Philippa Kaur

1.Karban, R. The ecology and evolution of induced responses to herbivory and how plants perceive risk. Ecol. Entomol. 45, 1–9 (2020).Article 

Google Scholar 
2.Heil, M. Plastic defence expression in plants. Evol. Ecol. 24, 555–569 (2010).Article 

Google Scholar 
3.Erb, M. & Reymond, P. Molecular interactions between plants and insect herbivores. Annu. Rev. Plant Biol. 70, 527–557 (2019).CAS 
Article 

Google Scholar 
4.Ohgushi, T. Indirect interaction webs: herbivore-induced effects through trait change in plants. Annu. Rev. Ecol. Evol. Syst. 36, 81–105 (2005).Article 

Google Scholar 
5.Poelman, E. H., van Loon, J. J. A., van Dam, N. M., Vet, L. E. M. & Dicke, M. Herbivore-induced plant responses in Brassica oleracea prevail over effects of constitutive resistance and result in enhanced herbivore attack. Ecol. Entomol. 35, 240–247 (2010).Article 

Google Scholar 
6.Erb, M., Meldau, S. & Howe, G. A. Role of phytohormones in insect-specific plant reactions. Trends Plant Sci. 17, 250–259 (2012).CAS 
Article 

Google Scholar 
7.Soler, R. et al. Plant-mediated facilitation between a leaf-feeding and a phloem-feeding insect in a brassicaceous plant: from insect performance to gene transcription. Funct. Ecol. 26, 156–166 (2012).Article 

Google Scholar 
8.Thaler, J. S., Humphrey, P. T. & Whiteman, N. K. Evolution of jasmonate and salicylate signal crosstalk. Trends Plant Sci. 17, 260–270 (2012).CAS 
Article 

Google Scholar 
9.Ehrlich, P. R. & Raven, P. H. Butterflies and plants—a study in coevolution. Evolution 18, 586–608 (1964).Article 

Google Scholar 
10.Labandeira, C. C., Johnson, K. R. & Wilf, P. Impact of the terminal Cretaceous event on plant–insect associations. Proc. Natl Acad. Sci. USA 99, 2061–2066 (2002).CAS 
Article 

Google Scholar 
11.Ramos, S. E. & Schiestl, F. P. Rapid plant evolution driven by the interaction of pollination and herbivory. Science 364, 193–196 (2019).CAS 
Article 

Google Scholar 
12.Züst, T. et al. Natural enemies drive geographic variation in plant defenses. Science 338, 116–119 (2012).Article 

Google Scholar 
13.Agrawal, A. A. Specificity of induced resistance in wild radish: causes and consequences for two specialist and two generalist caterpillars. Oikos 89, 493–500 (2000).Article 

Google Scholar 
14.Moreira, X., Abdala‐Roberts, L. & Castagneyrol, B. Interactions between plant defence signalling pathways: evidence from bioassays with insect herbivores and plant pathogens. J. Ecol. 106, 2353–2364 (2018).Article 

Google Scholar 
15.Voelckel, C. & Baldwin, I. T. Herbivore-induced plant vaccination. Part II. Array-studies reveal the transience of herbivore-specific transcriptional imprints and a distinct imprint from stress combinations. Plant J. 38, 650–663 (2004).CAS 
Article 

Google Scholar 
16.Caarls, L., Pieterse, C. M. J. & Van Wees, S. C. M. How salicylic acid takes transcriptional control over jasmonic acid signaling. Front. Plant Sci. 6, 170 (2015).Article 

Google Scholar 
17.Proietti, S. et al. Genome-wide association study reveals novel players in defense hormone crosstalk in Arabidopsis. Plant Cell Environ. 41, 2342–2356 (2018).CAS 
Article 

Google Scholar 
18.Davidson-Lowe, E., Szendrei, Z. & Ali, J. G. Asymmetric effects of a leaf-chewing herbivore on aphid population growth. Ecol. Entomol. 44, 81–92 (2019).Article 

Google Scholar 
19.Eisenring, M., Glauser, G., Meissle, M. & Romeis, J. Differential impact of herbivores from three feeding guilds on systemic secondary metabolite induction, phytohormone levels and plant-mediated herbivore interactions. J. Chem. Ecol. 44, 1178–1189 (2018).CAS 
Article 

Google Scholar 
20.Züst, T. & Agrawal, A. A. Mechanisms and evolution of plant resistance to aphids. Nat. Plants 2, 15206 (2016).Article 

Google Scholar 
21.Ali, J. G., Agrawal, A. A. & Fox, C. Asymmetry of plant-mediated interactions between specialist aphids and caterpillars on two milkweeds. Funct. Ecol. 28, 1404–1412 (2014).Article 

Google Scholar 
22.Bidart-Bouzat, M. G. & Kliebenstein, D. An ecological genomic approach challenging the paradigm of differential plant responses to specialist versus generalist insect herbivores. Oecologia 167, 677–689 (2011).Article 

Google Scholar 
23.Ali, J. G. & Agrawal, A. A. Specialist versus generalist insect herbivores and plant defense. Trends Plant Sci. 17, 293–302 (2012).CAS 
Article 

Google Scholar 
24.Mertens, D. et al. Predictability of biotic stress structures plant defence evolution. Trends Ecol. Evol. 36, 444–456 (2021).Article 

Google Scholar 
25.Appel, H. M. et al. Transcriptional responses of Arabidopsis thaliana to chewing and sucking insect herbivores. Front. Plant Sci. 5, 20 (2014).
Google Scholar 
26.Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
27.Connell, J. H. Diversity and the coevolution of competitors, or the ghost of competition past. Oikos 35, 131–138 (1980).Article 

Google Scholar 
28.Barton, K. E. & Koricheva, J. The ontogeny of plant defense and herbivory: characterizing general patterns using meta-analysis. Am. Nat. 175, 481–493 (2010).Article 

Google Scholar 
29.Barbosa, P., Letourneau, D. K. & Agrawal A. A. (eds) Insect Outbreaks Revisited (Wiley-Blackwell, 2012).30.Bischoff, A. & Trémulot, S. Differentiation and adaptation in Brassica nigra populations: interactions with related herbivores. Oecologia 165, 971–981 (2011).Article 

Google Scholar 
31.Schlinkert, H. et al. Plant size as determinant of species richness of herbivores, natural enemies and pollinators across 21 Brassicaceae species. PLoS ONE 10, e0135928 (2015).Article 

Google Scholar 
32.Snoeren, T. A. L., Broekgaarden, C. & Dicke, M. Jasmonates differentially affect interconnected signal-transduction pathways of Pieris rapae-induced defenses in Arabidopsis thaliana. Insect Sci. 18, 249–258 (2011).CAS 
Article 

Google Scholar 
33.Leon-Reyes, A. et al. Salicylate-mediated suppression of jasmonate-responsive gene expression in Arabidopsis is targeted downstream of the jasmonate biosynthesis pathway. Planta 232, 1423–1432 (2010).CAS 
Article 

Google Scholar 
34.Broekgaarden, C., Voorrips, R. E., Dicke, M. & Vosman, B. Transcriptional responses of Brassica nigra to feeding by specialist insects of different feeding guilds. Insect Sci. 18, 259–272 (2011).CAS 
Article 

Google Scholar 
35.Fernández de Bobadilla, M. et al. Insect species richness affects plant responses to multi‐herbivore attack. New Phytol. https://doi.org/10.1111/nph.17228 (2021).36.Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).Article 

Google Scholar 
37.Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).38.Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. nlme: Linear and nonlinear mixed effects models. R package version 3.1-141 https://CRAN.R-project.org/package=nlme (2019).39.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 
40.Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
41.Lenth, R. emmean: Estimated marginal means, aka least-squares means. R package version 1.2.3 https://CRAN.R-project.org/package=emmeans (2018).42.R Core Team. R: A Language and Environment for Statistical Computing version 3.2.4 (R Foundation for Statistical Computing, 2016).43.Lajeunesse, M. J. On the meta‐analysis of response ratios for studies with correlated and multi‐group designs. Ecology 92, 2049–2055 (2011).Article 

Google Scholar  More

Effects of storage and technical variationWe first validated our methods by assessing the effect of storage and technical variation on microbiome composition. To quantify the effect of the two storage methods on bacterial composition in fresh samples, we performed a separate pilot study with nine faecal samples sourced from nine captive meerkats at Zurich University. Samples were immediately frozen after collection, and then either freeze-dried or kept frozen at −80 °C for seven days. Microbiome composition clustered strongly by sample identity in their beta diversity (Supplementary Fig. 1b), and storage did not significantly affect composition (Weighted Unifrac: F = 0.7, p = 0.52; Unweighted Unifrac: F = 1.0, p = 0.37). Across samples analysed in this study, storage had significant yet small effects on estimated bacterial load, with frozen samples overall having slightly lower estimated abundance (t = 7.2, p  More

1.Chaney, R. L., Kim, W. I., Kunhikrishnan, A., Yang, J. E. & Ok, Y. S. Integrated management strategies for arsenic and cadmium in rice paddy environments. Geoderma 270, 1–116. https://doi.org/10.1016/j.geoderma.2016.03.001 (2016).ADS 
Article 

Google Scholar 
2.Nakashima, K., Yamaguchi-Shinozaki, K. & Shinozaki, K. The transcriptional regulatory network in the drought response and its crosstalk in abiotic stress responses including drought, cold, and heat. Front. Plant Sci. 5, 170. https://doi.org/10.3389/fpls.2014.00170 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
3.Senthil-Nathan, S. Physiological and biochemical effect of neem and other Meliaceae plants secondary metabolites against Lepidopteran insects. Front. Physiol. 4, 359. https://doi.org/10.3389/fphys.2013.00359 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Kalaivani, K., Maruthi-Kalaiselvi, M. & Senthil-Nathan, S. Seed treatment and foliar application of methyl salicylate (MeSA) as a defense mechanism in rice plants against the pathogenic bacterium, Xanthomonas oryzae pv. oryzae. Pest Biochem. Physiol. 171, 104718. https://doi.org/10.1016/j.pestbp.2020.104718 (2021).CAS 
Article 

Google Scholar 
5.Das, G. & Rao, G. J. N. Molecular marker assisted gene stacking for biotic and abiotic stress resistance genes in an elite rice cultivar. Front. Plant Sci. 6, 698. https://doi.org/10.3389/fpls.2015.00698 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Senthil-Nathan, S. A review of biopesticides and their mode of action against insect pests. Environ. Sustain. https://doi.org/10.1007/978-81-322-2056-5_3 (2015).Article 

Google Scholar 
7.Shi, W. et al. Grain yield and quality responses of tropical hybrid rice to high night-time temperature. Food Crop Res. 190, 18–25. https://doi.org/10.1016/j.fcr.2015.10.006 (2016).Article 

Google Scholar 
8.Farooq, M. et al. Rice direct seeding: Experiences, challenges and opportunities. Soil Till. Res. 111, 87–98. https://doi.org/10.1016/j.still.2010.10.008 (2011).Article 

Google Scholar 
9.Brown, J. K. M. Yield penalties of disease resistance in crops. Curr. Opin. Plant Biol. 5, 339–344. https://doi.org/10.1016/S1369-5266(02)00270-4 (2002).CAS 
Article 
PubMed 

Google Scholar 
10.Liu, H. et al. Antifungal effect and mechanism of chitosan against the rice sheath blight pathogen, Rhizoctonia solani. Biotechnol. Lett. 34, 2291–2298. https://doi.org/10.1007/s10529-012-1035-z (2012).CAS 
Article 
PubMed 

Google Scholar 
11.Orzali, L., Corsi, B., Forni, C. & Riccinoi, L. Chitosan in agriculture: A new challenge for managing plant disease, biological activities and application of marine polysaccharides. Biol. Act. Appl. Mar. Polysaccharides. 17–36. https://doi.org/10.5772/66840 (2017).
12.Anosheh, H. P., Sadeghi, H. & Emam, Y. Chemical priming with urea and KNO3 enhances maize hybrids (Zea mays L.) seed viability under abiotic stress. J. Crop Sci. Biotechnol. 14, 289–295. https://doi.org/10.1007/s12892-011-0039-x (2011).Article 

Google Scholar 
13.Hänsch, R. & Mendel, R. R. Physiological functions of mineral micronutrients (Cu, Zn, Mn, Fe, Ni, Mo, B, Cl). Curr. Opin. Plant Biol. 12, 259–266. https://doi.org/10.1016/j.pbi.2009.05.006 (2009).CAS 
Article 
PubMed 

Google Scholar 
14.Savvides, A., Ali, S., Tester, M. & Fotopoulos, V. Chemical priming of plants against multiple abiotic stresses: Mission possible?. Trends Plant Sci. 21, 329–340. https://doi.org/10.1016/j.tplants.2015.11.003 (2016).CAS 
Article 
PubMed 

Google Scholar 
15.Kurita, K. Chitin and chitosan: Functional biopolymers from marine crustaceans. Mar. Biotechnol. 8, 203–226. https://doi.org/10.1007/s10126-005-0097-5 (2006).CAS 
Article 

Google Scholar 
16.Hamed, I., Özogul, F. & Regenstein, J. M. Industrial applications of crustacean by-products (chitin, chitosan, and chitooligosaccharides): A review. Trends Food Sci. Technol. 48, 40–50. https://doi.org/10.1016/j.tifs.2015.11.007 (2016).CAS 
Article 

Google Scholar 
17.Badawy, M. E. I. & Rabea, E. I. A. Biopolymer chitosan and its derivatives as promising antimicrobial agents against plant pathogens and their applications in crop protection. Int. J. Carbohydr. Chem. https://doi.org/10.1155/2011/460381 (2011).Article 

Google Scholar 
18.Davydova, V. N. et al. Chitosan antiviral activity: Dependence on structure and depolymerization method. Appl. Biochem. Microbiol. 47, 103–108. https://doi.org/10.1134/S0003683811010042 (2011).CAS 
Article 

Google Scholar 
19.Park, B. K. & Kim, M. M. Applications of chitin and its derivatives in biological medicine. Int. J. Mol. Sci. 11, 5152–5164. https://doi.org/10.3390/ijms11125152 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Malerba, M. & Cerana, R. Chitosan effects on plant systems. Int. J. Mol. Sci. 17, 996. https://doi.org/10.3390/ijms17070996 (2016).CAS 
Article 
PubMed Central 

Google Scholar 
21.Liu, H. et al. Progress and constraints of dry direct-seeded rice in China. J. Food Agric. Environ. 2121, 465–472 (2014).
Google Scholar 
22.Li, B., Wang, X., Chen, R., Huangfu, W. & Xie, G. Antibacterial activity of chitosan solution against Xanthomonas pathogenic bacteria isolated from Euphorbia pulcherrima. Carbohydr. Polym. 72, 287–292. https://doi.org/10.1016/j.carbpol.2007.08.012 (2008).CAS 
Article 

Google Scholar 
23.Falcón-Rodríguez, A. B., Cabrera, J. C., Wégria, G., Onderwater, R. C. A., González, G., Nápoles, M. C., Costales, D., Rogers, H. J., Diosdado, E., González, S., Cabrera, G., González, L. & Wattiez, R. Practical use of oligosaccharins in agriculture. In Ist World Congress on the use of biostimulants in agriculture. Acta Hortic. 1009, 195–212 (2012).24.Yin, H. et al. Genome shuffling of Saccharomyces cerevisiae for enhanced glutathione yield and relative gene expression analysis using fluorescent quantitation reverse transcription polymerase chain reaction. J. Microbiol. Methods 127, 188–192. https://doi.org/10.1016/j.mimet.2016.06.012 (2016).CAS 
Article 
PubMed 

Google Scholar 
25.Borah, N. et al. Low energy rice stubble management through in situ decomposition. Procedia Environ. Sci. 35, 771–780. https://doi.org/10.1016/j.proenv.2016.07.092 (2016).CAS 
Article 

Google Scholar 
26.Singh, R., Srivastava, M. & Shukla, A. Environmental sustainability of bioethanol production from rice straw in India: A review. Renew. Sustain. Energy Rev. 54, 202–216. https://doi.org/10.1016/j.rser.2015.10.005 (2016).CAS 
Article 

Google Scholar 
27.Mrudula, S. & Murugammal, R. Production of cellulase by Aspergillus niger under submerged and solid state fermentation using coir waste as a substrate. Braz. J. Microbiol. 42, 1119–1127. https://doi.org/10.1590/S1517-83822011000300033 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.El-Sayed, S. M. & Mahdy, M. E. Effect of chitosan on root-knot nematode, Meloidogyne javanica on tomato plants. Int. J. ChemTech Res. 7, 1985–1992 (2015).
Google Scholar 
29.Iriti, M. & Varoni, E. M. Chitosan-induced antiviral activity and innate immunity in plants. Environ. Sci. Pollut. Res. 22, 2935–2944. https://doi.org/10.1007/s11356-014-3571-7 (2015).CAS 
Article 

Google Scholar 
30.Orzali, L. et al. Chitosan in agriculture: A new challenge for chitosan in agriculture: A new challenge for managing plant disease managing plant disease. InTech Open Publisher https://doi.org/10.5772/66840 (2017).ADS 
Article 

Google Scholar 
31.Nanda, S., Mohammad, J., Reddy, S. N., Kozinski, J. A. & Dalai, A. K. Pathways of lignocellulosic biomass conversion to renewable fuels. Biomass Convers. Biorefinery 4, 157–191. https://doi.org/10.1007/s13399-013-0097-z (2014).CAS 
Article 

Google Scholar 
32.Aggarwal, N. K., Goyal, V., Saini, A., Yadav, A. & Gupta, R. Enzymatic saccharification of pretreated rice straw by cellulases from Aspergillus niger BK01. 3 Biotech 7, 158. https://doi.org/10.1007/s13205-017-0755-0 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Fatma, H., Abd-EI-Zaher & Fadel, M. Production of bioethanol via enzymatic saccharification of rice straw by cellulase produced by Trichoderma Reesei under solid state fermentation. N. Y. Sci. J., 72–78. http://www.sciencepub.net/newyork (2010).34.Chang, A. K. T., Frias, R. R., Alvarez, L. V., Bigol, U. G. & Guzman, J. P. M. D. Comparative antibacterial activity of commercial chitosan and chitosan extracted from Auricularia sp. Biocatal. Agric. Biotechnol. 17, 189–195. https://doi.org/10.1016/j.bcab.2018.11.016 (2019).Article 

Google Scholar 
35.Lizárraga-Paulín, E. G., Miranda-Castro, S. P., Moreno-Martínez, E., Lara-Sagahón, A. V. & Torres-Pacheco, I. Maize seed coatings and seedling sprayings with chitosan and hydrogen peroxide: Their influence on some phenological and biochemical behaviors. J. Zhejiang Univ. Sci. B. 14, 87–96. https://doi.org/10.1631/jzus.B1200270 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Hadwiger, L. A., Fristensky, B. & Riggleman, R. C. Chitosan, a natural regulator in plant-fungal pathogen interactions, increases crop yields. Chitin Chitosan Relat. Enzymes. https://doi.org/10.1016/b978-0-12-780950-2.50024-1 (1984).Article 

Google Scholar 
37.Mrda, J., Crnobarac, J., Dušanić, N., Jocić, S. & Miklič, V. Germination energy as a parameter of seed quality in different sunflower genotypes. Genetika 43, 427–436. https://doi.org/10.2298/GENSR1103427M (2011).Article 

Google Scholar 
38.Singh, H. et al. Seed priming techniques in field crops—A review. Agric. Rev. 36, 1–14. https://doi.org/10.18805/ag.v36i4.6662 (2015).Article 

Google Scholar 
39.Hameed, A., Sheikh, M. A., Farooq, T., Basra, S. M. A. & Jamil, A. Chitosan priming enhances the seed germination, antioxidants, hydrolytic enzymes, soluble proteins and sugars in wheat seeds. Agrochimica LVII, 31–46 (2013).
Google Scholar 
40.Zhou, Y. G. et al. Effects of chitosan on some physiological activity in germinating seed of peanut. J. Peanut Sci. 31, 22–25 (2002).
Google Scholar 
41.Samarah, N. H., Wang, H. & Welbaum, G. E. Pepper (Capsicum annuum) seed germination and vigour following nanochitin, chitosan or hydropriming treatments. Seed Sci. Technol. 44, 1–15. https://doi.org/10.15258/sst.2016.44.3.18 (2016).Article 

Google Scholar 
42.Chen, J. L. & Zhao, Y. Effect of molecular weight, acid, and plasticizer on the physicochemical and antibacterial properties of β-chitosan based films. J. Food Sci. 77, E127–E136. https://doi.org/10.1111/j.1750-3841.2012.02686.x (2012).CAS 
Article 
PubMed 

Google Scholar 
43.Kulikov, S. N., Chirkov, S. N., Il’ina, A. V., Lopatin, S. A. & Varlamov, V. P. Effect of the molecular weight of chitosan on its antiviral activity in plants. Appl. Biochem. Microbiol. 42, 200–203. https://doi.org/10.1134/S0003683806020165 (2006).CAS 
Article 

Google Scholar 
44.El Hadrami, A., Adam, L. R., El Hadrami, I. & Daayf, F. Chitosan in plant protection. Mar. Drugs 8, 968–987. https://doi.org/10.3390/md8040968 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Orzali, L., Forni, C. & Riccioni, L. Effect of chitosan seed treatment as elicitor of resistance to Fusarium graminearum in wheat. Seed Sci. Technol. 42, 132–149. https://doi.org/10.15258/sst.2014.42.2.03 (2014).Article 

Google Scholar 
46.Rabea, E. I., Badawy, M. E. T., Stevens, C. V., Smagghe, G. & Steurbaut, W. Chitosan as antimicrobial agent: Applications and mode of action. Biomacromol 4, 1457–1465. https://doi.org/10.1021/bm034130m (2003).CAS 
Article 

Google Scholar 
47.Wang, X., El Hadrami, A., Adam, L. R. & Daayf, F. Differential activation and suppression of potato defence responses by Phytophthora infestans isolates representing US-1 and US-8 genotypes. Plant Pathol. 57, 1026–1037. https://doi.org/10.1111/j.1365-3059.2008.01866.x (2008).CAS 
Article 

Google Scholar 
48.Smits, J. P., Rinzema, A., Tramper, J., Schlösser, E. E. & Knol, W. Accurate determination of process variables in a solid-state fermentation system. Process Biochem. 31, 669–678. https://doi.org/10.1016/S0032-9592(96)00019-2 (1996).CAS 
Article 

Google Scholar 
49.Kalaivani, K., Kalaiselvi, M. M. & Senthil-Nathan, S. Effect of methyl salicylate (MeSA), an elicitor on growth, physiology and pathology of resistant and susceptible rice varieties. Sci. Rep. 6, 1–11 (2016).Article 

Google Scholar 
50.Rane, K. D. & Hoover, D. G. An evaluation of alkali and acid treatments for chitosan extraction from fungi. Process Biochem. 28, 115–118 (1993).CAS 
Article 

Google Scholar 
51.Crestini, C., Kovac, B. & Giovannozzi-Sermanni, G. Production of chitosan by fungi. 50, 207–210. https://doi.org/10.1002/bit.260500202 (1996).52.Khalaf, S. A. Production and characterization of fungal chitosan under solid-state fermentation conditions. Int. J. Agric. Biol. 6, 1033–1036 (2004).CAS 

Google Scholar 
53.Zhang, Z. T., Chen, D. H. & Chen, L. Preparation of two different serials of chitosan. J. Dong Hua Univ. Engl. Ed. 19, 36–39 (2002).
Google Scholar 
54.Chanthini, K. M. et al. Sustainable agronomic strategies for enhancing the yield and nutritional quality of wild tomato Solanum Lycopersicum (l) Var Cerasiforme Mill. Agronomy 9, 311 (2019).CAS 
Article 

Google Scholar 
55.Ellis, R. H. & Roberts, E. H. Improved equations for the prediction of seed longevity. Ann. Bot. 45, 13–30. https://doi.org/10.1093/oxfordjournals.aob.a085797 (1980).Article 

Google Scholar 
56.Chanthini, K. M. et al. Biocatalysis and agricultural biotechnology Chaetomorpha antennina (Bory) Kützing derived seaweed liquid fertilizers as prospective bio-stimulant for Lycopersicon esculentum (Mill). Biocatal. Agric. Biotechnol. 20, 101190 (2019).Article 

Google Scholar 
57.Murray, P. R., Baron, E. J., Pfaller, M. A., Tenover, F. C. & Yolke, R. H. Manual of clinical Microbiology 6th edn. (American Society of Microbiology Press, 1995).
Google Scholar 
58.French, E. R. Efficacy of five methods of inoculating potato plants with Pseudomonas solanacearum. Phytopathology 76, 1078 (1986).
Google Scholar 
59.Yasmin, S. et al. Biocontrol of Bacterial Leaf Blight of rice and profiling of secondary metabolites produced by rhizospheric Pseudomonas aeruginosa BRp3. Front. Microbiol. 8, 1895. https://doi.org/10.3389/fmicb.2017.01895 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
60.Hammerschmidt, R. & Kuć, J. Lignification as a mechanism for induced systemic resistance in cucumber. Physiol. Plant Pathol. 20, 61–71. https://doi.org/10.1016/0048-4059(82)90024-8 (1982).CAS 
Article 

Google Scholar 
61.Worthington, C. C. Worthington Enzyme Manual: Enzymes and Related Biochemicals (Worthington Biochemical Corporation, 1988).
Google Scholar  More

1.Rubel, F. et al. Geographical distribution of Dermacentor marginatus and Dermacentor reticulatus in Europe. Ticks Tick Borne Dis. 7, 224–233 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Medlock, J. M. et al. Driving forces for changes in geographical distribution of Ixodes ricinus ticks in Europe. Parasites Vectors 6, 1–11 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Jongejan, F. & Uilenberg, G. The global importance of ticks. Parasitology 129, 3–14 (2004).Article 

Google Scholar 
4.Földvári, G., Široký, P., Szekeres, S., Majoros, G. & Sprong, H. Dermacentor reticulatus: a vector on the rise. Parasites Vectors 9, 1–29 (2016).Article 

Google Scholar 
5.Ličková, M. et al. Dermacentor reticulatus is a vector of tick-borne encephalitis virus. Ticks Tick Borne Dis. 11, 101414 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Pawełczyk, A. et al. Long-term study of Borrelia and Babesia prevalence and co-infection in Ixodes ricinus and Dermacentor recticulatus ticks removed from humans in Poland, 2016–2019. Parasites Vectors 14, 1–13 (2021).Article 
CAS 

Google Scholar 
7.Karbowiak, G. et al. The competition between immatures of Ixodes ricinus and Dermacentor reticulatus (Ixodida: Ixodidae) ticks for rodent hosts. J. Med. Entomol. 56, 448–452 (2018).Article 

Google Scholar 
8.Karbowiak, G. The occurrence of the Dermacentor reticulatus tick-its expansion to new areas and possible causes. Ann. Parasitol. 60, 37–47 (2014).PubMed 
PubMed Central 

Google Scholar 
9.Drehmann, M. et al. The Spatial Distribution of Dermacentor Ticks (Ixodidae) in Germany: Evidence of a continuing spread of Dermacentor reticulatus. Front. Vet. Sci. 7, 578220 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Sands, B. O., Bryer, K. E. & Wall, R. Climate and the seasonal abundance of the tick Dermacentor reticulatus. Med. Vet. Entomol. https://doi.org/10.1111/mve.12518 (2021).Article 
PubMed 

Google Scholar 
11.Hasle, G. et al. Transport of ticks by migratory passerine birds to Norway. J. Parasitol. 95, 1342–1351 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Kjær, L. J. et al. A large-scale screening for the taiga tick, Ixodes persulcatus, and the meadow tick, Dermacentor reticulatus, in southern Scandinavia, 2016. Parasites Vectors 12, 1–4 (2019).Article 

Google Scholar 
13.García-Sanmartín, J., Barandika, J. F., Juste, R. A., García-Pérez, A. L. & Hurtado, A. Distribution and molecular detection of Theileria and Babesia in questing ticks from northern Spain. Med. Vet. Entomol. 22, 318–325 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Olivieri, E. et al. The southernmost foci of Dermacentor reticulatus in Italy and associated Babesia canis infection in dogs. Parasites Vectors 9, 1–9 (2016).Article 
CAS 

Google Scholar 
15.Široký, P. et al. The distribution and spreading pattern of Dermacentor reticulatus over its threshold area in the Czech Republic: How much is range of this vector expanding?. Vet. Parasitol. 183, 130–135 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Hornok, S. & Farkas, R. Influence of biotope on the distribution and peak activity of questing ixodid ticks in Hungary. Med. Vet. Entomol. 23, 41–46 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Chitimia-Dobler, L. Spatial distribution of Dermacentor reticulatus in Romania. Vet. Parasitol. 214, 219–223 (2015).PubMed 
Article 

Google Scholar 
18.Akimov, I. & Nebogatkin, I. Distribution of Ticks from of the Genus Dermacentor (Acari, Ixodidae) in Ukraine. Vestnik Zoologii 45, 6 (2011).
Google Scholar 
19.Kiewra, D., Szymanowski, M., Czułowska, A. & Kolanek, A. The local-scale expansion of Dermacentor reticulatus ticks in Lower Silesia, SW, Poland. Ticks Tick Borne Dis. 12, 101599 (2021).PubMed 
Article 

Google Scholar 
20.Dwużnik-Szarek, D. et al. Monitoring the expansion of Dermacentor reticulatus and occurrence of canine babesiosis in Poland in 2016–2018. Parasites Vectors 14, 1–18 (2021).Article 

Google Scholar 
21.Zając, Z., Woźniak, A. & Kulisz, J. Density of Dermacentor reticulatus ticks in eastern Poland. Int. J. Environ. Res. Public Health 17, 2814 (2020).PubMed Central 
Article 

Google Scholar 
22.Ogden, N. H., Ben Beard, C., Ginsberg, H. S. & Tsao, J. I. Possible effects of climate change on ixodid ticks and the pathogens they transmit: Predictions and observations. J. Med. Entomol. 58, 1536–1545 (2020).Article 

Google Scholar 
23.Zając, Z., Sędzikowska, A., Maślanko, W., Woźniak, A. & Kulisz, J. Occurrence and Abundance of Dermacentor reticulatus in the habitats of the ecological corridor of the Wieprz river, eastern Poland. Insects 12, 96 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
24.Zając, Z., Bartosik, K. & Woźniak, A. Monitoring Dermacentor reticulatus host-seeking activity in natural conditions. Insects 11, 264 (2020).PubMed Central 
Article 

Google Scholar 
25.Global and European temperature—European Environment Agency. https://www.eea.europa.eu/data-and-maps/indicators/global-and-european-temperature/global-and-european-temperature-assessment-1. Accessed 22 July 2021.26.Średnie i sumy miesięczne. Dane meteorologiczne https://meteomodel.pl/dane/srednie-miesieczne/?imgwid=351220495&par=sndp&max_empty=2. Accessed 22 July 2021.27.Vladimirov, L. N. et al. Quantifying the Northward Spread of Ticks (Ixodida) as climate warms in Northern Russia. Atmosphere 12, 233 (2021).ADS 
Article 

Google Scholar 
28.Mierzejewska, E. J., Alsarraf, M., Behnke, J. M. & Bajer, A. The effect of changes in agricultural practices on the density of Dermacentor reticulatus ticks. Vet. Parasitol. 211, 259–265 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Zając, Z., Woźniak, A. & Kulisz, J. Infestation of dairy cows by ticks Dermacentor reticulatus (Fabricius, 1794) and Ixodes ricinus (Linnaeus, 1758) in eastern Poland. Ann. Parasitol. 66, 87–96 (2020).PubMed 
PubMed Central 

Google Scholar 
30.Estrada-Peña, A. Climate, niche, ticks, and models: What they are and how we should interpret them. Parasitol. Res. 103, 87–95 (2008).Article 

Google Scholar 
31.Süss, J., Klaus, C., Gerstengarbe, F. W. & Werner, P. C. What makes ticks tick? Climate change, ticks, and tick-borne diseases. J. Travel Med. 15, 39–45 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Paulauskas, A. et al. New localities of Dermacentor reticulatus ticks in the Baltic countries. Ticks Tick Borne Dis. 6, 630–635 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Kubiak, K. et al. Dermacentor reticulatus ticks (Acari: Ixodidae) distribution in north-eastern Poland: An endemic area of tick-borne diseases. Exp. Appl. Acarol. 75, 289–298 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Silaghi, C., Weis, L. & Pfister, K. Dermacentor reticulatus and Babesia canis in Bavaria (Germany): A georeferenced field study with digital habitat characterization. Pathogens 9, 541 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
35.Kohn, M. et al. Dermacentor reticulatus in Berlin/Brandenburg (Germany): Activity patterns and associated pathogens. Ticks Tick Borne Dis. 10, 191–206 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Kiewra, D., Czułowska, A., Dyczko, D., Zieliński, R. & Plewa-Tutaj, K. First record of Haemaphysalis concinna (Acari: Ixodidae) in Lower Silesia, SW, Poland. Exp. Appl. Acarol. 77, 449–454 (2019).PubMed 
Article 

Google Scholar 
37.Zieba, P. et al. A new locality of the Haemaphysalis concinna tick (Koch, 1844) in Poland and its role as a potential vector of infectious diseases. Ann. Parasitol. 65, 281–286 (2019).PubMed 

Google Scholar 
38.Gray, J. S., Dautel, H., Estrada-Peña, A., Kahl, O. & Lindgren, E. Effects of climate change on ticks and tick-borne diseases in Europe. Interdiscip. Perspect. Infect. Dis. 2009, 593232 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Medlock, J. M. & Leach, S. A. Effect of climate change on vector-borne disease risk in the UK. Lancet Infect. Dis. 15, 721–730 (2015).PubMed 
Article 

Google Scholar 
40.Pfäffle, M., Littwin, N. & Petney, T. Host preferences of immature Dermacentor reticulatus (Acari: Ixodidae) in a forest habitat in Germany. Ticks Tick Borne Dis. 6, 508–515 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Zając, Z., Bartosik, K., Kulisz, J. & Woźniak, A. Ability of adult Dermacentor reticulatus ticks to overwinter in the temperate climate zone. Biology 9, 145 (2020).PubMed Central 
Article 

Google Scholar 
42.Kiewra, D., Czułowska, A. & Lonc, E. Winter activity of Dermacentor reticulatus (Fabricius, 1794) in the newly emerging population of Lower Silesia, south-west Poland. Ticks Tick Borne Dis. 7, 1124–1127 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Buczek, A., Bartosik, K. & Zając, Z. Changes in the activity of adult stages of Dermacentor reticulatus (Ixodida: Amblyommidae) induced by weather factors in eastern Poland. Parasites Vectors 7, 245 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Hubálek, Z., Halouzka, J. & Juricova, Z. Host-seeking activity of ixodid ticks in relation to weather variables. J. Vector Ecol. 28, 159–165 (2003).PubMed 
PubMed Central 

Google Scholar 
45.Bartosik, K., Wiśniowski, Ł & Buczek, A. Questing behavior of Dermacentor reticulatus adults (Acari: Amblyommidae) during diurnal activity periods in eastern Poland. J. Med. Entomol. 49, 859–864 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Buczek, A., Bartosik, K., Wisniowski, L. & Tomasiewicz, K. Changes in population abundance of adult Dermacentor reticulatus (Acari: Amblyommidae) in long-term investigations in eastern Poland. Ann. Agric. Environ. Med. 20, 269–272 (2013).PubMed 
PubMed Central 

Google Scholar 
47.Mierzejewska, E. J., Estrada-Peña, A., Alsarraf, M., Kowalec, M. & Bajer, A. Mapping of Dermacentor reticulatus expansion in Poland in 2012–2014. Ticks Tick Borne Dis. 7, 94–106 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Zając, Z. et al. Environmental determinants of the occurrence and activity of Ixodes ricinus ticks and the prevalance of tick-borne diseases in eastern Poland. Sci. Rep. 11, 15472 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Kulisz, J., Bartosik, K., Zając, Z., Woźniak, A. & Kolasa, S. Quantitative parameters of the body composition influencing host seeking behavior of Ixodes ricinus adults. Pathogens 10, 706 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Alasmari, S. & Wall, R. Metabolic rate and resource depletion in the tick Ixodes ricinus in response to temperature. Exp. Appl. Acarol. 83, 81–93 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Zajac, Z., Bartosik, K. & Buczek, A. Factors influencing the distribution and activity of Dermacentor reticulatus (F.) ticks in an anthropopressure-unaffected area in central-eastern Poland. Ann. Agric. Environ Med. 23, 270–275 (2016).PubMed 
Article 

Google Scholar 
52.Bogdaszewska, Z. Range and ecology of Dermacentor reticulatus (Fabricius, 1794) in Mazuria focus. II. Seasonal activity patterns of the adults. Wiad. Parazytol. 50, 731–738 (2004).PubMed 

Google Scholar 
53.Razumova, I. V. The activity of Dermacentor reticulatus Fabr. (Ixodidae) ticks in nature. Med. Parasitol. Parasites Dis. 4, 8–14 (1999).
Google Scholar 
54.Szymański, S. Seasonal activity of Dermacentor reticulatus (Fabricius, 1794) (Acarina, Ixodidae) in Poland I. Adults. Acta Parasitol. Pol. 31, 247–255 (1987).
Google Scholar 
55.Hornok, S. Allochronic seasonal peak activities of Dermacentor and Haemaphysalis spp. under continental climate in Hungary. Vet. Parasitol. 163, 366–369 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Randolph, S. E. & Storey, K. Impact of microclimate on immature tick-rodent host interactions (Acari: Ixodidae): Implications for parasite transmission. J. Med. Entomol. 36, 741–748 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Nowak-Chmura, M. Ticks (Ixodida) of Central Europe (Pedagogical University of Cracow Press, 2013).
Google Scholar  More

Altogether, we found that, while traffic volume declined by  > 7% during the pandemic year (with a maximum monthly decline of nearly 40%), the absolute number of annual WVCs was largely unchanged. This resulted in significant increases of  > 8% in collision rates between vehicles and wildlife during the pandemic year, peaking at a  > 27% nationwide increase in April 2020. Other studies from the first several months of the pandemic documented similar transient declines in the number of WVCs when the pandemic began which then reversed in many jurisdictions as the pandemic progressed and traffic rebounded26,27. We observed a similar pattern over the first five months of the pandemic at the national scale (Fig. 2): WVCs initially declined during the pandemic in step with declines in traffic volume, but then started to increase to baseline levels at a faster rate than traffic, possibly due to behavioral lags by wildlife following traffic-mediated increases in wildlife road use. Though based on coarse-scale data, our research aligns with assertions from studies during27 and prior to the pandemic3,15,16,28,29 that the relationship between traffic volume and WVCs is non-linear.We postulate that the observed non-linear relationship between traffic volume and WVCs is the result of greater use of roads and roadsides by certain wildlife species, namely large mammals (Table S1), in response to decreasing traffic volume, as prior research has suggested3,14,15,16. This explanation is consistent with accounts of various wildlife species making increased use of human spaces during the pandemic17,20,21: with less cars on the roads, wildlife might be less deterred from roads by the noise and light pollution that accompany high traffic volumes9,10,11,20 and perceive roads as less risky, thereby increasing their willingness to attempt road crossings3,8,15,16. Beyond incidentally crossing roads while moving about the landscape8,9, wildlife might be attracted to roads for travel, mates, or other resources8,10,11. Many animals are shown to utilize roads to move efficiently across the landscape11,12, and roads and the surrounding areas are comparatively open, such that wildlife might select roads and roadsides for enhanced visibility to find mates, detect predators, or locate prey10,13. Roadsides also can provide foraging opportunities and essential nutrients for wildlife via abundant, high-quality early successional vegetation and high salt concentrations10,11. As such, decreased road traffic during the pandemic might have caused certain wildlife species to tolerate the risks associated with roads in order to access the benefits of roads and roadsides.An alternative explanation for the observed increases in collision rates is that human driving behavior, rather than animal behavior, changed during the pandemic. With fewer cars on the road, people might drive faster35, rendering it more difficult for both humans and wildlife to avoid collisions3. Preliminary studies from throughout the United States have indeed suggested changes to human driving behavior during the pandemic, with several jurisdictions reporting increased vehicle speeds35,36. Despite reported increases in vehicle speeds, however, the total number of vehicle collisions (the sum of both wildlife and non-wildlife collisions) mirrored trends in traffic volume and declined considerably during the pandemic37,38. Thus, because changes to human behavior appear to have had a minimal effect on vehicle collisions overall, it is unlikely that the observed changes in collision rates are due to increased vehicle speeds alone. Still, we cannot discount the possibility that changes to human driving behavior contributed to the patterns documented here, and future work should more explicitly test the relative effects of changes in traffic volume on both human driving behavior and wildlife space-use, as well as the resultant impacts on WVCs.A greater understanding of human driving behavior would also help explain our findings regarding changes in traffic patterns during the pandemic. Nationwide, the severity of COVID-19 restrictions accounted for a large amount of the variation in changes in monthly traffic volume (R2 = 0.968), but the severity of restrictions was less influential on changes in yearly traffic across states (Tables S3 and S4). Restrictions implemented throughout the pandemic were largely enacted for the purpose of minimizing travel, and other research has demonstrated that these restrictions were effective at reducing human mobility18,21. Our state-level findings, however, imply that it was not only the restrictions themselves that reduced travel, but possibly also the associated anxiety regarding the risk of contracting the SARS-CoV-2 virus, as has been suggested in other studies21,22,23,24; although we observed the greatest declines in traffic volume early in the pandemic (Fig. 2A) when restrictions were most stringent (Fig. S2)21, there was widespread anxiety about the risks posed by SARS-CoV-2 during this time22,23, which likely motivated people to stay home independent of restrictions24. Indeed, anxiety and risk perception might explain the relationship between traffic volume and the other covariates in our top models (Table S4). Declines in traffic were greatest in the most densely populated states (Fig. 4A) and in states that had the highest and the lowest disease burdens (Fig. 4B). The risk of SARS-CoV-2 transmission is greater in more densely populated states due to the close proximity of and frequent interactions amongst people21. As such, people may have altered their road use more in densely populated states as compared to sparsely populated ones due to differing perceptions of disease transmission risk23—though differences in infrastructure in relation to population density likely contributed to this pattern as well39. Similarly, declines in traffic volume in states with larger outbreaks of SARS-CoV-2 might have been driven by increases in the perceived risk of contracting the virus21,23. Alternatively, traffic reductions in states with low disease burdens might reflect increased compliance with stay-at-home orders, and therefore less opportunity for disease spread40,41; essentially, reductions in traffic volume might be the cause of locally low disease burdens therein, rather than a consequence. Altogether, we posit that the observed heterogeneity in traffic volume between states is, at least in part, attributed to differences in the perceived risk posed by the SARS-CoV-2 virus.Regardless of the mechanisms underlying changes in traffic volume and WVCs, our observation that the annual number of WVCs was largely unchanged despite substantive declines in traffic volume has implications for mitigating WVCs going forward. Most directly, the lack of a directional change in WVCs suggests that road traffic levels in the United States are currently such that even large decreases in traffic volume would have minimal long-term effects on the absolute number of WVCs. As such, decreasing collisions by reducing traffic volume would require even larger and longer-lasting changes in traffic than those observed during the pandemic. Since such massive and sustained reductions in traffic are unlikely4,5,6, WVCs in the United States essentially represent a fixed cost as of now, both for human society and wildlife populations. As such, these transient decreases in traffic likely provided minimal reprieve to large mammals from collision-induced mortality, in contrast to speculation that changes in human mobility during the COVID-19 pandemic had substantial positive effects for wildlife populations by freeing wildlife from the pervasive direct and indirect effects of humans17,18,19,20,26,27,42.Indeed, it is possible that short-term decreases in traffic volume might ultimately be harmful to those wildlife species that increased their road use. Although the increases in collision rates we observed at the beginning of the pandemic were rapid and corresponded to nationwide declines in traffic volume (see also26,27), collision rates remained elevated even as traffic approached baseline levels in July (Fig. 2B). If wildlife responses to changes in traffic are asymmetric (i.e., increases in wildlife road use following declines in traffic occur more rapidly than decreases in wildlife road use in response to increased traffic), then short-term declines in traffic volume might lead to net increases in the number WVCs over longer timeframes, ultimately proving detrimental to certain wildlife populations1,3. Future work should evaluate the long-term effects of the pandemic on wildlife populations, specifically with regards to collision-induced mortality17,20,26,27,42.Although the COVID-19 pandemic provided an opportunity to examine the short-term effects of transient decreases in traffic volume on WVCs, the longer-term effects of expanding human populations, greater road densities, and altogether higher traffic volumes on WVCs are less clear. Similar to the increases in wildlife road use in response to decreases in traffic volume theorized here, steady increases in traffic might reduce wildlife road use long-term3,14,15,16; since road traffic is indeed increasing through time4,5,6, we might therefore see declines in WVCs as roads become more effective at repelling wildlife1,3,14. Although these reductions in vehicle-induced wildlife mortality are welcome, this would see roads increasingly serve as barriers to animal movement and gene flow43, further fragmenting already disconnected wildlife populations8. Thus, policy makers and urban planners should invest in infrastructure such as overpasses, underpasses, and fencing that enables wildlife to cross high-traffic roads safely or directs wildlife towards low-risk areas8,9. Even substantive short-term declines in road traffic are not sufficient to mitigate wildlife-vehicle conflict on their own. More

Gene Expression and Therapy Group, King’s College London, Faculty of Life Sciences & Medicine, Department of Medical and Molecular Genetics, Guy’s Hospital, London, UKRobin Mesnage & Michael N. AntoniouCentre for Ecology, Evolution & Behaviour, Department of Biological Sciences, School of Life Sciences and the Environment, Royal Holloway University of London, Egham, UKEdward A. Straw, Mark J. F. Brown & Ellouise LeadbeaterHeartland Health Research Alliance, Port Orchard, WA, USACharles BenbrookANSES, Sophia Antipolis Laboratory, Unit of Honey Bee Pathology, Sophia Antipolis, FranceMarie-Pierre ChauzatAgricultural Economics and Policy Group, ETH Zürich, Zürich, SwitzerlandRobert FingerSchool of Life Sciences, University of Sussex, Brighton, UKDave GoulsonBC3 — Basque Centre for Climate Change, Scientific Campus of the University of Basque Country, Leioa, SpainAna López-BallesterosCentre D’Études Biologiques de Chizé, UMR 7372, CNRS & La Rochelle Université, Villiers-en-bois, FranceNiklas MöhringInstitute of Bee Health, Vetsuisse Faculty, University of Bern, Bern, SwitzerlandPeter NeumannSchool of Agriculture and Food Science, University College Dublin, Dublin, IrelandEdward A. Straw, Dara Stanley & Linzi J. ThompsonDepartment of Botany, School of Natural Sciences, Trinity College Dublin, Dublin, IrelandJane C. Stout & Elena ZiogaDepartment of Ecoscience, Aarhus University, Aarhus, DenmarkChristopher J. ToppingSchool of Chemical Sciences, Glasnevin Campus, Dublin City University, Dublin, IrelandBlánaid WhiteInstitute of Zoology, University of Natural Resources and Life Sciences, Vienna, Vienna, AustriaJohann G. ZallerCorrespondence to
Robin Mesnage or Edward A. Straw. More

1.Abdel-Aal, H. K., Aggour, M. A. & Fahim, M. A. Petroleum and Gas Field Processing. Petroleum and Gas Field Processing (Marcel Dekker, 2015). doi:https://doi.org/10.1201/9780429021350.2.Vikram, A., Lipus, D. & Bibby, K. Produced water exposure alters bacterial response to biocides. Environ. Sci. Technol. 48, 13001–13009 (2014).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
3.Cluff, M. A., Hartsock, A., MacRae, J. D., Carter, K. & Mouser, P. J. Temporal changes in microbial ecology and geochemistry in produced water from hydraulically fractured marcellus shale gas wells. Environ. Sci. Technol. 48, 6508–6517 (2014).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
4.Gregory, K. & Mohan, A. M. Current perspective on produced water management challenges during hydraulic fracturing for oil and gas recovery. Environ. Chem. 12, 261 (2015).CAS 
Article 

Google Scholar 
5.da Silva Almeida, F. B. P., Esquerre, K. P. S. O. R., Soletti, J. I. & De Farias Silva, C. E. Coalescence process to treat produced water: an updated overview and environmental outlook. Environ. Sci. Pollut. Res. 26, 28668–28688 (2019).6.Kabyl, A., Yang, M., Abbassi, R. & Li, S. A risk-based approach to produced water management in offshore oil and gas operations. Process Saf. Environ. Prot. 139, 341–361 (2020).CAS 
Article 

Google Scholar 
7.Danforth, C. et al. An integrative method for identification and prioritization of constituents of concern in produced water from onshore oil and gas extraction. Environ. Int. 134, 105280 (2020).8.Bachmann, R. T., Johnson, A. C. & Edyvean, R. G. J. Biotechnology in the petroleum industry: An overview. Int. Biodeterior. Biodegrad. 86, 225–237 (2014).CAS 
Article 

Google Scholar 
9.Rellegadla, S., Jain, S. & Agrawal, A. Oil reservoir simulating bioreactors: tools for understanding petroleum microbiology. Appl. Microbiol. Biotechnol. 104, 1035–1053 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Weschenfelder, S. E. et al. Evaluation of ceramic membranes for oilfield produced water treatment aiming reinjection in offshore units. J. Pet. Sci. Eng. 131, 51–57 (2015).CAS 
Article 

Google Scholar 
11.Benka-Coker, M. O., Metseagharun, W. & Ekundayo, J. A. Abundance of sulphate-reducing bacteria in Niger Delta oilfield waters. Bioresour. Technol. 54, 151–154 (1995).CAS 
Article 

Google Scholar 
12.Magot, M., Ollivier, B. & Patel, B. K. C. Microbiology of petroleum reservoirs. Antonie van Leeuwenhoek, Int. J. Gen. Mol. Microbiol. 77, 103–116 (2000).13.Santillan, E. F. U., Choi, W., Bennett, P. C. & Diouma Leyris, J. The effects of biocide use on the microbiology and geochemistry of produced water in the Eagle Ford formation, Texas, U.S.A. J. Pet. Sci. Eng. 135, 1–9 (2015).14.Liu, H. et al. The effect of magneticfield on biomineralization and corrosion behavior of carbon steel induced by iron-oxidizing bacteria. Corros. Sci. 102, 93–102 (2016).CAS 
Article 
ADS 

Google Scholar 
15.Katebian, L. et al. Inhibiting quorum sensing pathways to mitigate seawater desalination RO membrane biofouling. Desalination 393, 135–143 (2016).CAS 
Article 

Google Scholar 
16.Tang, K., Baskaran, V. & Nemati, M. Bacteria of the sulphur cycle: An overview of microbiology, biokinetics and their role in petroleum and mining industries. Biochem. Eng. J. 44, 73–94 (2009).CAS 
Article 

Google Scholar 
17.Kahrilas, G. A., Blotevogel, J., Stewart, P. S. & Borch, T. Biocides in Hydraulic Fracturing Fluids: A Critical Review of Their Usage, Mobility, Degradation, and Toxicity. Environ. Sci. Technol. 49, 16–32 (2015).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
18.Tanji, Y., Toyama, K., Hasegawa, R. & Miyanaga, K. Biological souring of crude oil under anaerobic conditions. Biochem. Eng. J. 90, 114–120 (2014).CAS 
Article 

Google Scholar 
19.Vikram, A., Bomberger, J. M. & Bibby, K. J. Efflux as a Glutaraldehyde Resistance Mechanism in Pseudomonas fluorescens and Pseudomonas aeruginosa Biofilms. Antimicrob. Agents Chemother. 59, 3433–3440 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Struchtemeyer, C. G., Morrison, M. D. & Elshahed, M. S. A critical assessment of the efficacy of biocides used during the hydraulic fracturing process in shale natural gas wells. Int. Biodeterior. Biodegrad. 71, 15–21 (2012).CAS 
Article 

Google Scholar 
21.Turkiewicz, A., Brzeszcz, J. & Kapusta, P. The application of biocides in the oil and gas industry. Nafta-Gaz R. 69, nr, 103–111 (2013).22.Videla, H. A. & Herrera, L. K. Microbiologically influenced corrosion: looking to the future. Int. Microbiol. 8, 169–180 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Xu, D., Li, Y. & Gu, T. Mechanistic modeling of biocorrosion caused by biofilms of sulfate reducing bacteria and acid producing bacteria. Bioelectrochemistry 110, 52–58 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Sachan, R. & Singh, A. K. Comparison of microbial influenced corrosion in presence of iron oxidizing bacteria (strains DASEWM1 and DASEWM2). Constr. Build. Mater. 256, 119438 (2020).25.Hino, S., Kazuya, W. & Takahashi, N. Isolation and Characterization of Slime-Producing Bacteria Capable of Utilizing Petroleum Hydrocarbons as a Sole Carbon Source. J. Ferment. Bioeng. 84, 528–531 (1997).CAS 
Article 

Google Scholar 
26.Kim, H. S., Wright, K., Piccioni, J., Cho, D. J. & Cho, Y. I. Inactivation of bacteria by the application of spark plasma in produced water. Sep. Purif. Technol. 156, 544–552 (2015).CAS 
Article 

Google Scholar 
27.Nazina, T. N. et al. Functional and phylogenetic microbial diversity in formation waters of a low-temperature carbonate petroleum reservoir. Int. Biodeterior. Biodegrad. 81, 71–81 (2013).CAS 
Article 

Google Scholar 
28.Stipanicev, M. et al. Corrosion of carbon steel by bacteria from North Sea offshore seawater injection systems: Laboratory investigation. Bioelectrochemistry 97, 76–88 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Li, X. X. et al. Microbiota and their affiliation with physiochemical characteristics of different subsurface petroleum reservoirs. Int. Biodeterior. Biodegrad. 120, 170–185 (2017).CAS 
Article 

Google Scholar 
30.Jurelevicius, D. et al. Bacterial community response to petroleum hydrocarbon amendments in freshwater, marine, and hypersaline water-containing microcosms. Appl. Environ. Microbiol. 79, 5927–5935 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
31.Silva, T. R., Verde, L. C. L., Santos Neto, E. V. & Oliveira, V. M. Diversity analyses of microbial communities in petroleum samples from Brazilian oil fields. Int. Biodeterior. Biodegrad. 81, 57–70 (2013).32.Beech, I. B. & Gaylarde, C. C. Recent advances in the study of biocorrosion: an overview. Rev. Microbiol. 30, 117–190 (1999).Article 

Google Scholar 
33.Korenblum, E., Valoni, É., Penna, M. & Seldin, L. Bacterial diversity in water injection systems of Brazilian offshore oil platforms. Appl. Microbiol. Biotechnol. 85, 791–800 (2010).CAS 
PubMed 
Article 

Google Scholar 
34.Vasconcellos, S. P. et al. The potential for hydrocarbon biodegradation and production of extracellular polymeric substances by aerobic bacteria isolated from a Brazilian petroleum reservoir. World J. Microbiol. Biotechnol. 27, 1513–1518 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Postgate, J. R. The sulphate-reducing bacteria, 2nd edn. (Cambridge University Press, 1984).36.Baars, J. K. Over Sulphatreductie door Bacterien. (Dissertation, 1930).37.Whiteley, A. S. & Bailey, M. J. Bacterial community structure and physiological state within an industrial phenol bioremediation system. Appl. Environ. Microbiol. 66, 2400–2407 (2000).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
38.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
39.Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
PubMed 
PubMed Central 
Article 
ADS 

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

Google Scholar 
41.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.R Core Team. R: A Language and Environment for Statistical Computing. (2021).43.McMurdie, P. J. & Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One 8, e61217 (2013).44.Lahti, L., Shetty, S., Blake, T. & Salojarvi, J. Tools for microbiome analysis in R. Version 1(5), 28 (2017).
Google Scholar 
45.Muturi, E. J., Njoroge, T. M., Dunlap, C. & Cáceres, C. E. Blood meal source and mixed blood-feeding influence gut bacterial community composition in Aedes aegypti. Parasit. Vectors 14, 83 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
48.Oksanen, J. et al. vegan: Community Ecology Package. (2020).49.Zare, N. et al. Using enriched water and soil-based indigenous halophilic consortia of an oilfield for the biological removal of organic pollutants in hypersaline produced water generated in the same oilfield. Process Saf. Environ. Prot. 127, 151–161 (2019).CAS 
Article 

Google Scholar 
50.Belgini, D. R. B. et al. Integrated diversity analysis of the microbial community in a reverse osmosis system from a Brazilian oil refinery. Syst. Appl. Microbiol. 41, 473–486 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.de Sousa Pires, A. et al. Molecular diversity and abundance of the microbial community associated to an offshore oil field on the southeast of Brazil. Int. Biodeterior. Biodegradation 160, 105215 (2021).52.Tüccar, T., Ilhan-Sungur, E. & Muyzer, G. Bacterial Community Composition in Produced Water of Diyarbakır Oil Fields in Turkey. Johnson Matthey Technol. Rev. https://doi.org/10.1595/205651320X15911723486216 (2020).Article 

Google Scholar 
53.Aurepatipan, N., Champreda, V., Kanokratana, P., Chitov, T. & Bovonsombut, S. Assessment of bacterial communities and activities of thermotolerant enzymes produced by bacteria indigenous to oil-bearing sandstone cores for potential application in Enhanced Oil Recovery. J. Pet. Sci. Eng. 163, 295–302 (2018).CAS 
Article 

Google Scholar 
54.Li, X. X. et al. Dominance of Desulfotignum in sulfate-reducing community in high sulfate production-water of high temperature and corrosive petroleum reservoirs. Int. Biodeterior. Biodegrad. 114, 45–56 (2016).CAS 
Article 

Google Scholar 
55.Lan, G. Enrichment and diversity analysis of the thermophilic microbes in a high temperature petroleum reservoir. African J. Microbiol. Res. 5, 1850–1857 (2011).ADS 

Google Scholar 
56.Nguyen, T. T., Cochrane, S. K. J. & Landfald, B. Perturbation of seafloor bacterial community structure by drilling waste discharge. Mar. Pollut. Bull. 129, 615–622 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Ganesh Kumar, A., Nivedha Rajan, N., Kirubagaran, R. & Dharani, G. Biodegradation of crude oil using self-immobilized hydrocarbonoclastic deep sea bacterial consortium. Mar. Pollut. Bull. 146, 741–750 (2019).58.Birkeland, N. K. Sulfate-Reducing Bacteria and Archaea. in Petroleum Microbiology (eds. Ollivier, B. & Magot) 35–54 (American Society of Microbiology, 2005). doi:https://doi.org/10.1128/9781555817589.ch3.59.Gam, Z. B. A. et al. Desulfovibrio tunisiensis sp. nov., a novel weakly halotolerant, sulfate-reducing bacterium isolated from exhaust water of a Tunisian oil refinery. Int. J. Syst. Evol. Microbiol. 59, 1059–1063 (2009).60.Christman, G. D., León-Zayas, R. I., Zhao, R., Summers, Z. M. & Biddle, J. F. Novel clostridial lineages recovered from metagenomes of a hot oil reservoir. Sci. Rep. 10, 8048 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
61.Li, X.-X. et al. Diversity and Composition of Sulfate-Reducing Microbial Communities Based on Genomic DNA and RNA Transcription in Production Water of High Temperature and Corrosive Oil Reservoir. Front. Microbiol. 8, (2017).62.Popoola, L., Grema, A., Latinwo, G., Gutti, B. & Balogun, A. Corrosion problems during oil and gas production and its mitigation. Int. J. Ind. Chem. 4, 35 (2013).Article 

Google Scholar 
63.Street, C. N. & Gibbs, A. Eradication of the corrosion-causing bacterial strains Desulfovibrio vulgaris and Desulfovibrio desulfuricans in planktonic and biofilm form using photodisinfection. Corros. Sci. 52, 1447–1452 (2010).CAS 
Article 

Google Scholar 
64.Varjani, S. J. & Gnansounou, E. Microbial dynamics in petroleum oilfields and their relationship with physiological properties of petroleum oil reservoirs. Bioresour. Technol. 245, 1258–1265 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.da Rosa, J. P., Tibúrcio, S. R. G., Marques, J. M., Seldin, L. & Coelho, R. R. R. Streptomyces lunalinharesii 235 prevents the formation of a sulfate-reducing bacterial biofilm. Brazilian J. Microbiol. 47, 603–609 (2016).Article 
CAS 

Google Scholar 
66.Xie, C. H. & Yokota, A. Pleomorphomonas oryzae gen. nov., sp. nov., a nitrogen-fixing bacterium isolated from paddy soil of Oryza sativa. Int. J. Syst. Evol. Microbiol. 55, 1233–1237 (2005).67.Madhaiyan, M. et al. Pleomorphomonas diazotrophica sp. nov., an endophytic N-fixing bacterium isolated from root tissue of Jatropha curcas L. Int. J. Syst. Evol. Microbiol. 63, 2477–2483 (2013).68.Im, W.-T. Pleomorphomonas koreensis sp. nov., a nitrogen-fixing species in the order Rhizobiales. Int. J. Syst. Evol. Microbiol. 56, 1663–1666 (2006).69.Sung, H. R., Yoon, J. H. & Ghim, S. Y. Shewanella dokdonensis sp. nov., isolated from seawater. Int. J. Syst. Evol. Microbiol. 62, 1636–1643 (2012).70.Torri, A. et al. Shewanella algae infection in Italy: report of 3 years’ evaluation along the coast of the northern Adriatic Sea. New Microbes New Infect. 23, 39–43 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Semple, K. M. & Westlake, D. W. S. Characterization of iron-reducing Alteromonas putrefaciens strains from oil field fluids. Can. J. Microbiol. 33, 366–371 (1987).CAS 
Article 

Google Scholar 
72.Kim, D. D. et al. Microbial community analyses of produced waters from high-temperature oil reservoirs reveal unexpected similarity between geographically distant oil reservoirs. Microb. Biotechnol. 11, 788–796 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Angelova, A. G., Ellis, G. A., Wijesekera, H. W. & Vora, G. J. Microbial Composition and Variability of Natural Marine Planktonic and Biofouling Communities From the Bay of Bengal. Front. Microbiol. 10, (2019).74.Salgar-Chaparro, S. J., Lepkova, K., Pojtanabuntoeng, T., Darwin, A. & Machuca, L. L. Microbiologically influenced corrosion as a function of environmental conditions: A laboratory study using oilfield multispecies biofilms. Corros. Sci. 169, 108595 (2020).75.Michas, A. et al. More than 2500 years of oil exposure shape sediment microbiomes with the potential for syntrophic degradation of hydrocarbons linked to methanogenesis. Microbiome 5, 118 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
76.Tinker, K. et al. Geochemistry and Microbiology Predict Environmental Niches With Conditions Favoring Potential Microbial Activity in the Bakken Shale. Front. Microbiol. 11, (2020).77.Zhong, C., Nesbø, C. L., Goss, G. G., Lanoil, B. D. & Alessi, D. S. Response of aquatic microbial communities and bioindicator modelling of hydraulic fracturing flowback and produced water. FEMS Microbiol. Ecol. 96, (2020).78.Xiao, M. et al. Bacterial community diversity in a low-permeability oil reservoir and its potential for enhancing oil recovery. Bioresour. Technol. 147, 110–116 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Gulliver, D., Lipus, D., Tinker, K., Ross, D. & Sarkar, P. The Geochemistry and Microbial Ecology of Produced Waters from Three Different Unconventional Oil and Gas Regions. in Proceedings of the 8th Unconventional Resources Technology Conference (American Association of Petroleum Geologists, 2020). doi:https://doi.org/10.15530/urtec-2020-2979.80.Davis, J. P., Struchtemeyer, C. G. & Elshahed, M. S. Bacterial Communities Associated with Production Facilities of Two Newly Drilled Thermogenic Natural Gas Wells in the Barnett Shale (Texas, USA). Microb. Ecol. 64, 942–954 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Daly, R. A. et al. Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales. Nat. Microbiol. 1, 16146 (2016).82.Lipus, D. et al. Predominance and Metabolic Potential of Halanaerobium spp. in Produced Water from Hydraulically Fractured Marcellus Shale Wells. Appl. Environ. Microbiol. 83, (2017).83.Zdanowski, M. K. et al. Enrichment of Cryoconite Hole Anaerobes: Implications for the Subglacial Microbiome. Microb. Ecol. 73, 532–538 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
84.Guo, H., Chen, C., Lee, D.-J., Wang, A. & Ren, N. Sulfur–nitrogen–carbon removal of Pseudomonas sp. C27 under sulfide stress. Enzyme Microb. Technol. 53, 6–12 (2013).85.Brahmacharimayum, B. & Ghosh, P. K. Effects of different environmental and operating conditions on sulfate bioreduction in shake flasks by mixed bacterial culture predominantly Pseudomonas aeruginosa. Desalin. Water Treat. 57, 17911–17921 (2016).CAS 
Article 

Google Scholar 
86.Gao, P., Wang, H., Li, G. & Ma, T. Low-Abundance Dietzia Inhabiting a Water-Flooding Oil Reservoir and the Application Potential for Oil Recovery. Biomed Res. Int. 2019, 1–11 (2019).CAS 

Google Scholar 
87.Gao, P., Li, Y., Tan, L., Guo, F. & Ma, T. Composition of Bacterial and Archaeal Communities in an Alkali-Surfactant-Polyacrylamide-Flooded Oil Reservoir and the Responses of Microcosms to Nutrients. Front. Microbiol. 10, (2019).88.Liu, Y.-F. et al. Metabolic capability and in situ activity of microorganisms in an oil reservoir. Microbiome 6, 5 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
89.Li, D. et al. Microbial biodiversity in a Malaysian oil field and a systematic comparison with oil reservoirs worldwide. Arch. Microbiol. 194, 513–523 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Sierra-Garcia, I. N. et al. Microbial diversity in degraded and non-degraded petroleum samples and comparison across oil reservoirs at local and global scales. Extremophiles 21, 211–229 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Xingbiao, W., Yanfen, X., Sanqing, Y., Zhiyong, H. & Yanhe, M. Influences of microbial community structures and diversity changes by nutrients injection in Shengli oilfield, China. J. Pet. Sci. Eng. 133, 421–430 (2015).Article 
CAS 

Google Scholar 
92.Li, G. et al. The relative abundance of alkane-degrading bacteria oscillated similarly to a sinusoidal curve in an artificial ecosystem model from oil-well products. Environ. Microbiol. 20, 3772–3783 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Cui, K. et al. Stimulation of indigenous microbes by optimizing the water cut in low permeability reservoirs for green and enhanced oil recovery. Sci. Rep. 9, 15772 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
94.Cleary, A. et al. Bioremediation of strontium and technetium contaminated groundwater using glycerol phosphate. Chem. Geol. 509, 213–222 (2019).CAS 
Article 
ADS 

Google Scholar 
95.Xia, X. et al. Changes in groundwater bacterial community during cyclic groundwater‐table variations. Hydrol. Process. hyp.13917 (2020) doi:https://doi.org/10.1002/hyp.13917.96.Braun, K. & Gibson, D. T. Anaerobic degradation of 2-aminobenzoate (anthranilic acid) by denitrifying bacteria. Appl. Environ. Microbiol. 48, 102–107 (1984).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
97.Cai, M. et al. Crude oil as a microbial seed bank with unexpected functional potentials. Sci. Rep. 5, 16057 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
98.Arai, H. Regulation and Function of Versatile Aerobic and Anaerobic Respiratory Metabolism in Pseudomonas aeruginosa. Front. Microbiol. 2, (2011).99.Hilgarth, M., Lehner, E. M., Behr, J. & Vogel, R. F. Diversity and anaerobic growth of Pseudomonas spp. isolated from modified atmosphere packaged minced beef. J. Appl. Microbiol. 127, 159–174 (2019). More

Type-I errorIn the case of two identical unimodal von Mises distributions, seven tests did not maintain Type-I error near the nominal 5% level, at least when sample sizes were small. These tests were the Kuiper two-sample test, the non equal concentration parameters approach ANOVA, the P-test, the Watson’s large-sample nonparametric test, the Watson–Williams test and the Rao dispersion test (Fig. 2). The Type-I error results were similar for the unimodal wrapped skew-normal distribution, except that the Wallraff test and Fisher’s method also showed Type-I error inflation (Fig. S1). No other methods showed evidence of failure to control Type-I error rate across different testing situations (Figs. S2–S5), except for the Log-likelihood ratio ANOVA in the case of two identical asymmetrical bimodal distributions (Fig. S3). In summary, only eight out of 18 tests reliably controlled the Type-I error rate near the nominal 5% level across all the situations investigated. These included five tests for identical distribution, the Watson’s U2 test, the Large-sample Mardia–Watson–Wheeler test, the Watson-Wheeler test, the embedding approach ANOVA, the MANOVA approach, the Rao polar test for differences in mean direction, and two tests for differences in concentration, the Levene’s test and the concentration test. We focus only on these tests in our explorations of statistical power.Figure 2Type-I error of all tests using von Mises distributions for different sample sizes: 10 and 10 (A), 20 and 20 (B), 50 and 50 (C), 20 and 30 (D) and 10 and 50 (E). Concentration (κ, kappa) increases for both distributions from 0 to 8. Tests are grouped according to their null hypotheses.Full size imagePower to detect differences in concentrationThe most powerful test to detect concentration differences between two von Mises distributions was the MANOVA approach, which offered superior power especially at lower sample sizes (Fig. 3). The Watson’s U2 test was also very powerful, followed by the Watson–Wheeler and the Large-sample Mardia–Watson–Wheeler tests with only marginally lower power. The embedding approach ANOVA had lower power, but, notably, was still more powerful than the Concentration test and Levene’s test, both specifically designed to detect differences in concentration. As expected, the Rao polar test was not sensitive to differences in concentration. The general results for two unimodal wrapped skew-normal distributions were comparable to the results for unimodal von Mises distributions, with the only exception of superior performance of Levene’s test in situations with highly asymmetric samples sizes (Fig. S6).Figure 3Power of all included tests when comparing von Mises distributions of differing concentrations using different sample sizes: 10 and 10 (A), 20 and 20 (B), 50 and 50 (C), 20 and 30 (D) and 10 and 50 (E). The first distribution is fixed at κ = 0, the second increases from 0 towards 8.Full size imageWhen comparing axial von Mises distributions, only the Watson’s U2 test offered acceptable power (Fig. S7). For the symmetrical trimodal distributions, overall power was very low, and again, only the Watson’s U2 providing some power (Fig. S8). The asymmetrical bimodal (Fig. S9) situation showed acceptable power of the MANOVA approach and Watson’s U2, however, for the asymmetrical trimodal distribution power was low with the Watson’s U2 providing the best results (Fig. S10).Power to detect differences in the mean/medianThe power to detect angular differences between two von Mises distributions was highest for the MANOVA approach at small sample sizes (n = 10), followed by the Watson’s U2, Watson-Wheeler test and the Large-sample Mardia-Watson-Wheeler test (Fig. 4). Notably, the Levene’s test also showed acceptable power levels, clearly failing to detect specifically concentration differences (to which it was less sensitive, see Fig. 4). The concentration test was not sensitive to the differences in mean direction. Special cases were the embedding approach ANOVA and the Rao polar test. The ANOVA approach showed, with the exception of very unequal sample sizes (n = 10/50), a unimodal response, with increasing power levels from 0° to 90° difference, but then rapidly decreasing power towards 180° difference. The Rao polar test showed an even stranger pattern, with, at higher sample sizes, very good power when the difference was either around 45° or 135°, but with power levels dropping to 0.05 in between these two peaks (at 90°). The results were similar for the wrapped skew-normal distribution, with the exceptions that the Rao polar test showed strongly reduced power and switched from a bimodal to a unimodal power curve with a peak around 60°, and the Levene’s test completely lost its power (Fig. S11).Figure 4Power of all included tests when comparing von Mises distributions (kappa for both = 2) of differing directions using different sample sizes: 10 and 10 (A), 20 and 20 (B), 50 and 50 (C), 20 and 30 (D) and 10 and 50 (E). The first distribution is fixed at 0°, the second increases from 0° towards 180.Full size imageFor axial distributions, only the Watson’s U2 test offered acceptable power levels, although large sample sizes (~ n = 100) were required for the power to reach over 50% (Fig. S12). All other tests failed to detect the difference in mean direction between two axial distributions. For symmetric trimodal distributions none of the tests used was sensitive to differences in mean direction (Fig. S13).When comparing asymmetrical bimodal distributions, the general trends were similar to the unimodal case. However, over all sample sizes the MANOVA approach offered the best power. The Watson–Wheeler test was considerably less powerful in this situation, as were the Watson’s U2 test and the Large-sample Mardia–Watson–Wheeler test (Fig. S14). The Levene’s test showed a unimodal-shaped power curve. The asymmetrical trimodal situation was, again, similar to the asymmetrical bimodal situation (Fig. S15), with the exception of the Levene’s test, which showed steady power increase with angular difference (instead of the hump-shaped curve).Power to detect differences in distribution typeWhen comparing a unimodal and an axial bimodal distribution, which increased similarly in concentration, we found that the MANOVA approach again offered the best power in particular at low samples sizes, followed by the Watson’s U2 test, the Large-sample Mardia–Watson–Wheeler test and Watson–Wheeler test (Fig. 5). While the embedding approach ANOVA and the Levene’s test had varying but usable power levels, the concentration test was only sensitive to such differences at low concentration values. The Rao polar test was not sensitive to such differences.Figure 5Power of all included tests when comparing von Mises distributions of differing number of modes (unimodal and axially bimodal) using different sample sizes: 10 and 20 (A), 20 and 40 (B), 50 and 100 (C), 20 and 60 (D) and 10 and 100 (E). The concentration (κ) of both increases from 0 to 8.Full size imageThe picture was only marginally different when comparing a von Mises with a wrapped skew-normal distribution (Fig. S16). For low sample sizes (n = 10) the MANOVA approach offered great power, followed by the embedding approach ANOVA. The latter offered good power throughout the range of sample sizes tested, followed by the Watson’s U2 test, the Large-sample Mardia–Watson–Wheeler test and Levene’s test. Also, the Rao polar test showed lower, but acceptable sensitivity to distribution type. The concentration test only showed very low power, that (as expected) increased with increasing concentrations of the respective distributions.We summarize the results obtained in the power analysis in Table 2. In all situations, either the Watson’s U2 test or the MANOVA approach offered the best power.Table 2 Ranking of tests based on the power comparisons for the main scenarios encountered in potential data sets (using different distributions: unimodal, axial, asymmetrical bimodal, symmetrical trimodal, asymmetrical trimodal), in cases were only one test performed acceptable the others ranks were left blank (see Table 1 for abbreviations).Full size tableReal data examplesTesting the performance of the robust tests on real data sets revealed, predominantly, the expected test behavior. In the example of homing pigeons where a difference in concentration was expected, all tests, with the exception of the Rao polar test and, notably, the concentration test, showed a significant difference between the distributions (Fig. 6A). Therefore, we can conclude, in accordance with the respective publication19, that sectioning of the olfactory nerve disrupted the homing behavior of pigeons.Figure 6Results from example data. Shown are results of pigeon (A), ant (B) and bat orientations (C). Control groups are on the left panels and experimental groups on the right. The tests are abbreviated according to Table 1, significant test results are indicated in red with asterisk and non-significant in blue. For each circular plot directional data is shown as dots on the circle (each dot is one individual), the arrows represent the mean direction and the dashed line the 95% confidence interval.Full size imageIn the ant example, where no difference between the groups was expected, there was no significant difference between the distributions detected by most of the tests (Fig. 6B). Only the concentration test showed a significant difference. Based on the other tests we would conclude that there was no biological meaningful difference between the two distributions. Therefore, ants appear to be able to transfer visual information from one eye to the other.In the bat example, where a difference in mean direction was expected, the Watson’s U2, the Mardia–Watson–Wheeler, Watson-Wheeler test and the MANOVA approach showed a significant difference (Fig. 6C). Notably, the Rao polar, Levene’s, and concentration tests and the embedding approach ANOVA failed to show a significant difference. At least for the Rao polar test, one would have expected a significant difference, as the two distributions are clearly 180° apart. This outcome concurs with our simulation results where the Rao polar test failed to distinguish distributions on the same and orthogonal axes (Fig. 4). As the results of the tests where quite mixed this example highlights the need for choosing a test with appropriate power to detect the expected differences. Based on the results of the most powerful tests, we conclude that the bats showed a mirrored orientation, as expected in the experimental design. More