Philippa Kaur

1.Mnif, I., Sahnoun, R. & Ellouz-Chaabouni, S. Application of bacterial biosurfactants for enhanced removal and biodegradation of diesel oil. Process Saf. Environ. Prot. 109, 72–81 (2017).CAS 
Article 

Google Scholar 
2.Abioye, O. P. Biological remediation of hydrocarbon and heavy metals contaminated soil. In Soil Contamination (ed. Pascucci, S.) 127–142 (InTech Europe, 2011).
Google Scholar 
3.Zarinkamar, F., Reypour, F. & Soleimanpour, S. Effect of diesel fuel contaminated soil on the germination and the growth of Festuca arundinacea. Res. J. Chem. Environ. Sci. 1, 37–41 (2013).
Google Scholar 
4.Ashnani, M. H. M., Johari, A., Hashim, H. & Hasani, E. A source of renewable energy in Malaysia, why biodiesel? Renew. Sustain. Energy Rev. 35, 244–257 (2014).Article 

Google Scholar 
5.Bücker, F. et al. Impact of biodiesel on biodeterioration of stored Brazilian diesel oil. Int. Biodeterior. Biodegrad. 65, 172–178 (2011).Article 
CAS 

Google Scholar 
6.Hawrot-Paw, M. & Izwikow, M. Ecotoxicologial effects of biodiesel in the soil. J. Ecol. Eng. 16, 34–39 (2015).Article 

Google Scholar 
7.Restrepo-Flórez, J.-M., Bassi, A., Rehmann, L. & Thompson, M. R. Effect of biodiesel addition on microbial community structure in a simulated fuel storage system. Bioresour. Technol. 147, 456–463 (2013).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
8.Silva, G. S. et al. Biodegradability of soy biodiesel in microcosm experiments using soil from the Atlantic Rain Forest. Appl. Soil Ecol. 55, 27–35 (2012).Article 

Google Scholar 
9.Prosser, J. I. Dispersing misconceptions and identifying opportunities for the use of ‘omics’ in soil microbial ecology. Nat. Rev. Microbiol. 13, 439–446 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Hawrot-Paw, M. & Martynus, M. The influence of diesel fuel and biodiesel on soil microbial biomass. Pol. J. Environ. Stud. 20, 497–501 (2011).CAS 

Google Scholar 
11.Lahel, A. et al. Effect of process parameters on the bioremediation of diesel contaminated soil by mixed microbial consortia. Int. Biodeterior. Biodegrad. 113, 375 (2016).CAS 
Article 

Google Scholar 
12.Nwankwegu, A. S., Orji, M. U. & Onwosi, C. O. Studies on organic and in-organic biostimulants in bioremediation of diesel-contaminated arable soil. Chemosphere 162, 148–156 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Woźniak-Karczewska, M. et al. Effect of bioaugmentation on long-term biodegradation of diesel/biodiesel blends in soil microcosms. Sci. Total Environ. 671, 948–958 (2019).ADS 
Article 
CAS 

Google Scholar 
14.Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 46, D633–D639 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Lapinskiene, A., Martinkus, P. & Rebzdaite, V. Eco-toxicological studies of diesel and biodiesel fuels in aerated soil. Environ. Pollut. 142, 432–437 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Schiewer, S. & Horel, A. Biodiesel addition influences biodegradation rates of fresh and artificially weathered diesel fuel in Alaskan sand. J. Cold Reg. Eng. 31, 1–14 (2017).Article 

Google Scholar 
17.Schreier, C. G., Walker, W. J., Burns, J. & Wilkenfeld, R. Total organic carbon as a screening method for petroleum hydrocarbons. Chemosphere 39, 503–510 (1999).ADS 
CAS 
Article 

Google Scholar 
18.Nimmo, M. Carbon. In Encyclopedia of Analytical Science (eds Worsfold, P. & Alan Townshend, C. P.) 453–457 (Elsevier, 2005).
Google Scholar 
19.Margesin, R. & Schinner, F. Bioremediation of diesel-oil-contaminated alpine soils at low temperatures. Appl. Microbiol. Biotechnol. 47, 462–468 (1997).CAS 
Article 

Google Scholar 
20.Møller, J., Winther, P., Lund, B., Kirkebjerg, K. & Westermann, P. Bioventing of diesel oil-contaminated soil: Comparison of degradation rates in soil based on actual oil concentration and on respirometric data. J. Ind. Microbiol. 16, 110–116 (1996).Article 

Google Scholar 
21.Nakatsu, C. H. Microbial processes: Community analysis. Ref. Modul. Earth Syst. Environ. Sci. https://doi.org/10.1016/B978-0-12-409548-9.05218-0 (2013).Article 

Google Scholar 
22.Margesin, R., Hämmerle, M. & Tscherko, D. Microbial activity and community composition during bioremediation of diesel-oil-contaminated soil: Effects of hydrocarbon concentration, fertilizers, and incubation time. Microb. Ecol. 53, 259–269 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Owsianiak, M. et al. Biodegradation of diesel/biodiesel blends by a consortium of hydrocarbon degraders: Effect of the type of blend and the addition of biosurfactants. Bioresour. Technol. 100, 1497–1500 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Quideau, S. A. et al. Extraction and analysis of microbial phospholipid fatty acids in soils. J. Vis. Exp. https://doi.org/10.3791/54360 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
25.Frostegård, Å., Tunlid, A. & Bååth, E. Use and misuse of PLFA measurements in soils. Soil Biol. Biochem. 43, 1–5 (2010).
Google Scholar 
26.Ruess, L. & Chamberlain, P. M. The fat that matters: Soil food web analysis using fatty acids and their carbon stable isotope signature. Soil Biol. Biochem. 42, 1898–1910 (2010).CAS 
Article 

Google Scholar 
27.Davila, S. et al. Actinobacteria: Current research and perspectives for bioremediation of pesticides and heavy metals. Chemosphere 166, 41–62 (2017).ADS 
Article 
CAS 

Google Scholar 
28.Sutton, N. B. et al. Impact of long-term diesel contamination on soil microbial cummunity structure. Appl. Environ. Microbiol. 79, 619–630 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Kersters, K., Vos, P. D. E., Gillis, M., Swings, J. & Vandamme, P. Introduction to the Proteobacteria. In The Prokaryotes: A Handbook on the Biology of Bacteria (eds Dworkin, M. et al.) 3–37 (Springer, 2006).
Google Scholar 
30.Bell, T. H. et al. Predictable bacterial composition and hydrocarbon degradation in Arctic soils following diesel and nutrient disturbance. ISME J. 7, 1200–1210 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Brzeszcz, J. & Kaszycki, P. Aerobic bacteria degrading both n-alkanes and aromatic hydrocarbons: An undervalued strategy for metabolic diversity and flexibility. Biodegradation 29, 359–407 (2018).PubMed 
Article 

Google Scholar 
32.Elumalai, P. et al. Role of thermophilic bacteria (Bacillus and, Geobacillus) on crude oil degradation and biocorrosion in oil reservoir environment. 3Biotech 9, 79 (2019).
Google Scholar 
33.Mitter, E. K., de Freitas, J. R. & Germida, J. J. Bacterial root microbiome of plants growing in oil sands reclamation covers. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.00849 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Bundy, J. G., Paton, G. I. & Campbell, C. D. Microbial communities in different soil types do not converge after diesel contamination. J. Appl. Microbiol. 92, 276–288 (2002).CAS 
PubMed 
Article 

Google Scholar 
35.Korenblum, E., Souza, D. B., Penna, M. & Seldin, L. Molecular analysis of the bacterial communities in crude oil Samples from two Brazilian offshore petroleum platforms. Int. J. Microbiol. 2012, 1–8 (2012).Article 
CAS 

Google Scholar 
36.Kim, T. J., Lee, E. Y., Kim, Y. J., Cho, K. S. & Ryu, H. W. Degradation of polyaromatic hydrocarbons by Burkholderia cepacia 2A–12. World J. Microbiol. Biotechnol. 19, 411–417 (2003).CAS 
Article 

Google Scholar 
37.Revathy, T., Jayasri, M. A. & Suthindhiran, K. Biodegradation of PAHs by Burkholderia sp. VITRSB1 isolated from marine sediments. Scientifica (Cairo) 2015, 1–9 (2015).
Google Scholar 
38.Ramos, D. T., da Silva, M. L. B., Nossa, C. W., Alvarez, P. J. J. & Corseuil, H. X. Assessment of microbial communities associated with fermentative-methanogenic biodegradation of aromatic hydrocarbons in groundwater contaminated with a biodiesel blend (B20). Biodegradation 25, 681–691 (2014).CAS 
PubMed 
Article 

Google Scholar 
39.Whyte, L. G. et al. Gene cloning and characterization of multiple alkane hydroxylase systems in Rhodococcus strains Q15 and NRRL B-16531. Appl. Environ. Microbiol. 68, 5933–5942 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Lee, M., Kim, M. K., Singleton, I., Goodfellow, M. & Lee, S.-T. Enhanced biodegradation of diesel oil by a newly identified Rhodococcus baikonurensis EN3 in the presence of mycolic acid. J. Appl. Microbiol. 100, 325–333 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Bateman, J. N., Speer, B., Feduik, L. & Hartline, R. A. Naphthalene association and uptake in Pseudomonas putida. J. Bacteriol. 166, 155–161 (1986).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Rentz, J. A., Alvarez, P. J. J. & Schnoor, J. L. Repression of Pseudomonas putida phenanthrene-degrading activity by plant root extracts and exudates. Environ. Microbiol. 6, 574–583 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Shukor, M. Y. et al. Isolation and characterization of Pseudomonas diesel-degrading strain from Antartica. J. Environ. Biol. 30, 1–6 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Meyer, D. D. et al. Bioremediation strategies for diesel and biodiesel in oxisol from southern Brazil. Int. Biodeterior. Biodegrad. 95, 356–363 (2014).CAS 
Article 

Google Scholar 
45.Taccari, M., Milanovic, V., Comitini, F., Casucci, C. & Ciani, M. Effects of biostimulation and bioaugmentation on diesel removal and bacterial community. Int. Biodeterior. Biodegrad. 66, 39–46 (2012).CAS 
Article 

Google Scholar 
46.Fosso-Kankeu, E. et al. Adaptation behaviour of bacterial species and impact on the biodegradation of biodiesel-diesel. Braz. J. Chem. Eng. 34, 469–480 (2017).CAS 
Article 

Google Scholar 
47.Lutz, G., Chavarría, M., Arias, M. L. & Mata-Segreda, J. F. Microbial degradation of palm (Elaeis guineensis) biodiesel. Rev. Biol. Trop. 54, 59–63 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Holmes, A. J. et al. Diverse, yet-to-be-cultured members of the Rubrobacter subdivision of the Actinobacteria are widespread in Australian arid soils. FEMS Microbiol. Ecol. 33, 111–120 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Wollherr, A. et al. Pyrosequencing-based assessment of bacterial community structure along different management types in German forest and grassland soils. PLoS ONE 6, 1–12 (2011).
Google Scholar 
50.Crampon, M., Bodilis, J. & Portet-Koltalo, F. Linking initial soil bacterial diversity and polycyclic aromatic hydrocarbons (PAHs) degradation potential. J. Hazard. Mater. 359, 500–509 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Wang, L., Li, F., Zhan, Y. & Zhu, L. Shifts in microbial community structure during in situ surfactant-enhanced bioremediation of polycyclic aromatic hydrocarbon-contaminated soil. Environ. Sci. Pollut. Res. 23, 14451–14461 (2016).CAS 
Article 

Google Scholar 
52.van Beilen, J. B., Kingma, J. & Witholt, B. Substrate specificity of the alkane hydroxylase system of Pseudomonas oleovorans GPo1. Enzyme Microb. Technol. 16, 904–911 (1994).Article 

Google Scholar 
53.Mukherjee, A. et al. Bioinformatic approaches including predictive metagenomic profiling reveal characteristics of bacterial response to petroleum hydrocarbon contamination in diverse environments. Sci. Rep. 7, 1108 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Ono, K., Nozaki, M. & Hayaishi, O. Purification and some properties of protocatechuate 4,5-dioxygenase. Biochim. Biophys. Acta Enzymol. 220, 224–238 (1970).CAS 
Article 

Google Scholar 
55.Fung, H. K. H. et al. Biochemical and biophysical characterisation of haloalkane dehalogenases DmrA and DmrB in Mycobacterium strain JS60 and their role in growth on haloalkanes. Mol. Microbiol. 97, 439–453 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Kang, Y.-S. & Park, W. Protection against diesel oil toxicity by sodium chloride-induced exopolysaccharides in Acinetobacter sp. strain DR1. J. Biosci. Bioeng. 109, 118–123 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Ramadass, K., Megharaj, M., Venkateswarlu, K. & Naidu, R. Ecotoxicity of measured concentrations of soil-applied diesel: Effects on earthworm survival, dehydrogenase, urease and nitrification activities. Appl. Soil Ecol. 119, 1–7 (2017).Article 

Google Scholar 
58.Moreno, R. & Rojo, F. Enzymes for aerobic degradation of alkanes in bacteria. In Aerobic Utilization of Hydrocarbons, Oils and Lipids (ed. Rojo, F.) 1–25 (Springer, 2017).
Google Scholar 
59.Mitter, E. K., de Freitas, J. R. & Germida, J. J. Hydrocarbon-degrading genes in root endophytic communities on oil sands reclamation covers. Int. J. Phytoremediat. 22, 703–712 (2020).CAS 
Article 

Google Scholar 
60.Mitter, E. K., Kataoka, R., de Freitas, J. R. & Germida, J. J. Potential use of endophytic root bacteria and host plants to degrade hydrocarbons. Int. J. Phytoremediat. 21, 928–938 (2019).CAS 
Article 

Google Scholar 
61.Rojo, F. Degradation of alkanes by bacteria: Minireview. Environ. Microbiol. 11, 2477–2490 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Dincer, K. Lower emissions from biodiesel combustion. Energy Sources A Recov. Util. Environ. Eff. 30, 963–968 (2008).CAS 
Article 

Google Scholar 
63.Miri, M., Bambai, B., Tabandeh, F., Sadeghizadeh, M. & Kamali, N. Production of a recombinant alkane hydroxylase (AlkB2) from Alcanivorax borkumensis. Biotechnol. Lett. 32, 497–502 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Schomburg, D. & Stephan, D. Rubredoxin-NAD+ reductase. In Enzyme Handbook (eds Schomburg, D. & Stephan, D.) 917–920 (Springer, 1994).
Google Scholar 
65.Eggink, G., Engel, H., Vriend, G., Terpstra, P. & Witholt, B. Rubredoxin reductase of Pseudomonas oleovorans. J. Mol. Biol. 212, 135–142 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Hagelueken, G. et al. Crystal structure of the electron transfer complex rubredoxin rubredoxin reductase of Pseudomonas aeruginosa. Proc. Natl. Acad. Sci. 104, 12276–12281 (2007).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
67.Lyu, Y., Zheng, W., Zheng, T. & Tian, Y. Biodegradation of polycyclic aromatic hydrocarbons by Novosphingobium pentaromativorans US6-1. PLoS ONE 9, e101438 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Wang, J. et al. Comparative genomics of degradative Novosphingobium strains with special reference to microcystin-degrading Novosphingobium sp. THN1. Front. Microbiol. 9, 1–17 (2018).Article 

Google Scholar 
69.Dhillon, G. S., Amichev, B. Y., de Freitas, J. R. & van Rees, K. Accurate and precise measurement of organic carbon content in carbonate-rich soils. Commun. Soil Sci. Plant Anal. 3624, 2707–2720 (2015).Article 
CAS 

Google Scholar 
70.McKeague, J. A. Manual on SOIL sampling and Methods of Analysis (Canadian Society of Soil Science, 1978).
Google Scholar 
71.Laverty, D. H. & Bollo-Kamara, A. Recommended Methods of Soil Analysis for Canadian Prairie Agricultural Soils (Alberta Agriculture, 1988).
Google Scholar 
72.Qian, P., Schoenaru, J. J. & Karamanos, R. E. Simultaneous extraction of available phosphorus and potassium with a new soil test: A modification of Kelowna extraction. Commun. Soil Sci. Plant Anal. 25, 627–635 (1994).CAS 
Article 

Google Scholar 
73.Anderson, J. P. E. & Domsch, K. H. A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215–221 (1978).CAS 
Article 

Google Scholar 
74.de Freitas, J. R., Schoenau, J. J., Boyetchko, S. M. & Cyrenne, S. A. Soil microbial populations, community composition, and activity as affected by repeated applications of hog and cattle manure in eastern Saskatchewan. Can. J. Microbiol. 49, 538–548 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
75.Ramirez, K. S., Craine, J. M. & Fierer, N. Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob. Change Biol. 18, 1918–1927 (2012).ADS 
Article 

Google Scholar 
76.Craine, J. M., Fierer, N. & McLauchlan, K. K. Widespread coupling between the rate and temperature sensitivity of organic matter decay. Nat. Geosci. 3, 854–857 (2010).ADS 
CAS 
Article 

Google Scholar 
77.Helgason, B. L., Walley, F. L. & Germida, J. J. Long-term no-till management affects microbial biomass but not community composition in Canadian prairie agroecosytems. Soil Biol. Biochem. 42, 2192–2202 (2010).CAS 
Article 

Google Scholar 
78.Drenovsky, R. E., Elliott, G. N., Graham, K. J. & Scow, K. M. Comparison of phospholipid fatty acid (PLFA) and total soil fatty acid methyl esters (TSFAME) for characterizing soil microbial communities. Soil Biol. Biochem. 36, 1793–1800 (2004).CAS 
Article 

Google Scholar 
79.Macdonald, L. M., Paterson, E., Dawson, L. A. & McDonald, A. J. S. Short-term effects of defoliation on the soil microbial community associated with two contrasting Lolium perenne cultivars. Soil Biol. Biochem. 36, 489–498 (2004).CAS 
Article 

Google Scholar 
80.Zelles, L., Bai, Q. Y., Beck, T. & Beese, F. Signature fatty acids in phospholipids and lipopolysaccharides as indicators of microbial biomass and community structure in agricultural soils. Soil Biol. Biochem. 24, 317–323 (1992).CAS 
Article 

Google Scholar 
81.Hynes, H. M. & Germida, J. J. Relationship between ammonia oxidizing bacteria and bioavailable nitrogen in harvested forest soils of central Alberta. Soil Biol. Biochem. 46, 18–25 (2012).CAS 
Article 

Google Scholar 
82.McCune, B. & Mefford, M. J. Multivariate analysis of Ecological Data (2011).83.Helgason, B. L., Walley, F. L. & Germida, J. J. No-till soil management increases microbial biomass and alters community profiles in soil aggregates. Appl. Soil Ecol. 46, 390–397 (2010).Article 

Google Scholar 
84.McCune, B. & Grace, J. B. Analysis of Ecological Communities (2002).85.Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Boylen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. PeerJ Prepr. https://doi.org/10.7287/peerj.preprints.27295 (2018).Article 

Google Scholar 
87.Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, 1–8 (2011).Article 

Google Scholar 
88.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar 
90.Oksanen, J. et al. Community Ecology Package ‘vegan’ (2020).91.Hamilton, N. ggtern: An Extension to ‘ggplot2’, for the Creation of Ternary Diagrams (2018).92.Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
93.Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
94.Kanehisa, M., Sato, Y., Furumichi, M., Morishima, K. & Tanabe, M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 47, D590–D595 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
95.Parks, D. H. & Beiko, R. G. Identifying biologically relevant differences between metagenomic communities. Bioinformatics 26, 715–721 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Weber, A. W. & Vedder, A. Population dynamics of the Virunga gorillas: 1959–1978. Biol. Conserv. 26, 341–366 (1983).Article 

Google Scholar 
2.Granjon, A.-C. et al. Estimating abundance and growth rates in a wild mountain gorilla population. Anim. Conserv. 23, 455–465 (2020).Article 

Google Scholar 
3.Gray, M. et al. Virunga Massif Mountain Gorilla Census—2010 Summary Report (IGCP & Partners, 2010).
Google Scholar 
4.Gray, M. et al. Genetic census reveals increased but uneven growth of a critically endangered mountain gorilla population. Biol. Conserv. 158, 230–238 (2013).Article 

Google Scholar 
5.Robbins, M. M. et al. Extreme conservation leads to recovery of the Virunga mountain gorillas. PLoS One 6, e19788 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Hickey, J. R., Granjon, A.-C. & Vigilant, L. Virunga 2015–2016 Surveys: Monitoring Mountain Gorillas, Other Select Mammals, and Illegal Activities (IGCP & Partners, 2019).
Google Scholar 
7.Kalpers, J. et al. Gorillas in the crossfire: Population dynamics of the Virunga mountain gorillas over the past three decades. Oryx 37, 326–337 (2003).Article 

Google Scholar 
8.Robbins, M. M., Gray, M., Kagoda, E. & Robbins, A. M. Population dynamics of the Bwindi mountain gorillas. Biol. Conserv. 142, 2886–2895 (2009).Article 

Google Scholar 
9.Hickey, J. R., Uzabaho, E. & Akantorana, M. Bwindi-Sarambwe EM 2018 Surveys: Monitoring Mountain Gorillas, Other Select Mammals, and Human Activities 40 (GVTC, IGCP & Partners, 2019).
Google Scholar 
10.Roy, J. et al. Challenges in the use of genetic mark-recapture to estimate the population size of Bwindi mountain gorillas (Gorilla beringei beringei). Biol. Conserv. 180, 249–261 (2014).Article 

Google Scholar 
11.McNeilage, A. J. Mountain Gorillas in the Virunga Volcanoes: Ecology and Carrying Capacity (University of Bristol, 1995).
Google Scholar 
12.Caillaud, D., Ndagijimana, F., Giarrusso, A. J., Vecellio, V. & Stoinski, T. S. Mountain gorilla ranging patterns: Influence of group size and group dynamics. Am. J. Primatol. 76, 730–746 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Caillaud, D. et al. Violent encounters between social units hinder the growth of a high-density mountain gorilla population. Sci. Adv. 6, eaba0724 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Watts, D. P. Causes and consequences of variation in male mountain gorilla life histories and group membership. In Primate Males (ed. Kappeler, P. M.) 169–179 (Cambridge University Press, 2000).
Google Scholar 
15.Robbins, M. M., Robbins, A. M., Gerald-Steklis, N. & Steklis, H. D. Socioecological influences on the reproductive success of female mountain gorillas (Gorilla beringei beringei). Behav. Ecol. Sociobiol. 61, 919–931 (2007).Article 

Google Scholar 
16.Robbins, A. M. et al. Impact of male Infanticide on the social structure of mountain gorillas. PLoS One 8, e78256 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Grueter, C. C. et al. Quadratic relationships between group size and foraging efficiency in a herbivorous primate. Sci. Rep. 8, 16718 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Eckardt, W., Stoinski, T. S., Rosenbaum, S. & Santymire, R. Social and ecological factors alter stress physiology of Virunga mountain gorillas (Gorilla beringei beringei). Ecol. Evol. 9, 5248–5259 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Harcourt, A. H., Parks, S. A. & Woodroffe, R. Human density as an influence on species/area relationships: Double jeopardy for small African reserves?. Biodivers. Conserv. 10, 1011–1026 (2001).Article 

Google Scholar 
20.Citterio, C. V. et al. Abomasal nematode community in an alpine chamois (Rupicapra r. rupicapra) population before and after a die-off. J. Parasitol. 92, 918–927 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Hudson, P. J. Macroparasites: Observed patterns. Ecol. Infect. Dis. Nat. Popul. 20, 144–176 (1995).
Google Scholar 
22.Albon, S. D. et al. The role of parasites in the dynamics of a reindeer population. Proc. R. Soc. Lond. B Biol. Sci. 269, 1625–1632 (2002).CAS 
Article 

Google Scholar 
23.Anderson, R. M. & May, R. M. Age-related changes in the rate of disease transmission: Implications for the design of vaccination programmes. Epidemiol. Infect. 94, 365–436 (1985).CAS 

Google Scholar 
24.Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Anderson, R. M. & May, R. M. Regulation and stability of host-parasite population interactions: I. Regulatory processes. J. Anim. Ecol. 47, 219–247 (1978).Article 

Google Scholar 
26.Arneberg, P., Skorping, A., Grenfell, B. & Read, A. F. Host densities as determinants of abundance in parasite communities. Proc. R. Soc. Lond. B Biol. Sci. 265, 1283–1289 (1998).Article 

Google Scholar 
27.Gillespie, T. R. & Chapman, C. A. Forest fragmentation, the decline of an endangered primate, and changes in host–parasite interactions relative to an unfragmented forest. Am. J. Primatol. 70, 222–230 (2008).PubMed 
Article 
PubMed Central 

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

Google Scholar 
29.dos Santos, C. N. et al. Seasonal dynamics of cyathostomin (Nematoda–Cyathostominae) infective larvae in Brachiaria humidicola grass in tropical southeast Brazil. Vet. Parasitol. 180, 274–278 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Silangwa, S. M. & Todd, A. C. Vertical migration of trichostrongylid larvae on grasses. J. Parasitol. 50, 278–285 (1964).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Callinan, A. P. L. & Westcott, J. M. Vertical distribution of trichostrongylid larvae on herbage and in soil. Int. J. Parasitol. 16, 241–244 (1986).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Crofton, H. D. The ecology of immature phases of trichostrongyle nematodes: II. The effect of climatic factors on the availability of the infective larvae of Trichostrongylus retortaeformis to the host. Parasitology 39, 26–38 (1948).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Zanet, S. et al. Higher risk of gastrointestinal parasite infection at lower elevation suggests possible constraints in the distributional niche of Alpine marmots. PLoS One 12, e0182477 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
34.Derek Scasta, J. Livestock parasite management on high-elevation rangelands: Ecological interactions of climate, habitat, and wildlife. J. Integr. Pest Manag. 6, 20 (2015).Article 

Google Scholar 
35.Huffman, M. A., Gotoh, S., Turner, L. A., Hamai, M. & Yoshida, K. Seasonal trends in intestinal nematode infection and medicinal plant use among chimpanzees in the Mahale Mountains, Tanzania. Primates 38, 111–125 (1997).Article 

Google Scholar 
36.MacIntosh, A. J. J., Hernandez, A. D. & Huffman, M. A. Host age, sex, and reproductive seasonality affect nematode parasitism in wild Japanese macaques. Primates 51, 353–364 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Pafčo, B. et al. Do habituation, host traits and seasonality have an impact on protist and helminth infections of wild western lowland gorillas?. Parasitol. Res. 116, 3401–3410 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Rothman, J. M., Pell, A. N. & Bowman, D. D. Host-parasiteecology of the helminths in mountain gorillas. J. Parasitol. 94, 834–840 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Müller-Graf, C. D. M., Collins, D. A. & Woolhouse, M. E. J. Intestinal parasite burden in five troops of olive baboons (Papio cynocephalus anubis) in Gombe Stream National Park, Tanzania. Parasitology 112, 489–497 (1996).PubMed 
Article 

Google Scholar 
40.Alexander, J. & Stimson, W. H. Sex hormones and the course of parasitic infection. Parasitol. Today 4, 189–193 (1988).Article 

Google Scholar 
41.Bundy, D. A. P. Gender-dependent patterns of infections and disease. Parasitol. Today 4, 186–189 (1988).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Zuk, M. Reproductive strategies and disease susceptibility: An evolutionary viewpoint. Parasitol. Today 6, 231–233 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Nunn, C. & Altizer, S. Infectious Diseases in Primates: Behavior (Ecology and Evolution. Oxford University Press, Oxford, 2006).Book 

Google Scholar 
44.Wilson, K. et al. Heterogeneities in macroparasite infections: Patterns and processes. In The Ecology of Wildlife Diseases 6–44 (2002).45.Cattadori, I. M., Boag, B., Bjørnstad, O. N., Cornell, S. J. & Hudson, P. J. Peak shift and epidemiology in a seasonal host–nematode system. Proc. R. Soc. B Biol. Sci. 272, 1163–1169 (2005).CAS 
Article 

Google Scholar 
46.Terio, K. A. et al. Oesophagostomiasis in non-human primates of Gombe National Park, Tanzania. Am. J. Primatol. 80, e22572 (2018).Article 

Google Scholar 
47.Gillespie, T. R., Nunn, C. L. & Leendertz, F. H. Integrative approaches to the study of primate infectious disease: Implications for biodiversity conservation and global health. Am. J. Phys. Anthropol. 137, 53–69 (2008).Article 

Google Scholar 
48.Collett, M. G. et al. Gastric Ollulanus tricuspis infection identified in captive cheetahs (Acinonyx jubatus) with chronic vomiting: Case report. J. S. Afr. Vet. Assoc. 71, 251–255 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Dennis, M. M., Bennett, N. & Ehrhart, E. J. Gastric adenocarcinoma and chronic gastritis in two related Persian cats. Vet. Pathol. 43, 358–362 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Smetana, H. F. & Orihel, T. C. Gastric papillomata in Macaca speciosa induced by Nochtia nochti (Nematoda: Trichostrongyloidea). J. Parasitol. 55, 349–351 (1969).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Nybelin, O. Anoplocephala gorillae n. sp. Ark Zool. 19, 1–3 (1924).
Google Scholar 
52.Sleeman, J. M., Meader, L. L., Mudakikwa, A. B., Foster, J. W. & Patton, S. Gastrointestinal parasites of mountain gorillas (Gorilla gorilla beringei) in the Parc National des Volcans, Rwanda. J. Zool. Wildl. Med. 31, 322–328 (2000).CAS 
Article 

Google Scholar 
53.Ashford, R. W., Lawson, H., Butynski, T. M. & Reid, G. D. F. Patterns of intestinal parasitism in the mountain gorilla Gorilla gorilla in the Bwindi-Impenetrable Forest, Uganda. J. Zool. 239, 507–514 (1996).Article 

Google Scholar 
54.Kalema-Zikusoka, G., Rothman, J. M. & Fox, M. T. Intestinal parasites and bacteria of mountain gorillas (Gorilla beringei beringei) in Bwindi Impenetrable National Park, Uganda. Primates 46, 59–63 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Owiunji, I, et al. The biodiversity of the Virunga Volcanoes. https://programs.wcs.org/portals/49/media/file/volcanoes_biodiv_survey.pdf (2005).56.Langdale-Brown, I., Osmaston, H. & Wilson, J. G. The Vegetation of Uganda and Its Bearing on Land-Use (Governmentt of Uganda, 1964).
Google Scholar 
57.Ashford, R. W., Reid, G. D. F. & Butynski, T. M. The intestinal faunas of man and mountain gorillas in a shared habitat. Ann. Trop. Med. Parasitol. 84, 337–340 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Shutt, K. et al. Effects of habituation, research and ecotourism on faecal glucocorticoid metabolites in wild western lowland gorillas: Implications for conservation management. Biol. Conserv. 172, 72–79 (2014).Article 

Google Scholar 
59.Kayiranga, A. et al. Analysis of climate and topography impacts on the spatial distribution of vegetation in the Virunga Volcanoes Massif of East-Central Africa. Geosciences 7, 17 (2017).ADS 
Article 

Google Scholar 
60.Cousins, D. & Huffman, M. A. Medicinal properties in the diet of gorillas: An ethno-phramacological evaluation. Afr. Stud. Monogr. 23, 65–89 (2002).
Google Scholar 
61.Woolhouse, M. E. J. Patterns in parasite epidemiology: The peak shift. Parasitol. Today 14, 428–434 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Hayes, K. S., Bancroft, A. J. & Grencis, R. K. Immune-mediated regulation of chronic intestinal nematode infection. Immunol. Rev. 201, 75–88 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Maizels, R. M. et al. Helminth parasites—masters of regulation. Immunol. Rev. 201, 89–116 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Proudman, C. J., Holmes, M. A., Sheoran, A. S., Edwards, S. E. R. & Trees, A. J. Immunoepidemiology of the equine tapeworm Anoplocephala perfoliata: Age-intensity profile and age-dependency of antibody subtype responses. Parasitology 114, 89–94 (1997).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Gergócs, V., Garamvölgyi, Á., Homoródi, R. & Hufnagel, L. Seasonal change of oribatid mite communities (Acari, Oribatida) in three different types of microhabitats in an oak forest. Appl. Ecol. Environ. Res. 9, 181–195 (2011).Article 

Google Scholar 
66.Dobson, A. & Foufopoulos, J. Emerging infectious pathogens of wildlife. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1001–1012 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Xue, Y. et al. Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding. Science 348, 242–245 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17, 230–237 (2003).Article 

Google Scholar 
69.Pafčo, B. et al. Metabarcoding analysis of strongylid nematode diversity in two sympatric primate species. Sci. Rep. 8, 5933 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.McNeilage, A. Diet and habitat use of two mountain gorilla groups in contrasting habitats in the Virunga. In Mountain Gorillas: Three Decades of Research at Karisoke (Cambridge University Press, 2001).
Google Scholar 
71.Sinayitutse, E. et al. Daily defecation outputs of mountain gorillas (Gorilla beringei beringei) in the Volcanoes National Park, Rwanda. Primates https://doi.org/10.1007/s10329-020-00874-7 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
72.Burgunder, J. et al. Complexity in behavioural organization and strongylid infection among wild chimpanzees. Anim. Behav. 129, 257–268 (2017).Article 

Google Scholar 
73.Chapman, C. A., Speirs, M. L., Gillespie, T. R., Holland, T. & Austad, K. M. Life on the edge: Gastrointestinal parasites from the forest edge and interior primate groups. Am. J. Primatol. 68, 397–409 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
74.Anderson, R. M. & Schad, G. A. Hookworm burdens and faecal egg counts: An analysis of the biological basis of variation. Trans. R. Soc. Trop. Med. Hyg. 79, 812–825 (1985).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Warnick, L. D. Daily variability of equine fecal strongyle egg counts. Cornell Vet. 82, 453–463 (1992).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Tomczuk, K. et al. Comparison of the sensitivity of coprological methods in detecting Anoplocephala perfoliata invasions. Parasitol. Res. 113, 2401–2406 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Williamson, R., Beveridge, I. & Gasser, R. Coprological methods for the diagnosis of Anoplocephala perfoliata infection of the horse. Aust. Vet. J. 76, 618–621 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Cringoli, G. et al. The Mini-FLOTAC technique for the diagnosis of helminth and protozoan infections in humans and animals. Nat. Protoc. 12, 1723–1732 (2017).CAS 
PubMed 
Article 

Google Scholar 
79.Guschanski, K. et al. Counting elusive animals: Comparing field and genetic census of the entire mountain gorilla population of Bwindi Impenetrable National Park, Uganda. Biol. Conserv. 142, 290–300 (2009).Article 

Google Scholar 
80.Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).MATH 
Book 

Google Scholar 
81.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
82.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).83.Forstmeier, W. & Schielzeth, H. Cryptic multiple hypotheses testing in linear models: Overestimated effect sizes and the winner’s curse. Behav. Ecol. Sociobiol. 65, 47–55 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Engqvist, L. The mistreatment of covariate interaction terms in linear model analyses of behavioural and evolutionary ecology studies. Anim. Behav. 70, 20 (2005).Article 

Google Scholar 
85.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2007).
Google Scholar 
86.Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

Google Scholar 
87.Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
89.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020). More

N. V. PatinPresent address: Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, Miami, FL, USAN. V. PatinPresent address: Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USAN. V. PatinPresent address: Stationed at Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, USASchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USAN. V. Patin & F. J. StewartCenter for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USAN. V. Patin & F. J. StewartBowdoin College, Brunswick, ME, USAZ. A. DietrichHarbor Branch Oceanographic Institute, Florida Atlantic University, Ft. Pierce, FL, USAA. Stancil, M. Quinan & J. S. BecklerMote Marine Laboratory, Sarasota, FL, USAE. R. Hall & J. CulterU.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, USAC. G. SmithSchool of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USAM. TaillefertDepartment of Microbiology & Immunology, Montana State University, Bozeman, MT, USAF. J. Stewart More

1.Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.PubMed 
PubMed Central 
Article 

Google Scholar 
2.Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62.PubMed 
PubMed Central 
Article 

Google Scholar 
3.Rivett DW, Bell T. Abundance determines the functional role of bacterial phylotypes in complex communities. Nat Microbiol. 2018;3:767–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Bell T, Newman JA, Silverman BW, Turner SL, Lilley AK. The contribution of species richness and composition to bacterial services. Nature. 2005;436:1157–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Starke R, Capek P, Morais D, Callister SJ, Jehmlich N. The total microbiome functions in bacteria and fungi. J Proteom. 2020;213:1–5.Article 
CAS 

Google Scholar 
6.Saleem M, Hu J, Jousset A. More than the sum of its parts: microbiome biodiversity as a driver of plant growth and soil health. Annu Rev Ecol Evol Syst. 2019;50:145–68.Article 

Google Scholar 
7.Wagg C, Schlaeppi K, Banerjee S, Kuramae EE, Heijden van der MGA. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat Commun. 2019;10:1–10.CAS 
Article 

Google Scholar 
8.Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:1–8.Article 
CAS 

Google Scholar 
9.Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.PubMed 
Article 
PubMed Central 

Google Scholar 
10.Aanderud ZT, Jones SE, Fierer N, Lennon JT. Resuscitation of the rare biosphere contributes to pulses of ecosystem activity. Front Microbiol. 2015;6:1–11.Article 

Google Scholar 
11.Song H-K, Song W, Kim M, Tripathi BM, Kim H, Jablonski P, et al. Bacterial strategies along nutrient and time gradients, revealed by metagenomic analysis of laboratory microcosms. FEMS Microbiol Ecol. 2017;93:1–13.Article 
CAS 

Google Scholar 
12.Jiao S, Chen W, Wei G. Biogeography and ecological diversity patterns of rare and abundant bacteria in oil-contaminated soils. Mol Ecol. 2017;26:5305–5317.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Yu X, Polz MF, Alm EJ. Interactions in self-assembled microbial communities saturate with diversity. ISME J. 2019;13:1602–17.PubMed 
PubMed Central 
Article 

Google Scholar 
15.Li P, Liu J, Jiang C, Wu M, Liu M, Li Z. Distinct successions of common and rare bacteria in soil under humic acid amendment—a microcosm study. Front Microbiol. 2019;10:1–14.Article 

Google Scholar 
16.Nemergut DR, Costello EK, Hamady M, Lozupone C, Jiang L, Schmidt SK, et al. Global patterns in the biogeography of bacterial taxa. Environ Microbiol. 2011;13:135–44.PubMed 
PubMed Central 
Article 

Google Scholar 
17.Bickel S, Chen X, Papritz A, Or D. A hierarchy of environmental covariates control the global biogeography of soil bacterial richness. Sci Rep. 2019;9:1–10.CAS 
Article 

Google Scholar 
18.Clarke RT, Murphy JF. Effects of locally rare taxa on the precision and sensitivity of RIVPACS bioassessment of freshwaters. Freshw Biol. 2006;51:1924–40.Article 

Google Scholar 
19.Kurm V, Putten WH, van der, Boer W, de, Naus‐Wiezer S, Hol WHG. Low abundant soil bacteria can be metabolically versatile and fast growing. Ecology. 2017;98:555–64.PubMed 
Article 
PubMed Central 

Google Scholar 
20.Kurm V, Putten WH, van der, Hol WHG. Cultivation-success of rare soil bacteria is not influenced by incubation time and growth medium. PLoS ONE. 2019;14:1–14.Article 
CAS 

Google Scholar 
21.Meyer KM, Memiaghe H, Korte L, Kenfack D, Alonso A, Bohannan BJM. Why do microbes exhibit weak biogeographic patterns? ISME J. 2018;12:1404–13.PubMed 
PubMed Central 
Article 

Google Scholar 
22.Escalas A, Hale L, Voordeckers JW, Yang Y, Firestone MK, Alvarez‐Cohen L, et al. Microbial functional diversity: from concepts to applications. Ecol Evol. 2019;9:12000–16.PubMed 
PubMed Central 
Article 

Google Scholar 
23.Barberán A, Ramirez KS, Leff JW, Bradford MA, Wall DH, Fierer N. Why are some microbes more ubiquitous than others? Predicting the habitat breadth of soil bacteria. Ecol Lett. 2014;17:794–802.PubMed 
Article 
PubMed Central 

Google Scholar 
24.Dee LE, Cowles J, Isbell F, Pau S, Gaines SD, Reich PB. When do ecosystem services depend on rare species? Trends Ecol Evol. 2019;34:746–58.PubMed 
Article 
PubMed Central 

Google Scholar 
25.Pueyo S, He F, Zillio T. The maximum entropy formalism and the idiosyncratic theory of biodiversity. Ecol Lett. 2007;10:1017–28.PubMed 
PubMed Central 
Article 

Google Scholar 
26.Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Zhou J, Deng Y, Shen L, Wen C, Yan Q, Ning D, et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat Commun. 2016;7:1–10.
Google Scholar 
28.Thompson LR, Jex AR, Campbell AH, Linz AM, Berry A, Williams AE, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Bickel S, Or D. Soil bacterial diversity mediated by microscale aqueous-phase processes across biomes. Nat Commun. 2020;11:1–9.
Google Scholar 
30.Xu X, Thornton PE, Post WM. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems. Glob Ecol Biogeogr. 2013;22:737–49.Article 

Google Scholar 
31.Serna-Chavez HM, Fierer N, van Bodegom PM. Global drivers and patterns of microbial abundance in soil: global patterns of soil microbial biomass. Glob Ecol Biogeogr. 2013;22:1162–72.Article 

Google Scholar 
32.Wang G, Or D. A hydration-based biophysical index for the onset of soil microbial coexistence. Sci Rep. 2012;2:1–5.
Google Scholar 
33.Li CH, Lee CK. Minimum cross entropy thresholding. Pattern Recognit. 1993;26:617–625.Article 

Google Scholar 
34.Walt S, van der, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, et al. scikit-image: image processing in Python. PeerJ. 2014;2:1–18.
Google Scholar 
35.Homem-de-Mello T, Rubinstein RY. Estimation of rare event probabilities using cross-entropy. Proc Winter Simul Conf. 2002;1:310–19.Article 

Google Scholar 
36.Murali A, Bhargava A, Wright ES. IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome. 2018;6:1–14.Article 

Google Scholar 
37.Šťovíček A, Kim M, Or D, Gillor O. Microbial community response to hydration-desiccation cycles in desert soil. Sci Rep. 2017;7:1–9.Article 
CAS 

Google Scholar 
38.Zhao M, Heinsch FA, Nemani RR, Running SW. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens Environ. 2005;95:164–76.Article 

Google Scholar 
39.Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas: new climate surfaces for global land areas. Int J Climatol. 2017;37:4302–15.Article 

Google Scholar 
40.Schoolfield RM, Sharpe PJH, Magnuson CE. Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J Theor Biol. 1981;88:719–31.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Beck HE, Wood EF, Pan M, Fisher CK, Miralles DG, van Dijk AIJM, et al. MSWEP V2 Global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bull Am Meteorol Soc. 2019;100:473–500.Article 

Google Scholar 
42.Wang G, Or D. Hydration dynamics promote bacterial coexistence on rough surfaces. ISME J. 2013;7:395–404.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Kim M, Or D. Individual-based model of microbial life on hydrated rough soil surfaces. PLoS ONE. 2016;11:1–31.
Google Scholar 
44.Hermsen R, Okano H, You C, Werner N, Hwa T. A growth-rate composition formula for the growth of E.coli on co-utilized carbon substrates. Mol Syst Biol. 2015;11:1–6.Article 
CAS 

Google Scholar 
45.García FC, Bestion E, Warfield R, Yvon-Durocher G. Changes in temperature alter the relationship between biodiversity and ecosystem functioning. Proc Natl Acad Sci. 2018;115:10989–94.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
46.Slessarev EW, Lin Y, Bingham NL, Johnson JE, Dai Y, Schimel JP, et al. Water balance creates a threshold in soil pH at the global scale. Nature. 2016;540:567–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Treves DS, Xia B, Zhou J, Tiedje JM. A two-species test of the hypothesis that spatial isolation influences microbial diversity in soil. Micro Ecol. 2003;45:20–8.CAS 
Article 

Google Scholar 
48.Campbell BJ, Yu L, Heidelberg JF, Kirchman DL. Activity of abundant and rare bacteria in a coastal ocean. Proc Natl Acad Sci. 2011;108:12776–81.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Stauffer D. Scaling theory of percolation clusters. Phys Rep. 1979;54:1–74.Article 

Google Scholar 
50.Scher H, Zallen R. Critical density in percolation processes. J Chem Phys. 1970;53:3759–61.CAS 
Article 

Google Scholar 
51.Hengl T, de Jesus JM, Heuvelink GB, Gonzalez MR, Kilibarda M, Blagotić A, et al. SoilGrids250m: global gridded soil information based on machine learning. PloS ONE. 2017;12:1–40.Article 
CAS 

Google Scholar 
52.Chase AB, Arevalo P, Brodie EL, Polz MF, Karaoz U, Martiny JBH. Maintenance of sympatric and allopatric populations in free-living terrestrial bacteria. mBio. 2019;10:1–11.Article 

Google Scholar 
53.Fisher CK, Mehta P. The transition between the niche and neutral regimes in ecology. Proc Natl Acad Sci. 2014;111:13111–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Ratzke C, Barrere J, Gore J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat Ecol Evol. 2020;4:376–83.PubMed 
Article 
PubMed Central 

Google Scholar 
55.Doud DFR, Bowers RM, Schulz F, Raad MD, Deng K, Tarver A, et al. Function-driven single-cell genomics uncovers cellulose-degrading bacteria from the rare biosphere. ISME J. 2020;14:659–75.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Shade A, Jones SE, Caporaso JG, Handelsman J, Knight R, Fierer N, et al. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. mBio. 2014;5:1–9.Article 
CAS 

Google Scholar 
57.Kaminsky R, Morales SE. Conditionally rare taxa contribute but do not account for changes in soil prokaryotic community structure. Front Microbiol. 2018;9:1–6.Article 

Google Scholar 
58.Price PB, Sowers T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc Natl Acad Sci U S A. 2004;101:4631–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.McGowan, P. J. K., Traylor-Holzer, K. & Leus, K. IUCN guidelines for determining when and how ex situ management should be used in species conservation. Conserv. Lett. 10, 361–366 (2017).Article 

Google Scholar 
2.Conde, D. A., Flesness, N., Colchero, F., Jones, O. R. & Scheuerlein, A. An emerging role of zoos to conserve biodiversity. Science 331, 1390–1391 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Lacy, R. C. Conservation Genetics in the Age of Genomics (eds Amato, G., DeSalle, R., Ryder, O. A. & Rosenbaum, H. C.) (Columbia Univ. Press, 2009).4.Frankham, R. Genetic adaptation to captivity in species conservation programs. Mol. Ecol. 17, 325–333 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Jule, K. R., Leaver, L. A. & Lea, S. E. G. The effects of captive experience on reintroduction survival in carnivores: a review and analysis. Biol. Conserv. 141, 355–363 (2008).Article 

Google Scholar 
6.Araki, H., Cooper, B. & Blouin, M. S. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318, 100–103 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Lacy, R. C., Alaks, G. & Walsh, A. Evolution of Peromyscus leucopus mice in response to a captive environment. PLOS One 8, e72452 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Milot, E., Perrier, C., Papillon, L., Dodson, J. J. & Bernatchez, L. Reduced fitness of Atlantic salmon released in the wild after one generation of captive breeding. Evol. Appl. 6, 472–485 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Frankham, R. Where are we in conservation genetics and where do we need to go? Conserv. Genet. 11, 661–663 (2010).Article 

Google Scholar 
10.Williams, S. E. & Hoffman, E. A. Minimizing genetic adaptation in captive breeding programs: a review. Biol. Conserv. 142, 2388–2400 (2009).Article 

Google Scholar 
11.Christie, M. R., Marine, M. L., Fox, S. E., French, R. A. & Blouin, M. S. A single generation of domestication heritably alters the expression of hundreds of genes. Nat. Commun. 7, 10676 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Farquharson, K. A., Hogg, C. J. & Grueber, C. E. A meta-analysis of birth-origin effects on reproduction in diverse captive environments. Nat. Commun. 9, 1055 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Christie, M. R., Marine, M. L., French, R. A. & Blouin, M. S. Genetic adaptation to captivity can occur in a single generation. Proc. Natl Acad. Sci. USA 109, 238–242 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Matos, M. Maternal effects can inflate rate of adaptation to captivity. Proc. Natl Acad. Sci. USA 109, e2380 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Grueber, C. E., Laws, R. J., Nakagawa, S. & Jamieson, I. G. Inbreeding depression accumulation across life-history stages of the endangered takahe. Conserv. Biol. 24, 1617–1625 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Harrisson, K. A. et al. Lifetime fitness costs of inbreeding and being inbred in a critically endangered bird. Curr. Biol. 29, 2711–2717 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Boakes, E. H., Wang, J. & Amos, W. An investigation of inbreeding depression and purging in captive pedigreed populations. Heredity 98, 172–182 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Kennedy, E. S., Grueber, C. E., Duncan, R. P. & Jamieson, I. G. Severe inbreeding depression and no evidence of purging in an extremely inbred wild species – the Chatham Island black robin. Evolution 68, 987–995 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Frankham, R., Ballou J. D., Briscoe D. A. Introduction to Conservation Genetics 2nd edn, (Cambridge Univ. Press, 2010).20.Hedrick, P. W. & Kalinowski, S. T. Inbreeding depression in conservation biology. Annu. Rev. Ecol. Syst. 31, 139–162 (2000).Article 

Google Scholar 
21.Fa, J. E., Gusset, M., Flesness, N. & Conde, D. A. Zoos have yet to unveil their full conservation potential. Anim. Conserv. 17, 97–100 (2014).Article 

Google Scholar 
22.Martin, T. E., Lurbiecki, H., Joy, J. B. & Mooers, A. O. Mammal and bird species held in zoos are less endemic and less threatened than their close relatives not held in zoos. Anim. Conserv. 17, 89–96 (2014).Article 

Google Scholar 
23.Fisher, D. O. & Owens, I. P. F. The comparative method in conservation biology. Trends Ecol. Evol. 19, 391–398 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Conde, D. A. et al. Data gaps and opportunities for comparative and conservation biology. Proc. Natl Acad. Sci. USA 116, 9658–9664 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Species 360. Zoological Information Management System (ZIMS) http://zims.species360.org (2018).26.Charlesworth, D. & Willis, J. H. The genetics of inbreeding depression. Nat. Rev. Genet. 10, 783–796 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Packer, C., Tatar, M. & Collins, A. Reproductive cessation in female mammals. Nature 392, 807–811 (1998).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Farquharson, K. A., Hogg, C. J. & Grueber, C. E. Pedigree analysis reveals a generational decline in reproductive success of captive Tasmanian devil (Sarcophilus harrisii): implications for captive management of threatened species. J. Hered. 108, 488–495 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Hammerly, S. C., de la Cerda, D. A., Bailey, H. & Johnson, J. A. A pedigree gone bad: increased offspring survival after using DNA-based relatedness to minimize inbreeding in a captive population. Anim. Conserv. 19, 296–303 (2016).Article 

Google Scholar 
30.Woodworth, L. M., Montgomery, M. E., Briscoe, D. A. & Frankham, R. Rapid genetic deterioration in captive populations: causes and conservation implications. Conserv. Genet. 3, 277–288 (2002).CAS 
Article 

Google Scholar 
31.Fraser, D. J. et al. Population correlates of rapid captive-induced maladaptation in a wild fish. Evol. Appl. 12, 1305–1317 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Frankham, R. & Loebel, D. A. Modeling problems in conservation genetics using captive Drosophila populations: rapid genetic adaptation to captivity. Zoo. Biol. 11, 333–342 (1992).Article 

Google Scholar 
33.Lacy, R. C. Loss of genetic diversity from managed populations: interacting effects of drift, mutation, immigration, selection, and population subdivision. Conserv. Biol. 1, 143–158 (1987).Article 

Google Scholar 
34.Mason, G. et al. Plastic animals in cages: behavioural flexibility and responses to captivity. Anim. Behav. 85, 1113–1126 (2013).Article 

Google Scholar 
35.Courtney Jones, S. K. & Byrne, P. G. What role does heritability play in transgenerational phenotypic responses to captivity? Implications for managing captive populations. Zoo. Biol. 36, 397–406 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Kokko, H. & Jennions, M. D. The Evolution of Parental Care (eds Royle, N. J., Smiseth, P. T. & Kölliker, M.) (Oxford Univ. Press, 2012).37.Grueber, C. E., Hogg, C. J., Ivy, J. A. & Belov, K. Impacts of early viability selection on management of inbreeding and genetic diversity in conservation. Mol. Ecol. 24, 1645–1653 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Wells, J. C. Commentary: paternal and maternal influences on offspring phenotype: the same, only different. Int J. Epidemiol. 43, 772–774 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Calkins, E. S., Fuller, T. K., Asa, C. S., Sievert, P. R. & Coonan, T. J. Factors influencing reproductive success and litter size in captive island foxes. J. Wildl. Manag. 77, 346–351 (2013).Article 

Google Scholar 
40.Hogg, C. J. et al. Influence of genetic provenance and birth origin on productivity of the Tasmanian devil insurance population. Conserv. Genet. 16, 1465–1473 (2015).Article 

Google Scholar 
41.O’Grady, J. J. et al. Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol. Conserv. 133, 42–51 (2006).Article 

Google Scholar 
42.Hoeck, P. E. A., Wolak, M. E., Switzer, R. A., Kuehler, C. M. & Lieberman, A. A. Effects of inbreeding and parental incubation on captive breeding success in Hawaiian crows. Biol. Conserv. 184, 357–364 (2015).Article 

Google Scholar 
43.Menotti-Raymond, M. & O’Brien, S. J. Dating the genetic bottleneck of the African cheetah. Proc. Natl Acad. Sci. USA 90, 3172–3176 (1993).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Brüniche-Olsen, A., Jones, M. E., Austin, J. J., Burridge, C. P. & Holland, B. R. Extensive population decline in the Tasmanian devil predates European settlement and devil facial tumour disease. Biol. Lett. 10, 20140619 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Hedrick, P. W. & Fredrickson, R. J. Captive breeding and the reintroduction of Mexican and red wolves. Mol. Ecol. 17, 344–350 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Hogg, C. J. et al. Founder relationships and conservation management: empirical kinships reveal the effect on breeding programmes when founders are assumed to be unrelated. Anim. Conserv. 22, 348–361 (2019).Article 

Google Scholar 
47.Ivy, J. A. & Lacy, R. C. A comparison of strategies for selecting breeding pairs to maximize genetic diversity retention in managed populations. J. Hered. 103, 186–196 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Norman, A. J., Putnam, A. S. & Ivy, J. A. Use of molecular data in zoo and aquarium collection management: benefits, challenges, and best practices. Zoo. Biol. 38, 106–118 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Leberg, P. L. & Firmin, B. D. Role of inbreeding depression and purging in captive breeding and restoration programmes. Mol. Ecol. 17, 334–343 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Tennenhouse, E. M., Weladji, R. B., Holand, Ø. & Nieminen, M. Timing of reproductive effort differs between young and old dominant male reindeer. Ann. Zool. Fenn. 49, 152–160 (2012). 159.Article 

Google Scholar 
51.L’Italien, L. et al. Mating group size and stability in reindeer Rangifer tarandus: the effects of male characteristics, sex ratio and male age structure. Ethology 118, 783–792 (2012).Article 

Google Scholar 
52.Imlay, T. L., Steiner, J. C. & Bird, D. M. Age and experience affect the reproductive success of captive Loggerhead Shrike (Lanius ludovicianus) subspecies. Can. J. Zool. 95, 547–554 (2017).Article 

Google Scholar 
53.Henry, M. D., Hankerson, S. J., Siani, J. M., French, J. A. & Dietz, J. M. High rates of pregnancy loss by subordinates leads to high reproductive skew in wild golden lion tamarins (Leontopithecus rosalia). Horm. Behav. 63, 675–683 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Descamps, S., Boutin, S., Berteaux, D. & Gaillard, J.-M. Age-specific variation in survival, reproductive success and offspring quality in red squirrels: evidence of senescence. Oikos 117, 1406–1416 (2008).Article 

Google Scholar 
55.Ruiz-López, M. J., Espeso, G., Evenson, D. P., Roldan, E. R. S. & Gomendio, M. Paternal levels of DNA damage in spermatozoa and maternal parity influence offspring mortality in an endangered ungulate. Proc. R. Soc. B 277, 2541–2546 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl Acad. Sci. USA 114, 10678–10683 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Kellermann, V., Hoffmann, A. A., Overgaard, J., Loeschcke, V. & Sgrò, C. M. Plasticity for desiccation tolerance across Drosophila species is affected by phylogeny and climate in complex ways. Proc. R. Soc. B 285, 20180048 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Mellor, E., McDonald Kinkaid, H. & Mason, G. Phylogenetic comparative methods: harnessing the power of species diversity to investigate welfare issues in captive wild animals. Zoo. Biol. 37, 369–388 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
59.Araki, H., Cooper, B. & Blouin, M. S. Carry-over effect of captive breeding reduces reproductive fitness of wild-born descendants in the wild. Biol. Lett. 5, 621–624 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
60.Christie, M. R., Ford, M. J. & Blouin, M. S. On the reproductive success of early-generation hatchery fish in the wild. Evol. Appl. 7, 883–896 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
61.González, A., Quevedo, M. Á. & Cuadrado, M. Comparison of reproductive success between parent-reared and hand-reared northern bald ibis Geronticus eremita in captivity during Proyecto Eremita. J. Zoo. Aquar. Res. 8, 246–252 (2020).
Google Scholar 
62.Lacy, R. C., Ballou, J. D. & Pollak, J. P. PMx: software package for demographic and genetic analysis and management of pedigreed populations. Methods Ecol. Evol. 3, 433–437 (2012).Article 

Google Scholar 
63.Ballou, J. D., Lacy R. C., Pollak J. P. PMx: software for demographic and genetic analysis and mangement of pedigreed populations. Chicago Zoological Society (2010).64.Ballou, J. Genetics and Conservation: a Reference for Managing Wild Animal and Plant Populations (eds Schonewald-Cox, C. M., Chambers, S. M., MacBryde, B., Thomas, W. L.) (The Benjamin/Cummings Publishing Company Inc., 1983).65.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2018).66.Tacutu, R. et al. Human ageing genomic resources: new and updated databases. Nucleic Acids Res. 46, D1083–D1090 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
67.Eager, C. D. standardize: tools for standardizing variables for regression in R. https://CRAN.R-project.org/package=standardize (2017).68.Michonneau, F., Brown, J. W. & Winter, D. J. rotl: an R package to interact with the Open Tree of Life data. Methods Ecol. Evol. 7, 1476–1481 (2016).Article 

Google Scholar 
69.Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc. Natl Acad. Sci. USA 112, 12764–12769 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).Article 

Google Scholar 
71.Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. https://CRAN.R-project.org/package=DHARMa (2019).72.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Barton, K. MuMIn: multi-model inference. R package https://CRAN.R-project.org/package=MuMIn (2018).75.IUCN. The IUCN Red List of Threatened Species. Version 2020-2 https://www.iucnredlist.org (2020).76.Wright, S. Evolution in Mendelian populations. Genetics 16, 97–159 (1931).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Metabolic profiling of EP and LE root exudates and root periderm samplesHigh performance liquid chromatography (HPLC) analysis of root exudates and root periderm reported the presence of five bioactive NQs. The identified NQs included shikonin (SK), acetylshikonin (AS); isobutyrylshikonin (IBS); β, β-dimethylacrylshikonin (DMAS); and isovalerylshikonin (IVS) (Fig. 1a–d). This suggests that SK and its derivatives accumulate in the rhizosphere of both EP and LE via root exudation. Though all the five NQs were found to be exuded in the rhizosphere however they varied quantitatively among EP and LE species. LE samples had higher SK and its derivatives production compared to EP (Figs. S5–S6). Our results also displayed quantitative variations in SK and its derivatives production among two soil types (Table 1a,b). However, regardless of variation, SK, AS, DMAS, and IVS were consistently present among all the samples.Figure 1Images and chromatograms representing qualitative and quantitative variation of SK and its derivatives production in root periderm extracts. Chromatograms of root extracts of E. plantagenium (EP) and L.erythrorhizon (LE) specimens grown in Peat potting artificial soil (a) EP.PP, (c) LE.PP; and Natural campus soil (b) EP.NC, (d) LE.NC. Resulting peaks correspond to shikonin (SK), acetylshikonin (AS); isobutylshikonin (IBS); β, β-dimethylacrylshikonin (DMAS); and isovalerylshikonin (IVS). Chromatogram for each sample represents a composite sample of 3–4 individual plants. Figure represents only one replicate for each sample while the rest of the two replicates for each sample with standard chromatogram are provided in Fig. S5.Full size imageTable 1 Quantitative analysis of shikonin and its derivatives via HPLC in (a) root periderm; (b) root exudates samples of E. plantagineum (EP) and L.erythrorhizon (LE).Full size tablePacBio sequence reads statistics and taxonomic profilingAfter quality filtering, removal of chimera, chloroplast and mitochondrial sequences, approximately 165,570 high quality sequences (Tags) were obtained. Tags were clustered into 14,429 microbial operational taxonomic units (OTUs) at a 97% sequence similarity cutoff level (Table S2). All OTUs with species annotation are summarized in Table S3. Taxonomic profiling for taxonomic affiliations revealed Proteobacteria, Bacteroidetes, Planctomycetes, Cyanobacteria, Acidobacteria, and Actinobacteria to be the dominant phyla among all the samples (Fig. S7). These 6 phyla accounted for 70.97–96.61% of the total microbial OTUs. The Proteobacterial microbes mainly belonged to Classes Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria that accounted for 13.94–40.54% of the total microbes (Table S4).Host plant genetics are the drivers for distinct microbiomeTo identify the effects of host plant genetics on microbial acquisition, microbial community composition of bulk soil was compared with root and rhizospher soils of EP and LE. α-diversity estimates revealed a significantly higher observed species richness (Sobs), and shannon diversity for bulk soil (Fig. 3a,b; Table S5). This indicates that bulk soil serves as a reservoir for microbial acquisition in other rhizo-compartments. At different taxonomic levels, microbes associated with Proteobacteria, Planctomycetes, Bacteroidetes and Cyanobacteria were all present in relatively higher abundance in EP and LE rhizo-compartments compared to bulk soil in two different soil types (Fig. 2a; Table S6). Wilcox test also displayed quantitative variation in microbial acquisition at order level. For example, compared to bulk soil, Flavobacteriales, Sphingomonadales, and Verrucomicrobiales had a relatively higher abundance in EP rhizosphere, while Caulobacterales, and Sphingomonadales were significantly higher in LE rhizosphere (Fig. S8, P  More

1.Hardoim, P. R. et al. The hidden world within plants: Ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol. Mol. Biol. Rev. 79, 293–320 (2015).Article 

Google Scholar 
2.Zhang, H. W., Song, Y. C. & Tan, R. X. Biology and chemistry of endophytes. Nat. Prod. Rep. 23, 753–771 (2006).CAS 
Article 

Google Scholar 
3.Köberl, M., Schmidt, R., Ramadan, E. M., Bauer, R. & Berg, G. The microbiome of medicinal plants: Diversity and importance for plant growth, quality and health. Front. Microbiol. 4, 400 (2013).Article 

Google Scholar 
4.Soto, M. J., Domínguez-Ferreras, A., Pérez-Mendoza, D., Sanjuán, J. & Olivares, J. Mutualism versus pathogenesis: The give-and-take in plant–bacteria interactions. Cell. Microbiol. 11, 381–388 (2009).CAS 
Article 

Google Scholar 
5.Leff, J. W., Del Tredici, P., Friedman, W. E. & Fierer, N. Spatial structuring of bacterial communities within individual Ginkgo biloba trees. Environ. Microbiol. 17, 2352–2361. https://doi.org/10.1111/1462-2920.12695 (2015).Article 
PubMed 

Google Scholar 
6.Berg, G., Rybakova, D., Grube, M. & Köberl, M. The plant microbiome explored: Implications for experimental botany. J. Exp. Bot. 67, 995–1002. https://doi.org/10.1093/jxb/erv466 (2016).CAS 
Article 
PubMed 

Google Scholar 
7.Cregger, M. A. et al. The Populus holobiont: Dissecting the effects of plant niches and genotype on the microbiome. Microbiome 6, 31. https://doi.org/10.1186/s40168-018-0413-8 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Wang, Y., Liu, Y., Wu, Q., Yao, X. & Cheng, Z. Rapid and sensitive determination of major active ingredients and toxic components in Ginkgo biloba leaves extract (EGb 761) by a validated UPLC–MS-MS method. J. Chromatogr. Sci. 55, 459–464. https://doi.org/10.1093/chromsci/bmw206 (2017).CAS 
Article 
PubMed 

Google Scholar 
9.Mesquita, T. R. R. et al. Cardioprotective action of Ginkgo biloba extract against sustained β-adrenergic stimulation occurs via activation of M2/NO pathway. Front. Pharmacol. 8, 220. https://doi.org/10.3389/fphar.2017.00220 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Woelk, H., Arnoldt, K. H., Kieser, M. & Hoerr, R. Ginkgo biloba special extract EGb 761® in generalized anxiety disorder and adjustment disorder with anxious mood: A randomized, double-blind, placebo-controlled trial. J. Psychiatr. Res. 41, 472–480. https://doi.org/10.1016/j.jpsychires.2006.05.004 (2007).CAS 
Article 
PubMed 

Google Scholar 
11.Rojas, P., Montes, P., Rojas, C., Serrano-Garcia, N. & Rojas-Castaneda, J. C. Effect of a phytopharmaceutical medicine, Ginko biloba extract 761, in an animal model of Parkinson’s disease: Therapeutic perspectives. Nutrition 28, 1081–1088. https://doi.org/10.1016/j.nut.2012.03.007 (2012).CAS 
Article 
PubMed 

Google Scholar 
12.Tan, M.-S. et al. Efficacy and adverse effects of Ginkgo biloba for cognitive impairment and dementia: A systematic review and meta-analysis. J. Alzheimer’s Dis. 43, 589–603 (2015).CAS 
Article 

Google Scholar 
13.Kennedy, D. O., Jackson, P. A., Haskell, C. F. & Scholey, A. B. Modulation of cognitive performance following single doses of 120 mg Ginkgo biloba extract administered to healthy young volunteers. Hum. Psychopharm. Clin. 22, 559–566. https://doi.org/10.1002/hup.885 (2007).Article 

Google Scholar 
14.Yao, Z.-X., Han, Z., Drieu, K. & Papadopoulos, V. Ginkgo biloba extract (Egb 761) inhibits β-amyloid production by lowering free cholesterol levels. J. Nutr. Biochem. 15, 749–756. https://doi.org/10.1016/j.jnutbio.2004.06.008 (2004).CAS 
Article 
PubMed 

Google Scholar 
15.Chen, D., Sun, S., Cai, D. & Kong, G. Induction of mitochondrial-dependent apoptosis in T24 cells by a selenium (Se)-containing polysaccharide from Ginkgo biloba L. leaves. Int. J. Biol. Macromol. 101, 126–130 (2017).CAS 
Article 

Google Scholar 
16.Hamdoun, S. & Efferth, T. Ginkgolic acids inhibit migration in breast cancer cells by inhibition of NEMO sumoylation and NF-κB activity. Oncotarget 8, 35103 (2017).Article 

Google Scholar 
17.Fei, R. et al. Purified polysaccharide from Ginkgo biloba leaves inhibits P-selectin-mediated leucocyte adhesion and inflammation. Acta Pharmacol. Sin. 29, 499–506 (2008).CAS 
Article 

Google Scholar 
18.Mahadevan, S. & Park, Y. Multifaceted therapeutic benefits of Ginkgo biloba L.: Chemistry, efficacy, safety, and uses. J. Food Sci. 73, R14–R19 (2008).CAS 
Article 

Google Scholar 
19.Zimmermann, M., Colciaghi, F., Cattabeni, F. & Di Luca, M. Ginkgo biloba extract: From molecular mechanisms to the treatment of Alzheimer’s disease. Cell. Mol. Biol. 48, 613–623 (2002).CAS 
PubMed 

Google Scholar 
20.van Beek, T. A. & Montoro, P. Chemical analysis and quality control of Ginkgo biloba leaves, extracts, and phytopharmaceuticals. J. Chromatogr. A 1216, 2002–2032 (2009).Article 

Google Scholar 
21.Lu, X. et al. Combining metabolic profiling and gene expression analysis to reveal the biosynthesis site and transport of ginkgolides in Ginkgo biloba L.. Front. Plant Sci. 8, 872. https://doi.org/10.3389/fpls.2017.00872 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
22.Mancuso, C. & Santangelo, R. Panax ginseng and Panax quinquefolius: From pharmacology to toxicology. Food Chem. Toxicol. 107, 362–372 (2017).CAS 
Article 

Google Scholar 
23.Karmazyn, M., Moey, M. & Gan, X. T. Therapeutic potential of Ginseng in the management of cardiovascular disorders. Drugs 71, 1989–2008. https://doi.org/10.2165/11594300-000000000-00000 (2011).CAS 
Article 
PubMed 

Google Scholar 
24.Predy, G. N. et al. Efficacy of an extract of North American ginseng containing poly-furanosyl-pyranosyl-saccharides for preventing upper respiratory tract infections: A randomized controlled trial. Can. Med. Assoc. J. 173, 1043–1048. https://doi.org/10.1503/cmaj.1041470 (2005).Article 

Google Scholar 
25.Yuan, C.-S., Wang, C.-Z., Wicks, S. M. & Qi, L.-W. Chemical and pharmacological studies of saponins with a focus on American ginseng. J. Ginseng Res. 34, 160 (2010).CAS 
Article 

Google Scholar 
26.Yang, W.-Z., Hu, Y., Wu, W.-Y., Ye, M. & Guo, D.-A. Saponins in the genus Panax L. (Araliaceae): A systematic review of their chemical diversity. Phytochemistry 106, 7–24. https://doi.org/10.1016/j.phytochem.2014.07.012 (2014).CAS 
Article 
PubMed 

Google Scholar 
27.Solieri, L., Dakal, T. C. & Giudici, P. Next-generation sequencing and its potential impact on food microbial genomics. Ann. Microbiol. 63, 21–37 (2013).CAS 
Article 

Google Scholar 
28.Ercolini, D. High-throughput sequencing and metagenomics: Moving forward in the culture-independent analysis of food microbial ecology. Appl. Environ. Microbiol. 79, 3148–3155 (2013).CAS 
Article 

Google Scholar 
29.Metzker, M. L. Sequencing technologies—the next generation. Nat. Rev. Genet. 11, 31 (2010).CAS 
Article 

Google Scholar 
30.Riesenfeld, C. S., Schloss, P. D. & Handelsman, J. Metagenomics: Genomic analysis of microbial communities. Annu. Rev. Genet. 38, 525–552 (2004).CAS 
Article 

Google Scholar 
31.Robinson, R. J. et al. Endophytic bacterial community composition in wheat (Triticum aestivum) is determined by plant tissue type, developmental stage and soil nutrient availability. Plant Soil 405, 381–396. https://doi.org/10.1007/s11104-015-2495-4 (2016).CAS 
Article 

Google Scholar 
32.Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol 10, 828–840. https://doi.org/10.1038/nrmicro2910 (2012).CAS 
Article 
PubMed 

Google Scholar 
33.Dasgupta, M. G. et al. Diversity of bacterial endophyte in Eucalyptus clones and their implications in water stress tolerance. Microbiol. Res. 241, 126579. https://doi.org/10.1016/j.micres.2020.126579 (2020).CAS 
Article 
PubMed 

Google Scholar 
34.Knief, C. et al. Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. ISME J. 6, 1378–1390. https://doi.org/10.1038/ismej.2011.192 (2012).CAS 
Article 
PubMed 

Google Scholar 
35.Idris, R., Trifonova, R., Puschenreiter, M., Wenzel, W. W. & Sessitsch, A. Bacterial communities associated with flowering plants of the Ni hyperaccumulator Thlaspi goesingense. Appl. Environ. Microbiol. 70, 2667–2677. https://doi.org/10.1128/aem.70.5.2667-2677.2004 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Rastogi, G. et al. Leaf microbiota in an agroecosystem: Spatiotemporal variation in bacterial community composition on field-grown lettuce. Isme J. 6, 1812–1822. https://doi.org/10.1038/ismej.2012.32 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95. https://doi.org/10.1038/nature11336 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
38.Kielak, A. M., Cipriano, M. A. P. & Kuramae, E. E. Acidobacteria strains from subdivision 1 act as plant growth-promoting bacteria. Arch. Microbiol. 198, 987–993. https://doi.org/10.1007/s00203-016-1260-2 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Fuerst, J. A. & Sagulenko, E. Beyond the bacterium: Planctomycetes challenge our concepts of microbial structure and function. Nat. Rev. Microbiol 9, 403–413. https://doi.org/10.1038/nrmicro2578 (2011).CAS 
Article 
PubMed 

Google Scholar 
40.Wiegand, S., Jogler, M. & Jogler, C. On the maverick planctomycetes. FEMS Microbiol. Rev. 42, 739–760. https://doi.org/10.1093/femsre/fuy029 (2018).CAS 
Article 
PubMed 

Google Scholar 
41.Kim, H. et al. High population of Sphingomonas species on plant surface. J. Appl. Microbiol. 85, 731–736. https://doi.org/10.1111/j.1365-2672.1998.00586.x (1998).Article 

Google Scholar 
42.Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl. Acad. Sci. 106, 16428–16433. https://doi.org/10.1073/pnas.0905240106 (2009).ADS 
Article 
PubMed 

Google Scholar 
43.Kampfer, P., Busse, H. J., McInroy, J. A. & Glaeser, S. P. Sphingomonas zeae sp nov., isolated from the stem of Zea mays. Int. J. Syst. Evol. Microbiol. 65, 2542–2548. https://doi.org/10.1099/ijs.0.000298 (2015).CAS 
Article 
PubMed 

Google Scholar 
44.Xie, C.-H. & Yokota, A. Sphingomonas azotifigens sp. nov., a nitrogen-fixing bacterium isolated from the roots of Oryza sativa. Int. J. Syst. Evol. Microbiol. 56, 889–893. https://doi.org/10.1099/ijs.0.64056-0 (2006).CAS 
Article 
PubMed 

Google Scholar 
45.Videira, S. S., De Araujo, J. L. S., Da Silva Rodrigues, L., Baldani, V. L. D. & Baldani, J. I. Occurrence and diversity of nitrogen-fixing Sphingomonas bacteria associated with rice plants grown in Brazil. FEMS Microbiol. Lett. 293, 11–19. https://doi.org/10.1111/j.1574-6968.2008.01475.x (2009).CAS 
Article 
PubMed 

Google Scholar 
46.Innerebner, G., Knief, C. & Vorholt, J. A. Protection of Arabidopsis thaliana against leaf-pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Appl. Environ. Microbiol. 77, 3202–3210. https://doi.org/10.1128/aem.00133-11 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Khan, A. L. et al. Bacterial endophyte Sphingomonas sp LK11 produces gibberellins and IAA and promotes tomato plant growth. J. Microbiol. 52, 689–695. https://doi.org/10.1007/s12275-014-4002-7 (2014).CAS 
Article 
PubMed 

Google Scholar 
48.Asaf, S., Numan, M., Khan, A. L. & Al-Harrasi, A. Sphingomonas: From diversity and genomics to functional role in environmental remediation and plant growth. Crit. Rev. Biotechnol. 40, 138–152. https://doi.org/10.1080/07388551.2019.1709793 (2020).CAS 
Article 
PubMed 

Google Scholar 
49.Ali, A. et al. Biotransformation of benzoin by Sphingomonas sp. LK11 and ameliorative effects on growth of Cucumis sativus. Arch. Microbiol. 201, 591–601. https://doi.org/10.1007/s00203-019-01623-1 (2019).CAS 
Article 
PubMed 

Google Scholar 
50.Chhetri, G., Kim, J., Kim, I., Kim, H. & Seo, T. Hymenobacter setariae sp. nov., isolated from the ubiquitous weedy grass Setaria viridis. Int. J. Syst. Evol. Microbiol. 70, 3724–3730. https://doi.org/10.1099/ijsem.0.004226 (2020).CAS 
Article 
PubMed 

Google Scholar 
51.Dai, Y. et al. Wheat-associated microbiota and their correlation with stripe rust reaction. J. Appl. Microbiol. 128, 544–555. https://doi.org/10.1111/jam.14486 (2020).CAS 
Article 
PubMed 

Google Scholar 
52.Buczolits, S. et al. Classification of three airborne bacteria and proposal of Hymenobacter aerophilus sp nov. Int. J. Syst. Evol. Microbiol. 52, 445–456. https://doi.org/10.1099/00207713-52-2-445 (2002).CAS 
Article 
PubMed 

Google Scholar 
53.Su, S. Y. et al. Hymenobacter kanuolensis sp nov., a novel radiation-resistant bacterium. Int. J. Syst. Evol. Microbiol. 64, 2108–2112. https://doi.org/10.1099/ijs.0.051680-0 (2014).CAS 
Article 
PubMed 

Google Scholar 
54.Dimitrijevic, S. et al. Plant growth-promoting bacteria elevate the nutritional and functional properties of black cumin and flaxseed fixed oil. J. Sci. Food Agric. 98, 1584–1590. https://doi.org/10.1002/jsfa.8631 (2018).CAS 
Article 
PubMed 

Google Scholar 
55.Yang, R. X., Fan, X. J., Cai, X. Q. & Hu, F. P. The inhibitory mechanisms by mixtures of two endophytic bacterial strains isolated from Ginkgo biloba against pepper phytophthora blight. Biol. Control 85, 59–67. https://doi.org/10.1016/j.biocontrol.2014.09.013 (2015).Article 

Google Scholar 
56.Islam, M. N., Choi, J. & Baek, K. H. Control of foodborne pathogenic bacteria by endophytic bacteria isolated from Ginkgo biloba L. Foodborne Pathog. Dis. 16, 661–670. https://doi.org/10.1089/fpd.2018.2496 (2019).CAS 
Article 
PubMed 

Google Scholar 
57.Datta, S. et al. Endophytic bacteria in xenobiotic degradation In Microbial endophytes (eds. Kumar, A. & Singh, V. K.) 125–156 (Woodhead Publishing, 2020).58.Newmaster, S. G., Grguric, M., Shanmughanandhan, D., Ramalingam, S. & Ragupathy, S. DNA barcoding detects contamination and substitution in North American herbal products. BMC Med. 11, 222 (2013).Article 

Google Scholar 
59.Gao, Z. et al. Derivative technology of DNA barcoding (Nucleotide Signature and SNP Double Peak methods) detects adulterants and substitution in Chinese patent medicines. Sci. Rep. 7, 5858. https://doi.org/10.1038/s41598-017-05892-y (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
60.Ichim, M. C. & de Boer, H. J. A review of authenticity and authentication of commercial ginseng herbal medicines and food supplements. Front. Pharmacol. https://doi.org/10.3389/fphar.2020.612071 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
61.Dhivya, S. et al. Validated identity test method for Ginkgo biloba NHPs using DNA-based species-specific hydrolysis PCR probe. J. AOAC Int. 102, 1779–1786. https://doi.org/10.5740/jaoacint.18-0319 (2019).CAS 
Article 
PubMed 

Google Scholar 
62.Singh, A., Bajpai, V., Srivastava, M., Arya, K. R. & Kumar, B. Rapid screening and distribution of bioactive compounds in different parts of Berberis petiolaris using direct analysis in real time mass spectrometry. J. Pharm. Anal. 5, 332–335 (2015).CAS 
Article 

Google Scholar 
63.Kim, H. K., Choi, Y. H. & Verpoorte, R. NMR-based metabolomic analysis of plants. Nat. Protoc. 5, 536 (2010).CAS 
Article 

Google Scholar 
64.Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).ADS 
CAS 
Article 

Google Scholar 
65.Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D. & Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nat. Methods 10, 999–1002 (2013).CAS 
Article 

Google Scholar 
66.Illumina. 16S Metagenomic Sequencing Library Preparation. Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System (Part 15044223 Rev. B). (2013), Accessed 07-2017, available at https://support.illumina.com/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf.67.Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821. https://doi.org/10.1038/s41596-019-0264-1 (2020).CAS 
Article 
PubMed 

Google Scholar 
68.Dhariwal, A. et al. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188. https://doi.org/10.1093/nar/gkx295 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
69.Hulsen, T., de Vlieg, J. & Alkema, W. BioVenn—A web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genom. 9, 488. https://doi.org/10.1186/1471-2164-9-488 (2008).CAS 
Article 

Google Scholar 
70.vegan: Community Ecology Package v. 2.5-6 (2019), available at https://cran.r-project.org/web/packages/vegan/index.html71.Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

P. aeruginosa produces novel glycolipids in response to Pi stressTo determine changes in the membrane lipidome in response to P-stress, the model P. aeruginosa strain PAO1 was grown in minimal medium under high (1 mM) or low Pi (50 µM) conditions (Fig. 1a). The latter condition elicited strong alkaline phosphatase activity, measured through the liberation of para-nitrophenol (pNP) from pNPP (Fig. 1b), this being a strong indication that cells were P-stressed. Analysis of membrane lipid profiles using high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS) revealed the presence of several new lipids under Pi stress conditions (Fig. 1c). Thus, during Pi-replete growth (1 mM phosphate), the lipidome is dominated by two glycerophospholipids: PG (eluted at 6.8 min) and PE (eluted at 12.2 min). During Pi-stress a lipid species with mass to charge ratio (m/z) of 623 and 649 were also found, with MS fragmentation resulting in a 131 m/z peak, a diagnostic ion for the amino-acid containing ornithine lipid. This is consistent with previous reports of ornithine lipids in the P. aeruginosa membrane in response to Pi stress [29, 30].Fig. 1: Lipidomics analysis uncovers novel glycolipid formation in Pseudomonas aeruginosa strain PAO1 in response to phosphorus limitation.a Growth of strain PAO1 WT in minimal medium A containing 1 mM phosphate (+Pi, blue) or 50 µM phosphate (−Pi, black) over 12 h. Data are the average of three independent replicates. b Liberation of para-nitrophenol (pNP) from para-nitrophenol phosphate (pNPP) through alkaline phosphatase activity, under Pi-replete (1 mM, black) and Pi-deplete (50 µM, yellow) conditions. Error bars represent the standard deviation of three independent replicates. c Representative chromatograms in negative ionisation mode of the P. aeruginosa lipidome when grown under phosphorus stress (−Pi, black) compared to growth under phosphorus sufficient conditions (+Pi, orange). PG phosphatidylglycerol, PE phosphatidylethanolamine, OL ornithine lipids. Lower panel: extracted ion chromatograms of three new glycolipid species in P. aeruginosa which are only produced during Pi-limitation (black, 1 mM; orange, 50 µM). MGDG monoglucosyldiacylglycerol, GADG glucuronic acid-diacylglycerol and UGL unconfirmed glycolipid. d Mass spectrometry fragmentation spectra of three glycolipid species present under Pi stress in P. aeruginosa, at retention times of 7.7 (m/z 774.7), 8.7 (m/z 786.7) and 9.8 (m/z 788.6) minutes, respectively. Each spectrum depicts an intact lipid mass with an ammonium (NH4+) adduct exhibiting neutral loss of a head group, yielding diacylglycerol (DAG) (595 m/z). Further fragmentation yields monoacylglycerols (MAG) with C16:0 or C18:1 fatty acyl chains.Full size imageFurther to ornithine lipids, three unknown lipids eluting at 7.7, 8.7 and 9.8 min, were only present under Pi stress conditions (Fig. 1c). Using several rounds of MS fragmentation (MSn), with a quadrupole ion trap MS, fragmentation patterns characteristic of glycolipids were found for all three peaks. For each peak of interest, the most predominant lipid masses of 774.7, 786.8 and 788.6 m/z were analysed by MSn in positive ionisation mode (Fig. 1d). In each case, an initial head group was lost leaving a significant signal of 595.6 m/z, the mass of the glycolipid building block diacylglycerol (DAG). Further fragmentation leads to the loss of either fatty acyl chain from DAG, leaving monoacylglycerols of 313.2 and 339.3 m/z. Two monoacylglycerols with different masses are produced as a result of the original lipid containing 16:0 and 18:1 fatty acids (313.2 and 339.3 m/z monoacylglycerols, respectively). To further elucidate the identity of the peaks, a search for a neutral loss of a polar head group was carried out. Thus, the intact masses of 774.7 and 788.6 m/z in positive ionisation mode leads to the loss of a head group of −179 and −193 m/z, which corresponds to a hexose- and a glucuronate- group, respectively (Fig. 1d), suggesting the occurrence of novel monoglucosyldiacylglycerol (MGDG) and glucuronic acid diacylglycerol (GADG) glycolipids in P. aeruginosa. The third glycolipid peak at 8.7 min remains an unknown lipid with intact mass of 786.8 m/z (hereafter designated as a putative unknown glycolipid, UGL). Together, these data confirm the production of new glycolipids in P. aeruginosa in response to Pi stress.Comparative proteomics uncover the lipid renovation pathway in P. aeruginosa
To determine the proteomic response of P. aeruginosa to phosphorus limitation, and identify the genes involved in glycolipid formation, strain PAO1 was cultivated under high and low Pi conditions for 8 h and the cellular proteome then analysed. A total of 2844 proteins were detected, 175 of which were found to be differentially regulated by Pi availability (Fig. 2a, Table S1). In line with previous transcriptomic studies of strain PAO1 [18], major phosphorus acquisition mechanisms were highly expressed under Pi stress conditions, e.g. the Pi-specific transporter PstSCAB, the two-component regulator PhoBR (Table S1) [31].Fig. 2: Comparative multi-omic analyses for the identification of the PlcP-Agt pathway responsible for glycolipid formation in Pseudomonas aeruginosa strain PAO1.a Volcano plot depicting differentially expressed proteins when comparing Pi-replete and Pi-deplete conditions. Significantly upregulated proteins when under Pi stress are shown in red (left), and those that are significantly upregulated when Pi is sufficient are in green (right). Significance was accepted when the false discovery rate (FDR) was More