagenomic sequencing reads (in PE150 mode) were trimmed and quality filtered with in-house Perl scripts as described previously.25 Briefly, duplicated reads caused by the PCR bias during the amplification step were dereplicated. Reads were eliminated if both paired-end reads contained >10% ambiguous bases (that is, “N”). Low-quality bases with phred values 2.5 kbp were retained for later analysis. Genome binning was conducted for each sample using sequencing depth and tetranucleotide frequency. To calculate coverage, high-quality reads from all samples were mapped to the contigs using BBMap v38.85 (http://sourceforge.net/projects/bbmap/) with minimal identity set to 90%. The generated bam files were sorted using samtools v1.3.1.27 Then, sequencing depth was calculated using the script “jgi_summarize_bam_contig_depths” in MetaBAT.28 Metagenome-assembled genomes (MAGs) were obtained in MetaBAT. MAG quality, including completeness, contamination, and heterogeneity, was estimated using CheckM v1.0.12.29 To optimize the MAGs, emergent self-organizing maps30 were used to visualize the bins, and contigs with abnormal coverage or discordant tetranucleotide frequencies were removed manually. Finally, all MAGs were reassembled using SPAdes with the following parameters: –careful –k 21,33,55,77,99,127. The reads used for reassembly were recruited by mapping all high-quality reads to each MAG using BBMap with the same parameter settings as described above.Functional annotation of metagenomic assemblies and metagenome-assembled genomesGene calling was conducted for the complete metagenomic assemblies and all retrieved MAGs using Prodigal v2.6.3.31 For the MAGs, predicted protein-coding sequences (CDSs) were subsequently aligned to a manually curated database containing amoCAB, hao, and nxrAB genes collected from public database using DIAMOND v0.7.9 (E-values < 1e−5 32) MAGs found to contain all these genes were labeled as comammox Nitrospira MAGs and kept for later analysis. Functional annotations were obtained by searching all CDSs in the complete metagenomic assemblies and the retrieved MAGs against the NCBI-nr, eggNOG, and KEGG databases using DIAMOND (E-values < 1e−5).Phylogenetic analysisPhylogenomic treeThe taxonomic assignment of the three identified comammox Nitrospira MAGs was determined using GTDB-tk v0.2.2.33 To reveal the phylogenetic placement of these MAGs within the Nitrospirae, 296 genomes from this phylum were downloaded from the NCBI-RefSeq database. The download genomes were dereplicated using dRep v2.3.234 (-con 10 -comp 80) to reduce the complexity and redundancy of the phylogenetic tree, which resulted in the removal of 166 genomes. In the remaining genomes, the three comammox Nitrospira MAGs and 25 genomes from phylum Thermotogae which were treated as outgroups, a set of 16 ribosomal proteins were identified using AMPHORA2.35 Each gene set was aligned separately using MUSCLE v3.8.31 with default parameters,36 and poorly aligned regions were filtered by TrimAl v1.4.rev22 (-gt 0.95 –cons 5037) The individual alignments of the 16 marker genes were concatenated, resulting in an alignment containing 118 species and 2665 amino acid positions. Subsequently, the best phylogenetic model LG + F + R8 was determined using ModelFinder38 integrated into IQ-tree v1.6.10.39 Finally, a phylogenetic tree was reconstructed using IQ-tree with the following options: -bb 1000 –alrt 1000. The generated tree in newick format was visualized by iTOL v3.40 amoA treeReference amoA sequences of AOB, AOA, and comammox Nitrospira were obtained from NCBI. Together with the amoA genes from the present study, all sequences were aligned and trimmed as described above. IQ-tree was used to generate the phylogenetic tree, with “LG + G4” determined as the best model.ureABC gene treeureABC gene sequences detected in this study were extracted and used to build a database using “hmmbuild” command in HMMER.41 ureABC gene sequences from genomes in NCBI-RefSeq database (downloaded on July 1st, 2019) were identified by searching against the built database using AMPHORA2. The same procedures as above were conducted to construct the phylogenetic tree of concatenated ureABC genes, except for the sequence collection step. To reduce the complexity of the phylogenetic tree, the alignment of concatenated ureABC genes was clustered using CD-HIT42 with the following parameters: -aS 1 -c 0.8 -g 1. Only representative sequences were kept for phylogeny reconstruction, which resulted in an alignment containing 858 sequences and 1263 amino acids positions. “LG + R10” was determined as the best model and used to build the phylogenetic tree. Regarding the Nitrospirae-specific ureABC gene tree, ureABC gene sequences were recruited from the genomes as described above, but without the sequence clustering step. The final Nitrospirae-specific phylogeny of ureABC genes was built on an alignment containing 62 sequences and 1015 amino acid positions with “LG + F + I + G4” as the best model. More

1.Bennett, J. R. et al. Polar lessons learned: long-term management based on shared threats in Arctic and Antarctic environments. Front. Ecol. Environ. 13, 316–324 (2015).Article 

Google Scholar 
2.Pyšek, P. et al. Scientists’ warning on invasive alien species. Biol. Rev. 95, 1511–1534 (2020).PubMed 
Article 

Google Scholar 
3.Convey, P. et al. The spatial structure of Antarctic biodiversity. Ecol. Monogr. 84, 203–244 (2014).Article 

Google Scholar 
4.Turner, J. et al. Antarctic climate change and the environment: an update. Polar Rec. 50, 237–259 (2014).Article 

Google Scholar 
5.Siegert, M., et al. The Antarctic Peninsula under a 1.5 °C global warming scenario. Front. Environ. Sci. 7 (2019).6.Huiskes, A. H. L. et al. Aliens in Antarctica: assessing transfer of plant propagules by human visitors to reduce invasion risk. Biol. Conserv. 171, 278–284 (2014).Article 

Google Scholar 
7.Hughes, K. A., Pertierra, L. R., Molina-Montenegro, M. A. & Convey, P. Biological invasions in terrestrial Antarctica: what is the current status and can we respond? Biodivers. Conserv. 24, 1031–1055 (2015).Article 

Google Scholar 
8.Molina-Montenegro, M. A., et al. Assessing the importance of human activities for the establishment of the invasive Poa annua in Antarctica. Polar Res. 33, https://doi.org/10.3402/polar.v33.21425 (2014).9.Whinam, J., Chilcott, N. & Bergstrom, D. M. Subantarctic hitchhikers: expeditioners as vectors for the introduction of alien organisms. Biol. Conserv. 121, 207–219 (2005).Article 

Google Scholar 
10.Hughes, K. A. et al. Invasive non-native species likely to threaten biodiversity and ecosystems in the Antarctic Peninsula region. Glob. Change Biol. 26, 2702–2716 (2020).Article 

Google Scholar 
11.Osyczka, P. Alien lichens unintentionally transported to the “Arctowski” station (South Shetlands, Antarctica). Polar Biol. 33, 1067–1073 (2010).Article 

Google Scholar 
12.Chown, S. L. et al. Continent-wide risk assessment for the establishment of nonindigenous species in Antarctica. Proc. Natl Acad. Sci. USA 109, 4938–4943 (2012).CAS 
PubMed 
Article 

Google Scholar 
13.Hughes, K. A., Greenslade, P. & Convey, P. The fate of the non-native Collembolon, Hypogastrura viatica, at the southern extent of its introduced range in Antarctica. Polar Biol. 40, 2127–2131 (2017).Article 

Google Scholar 
14.Lee, J. E. & Chown, S. L. Breaching the dispersal barrier to invasion: quantification and management. Ecol. Appl. 19, 1944–1959 (2009).PubMed 
Article 

Google Scholar 
15.Tsujimoto, M. & Imura, S. Does a new transportation system increase the risk of importing non-native species to Antarctica? Antarct. Sci. 24, 441–449 (2012).Article 

Google Scholar 
16.Duffy, G. A. et al. Barriers to globally invasive species are weakening across the Antarctic. Divers. Distrib. 23, 982–996 (2017).Article 

Google Scholar 
17.Convey, P., Hopkins, D. W., Roberts, S. J. & Tyler, A. N. Global southern limit of flowering plants and moss peat accumulation. Polar Res. 30, 8929 (2011).Article 

Google Scholar 
18.Bergstrom, D. M. & Chown, S. L. Life at the front: history, ecology and change on southern ocean islands. Trends Ecol. Evolut. 14, 472–477 (1999).CAS 
Article 

Google Scholar 
19.Gremmen, N. J. M., Chown, S. L. & Marshall, D. J. Impact of the introduced grass Agrostis stolonifera on vegetation and soil fauna communities at Marion Island, sub-Antarctic. Biol. Conserv. 85, 223–231 (1998).Article 

Google Scholar 
20.Cavieres, L. A., Sanhueza, A. K., Torres-Mellado, G. & Casanova-Katny, A. Competition between native Antarctic vascular plants and invasive Poa annua changes with temperature and soil nitrogen availability. Biol. Invasions 20, 1597–1610 (2018).Article 

Google Scholar 
21.Molina-Montenegro, M. A., et al. Increasing impacts by Antarctica’s most widespread invasive plant species as result of direct competition with native vascular plants. Neobiota 51, 19–40 (2019).22.Molina-Montenegro, M. A. et al. Occurrence of the non-native annual bluegrass on the Antarctic mainland and its negative effects on native plants. Conserv. Biol. 26, 717–723 (2012).PubMed 
Article 

Google Scholar 
23.Frenot, Y. et al. Biological invasions in the Antarctic: extent, impacts and implications. Biol. Rev. 80, 45–72 (2005).PubMed 
Article 

Google Scholar 
24.Leihy, R. I., Duffy, G. A. & Chown, S. L. Species richness and turnover among indigenous and introduced plants and insects of the Southern Ocean Islands. Ecosphere 9, 15 (2018).Article 

Google Scholar 
25.Graae, B. J. et al. On the use of weather data in ecological studies along altitudinal and latitudinal gradients. Oikos 121, 3–19 (2012).Article 

Google Scholar 
26.Convey, P., Coulson, S. J., Worland, M. R. & Sjöblom, A. The importance of understanding annual and shorter-term temperature patterns and variation in the surface levels of polar soils for terrestrial biota. Polar Biol. 41, 1587–1605 (2018).Article 

Google Scholar 
27.Edwards, J. A. An experimental introduction of vascular plants from South Georgia to the Maritime Antarctic. Br. Antarct. Surv. Bull. 49, 73–80 (1979).
Google Scholar 
28.Corte, A. La primera fanerogama adventicia hallada en el continente Antartico. Inst. Antártico Argent. 62, 1–14 (1961).
Google Scholar 
29.Pertierra, L. R. et al. Global thermal niche models of two European grasses show high invasion risks in Antarctica. Glob. Change Biol. 23, 2863–2873 (2017).Article 

Google Scholar 
30.Macloskie, G. The Patagonian flora. Plant World 10, 97–103 (1907).
Google Scholar 
31.Pertierra, L. R. et al. Assessing the invasive risk of two non-native Agrostis species on sub-Antarctic Macquarie Island. Polar Biol. 39, 2361–2371 (2016).Article 

Google Scholar 
32.Bokhorst, S. et al. Climate change effects on soil arthropod communities from the Falkland Islands and the Maritime Antarctic. Soil Biol. Biochem. 40, 1547–1556 (2008).CAS 
Article 

Google Scholar 
33.Chwedorzewska, K. J. et al. Poa annua L. in the maritime Antarctic: an overview. Polar Rec. 51, 637–643 (2015).Article 

Google Scholar 
34.Zhang, H. et al. Is the proportion of clonal species higher at higher latitudes in Australia? Austral. Ecol. 43, 69–75 (2018).Article 

Google Scholar 
35.Holtom, A. & Greene, S. W. The growth and reproduction of Antarctic flowering plants. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 252, 323–337 (1967).
Google Scholar 
36.Vera, M. L. Colonization and demographic structure of Deschampsia antarctica and Colobanthus quitensis along an altitudinal gradient on Livingston Island, South Shetland Islands, Antarctica. Polar Res. 30, 7146 (2011).Article 

Google Scholar 
37.Convey, P. The influence of environmental characteristics on life history attributes of Antarctic terrestrial biota. Biol. Rev. Camb. Philos. Soc. 71, 191–225 (1996).Article 

Google Scholar 
38.Pertierra, L. R., Lara, F., Benayas, J. & Hughes, K. A. Poa pratensis L., current status of the longest-established non-native vascular plant in the Antarctic. Polar Biol. 36, 1473–1481 (2013).Article 

Google Scholar 
39.Williams, L. K. et al. Longevity, growth and community ecology of invasive Poa annua across environmental gradients in the subantarctic. Basic Appl. Ecol. 29, 20–31 (2018).Article 

Google Scholar 
40.Pertierra, L. et al. Eradication of the non-native Poa pratensis colony at Cierva Point, Antarctica: a case study of international cooperation and practical management in an area under multi-party governance. Environ. Sci. Policy 69, 50–56 (2016).Article 

Google Scholar 
41.Hughes, K. A. & Convey, P. The protection of Antarctic terrestrial ecosystems from inter- and intra-continental transfer of non-indigenous species by human activities: a review of current systems and practices. Glob. Environ. Change 20, 96–112 (2010).Article 

Google Scholar 
42.Smith, R. I. L. Introduced plants in Antarctica: potential impacts and conservation issues. Biol. Conserv. 76, 135–146 (1996).Article 

Google Scholar 
43.Thompson, K., Grime, J. P. & Mason, G. Seed germination in response to diurnal fluctuations of temperature. Nature 267, 147–149 (1977).CAS 
PubMed 
Article 

Google Scholar 
44.McGeoch, M. A. et al. Monitoring biological invasion across the broader Antarctic: a baseline and indicator framework. Glob. Environ. Change 32, 108–125 (2015).Article 

Google Scholar 
45.Kellmann-Sopyła, W. & Giełwanowska, I. Germination capacity of five polar Caryophyllaceae and Poaceae species under different temperature conditions. Polar Biol. 38, 1753–1765 (2015).Article 

Google Scholar 
46.Körner, C. Alpine Treelines: Functional Ecology of the Global High Elevation Tree Limits. 1–220 (2012).47.Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Change 2, 453–457 (2012).Article 

Google Scholar 
48.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
Article 
PubMed 

Google Scholar 
49.Billings, W. D. Constraints to plant growth, reproduction, and establishment in Arctic environments. Arct. Alp. Res. 19, 357–365 (1987).Article 

Google Scholar 
50.Block, W., Smith, R. I. L. & Kennedy, A. D. Strategies of survival and resource exploitation in the Antarctic fellfield ecosystem. Biol. Rev. 84, 449–484 (2009).CAS 
PubMed 
Article 

Google Scholar 
51.Aerts, R. & Chapin, F. S. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns. Adv. Ecol. Res. 30, 1–67 (2000).CAS 

Google Scholar 
52.Barrand, N. E. et al. Trends in Antarctic Peninsula surface melting conditions from observations and regional climate modeling. J. Geophys. Res. 118, 315–330 (2013).Article 

Google Scholar 
53.Walton, D. W. H. The Signy Island terrestrial reference sites: XV. Micro-climate monitoring, 1972-1974. Br. Antarct. Surv. Bull. 55, 111–126 (1982).
Google Scholar 
54.Smith, R. I. L. Bryophyte oases in Ablation Valleys on Alexander Island, Antarctica. Bryologist 91, 45–50 (1988).55.Hunt, H. W., Fountain, A. G., Doran, P. T. & Basagic, H. A dynamic physical model for soil temperature and water in Taylor Valley, Antarctica. Antarct. Sci. 22, 419–434 (2010).Article 

Google Scholar 
56.Bracegirdle, T. J., Barrand, N. E., Kusahara, K. & Wainer, I. Predicting Antarctic climate using climate models. Antarctic Environ. Portal https://doi.org/10.18124/5wq2-0154 (2016).57.De Boeck, H. J., De Groote, T. & Nijs, I. Leaf temperatures in glasshouses and open-top chambers. N. Phytol. 194, 1155–1164 (2012).Article 

Google Scholar 
58.Greenspan, S. E. et al. Low-cost fluctuating-temperature chamber for experimental ecology. Methods Ecol. Evolut. 7, 1567–1574 (2016).Article 

Google Scholar 
59.Bokhorst, S. et al. Contrasting survival and physiological responses of sub-Arctic plant types to extreme winter warming and nitrogen. Planta 247, 635–648 (2018).CAS 
PubMed 
Article 

Google Scholar 
60.Litaor, M. I., Williams, M. & Seastedt, T. R. Topographic controls on snow distribution, soil moisture, and species diversity of herbaceous alpine vegetation, Niwot Ridge, Colorado. J. Geophys. Res. 113 (2008).61.Taulavuori, K., Sarala, M. & Taulavuori, E. Growth responses of trees to Arctic light environment. (eds U. Lüttge, W. Beyschlag, B. Büdel, and D. Francis). 157–168. (Springer, Berlin).62.Goncharova, O. et al. Influence of snow cover on soil temperatures: meso- and micro-scale topographic effects (a case study from the northern West Siberia discontinuous permafrost zone). Catena 183, 104224 (2019).Article 

Google Scholar 
63.Upson, R., et al. Field Guide to the Introduced Flora of South Georgia. (Royal Botanical Gardens, Kew, 2017).64.Allen, S. E. & Heal, O. W. Soils of the Maritime Antarctic zone. (eds M. W. Holdgate). 693–696, (Academic Press, London, 1970).65.Bölter, M. Soil development and soil biology on King George Island, Maritime Antarctic. Pol. Polar Res. 32, 105–116 (2011).Article 

Google Scholar 
66.Duffy, G. A. & Lee, J. R. Ice-free area expansion compounds the non-native species threat to Antarctic terrestrial biodiversity. Biol. Conserv. 232, 253–257 (2019).Article 

Google Scholar 
67.Lee, J. R. et al. Climate change drives expansion of Antarctic ice-free habitat. Nature 547, 49–54 (2017).CAS 
PubMed 
Article 

Google Scholar 
68.Bokhorst, S., Huiskes, A., Convey, P. & Aerts, R. The effect of environmental change on vascular plant and cryptogam communities from the Falkland Islands and the Maritime Antarctic. BMC Ecol. 7, 15 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
69.IPCC, Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (ed T. F. Stocker, et al.) 1535 (Cambridge, 2013).70.Bracegirdle, T. J. et al. Back to the future: using long-term observational and paleo-proxy reconstructions to improve model projections of antarctic climate. Geosciences 9, 255 (2019).Article 

Google Scholar 
71.Royles, J. et al. Carbon isotope evidence for recent climate-related enhancement of CO2 assimilation and peat accumulation rates in Antarctica. Glob. Change Biol. 18, 3112–3124 (2012).Article 

Google Scholar 
72.Tang, M. S. Y. et al. Precipitation instruments at Rothera Station, Antarctic Peninsula: a comparative study. Polar Res. 37, 1503906 (2018).Article 

Google Scholar 
73.Bokhorst, S. et al. Microclimate impacts of passive warming methods in Antarctica: implications for climate change studies. Polar Biol. 34, 1421–1435 (2011).Article 

Google Scholar 
74.Hiltbrunner, E. et al. Ecological consequences of the expansion of N2-fixing plants in cold biomes. Oecologia 176, 11–24 (2014).PubMed 
Article 

Google Scholar 
75.Convey, P., et al. Microclimate data from Anchorage Island, 2001–2009. (2020).76.Convey, P., et al. Microclimate data from Coal Nunatak, 2006–2019. (2020).77.Convey, P., et al. Microclimate data from Mars Oasis, 2000–2019. (2020).78.Moore, D. M. The vascular Flora of the Falkland Islands. Br. Antarct. Surv. Sci. Rep. 60, 4–202 (1968).
Google Scholar 
79.Oliva, M. et al. Recent regional climate cooling on the Antarctic Peninsula and associated impacts on the cryosphere. Sci. Total Environ. 580, 210–223 (2017).CAS 
PubMed 
Article 

Google Scholar 
80.Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

Google Scholar 
81.RCoreTeam, R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2015).82.Bokhorst, S., Convey, P., Casanova, A. & Aerts, R. Warming impacts on potential germination of non-native plants on the Antarctic Peninsula. https://npdc.nl/dataset/d350edc1-e31e-51b9-aa37-d11365e6bc2b (2020). More

1.Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Summary for Policymakers of the Global Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) (IPBES secretariat, 2019).2.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 
CAS 

Google Scholar 
3.Adams, W. M. Against Extinction: The Story of Conservation (Earthscan, 2004).4.Escobar, A. Whose knowledge, whose nature? Biodiversity, conservation, and the political ecology of social movements. J. Polit. Ecol. 5, 53–82 (1998).
Google Scholar 
5.Meine, C., Soulé, M. & Noss, R. F. A mission-driven discipline: the growth of conservation biology. Conserv. Biol. 20, 631–651 (2006).Article 

Google Scholar 
6.Sandbrook, C., Fisher, J. A., Holmes, G., Luque-Lora, R. & Keane, A. The global conservation movement is diverse but not divided. Nat. Sustain. 2, 316–323 (2019).Article 

Google Scholar 
7.Takacs, D. The Idea of Biodiversity: Philosophies of Paradise (Johns Hopkins Univ. Press, 1996).8.Garland, E. The elephant in the room: confronting the colonial character of wildlife conservation in Africa. Afr. Stud. Rev 51, 51–74 (2008).Article 

Google Scholar 
9.Thekaekara, T. Botswana elephants episode: there’s a colonial underpinning to conservation. DownToEarth (22 July 2020); https://www.downtoearth.org.in/blog/wildlife-and-biodiversity/botswana-elephants-episode-there-s-a-colonial-underpinning-to-conservation-7242910.Cronon, W. et al. Uncommon Ground: Toward Reinventing Nature (WW Norton & Company, 1995).
Google Scholar 
11.Stephens, L. et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).CAS 
Article 

Google Scholar 
12.Brockington, D., Duffy, R. & Igoe, J. Nature Unbound: Conservation, Capitalism and the Future of Protected Areas (Earthscan, 2008).13.Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).CAS 
Article 

Google Scholar 
14.Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).Article 

Google Scholar 
15.Lele, S., Springate-Baginski, O., Lakerveld, R., Deb, D. & Dash, P. Ecosystem services: origins, contributions, pitfalls, and alternatives. Conserv. Soc. 11, 343–358 (2013).Article 

Google Scholar 
16.Martin, J.-L., Maris, V. & Simberloff, D. S. The need to respect nature and its limits challenges society and conservation science. Proc. Natl Acad. Sci. USA 113, 6105–6112 (2016).CAS 
Article 

Google Scholar 
17.Díaz, S. et al. The IPBES Conceptual Framework: connecting nature and people. Curr. Opin. Environ. Sustain 14, 1–16 (2015).Article 

Google Scholar 
18.Turnhout, E., Waterton, C., Neves, K. & Buizer, M. Rethinking biodiversity: from goods and services to ‘living with’. Conserv. Lett. 6, 154–161 (2013).Article 

Google Scholar 
19.Kenter, J. O. et al. Loving the mess: navigating diversity and conflict in social values for sustainability. Sustain. Sci. 14, 1439–1461 (2019).Article 

Google Scholar 
20.Lele, S. From wildlife-ism to ecosystem-service-ism to a broader environmentalism. Environ. Conserv. https://doi.org/10.1017/S0376892920000466 (2020).21.Muradian, R. & Pascual, U. A typology of elementary forms of human-nature relations: a contribution to the valuation debate. Curr. Opin. Environ. Sustain 35, 8–14 (2018).Article 

Google Scholar 
22.Robertson, D. P. & Hull, R. B. Beyond biology: toward a more public ecology for conservation. Conserv. Biol. 15, 970–979 (2001).Article 

Google Scholar 
23.Tallis, H. & Lubchenco, J. Working together: a call for inclusive conservation. Nature 515, 27 (2014).CAS 
Article 

Google Scholar 
24.Kareiva, P. M., Marvier, M. & Silliman, B. Effective Conservation Science: Data Not Dogma (Oxford Univ. Press, 2018).25.Wilshusen, P. R., Brechin, S. R., Fortwangler, C. L. & West, P. C. Reinventing a square wheel: critique of a resurgent “protection paradigm” in international biodiversity conservation. Soc. Nat. Resour. 15, 17–40 (2002).Article 

Google Scholar 
26.Turnhout, E. The politics of environmental knowledge. Conserv. Soc. 16, 363–371 (2018).Article 

Google Scholar 
27.Louder, E. & Wyborn, C. Biodiversity narratives: stories of the evolving conservation landscape. Environ. Conserv. 47, 251–259 (2020).Article 

Google Scholar 
28.Gadgil, M., Seshagiri Rao, P., Utkarsh, G., Pramod, P. & Chhatre, A. New meanings for old knowledge: the people’s biodiversity registers program. Ecol. Appl. 10, 1307–1317 (2000).Article 

Google Scholar 
29.Buijs, A. E., Fischer, A., Rink, D. & Young, J. C. Looking beyond superficial knowledge gaps: understanding public representations of biodiversity. Int. J. Biodivers. Sci. Manag. 4, 65–80 (2008).Article 

Google Scholar 
30.Wyborn, C. et al. An agenda for research and action towards diverse and just futures for life on Earth. Conserv. Biol. https://doi.org/10.1111/cobi.13671 (2020).31.Wyborn, C. et al. Imagining transformative biodiversity futures. Nat. Sustain. 3, 670–672 (2020).Article 

Google Scholar 
32.Samper, C. Planetary boundaries: rethinking biodiversity. Nat. Clim. Change 1, 118–119 (2009).Article 

Google Scholar 
33.Mayer, P. Biodiversity: the appreciation of different thought styles and values helps to clarify the term. Restor. Ecol. 14, 105–111 (2006).Article 

Google Scholar 
34.Morar, N., Toadvine, T. & Bohannan, B. J. Biodiversity at twenty-five years: revolution or red herring? Ethics Policy Environ. 18, 16–29 (2015).Article 

Google Scholar 
35.Purvis, A. et al. in Global Assessment Report on Biodiversity and Ecosystem Services (eds Brondízio, E. S. et al.) Ch. 2.2 (Secretariat of the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services, 2019).36.Dasgupta, P. The Economics of Biodiversity: The Dasgupta Review (HM Treasury, 2021).37.Perrings, C. Our Uncommon Heritage: Biodiversity Change, Ecosystem Services, and Human Well-Being (Cambridge Univ. Press, 2014).38.Gowdy, J. M. The value of biodiversity: markets, society, and ecosystems. Land Econ. 73, 25–41 (1997).Article 

Google Scholar 
39.Keulartz, J. Boundary work in ecological restoration. Environ. Phil. 6, 35–55 (2009).Article 

Google Scholar 
40.Chan, K. M. et al. Why protect nature? Rethinking values and the environment. Proc. Natl Acad. Sci. USA 113, 1462–1465 (2016).CAS 
Article 

Google Scholar 
41.Descola, P. The Ecology of Others (Prickly Paradigm, 2013).42.Raffles, R. Intimate knowledge. Int. Soc. Sci. J. 54, 325–335 (2002).Article 

Google Scholar 
43.Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. AMBIO 43, 579–591 (2014).Article 

Google Scholar 
44.Zafra-Calvo, N. et al. Plural valuation of nature for equity and sustainability: insights from the Global South. Glob. Environ. Change 63, 102115 (2020).Article 

Google Scholar 
45.Lele, S., Wilshusen, P., Brockington, D., Seidler, R. & Bawa, K. Beyond exclusion: alternative approaches to biodiversity conservation in the developing tropics. Curr. Opin. Environ. Sustain. 2, 94–100 (2010).Article 

Google Scholar 
46.Pascual, U. et al. Social equity matters in payments for ecosystem services. BioScience 64, 1027–1036 (2014).Article 

Google Scholar 
47.Wunder, S. et al. From principles to practice in paying for nature’s services. Nat. Sustain. 1, 145–150 (2018).Article 

Google Scholar 
48.Büscher, B. et al. Half-Earth or whole Earth? Radical ideas for conservation, and their implications. Oryx 51, 407–410 (2017).Article 

Google Scholar 
49.Adams, W. M. in The Anthropology of Sustainability, Palgrave Studies in Anthropology of Sustainability (eds Brightman, M. & Lewis, J.) 111–126 (Palgrave Macmillan, 2017).50.Vatn, A. An institutional analysis of methods for environmental appraisal. Ecol. Econ. 68, 2207–2215 (2009).Article 

Google Scholar 
51.Büscher, B., Sullivan, S., Neves, K., Igoe, J. & Brockington, D. Towards a synthesized critique of neoliberal biodiversity conservation. Capital. Nat. Social. 23, 4–30 (2012).Article 

Google Scholar 
52.Lliso, B., Mariel, P., Pascual, U. & Engel, S. Increasing the credibility and salience of valuation through deliberation: lessons from the Global South. Glob. Environ. Change 62, 102065 (2020).Article 

Google Scholar 
53.Rudel, T. K., Defries, R., Asner, G. P. & Laurance, W. F. Changing drivers of deforestation and new opportunities for conservation. Conserv. Biol. 23, 1396–1405 (2009).Article 

Google Scholar 
54.Mazor, T. et al. Global mismatch of policy and research on drivers of biodiversity loss. Nat. Ecol. Evol. 2, 1071–1074 (2018).Article 

Google Scholar 
55.Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).CAS 
Article 

Google Scholar 
56.Folke, C. et al. Transnational corporations and the challenge of biosphere stewardship. Nat. Ecol. Evol. 3, 1396–1403 (2019).Article 

Google Scholar 
57.Ceddia, M. G. Investments’ role in ecosystem degradation. Science 368, 377–377 (2020).
Google Scholar 
58.Neumann, R. P. Moral and discursive geographies in the war for biodiversity in Africa. Polit. Geogr. 23, 813–837 (2004).Article 

Google Scholar 
59.Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).CAS 
Article 

Google Scholar 
60.Svarstad, H., Petersen, L. K., Rothman, D., Siepel, H. & Wätzold, F. Discursive biases of the environmental research framework DPSIR. Land Use Policy 25, 116–125 (2008).Article 

Google Scholar 
61.Gari, S. R., Newton, A. & Icely, J. D. A review of the application and evolution of the DPSIR framework with an emphasis on coastal social-ecological systems. Ocean Coast. Manage. 103, 63–77 (2015).Article 

Google Scholar 
62.Muradian, R. et al. Payments for ecosystem services and the fatal attraction of win-win solutions. Conserv. Lett. 6, 274–279 (2013).Article 

Google Scholar 
63.Otero, I. et al. Biodiversity policy beyond economic growth. Conserv. Lett. 13, e12713 (2020).Article 

Google Scholar 
64.Nielsen, J. Ø. et al. Toward a normative land systems science. Curr. Opin. Environ. Sustain. 38, 1–6 (2019).Article 

Google Scholar 
65.Lele, S. & Kurien, A. Interdisciplinary analysis of the environment: insights from tropical forest research. Environ. Conserv. 38, 211–233 (2011).Article 

Google Scholar 
66.West, S., Haider, L. J., Stålhammar, S. & Woroniecki, S. A relational turn for sustainability science? Relational thinking, leverage points and transformations. Ecosyst. People 16, 304–325 (2020).Article 

Google Scholar 
67.Boivin, N. L. et al. Ecological consequences of human niche construction: examining long-term anthropogenic shaping of global species distributions. Proc. Natl Acad. Sci. USA 113, 6388–6396 (2016).CAS 
Article 

Google Scholar 
68.Jacobs, S. et al. Use your power for good: plural valuation of nature – the Oaxaca statement. Glob. Sustain. 3, e8 (2020).Article 

Google Scholar 
69.Turnhout, E., Tuinstra, W. & Halffman, W. Environmental Expertise: Connecting Science, Policy and Society (Cambridge Univ. Press, 2019).70.Saberwal, V. & Chhatre, A. Democratizing Nature: Politics, Conservation, and Development in India (Oxford Univ. Press, 2006). More