Ecology

Insects and bamboosFive internodes (length: mean ± SD = 44.8 ± 1.1 cm, n = 5; diameter in the middle part of internodes: 21.4 ± 0.8 mm, n = 5) of five living mature culms of P. simonii bamboo were sampled at Kawaminami, Miyazaki Prefecture, Japan [32°9′ N, 131°29′ E] on 6 June, 2019. Per internode, four semi-cylindrical strips (ca. 15 × 2 cm) were made and stored at − 25 °C until use.To obtain fungus-free larvae of D. bucculenta, we sampled five beetle eggs from P. simonii bamboo obtained at Toyota, Aichi Prefecture, Japan [35°9′ N, 137°13′ E] on 9 May, 2019 in the laboratory from ovipositing females collected at Kawaminami on 10 and 11 April, 2019. The eggs were immersed in 99.5% ethanol for 10 s followed by 70% ethanol for 10 s for surface sterilization and then individually placed on potato dextrose agar (PDA) (Difco, Detroit, MI, USA) plates. The plates were incubated at 25 °C in the dark until 30 days after larval hatching to confirm the absence of the formation of yeast or other microbial colonies. Consequently, all five larvae hatched successfully and aseptically.The bamboo used in this study was morphologically identified using the literature29. This is native to the study areas and no other host bamboo species are distributed there29. Therefore, no voucher specimen of this bamboo has been deposited in a publicly available herbarium. No specific permits were required for the described field studies. The location is not privately-owned or protected in any way. The field studies did not involve endangered or protected species. All applicable international, national, and/or institutional guidelines for the care and use of animals and plants were followed. This study is reported in accordance with ARRIVE guidelines.Component analyses of bamboo tissuesFor YP and LP, the yeast W. anomalus originating from D. bucculenta in Kawaminami (strain: DBL05Kawaminami) was cultured on a 9-cm PDA plate to obtain enough biomass for further experiments. Afterwards, yeast cells were suspended in ca. 10 mL of sterilized water, and were inoculated on the inner surface of the autoclaved internode strips using an autoclaved tissue paper immersed with the yeast suspension. For LP, additionally, the fungus-free 2nd instar larvae (weight: mean ± SD = 2.4 ± 0.4 mg, n = 5) were individually placed on the yeast-inoculated strips. Each of these yeast-inoculated and yeast-and-larva-inoculated strips was then put in a sterilized test tube (3.0 cm in diameter and 20 cm tall) with moistened cotton placed at the bottom. Each of the test tubes was covered with a sterilized polypropylene cap, sealed with Parafilm Sealing Film (Pechiney Plastic Packaging, Chicago, IL, USA) on which three small holes were made using a fire-sterilized insect pin to avoid oxygen shortage, and individually put in a plastic zipper bag. These yeasts and insects were incubated at 25 °C in the dark for 47 days for YP (n = 5), and 47 (n = 4) and 73 (n = 1) days until these larvae reached adulthood for LP (adult elytral length: mean ± SD = 9.2 ± 0.4 mm, n = 5). Microbial contamination was invisible to the naked eye.For FP, YP and LP, the inner surface (up to 0.3 mm in thickness, dry weight: 336 to 935 mg) of a strip was sampled using a small U-shaped gouge. In the case of FX, first, the pith of a strip was completely removed, and then xylem tissue (up to 0.5 mm in thickness, dry weight: 729 to 872 mg) was sampled using a small U-shaped gouge. These tissues were individually sampled from five strips derived from five different internodes for each tissue type.Samples were extracted by aqueous ethanol and hydrolyzed by sulfuric acid with reference to the literature30,31,32 as follows. Four types of samples were freeze-dried and pulverized using a rotor-speed mill (Fritsch, PULVERISETTE 14, 0.2 mm mesh). About 80 mg of powdered sample was extracted using 5-mL 80% ethanol aqueous solution (aq.) at 63 °C three times. The volume of the extracts was adjusted to 25 mL, filtered, and analyzed using ion exchange chromatography measurements (extractable sugar analysis). Their extracted residues were hydrolyzed using sulfuric acid as follows: 50-mg samples were immersed in 1.64-g 72% sulfuric acid aq. at 30 °C for 2 h, boiled in 39.4-g 3% sulfuric acid aq. for 4 h, and filtered to collect sulfuric acid residues as sulfuric acid lignin fractions. The volumes of the filtrates were fixed to 100 mL, passed through a sulfuric acid-removing filter (DIONEX OnGuard IIA), and submitted to ion exchange chromatography measurements (structural sugar analysis). For the uronic acid measurements, the sulfuric acid-removing filter was not used.Ion exchange chromatography measurements were conducted using a DIONEX ICS-3000 apparatus. The measurement conditions were as follows: column, CarboPac PA-1 (2.0 mm I.D. × 250 mm L, Dionex corp.); flow rate, 0.3 mL min−1; column temperature, 30 °C; injection volume, 25 µL; eluent, H2O (solvent A), 100 mM NaOHaq. (solvent B), aqueous solution containing 100 mM NaOH and 1.0 M CH3COONa (solvent C), and aqueous solution containing 100 mM NaOH and 150 mM CH3COONa (solvent D). The gradient conditions for monomers, dimers, and uronic acids were as follows: for monomers, with a gradient of B 0.5% C 0% 45 min, C 100% 10 min, B 100% 10 min, B 0.5% C 0% 20 min; for dimers, with a gradient of B 50% C 0% 50 min, C 100% 10 min, B 100% 10 min, B 50% C 0% 15 min; for uronic acids, with a gradient of D 100% 10 min. These extraction, hydrolysis, and measurement procedures were conducted using n = 5 samples. For the structural sugars, their yield was calculated as the dehydrated state. The values of other extractives % were calculated by the subtraction of total extractable sugars % from total extractives %.Elemental analysis (carbon, hydrogen, nitrogen) was conducted by 2400 CHNS Organic Elemental Analyzer (PerkinElmer Japan, Yokohama, Japan). About 1-mg dried samples were burned completely and the produced CO2, H2O, and N2 (after reduction of NOx species) gasses were quantified by a thermal conductivity detector.Means of components of bamboo tissues were compared among tissue types using the Steel–Dwass test after the Kruskal–Wallis test. Calculations were performed using R 3.5.133.Carbon assimilation testThe yeast W. anomalus (DBL05Kawaminami) was cultured aerobically in 20 mL of yeast nitrogen base (YNB) (Difco) containing 0.5% glucose at 25 °C in the dark for 2 days with shaking at 85 rpm. The culture media were centrifuged and cell pellets were suspended in sterile water, in which the OD600 was adjusted to 0.10. Fifty μL of the cell suspension was added into a tube (2 mL) with 1 mL of each of 14 different media containing YNB and one of the following carbon sources: d-glucose, d-galactose, d-mannose, d-xylose, l-arabinose, d-fructose, d-galacturonic acid, d-glucuronic acid, sucrose, cellobiose, starch from corn, xylan from corn, carboxymethyl cellulose, and no carbon source (n = 5 to 6). The concentration of each carbon source was 0.5 g L−1, except for xylan at 1.5 g L−1. The tubes were shaken at 85 rpm and incubated at 25 °C in the dark for 7 days. Afterwards, the presence of visible pellets of yeasts and OD600 were recorded to determine the growth of the strain. The degree of assimilation was scored according to the presence of the pellets and the difference in the turbidity increase (ΔOD600) between culture media containing no and a given carbon source as follows: no growth (without a pellet, ΔOD600  More

1.Hughes, T. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).Article 

Google Scholar 
3.Brander, L. M., Van Beukering, P. & Cesar, H. S. The recreational value of coral reefs: A meta-analysis. Ecol. Econ. 63, 209–218 (2007).Article 

Google Scholar 
4.Jackson, J. B. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Bruno, J. F., Sweatman, H., Precht, W. F., Selig, E. R. & Schutte, V. G. Assessing evidence of phase shifts from coral to macroalgal dominance on coral reefs. Ecology 90, 1478–1484 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Hughes, T. Catastrophes, phase shifts, and large-scale degradation of a Caribbean Coral Reef. Science 265, 1547–1551 (1994).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Hughes, T. P., Bellwood, D. R., Folke, C. S., McCook, L. J. & Pandolfi, J. M. No-take areas, herbivory and coral reef resilience. Trends Ecol. Evol. 22, 1–3 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Edgar, G. J. et al. Reef Life Survey: Establishing the ecological basis for conservation of shallow marine life. Biol. Conserv. 252, 108855 (2020).Article 

Google Scholar 
10.Chabanet, P., Bigot, L., Garnier, R., Tessier, E. & Moyne-Picard, M. Coral reef monitoring at Reunion island (Western Indian Ocean) using the GCRMN method. Proc. 9th Int. Coral Reef Symp. 2, 873–878 (2000).
Google Scholar 
11.Lang, J. C., Marks, K. W., Kramer, P. A., Kramer, P. R. & Ginsburg, R. N. AGRRA Protocols Version 5.4. (2010).12.Cortés, J. et al. The CARICOMP network of Caribbean Marine Laboratories (1985–2007): History, key findings, and lessons learned. Front. Mar. Sci. 5, 519 (2019).Article 

Google Scholar 
13.Dethier, M. N., Graham, E. S., Cohen, S. & Tear, L. M. Visual versus random-point percent cover estimations: ‘objective’ is not always better. Mar. Ecol. Prog. Ser. 96, 93–100 (1993).Article 

Google Scholar 
14.Beijbom, O., Edmunds, P. J., Kline, D. I., Mitchell, B. G. & Kriegman, D. Automated annotation of coral reef survey images. In 2012 IEEE Conference on Computer Vision and Pattern Recognition 1170–1177 (IEEE, 2012).15.Beijbom, O. et al. Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation. PloS One 10, e0130312 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Williams, I. D. et al. Leveraging automated image analysis tools to transform our capacity to assess status and trends of coral reefs. Front. Mar. Sci. 6, 222 (2019).Article 

Google Scholar 
17.Roelfsema, C. et al. Benthic and coral reef community field data for Heron Reef, Southern Great Barrier Reef, Australia, 2002–2018. Sci. Data 8, 1–7 (2021).MathSciNet 
Article 

Google Scholar 
18.Gonzalez-Rivero, M. et al. Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach. Remote Sens. 12, 489 (2020).Article 

Google Scholar 
19.Cairns, S. D. Stony corals (Cnidaria: Hydrozoa, Scleractinia) of Carrie Bow Cay, Belize. Smithson. Contrib. Mar. Sci. 21, 271–302 (1982).
Google Scholar 
20.Rutzler, K. & Macintyre, I. G. The Atlantic Barrier Reef Ecosystem at Carrie Bow Cay, Belize, 1: Structure and Communities (Smithsonian Institution Press, 1982). https://doi.org/10.5479/si.01960768.12.539.Book 

Google Scholar 
21.Rutzler, K. Caribbean coral reef ecosystems: Thirty-five years of smithsonian marine science in Belize. In Proceedings of the Smithsonian Marine Science Symposium (2009).22.McField, M. et al. Mesoamerican Reef Report Card. (2020).23.Cox, C. E. et al. Genetic testing reveals some mislabeling but general compliance with a ban on herbivorous fish harvesting in Belize. Conserv. Lett. 6, 132–140 (2013).Article 

Google Scholar 
24.Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5, 9–13 (2005).
Google Scholar 
25.Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446 (2018).Article 

Google Scholar 
26.R Core Team. A language and environment for statistical computing. (2020).27.Althaus, F. et al. A standardised vocabulary for identifying benthic biota and substrata from underwater imagery: The CATAMI classification scheme. PLoS One 10, e0141039 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Goatley, C. H., Bonaldo, R. M., Fox, R. J. & Bellwood, D. R. Sediments and herbivory as sensitive indicators of coral reef degradation. Ecol. Soc. 21(1), 29 (2016).Article 

Google Scholar 
29.Connell, S., Foster, M. & Airoldi, L. What are algal turfs? Towards a better description of turfs. Mar. Ecol. Prog. Ser. 495, 299–307 (2014).Article 

Google Scholar 
30.Lozada-Misa, P., Schumacher, B. D. & Vargas-Angel, B. Analysis of benthic survey images via CoralNet: A summary of standard operating procedures and guidelines. Pacific
Islands Fish. Sci. Cent. Natl. Mar. Fish. Serv. https://doi.org/10.7289/V5%2FAR-PIFSC-H-17-02 (2017).Article 

Google Scholar 
31.Obura, D. & Grimsditch, G. Resilience Assessment of Coral Reefs: Assessment Protocol for Coral Reefs, Focusing on Coral Bleaching and Thermal Stress (Citeseer, 2009).
Google Scholar 
32.Broeke, J., Pérez, J. M. M., & Pascau, J. Image processing with ImageJ. (Packt Publishing Ltd, 2015).
Google Scholar 
33.Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press, 2017).MATH 
Book 

Google Scholar 
34.Fasiolo, M., Nedellec, R., Goude, Y. & Wood, S. N. Scalable visualization methods for modern generalized additive models. J. Comput. Graph. Stat. 29, 78–86 (2020).MathSciNet 
Article 

Google Scholar 
35.Oksanen, J. et al. Community ecology package. R Package Version 2, (2013).36.Arnold, S. N. & Steneck, R. S. Settling into an increasingly hostile world: The rapidly closing ‘“Recruitment Window”’ for corals. PLoS One. https://doi.org/10.1371/journal.pone.0028681 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Mumby, P. J. & Harborne, A. R. Marine reserves enhance the recovery of corals on Caribbean reefs. PLoS One 5, e8657 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Adam, T. C., Burkepile, D. E., Ruttenberg, B. I. & Paddack, M. J. Herbivory and the resilience of Caribbean coral reefs: Knowledge gaps and implications for management. Mar. Ecol. Prog. Ser. 520, 1–20 (2015).Article 

Google Scholar 
39.Suchley, A., McField, M. D. & Alvarez-Filip, L. Rapidly increasing macroalgal cover not related to herbivorous fishes on Mesoamerican reefs. PeerJ 4, e2084 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Arnold, S. N., Steneck, R. S. & Mumby, P. J. Running the gauntlet: Inhibitory effects of algal turfs on the processes of coral recruitment. Mar. Ecol. Prog. Ser. 414, 91–105 (2010).Article 

Google Scholar 
41.Box, S. J. & Mumby, P. J. Effect of macroalgal competition on growth and survival of juvenile Caribbean corals. Mar. Ecol. Prog. Ser. 342, 139–149 (2007).Article 

Google Scholar 
42.Williams, I. & Polunin, N. Large-scale associations between macroalgal cover and grazer biomass on mid-depth reefs in the Caribbean. Coral Reefs 19, 358–366 (2001).Article 

Google Scholar 
43.Newman, M. J., Paredes, G. A., Sala, E. & Jackson, J. B. Structure of Caribbean coral reef communities across a large gradient of fish biomass. Ecol. Lett. 9, 1216–1227 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Mumby, P. J., Steneck, R. S., Roff, G. & Paul, V. J. Marine reserves, fisheries ban, and 20 years of positive change in a coral reef ecosystem. Conserv. Biol. https://doi.org/10.1111/cobi.13738 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Aronson, R., Precht, W., Toscano, M. & Koltes, K. The 1998 bleaching event and its aftermath on a coral reef in Belize. Mar. Biol. 141, 435–447 (2002).Article 

Google Scholar 
46.Green, D. H., Edmunds, P. J. & Carpenter, R. C. Increasing relative abundance of Porites astreoides on Caribbean reefs mediated by an overall decline in coral cover. Mar. Ecol. Prog. Ser. 359, 1–10 (2008).Article 

Google Scholar 
47.Roff, G., Joseph, J. & Mumby, P. J. Multi-decadal changes in structural complexity following mass coral mortality on a Caribbean reef. Biogeosciences 17, 5909–5918 (2020).Article 

Google Scholar 
48.Graham, N. & Nash, K. The importance of structural complexity in coral reef ecosystems. Coral Reefs 32, 315–326 (2013).Article 

Google Scholar 
49.Aronson, R. B. & Precht, W. F. White-band disease and the changing face of Caribbean coral reefs. Ecol. Etiol. New. Emerg. Mar. Dis. 159, 25–38 (2001).
Google Scholar 
50.Aronson, R. B., Macintyre, I. G., Precht, W. F., Murdoch, T. J. & Wapnick, C. M. The expanding scale of species turnover events on coral reefs in Belize. Ecol. Monogr. 72, 233–249 (2002).Article 

Google Scholar 
51.McField, M. et al. Status of the Mesoamerican Reef after the 2005 coral bleaching event. Status Caribb. Coral Reefs Bleach. Hurric. In 45–60 (2005).52.Arias-González, J. E. et al. A coral-algal phase shift in Mesoamerica not driven by changes in herbivorous fish abundance. PLoS One 12, e0174855 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Miller, S. Automatically Annotating 175,000+ Images with the CoralNet API. CoralNet (Accessed 23 August 2021); https://coralnet.ucsd.edu/blog/automatically-annotating-175000-images-with-the-coralnet-api/ (2020).54.Muller, E. M., Sartor, C., Alcaraz, N. I. & van Woesik, R. Spatial epidemiology of the stony-coral-tissue-loss disease in Florida. Front. Mar. Sci. 7, 163 (2020).Article 

Google Scholar 
55.Alvarez-Filip, L., Estrada-Saldívar, N., Pérez-Cervantes, E., Molina-Hernández, A. & González-Barrios, F. J. A rapid spread of the stony coral tissue loss disease outbreak in the Mexican Caribbean. PeerJ 7, e8069 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Weil, E. et al. Spread of the new coral disease “SCTLD” into the Caribbean: implications for Puerto Rico. Reef Encount. 34, 38–43 (2019).
Google Scholar 
57.Heres, M. M., Farmer, B. H., Elmer, F. & Hertler, H. Ecological consequences of Stony Coral Tissue Loss Disease in the Turks and Caicos Islands. Coral Reefs 40, 609–624 (2021).Article 

Google Scholar 
58.Walton, C. J., Hayes, N. K. & Gilliam, D. S. Impacts of a regional, multi-year, multi-species coral disease outbreak in Southeast Florida. Front. Mar. Sci. 5, 323 (2018).Article 

Google Scholar  More

1.Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
PubMed 
Article 

Google Scholar 
2.Philippot, L., Raaijmakers, J. M., Lemanceau, P. & Putten, W. Hvander Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol 11, 789–799 (2013).CAS 
PubMed 
Article 

Google Scholar 
3.Bais, H. P., Weir, T. L., Perry, L. G., Gilroy, S. & Vivanco, J. M. The role of root exudates in rhizosphere interactions with plants and other organisms. Annu Rev. Plant Biol. 57, 233–266 (2006).CAS 
PubMed 
Article 

Google Scholar 
4.De Long, J. R., Fry, E. L., Veen, G. F. & Kardol, P. Why are plant–soil feedbacks so unpredictable, and what to do about it? Funct. Ecol. 33, 118–128 (2019).Article 

Google Scholar 
5.Meisner, A., De Deyn, G. B., de Boer, W. & van der Putten, W. H. Soil biotic legacy effects of extreme weather events influence plant invasiveness. Proc. Natl. Acad. Sci. USA. 110, 9835–9838 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Kostenko, O., van de Voorde, T. F. J., Mulder, P. P. J., van der Putten, W. H. & Bezemer, T. M. Legacy effects of aboveground-belowground interactions. Ecol. Lett. 15, 813–821 (2012).PubMed 
Article 

Google Scholar 
7.Heinen, R. et al. Plant community composition steers grassland vegetation via soil legacy effects. Ecol. Lett. 23, 973–982 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Semchenko, M. et al. Fungal diversity regulates plant-soil feedbacks in temperate grassland. Sci. Adv. 4, eaau4578 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Cordovez, V., Dini-Andreote, F., Carrión, V. J. & Raaijmakers, J. M. Ecology and evolution of plant microbiomes. Annu. Rev. Microbiol. 73, 69–88 (2019).CAS 
PubMed 
Article 

Google Scholar 
10.Bennett, J. A. & Klironomos, J. Mechanisms of plant–soil feedback: interactions among biotic and abiotic drivers. New Phytol. 222, 91–96 (2019).PubMed 
Article 

Google Scholar 
11.van der Putten, W. H. et al. Plant–soil feedbacks: the past, the present and future challenges. J. Ecol. 101, 265–276 (2013).Article 

Google Scholar 
12.Bever, J. D., Platt, T. G. & Morton, E. R. Microbial population and community dynamics on plant roots and their feedbacks on plant communities. Annu. Rev. Microbiol. 66, 265–283 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Petermann, J. S., Fergus, A. J. F., Turnbull, L. A. & Schmid, B. Janzen-connell effects are widespread and strong enough to maintain diversity in grasslands. Ecology 89, 2399–2406 (2008).PubMed 
Article 

Google Scholar 
14.Cortois, R., Schröder‐Georgi, T., Weigelt, A., van der Putten, W. H. & De Deyn, G. B. Plant–soil feedbacks: role of plant functional group and plant traits. J. Ecol. 104, 1608–1617 (2016).Article 

Google Scholar 
15.Bezemer, T. M., Jing, J., Bakx‐Schotman, J. M. T. & Bijleveld, E.-J. Plant competition alters the temporal dynamics of plant-soil feedbacks. J. Ecol. 106, 2287–2300 (2018).Article 

Google Scholar 
16.Kardol, P., Deyn, G. B. D., Laliberté, E., Mariotte, P. & Hawkes, C. V. Biotic plant–soil feedbacks across temporal scales. J. Ecol. 101, 309–315 (2013).Article 

Google Scholar 
17.Dudenhöffer, J.-H., Ebeling, A., Klein, A.-M. & Wagg, C. Beyond biomass: Soil feedbacks are transient over plant life stages and alter fitness. J. Ecol. 106, 230–241 (2018).Article 
CAS 

Google Scholar 
18.Elger, A., Lemoine, D. G., Fenner, M. & Hanley, M. E. Plant ontogeny and chemical defence: older seedlings are better defended. Oikos. 118, 767–773 (2009).CAS 
Article 

Google Scholar 
19.Nelson, E. B. The seed microbiome: origins, interactions, and impacts. Plant Soil 422, 7–34 (2018).CAS 
Article 

Google Scholar 
20.Wei, Z. et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci. Adv. 5, eaaw0759 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Rosenblueth, M. & Martínez-Romero, E. Bacterial endophytes and their interactions with hosts. Mol. Plant Microbe Interact. 19, 827–837 (2006).CAS 
PubMed 
Article 

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

Google Scholar 
23.Gaiero, J. R. et al. Inside the root microbiome: bacterial root endophytes and plant growth promotion. Am. J. Bot. 100, 1738–1750 (2013).PubMed 
Article 

Google Scholar 
24.Rodriguez, R. J. Jr, Arnold, J. F. W. & Redman, A. E. Fungal endophytes: diversity and functional roles. New Phytol. 182, 314–330 (2009).CAS 
PubMed 
Article 

Google Scholar 
25.Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).ADS 
PubMed 
Article 
CAS 

Google Scholar 
26.Fitzpatrick, C. R. et al. Ecological role of the angiosperm root microbiome. Proc. Natl. Acad. Sci. USA. 115, E1157–E1165 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Hardoim, P. R., van Overbeek, L. S. & Elsas, J. Dvan Properties of bacterial endophytes and their proposed role in plant growth. Trends Microbiol. 16, 463–471 (2008).CAS 
PubMed 
Article 

Google Scholar 
28.Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
29.Hannula, S. E., Zhu, F., Heinen, R. & Bezemer, T. M. Foliar-feeding insects acquire microbiomes from the soil rather than the host plant. Nat. Commun. 10, 1–9 (2019).CAS 
Article 

Google Scholar 
30.Sikes, B. A., Hawkes, C. V. & Fukami, T. Plant and root endophyte assembly history: interactive effects on native and exotic plants. Ecology 97, 484–493 (2016).PubMed 
Article 

Google Scholar 
31.Bezemer, T. M. et al. Plant species and functional group effects on abiotic and microbial soil properties and plant–soil feedback responses in two grasslands. J. Ecol. 94, 893–904 (2006).CAS 
Article 

Google Scholar 
32.van de Voorde, T. F., van der Putten, W. H. &  Bezemer, T. M. Intra‐and interspecific plant–soil interactions, soil legacies and priority effects during old‐field succession. J. Ecol. 99, 945–953 (2011).Article 

Google Scholar 
33.Hannula, S. E. et al. Time after time: temporal variation in the effects of grass and forb species on soil bacterial and fungal communities. mBio 10, e02635–19 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663 (2013).CAS 
PubMed 
Article 

Google Scholar 
35.Ampt, E. A., van Ruijven, J., Raaijmakers, J. M., Termorshuizen, A. J. & Mommer, L. Linking ecology and plant pathology to unravel the importance of soil-borne fungal pathogens in species-rich grasslands. Eur. J. Plant Pathol. 154, 141–156 (2019).Article 

Google Scholar 
36.Allison, S. D. & Martiny, J. B. H. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. USA. 105, 11512–11519 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Rousk, J. & Bååth, E. Fungal biomass production and turnover in soil estimated using the acetate-in-ergosterol technique. Soil Biol. Biochem. 39, 2173–2177 (2007).CAS 
Article 

Google Scholar 
38.Phillips, M. L. et al. Fungal community assembly in soils and roots under plant invasion and nitrogen deposition. Fungal Ecol. 40, 107–117 (2019).Article 

Google Scholar 
39.Carini, P., Marsden, P. & Leff, J. E. A. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2, 16242 (2017).CAS 
Article 

Google Scholar 
40.Hannula, S. E., Morrien, E., van der Putter, W. H. & de Boer, W. Rhizosphere fungi actively assimilating plant-derived carbon in a grassland soil. Fungal Ecol. 48, 100988 (2020).Article 

Google Scholar 
41.Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant–soil feedbacks: a meta‐analytical review. Ecol. Lett. 11, 980–992 (2008).PubMed 
Article 

Google Scholar 
42.Dassen, S. et al. Differential responses of soil bacteria, fungi, archaea and protists to plant species richness and plant functional group identity. Mol. Ecol. 26, 4085–4098 (2017).CAS 
PubMed 
Article 

Google Scholar 
43.Hannula, S. E. et al. Structure and ecological function of the soil microbiome affecting plant–soil feedbacks in the presence of a soil‐borne pathogen. Environ. Microbiol. 22, 660–676 (2020).CAS 
PubMed 
Article 

Google Scholar 
44.Francioli, D. et al. Plant functional group drives the community structure of saprophytic fungi in a grassland biodiversity experiment. Plant Soil https://doi.org/10.1007/s11104-020-04454-y (2020).45.Craine, J., Froehle, J., Tilman, D., Wedin, D. & Chapin, F. S. III The relationships among root and leaf traits of 76 grassland species and relative abundance along fertility and disturbance gradients. Oikos 93, 274–285 (2001).Article 

Google Scholar 
46.Tjoelker, M., Craine, J. M., Wedin, D., Reich, P. B. & Tilman, D. Linking leaf and root trait syndromes among 39 grassland and savannah species. New Phytol. 167, 493–508 (2005).CAS 
PubMed 
Article 

Google Scholar 
47.Herz, K. et al. Linking root exudates to functional plant traits. PLoS ONE 13, e0204128 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Huberty, M., Choi, Y. H., Heinen, R. & Bezemer, T. M. Above-ground plant metabolomic responses to plant–soil feedbacks and herbivory. J. Ecol. 108, 1703–1712 (2020).CAS 
Article 

Google Scholar 
49.Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl. Acad. Sci. USA. 112, E911–E920 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Jones, D. L., Nguyen, C. & Finlay, R. D. Carbon flow in the rhizosphere: carbon trading at the soil–root interface. Plant Soil 321, 5–33 (2009).CAS 
Article 

Google Scholar 
51.Hannula, S. E. et al. Shifts in rhizosphere fungal community during secondary succession following abandonment from agriculture. ISME J. 11, 2294–2304 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Koyama, A., Maherali, H. & Antunes, P. M. Plant geographic origin and phylogeny as potential drivers of community structure in root‐inhabiting fungi. J. Ecol. 107, 1720–1736 (2019).Article 

Google Scholar 
53.Wemheuer, F., Wemheuer, B., Daniel, R. & Vidal, S. Deciphering bacterial and fungal endophyte communities in leaves of two maple trees with green islands. Sci. Rep. 9, 1–14 (2019).ADS 
CAS 
Article 

Google Scholar 
54.Ma, H. et al. Steering root microbiomes of a commercial horticultural crop with plant-soil feedbacks. Appl. Soil Ecol. 150, 103468 (2020).Article 

Google Scholar 
55.Suárez-Moreno, Z. R. et al. Plant-growth promotion and biocontrol properties of three streptomyces spp. isolates to control bacterial rice pathogens. Front. Microbiol. 10, 290 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Treseder, K. K. The extent of mycorrhizal colonization of roots and its influence on plant growth and phosphorus content. Plant Soil 371, 1–13 (2013).CAS 
Article 

Google Scholar 
57.Liang, M. et al. Arbuscular mycorrhizal fungi counteract the Janzen-Connell effect of soil pathogens. Ecology 96, 562–574 (2015).PubMed 
Article 

Google Scholar 
58.Teste, F. P., Veneklaas, E. J., Dixon, K. W. & Lambers, H. Complementary plant nutrient-acquisition strategies promote growth of neighbour species. Funct. Ecol. 28, 819–828 (2014).Article 

Google Scholar 
59.Mommer, L. et al. Lost in diversity: the interactions between soil-borne fungi, biodiversity and plant productivity. New Phytol. 218, 542–553 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
60.Hassani, M. A., Durán, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6, 58 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
61.De Long, J. R. et al. How plant–soil feedbacks influence the next generation of plants?. Ecol. Res. 36, 32–44 https://doi.org/10.1111/1440-1703.12165 (2021).CAS 
Article 

Google Scholar 
62.Tedersoo, L. et al. Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10, 1–43 (2015).Article 

Google Scholar 
63.Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 
Article 

Google Scholar 
65.Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Micro. Ecol. 75, 129–137 (2015).Article 

Google Scholar 
66.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 
67.Gweon, H. S. et al. PIPITS: an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform. Methods Ecol. Evol. 6, 973–980 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 
Article 

Google Scholar 
69.Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
70.Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Robust methods for differential abundance analysis in marker gene surveys. Nat. Methods 10, 1200–1202 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
72.Oksanen, J. et al. Vegan: Ordination Methods, Diversity Analysis And Other Functions For Community And Vegetation Ecologists (Community Ecol Package Vegan, 2013).73.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar  More

Synergy between S. cerevisiae and R. etli in biofilm formationWhen S. cerevisiae Mat α Σ1278h and R.etli CE3 were grown in minimal medium with low glucose concentrations (0.1%), these species adhered to abiotic surfaces to form biofilms (Fig. 1). Interestingly, R. etli and S. cerevisiae formed a mixed biofilm whose biomass was ~ 3 times greater than that of either single-species biofilm (Fig. 1a). In addition, at 24 h, the number of colony-forming units (CFU)/cm2 of R. etli CE3 in the mixed biofilm was higher than that in the pure biofilm (Supplementary Fig. 1). Confocal laser scanning microscopy of biofilms stained with the Live/Dead Kit (propidium iodide and SYTO9) showed that in the mixed biofilm, the yeast cells formed patches, and the bacterial cells covered most of the surface (Fig. 1b). In contrast, monospecies biofilms of R. etli and S. cerevisiae had lower structural complexity and contained a greater (80%) number of dead cells, and their individual densities were lower than their populations in the mixed biofilm (Fig. 1b). These results suggest that in mixed biofilms, S. cerevisiae promotes bacterial growth.Figure 1The interaction between S. cerevisiae and Rhizobium etli CE3 results in the formation of a structurally complex and more productive biofilm in terms of biomass. (a) Biofilm formation of R. etli CE3 and S. cerevisiae Σ1278h Mat α and biofilm growth over time in minimal dextrose medium. The data are representative of 3 independent experiments +/− the S.D. values. (b) Top view and cross section of confocal micrographs of the S. cerevisiae-R. etli mixed biofilm and the single-species biofilms. Magnification 40 × . The images are representative of 3 independent experiments. Biofilms were developed on glass microscope slides and stained with a LIVE/DEAD viability kit. Red fluorescence indicates dead cells, and live cells are colored green. Images were acquired 24 h after inoculation.Full size image
S. cerevisiae secretes dicarboxylic acids that promote R. etli growth and biofilm formationWe found that the R. etli colonies that grew close to S. cerevisiae on solid glucose minimal medium were larger than those growing far from yeast colony (Fig. 2).Figure 2Yeast cells produce dicarboxylic acids that promote the growth of R. etli. (a) R. etli growth in coculture with S. cerevisiae BY4741 mutants (aco1Δ, fum1Δ, sdh1Δ and mdh1Δ) that accumulate dicarboxylic acids and a BY4741 strain with blockade of the aerobic respiratory chain (rho-). (b) Test on solid medium showing that S. cerevisiae BY4741 (*) secretes compounds that promote bacterial growth (  >). In contrast, BY4741 rho- cells (ρ), which do not produce dicarboxylic acids, do not promote the growth of R. etli CE3. R. etli CE3 cells were spread over MMD agar, and yeast cells were spotted in the center. (c) Top view of light micrographs of dual-species biofilms; S. cerevisiae (arrowhead) and R. etli (arrow). Biofilms were developed on glass microscope slides and stained with crystal violet. Magnification 20 × . The images are representative of 3 independent experiments. (d) Growth of R. etli strains in coculture with S. cerevisiae BY4741. The growth of the rhizobium strains was estimated at 24 h. R. etli CE3 strains: wild-type (wt), dctA- containing an empty expression plasmid (dctA-) and dctA- containing a plasmid expressing dctA (dctA-/dctA). The data are representative of 3 independent experiments +/− the S.D. values.Full size imageWe used a visual growth promotion assay on solid medium to screen for S. cerevisiae knockout strains (YKO library) that influenced bacterial growth. 159 yeast mutants were unable to promote R. etli CE3 growth (Supplementary Table 3). In general, these mutants were defective in mitochondrial function. Interestingly, we found that 5 strains with mutations in genes coding for enzymes involved in the TCA cycle showed an enhanced ability to promote bacterial growth compared to that of the wild-type strain (Fig. 2a).To determine how the S. cerevisiae mutants may affect the fungal-bacterial interaction, we analyzed factors that may be altered in mutants with mitochondrial function defects and a compromised TCA cycle.We compared the production of TCA intermediates between the wild-type and mutant yeast strains. Mutants defective in mitochondrial function (mef1Δ, gep5Δ, sdh2Δ, ppa2Δ, imp1Δ, cox7Δ, cyc1Δ and cyc2Δ) produced low amounts of tricarboxylic acids (Supplementary Fig. 2a). In contrast, the aconitase mutant (aco1Δ) produced 60% more citrate and succinate; the fumarase mutant (fum1Δ) resulted in fumarate accumulation; the succinate dehydrogenase mutants (sdh1Δ and sdh4Δ) produced 80% more succinate; and the mitochondrial malate dehydrogenase mutant (mdh1Δ) produced 60% more malate and succinate (Supplementary Fig. 2b). These results suggested that the large quantities of tricarboxylic acids secreted by the mutant yeast played a role in promoting bacterial growth in the cocultures.We analyzed the biomass of mixed biofilms formed by yeast cells defective in mitochondrial function (Σ1278B petit mutant). The ability of the wild-type and the petit mutant strains to form a monospecies biofilm was similar (Supplementary Fig. 3). In contrast, the mixed biofilm formed by yeast cells defective in mitochondrial function was significantly lower in biomass than that formed by the wild-type yeast strain (Fig. 2c). Also, Σ1278B petit mutant produced low amounts of tricarboxylic acids (Supplementary Fig. 2a).We next measured the biomass of the mixed biofilm formed by S. cerevisiae and a Rhizobium mutant unable to take up C4-dicarboxylic acids (dctA-). This evaluation revealed that C4-dicarboxylate uptake by R. etli is necessary to form mixed biofilms with high biomass (Fig. 2d).A symbiotic plasmid is involved in the phenotypic plasticity of R. etli.
The genome of Rhizobium etli CE3 is composed of a chromosome and 6 plasmids (pA, pB, pC, pD, PE and pF)11. To determine whether elements encoded by these replicons can participate in the establishment of commensalism, we evaluated the formation of biofilms by yeast and R. etli strains lacking these replicons12. We found that lack of pA, pB, pC or pF did not affect the ability of bacteria to coexist with yeast (Fig. 3a). Interestingly, a strain cured of plasmids pA-/pD- could not coexist with S. cerevisiae to form a mixed biofilm and obtain the benefits provided by the fungus (Fig. 3a).Figure 3Plasmids pA and pD encode proteins performing functions that are necessary for the coexistence of bacterial cells with yeast. Growth of R. etli strains in biofilms with S. cerevisiae S1278B. (a) Growth in mixed biofilms of R. etli strains lacking the plasmids; pA, pB, pC, pF and in one case of two plasmids, pA-/pD-. The growth of the rhizobia strains was assessed at 24 h. (b) Scheme of the genes contained in a cosmid that partially complements the growth of the pA-/pD- strain in mixed biofilms. Here, 3, 2 and only one gene was amplified to generate the plasmids AD1, AD2 and AD3, respectively, as indicated in the figure. (c) Growth of R. etli strains in mixed biofilms. Strains AD1 and AD2 are R. etli pA-/pD- cells that carried plasmids AD1 and AD2, respectively. The growth of rhizobium strains in mixed biofilms was estimated at 24 h. The data are representative of 3 independent experiments +/− the S.D. values.Full size imageTo determine the genetic elements from the symbiotic plasmid involved in the interaction with yeast, we complemented the R. etli pA-/pD- strain with a cosmid library containing fragments of partial digestion (EcoRI) of the R. etli CE3 genome13. We found that a cosmid containing 9 ORFs from plasmid pD (GenBank: U80928.5) partially restored the ability of R. etli pA-/pD- to form a mixed biofilm (Fig. 3b). This cosmid contains 7 insertion sequences (IS) and a predicted operon encoding a probable peptide pheromone/bacteriocin exporter (RHE_PD00332) and a probable bacteriocin/lantibiotic ABC transporter (RHE_PD00333) (Fig. 3b).The complete operon or only the ABC transporter gene, including its endogenous promoter and terminator regions, was cloned into plasmid pBBR1MCS-3, and the resultant plasmids were named AD1, AD2 and AD32, respectively (Supplementary table 1 and 2). We found that complementation with the complete operon (plasmid AD2) partially restored the ability of R. etli pA-/pD- to form a mixed biofilm with yeast (Fig. 3c). In contrast, complementing with the RHE_PD00332 gene (plasmid AD3) does not restore the phenotype. It is necessary to complement only with the RHE_PD00333 gene to determine if its product is involved in the phenotypic plasticity of R. etli. These results suggest that the ABC transporter gene (RHE_PD00333) is involved in the fungal-bacteria interaction.
S. cerevisiae produces a small molecule that affects R. etli growthTo determine how S. cerevisiae affects the growth of R. etli pA-/pD- (Fig. 4a), we evaluated the inhibitory activity of methanol extracts of S. cerevisiae culture supernatants.Figure 4S. cerevisiae s1278B produces a small molecule that only affects the growth of R. etli strains that do not harbor the symbiotic plasmid and plasmid A. (a) S. cerevisiae and R. etli strains were inoculated in close proximity onto MMD soft agar. R. etli pA-/pD- grew, forming a swarm far from the yeast colony. (b) Inhibition of R. etli pA-/pD- growth by 5 µg/mL of a purified compound from the yeast supernatant, which we named Sc2A. (c) Proposed molecular structure of Sc2A.Full size imageInterestingly, we found that the methanol extract inhibited R. etli pA-/pD- growth but had no activity against wild-type R. etli (Fig. 4b). We investigated the chemical constituents of the S. cerevisiae culture supernatants. After succesive organic solvent extractions, the methanolic extract was fractionated by HPLC and 8 fractions were obtained. Each fraction was tested for its determine its effect on the growth of R. etli pA-/pD-. Only a fraction with the ability to inhibit the growth of R. etli pA-/pD- was identified. This resulted in ~ 90% pure sophoroside, judging by its appearance as a dominant peak in the mass spectra obtained by Fast Atom Bombardment Mass Spectroscopy (FAB). As a result, a new sophoroside with bacteriostatic activity, named Sc2A, was isolated (Fig. 4c). The structure of Sc2A was elucidated by a combination of extensive spectroscopic analyses, including 2D NMR and HR-MS.Sc2A was isolated as a crystalline powder with a positive optical rotation ([α]D25 + 13.7°, c0.58, H2O). The molecular formula of Sc2A was determined to be C30H50O24 from its positive-mode FAB data (m/z 794.26 [M + H]+), which was consistent with the 13C NMR data. RMN1H (CD3OD, 400 MHz) data for Sc2A: δ 5.1 d (J = 3.6 Hz), 4.4 d (J = 8 Hz), 4.23 dd (J = 9, 4.8 Hz), 3.79 t (J = 10.8, 14.4 Hz), 3.73 m, 3.67 m, 3.639 m, 3.63 dd (J = 8, 9.2 Hz), 3.53 dd (J = 5.6, 5.2 Hz), 3.36 dd (J = 3.6, 4 Hz), 3.31 dd (J = 8, 8 Hz), 3.10 dd (J = 8, 7.6 Hz), 2.77 dd (J = 4.4, 6.8 Hz), 2.61 m, 2.46 m, 2.33 m, 2.12 m. RMN13C-DEPT (CD3OD, 400 MHz) data for Sc2A: δ 181.2 (C), 175.9 (C), 98.1(CH), 93.8 (CH), 78.05 (CH), 78.02 (CH), 76.30 (CH), 74.92 (CH), 73.80 (CH), 73.11 (CH), 71.78 (CH), 71.72 (CH), 64.37 (CH2), 62.87 (CH2),62.72 (CH2), 57.24 (CH), 30.70 (CH2), 26.19 (CH2), 28.21 (CH2).The IR spectrum of Sc2A displayed characteristic absorptions of 3416.34 cm-1 (O–H), 1642.10 (C = O), 1405.44 (C–OH), 1242.93 (C–O–C), 1040.36 (C-H), and 598.48 (O-C-O).Sc2A possesses a sophorose linked by 2,5 hexanedione to another molecule of sophorose (Fig. 4c).Sc2A induces the expression of genes involved in symbiosisExpression from the nifH and fixA promoters was studied in R. etli monocultures and cocultures with yeast by monitoring GUS activity in living cells. Cells were grown on solid PY-D medium for 1 day, and monitoring of GUS expression showed that the nifH promoter was strongly induced when R. etli was grown with yeast in liquid medium and on solid medium (Fig. 5).Figure 5The expression of Rhizobium etli genes involved in symbiosis is induced in cocultures with yeast or by exposure to the small molecule Sc2A. (a) Activity of different R. etli promoters in monoculture (Re) or in coculture with yeast (+ Sc). Cells were cultured for 24 h in 1 ml of PY-D in 1.5-mL tubes. The tubes were kept closed to generate an environment with a low oxygen concentration. (b) Activity of the nifH promoter in R. etli cells grown alone (Re) or in coculture with yeast (+ Sc) on PY-D agar. (c) Effect of Sc2A on the expression of the nodA gene in R. etli cells grown in liquid culture. Cells stimulated with the flavonoid naringenin were included as a positive induction control. The data are representative of 3 independent experiments +/− the S.D. values.Full size imageAt the beginning of the symbiosis, the legume roots exude flavonoids, which induces in R. etli the expression of a group of genes (nod) involved in the synthesis of lipochitooligosaccharides, also called nodulation factors (NFs). Recognition of NFs by the host plant triggers both rhizobial infection and initiation of nodule organogenesis14. NodA protein is involved in N-acylation of the chitooligosaccharide backbone of NFs. Given the participation of nodA in the interaction of R. etli with a eukaryote, we decided to evaluate the expression of this gene in response to exposure to 5 µg/mL of Sc2A (this concentration is similar to that found in cocultures). We found that Sc2A induces the expression of nodA (Fig. 5c). However, the levels of induction of nodA were moderated compared to the values obtained upon naringenin induction (Fig. 5c). More

Wet and dry weight of aerial partsDry weight of aerial parts was significantly affected by drought stress and genotype treatments and their interactions (Table 2). With increasing drought stress the amount of dry weight of aerial parts in all genotypes was decreased. Dry functions in I2, I3, and I4 levels in H genotype (Lavandula gngustifolia cv. Hidecot) were 15.68%, 40.35% and 48.15%, respectively. In S genotype (Lavandula stricta) these amounts were 0.78%, 48.58% and 51.72%, respectively; and in M genotype (Lavandula angustifolia cv. Muneasted) they were 22.29%, 49.38% and 52.63%, respectively. Compared to the control group, the most reduction in dry weight of aerial parts was in M genotype. The highest amount of dry weight (11.40 g in plant) was observed in H genotype in drought stress of 90–100% of field capacity. The lowest amount of dry weight of aerial parts (3.07 g) was seen in S genotype in drought stress of 30–40% field capacity (Fig. 2).Table 2 Variance analysis of the effect of drought stress on enzymatic activity of antioxidant enzymes, and quantity of essential oil from different lavender genotypes.Full size tableFigure 2The effect of drought stress on dry weight of aerial parts in different lavender genotypes.Full size imageIn this study drought stress had a negative effect on biomass of lavender plants. This effect can be due to water shortage. Because drought stress cause reduction in swelling, total water potential in cell and withering, it also results in closing stomata, reduction in cell division, and cell enlargement47,48. Reduction in cell division and cell enlargement as a result of drought, reduce the leaf surface, photosynthesis and growth function of the plant. In other words, reduction in photosynthesis products, cause reduction in leaf’s surface; and reduction in transfer of assimilated materials to aerial part, as a result of drought, cause decrease in aerial yield of the plant49. In this regard, Abbaszadeh et al. (2020) reported that due to drought stress of 30% and 60% of field capacity, dry weight of aerial parts in Rosmarinus officinalis L. has decreased. While contrary to our results Rhizopoulou and Diamantoglou (1991) observed that dry weight of leaves from Marjoram plant (Origanum majorana) was increased with increased soil moisture deficiency; which can be due to differences in plant species and ecological conditions50,51.Proline content of leavesThe results of variance analysis showed that drought stress, genotype and their interactions have significantly affected proline content of leaves (Table 2). With increasing drought stress the proline content was increased. The highest amount of proline content (4.96 mg per g) was observed in H genotype in I4 drought level (30–40% of field capacity). While the lowest amount (1.08 mg per g) was observed in S genotype in irrigation of 90–100% of field capacity (Fig. 3). In each genotype separately, in I2 to I3 drought levels the amount of proline was equal, but in H and M genotypes with increasing drought stress, the amount of proline was increased, While in S genotype with increasing water deficit proline did not show a significant increase. This indicates that two genotypes (H and M) have a similar function for using these types of osmolyte to deal with this level of drought. Which this result may be exist another osmolite production as a resistance mechanism in S genotype52.Figure 3The effect of drought stress on proline content in different genotypes of lavender.Full size imageOne change that happens in biological and non-biological stresses is increasing the amount of osmolytes in plant. To prevent negative effects of drought stress, the plant increases the amount of its osmolytes including proline53. Proline is an amino acid which in addition to act as an osmolyte, plays an important role in maintaining and stabilizing membranes by adding membrane phospholipids and changing the hydrated layer around macromolecules. Proline is also recognized as a stabilizer for cellular homeostasis under stressful conditions. This is due to high ability of proline to stabilize sub-cellular structures such as proteins and cell membranes and its ability to eliminate free radicals54. In present study, increasing proline content in different genotypes of lavender as a result of drought, can be for the same reason. It is proved that in some plants, changes in amount of proline is related to their ability to tolerate and adapt with drought stress; so, the proline content can be used as an indicator to select drought-resistant plants. Hosseinpour et al. (2020), reported that in response to drought stress, accumulation of compatible metabolites such as proline can participate in water absorption. In accordance with our results, an increase in proline content in different genotypes of Calendula officinalis plant due to drought stress has been reported as well55,56. However, in some plant species, other osmolites are produced under biological stress, the most important of them is glycine betaine. So that it is probable that the relationship between glycine betaine accumulation and stress tolerance, such as drought stress, is species- or even genotype specific57. As a results, the S genotype likely produced glycine betaine under drought stress, obviously, completed studies are needed to confirm this hypothesis.Relative water content of leavesThe relative water content (RWC) of leaves was significantly affected by drought stress, genotype and their interaction (Table 2). The highest amount of RWC (87.43%) was observed in H genotype in no drought stress condition. The lowest RWC (19.60%) was observed in S genotype in 30–40% of field capacity (Fig. 4). The results of comparing average data showed that in highest level of drought stress RWC in H, S and M genotypes is 57.25%, 65.19% and 58.88%, respectively; which compared to the control group, it is decreased in all genotypes. This suggests higher resistance of H genotype to maintain RWC of leaves (Fig. 4). In all evaluated genotypes, with increasing drought, RWC was decreased.Figure 4The effect of drought stress on RWC of leaves in different lavender genotypes.Full size imageRWC is a suitable indicator for water stress in plants. Drought stress by reducing RWC and total water potential of cell, result in reduction in growth of plants. The osmoregulation mechanisms in drought-resistant plants, maintains high RWC in them. Reduction in RWC of leaves as a result of water deficiency stress, is due to reduction in amount of water in tissue, reduction in amount of water in soil, and the negative soil water potential58. Alinejad et al. (2020), reported that RWC of leaves in Datura stramonium L. plant was decreased due to drought, in a way that the highest amount of RWC (80.22%) was seen in 55% of field capacity, compared to 35% and 15% of field capacity59. Also Mohammadi et al. (2018) suggested that RWC of leaves in Thymu vulgari L. was decreased to 18.41%, after being exposed to drought60.Total phenolic and flavonoids contents in leavesDrought, genotype and their interaction had a significant effect on total phenolic content of leaves (Table 2). The results suggest that in different levels of drought, total phenolic content was different in lavender genotypes. In the highest level of drought, total phenolic content in H, S, and M genotypes was respectively increased 18.64%, 28.57% and 98.07% in comparison with the control group. The highest difference in total phenolic content compared to control group was observed in M genotype (Fig. 5).Figure 5The effect of total phenolic content of leaves in different genotypes of lavender.Full size imageTotal flavonoids content of leaves was significantly (p ≤ 0.01) affected by drought and genotype (Table 2). The results of comparing averages showed that the highest amount of total flavonoids (1.12 mg quercetin per g of fresh weight) was in H genotype, and the lowest amount (0.95 mg quercetin per g of fresh weight) was in M genotype (Table 3). Moreover, our results showed that drought level from I2 to I4 caused an increase of 12.74%, 14.61% and 15.38% in total flavonoid content of leaves, respectively. Which indicates an increase in flavonoid amount with increasing drought level (Table 3). Table 3 Comparing simple effects of genotype and drought stress on traits of lavender plant.Full size tableTotal phenolic content is related to stress-resistance, indirectly by helping cell protection, and directly as an antioxidant61. Phenolic compounds due to their reductive properties, act as a free radical remover62. Our findings are similar to those of a study on growth of Mentha piperita in drought stress54.Total antioxidant activityTotal antioxidant activity was significantly affected by drought stress and genotype (Table 2). With increasing drought, antioxidant activity in H and S genotypes was increased. The results of comparing average data showed that compared to the control group, in drought levels of I2, I3 and I4, antioxidant activity in H genotype was increased by 98.43%, 98.36% and 118.78%, respectively; and in S genotype this amounts were increased by 89.85%, 111.78%, and 131.90% respectively (Table 5). In M genotype the antioxidant activity has reached its highest amount (49.38 mg/g) in I3 level of drought, and then with increasing drought stress the antioxidant activity was decreased, in a way that in highest drought level it had the lowest antioxidant activity (23.18 mg/g). M genotype was used as control (Fig. 6). Our results indicate that in highest drought level, antioxidant activity of S genotype was more than others. Figure 6The effect of drought stress on antioxidant activity in different lavender genotypes.Full size imageAntioxidant enzymesEnzymatic activity of antioxidant enzymes in lavender leaves was significantly affected by genotype and drought stress (Table 2). Our results showed that the highest activity of SOD (304.75 μmol min−1 mg−1 protein) was observed in interaction of H genotype and I4 drought level, and the lowest activity of SOD (144.52 μmol min−1 mg−1 protein) was observed in S genotype with no drought (Fig. 7). Moreover, our observations showed that in I2 and I3 drought levels, the highest amount of SOD enzymatic activity was related to M genotype (Fig. 7). In the highest drought level, enzymatic activity of SOD was increased in H and S genotypes, and it decreased in M genotype.Figure 7The effect of drought stress on enzymatic activity of SOD, POX and CAT in different lavender genotypes.Full size imageEnzymatic activity of peroxidase (POX) enzyme was increased in all three genotypes, with increasing drought. In all drought levels, H genotype had the highest amount of POX activity, compared to other genotypes. There was no significant difference in POX activity in S and H genotypes. The results showed that the highest amount of POX activity (274.48 μmol min−1 mg−1 protein) was observed in interaction of H genotype and 30–40% field capacity, and the lowest amount (117.66 μmol min−1 mg−1 protein) was observed in interaction of S genotype and no drought condition (control) (Fig. 7).Catalase (CAT) enzyme was affected by drought, genotype and their interaction (Table 2). The results of catalase enzyme activity assessment showed that with increasing drought, catalase activity is different in H, M and S genotypes. The most different reaction in production of CAT was related to H genotype, which with increasing drought stress up to I3 level, the enzyme activity was increased. But regarding M and S genotypes, with increasing drought level, CAT activity was increased in both genotypes. In this study the highest amount of CAT (460.51 μmol min−1 mg−1 protein) was observed in interaction of S genotype with 30–40% of field capacity; and the lowest amount (157.06 μmol min−1 mg−1 protein) was observed in interaction of H genotype with 90–100% of field capacity (Fig. 7).No significant effect was observed for APX enzyme in interaction of genotype and drought (Table 2). The results of comparing average data, suggest that the highest amount of APX activity (284.96 μmol min−1 mg−1 protein) was observed in H genotype (Table 3). Also the results showed that I2, I3 and I4 drought level resulted in an increase in APX enzyme activity by 32.38%, 49.16%, and 65.53% respectively. This indicates that APX enzymatic activity increases with increasing drought level (Table 3).Using physiological and biochemical mechanisms to reduce effects of stress shows that to overcome drought, oxidative stress and to eliminate ROS, plants will increase the amount of antioxidant content55. One of major mechanisms to cope with oxidative stress in plants, is activation of antioxidant enzymes61. Findings of the present study indicates that different lavender genotypes showed partial resistance against drought. In this research, increased activity of antioxidant enzymes in lavender genotypes under drought condition, was considered as an important drought-resistance factor. Among all antioxidant enzymes, SOD can have a good response against drought stress. In a way that H, S, and M genotypes of lavender in the highest level of drought stress (I4), showed an increased amount of SOD, by 57.42%, 35.85% and 60.69% compared to normal conditions (Fig. 7).In this study, the minimum enzymatic changes were related to the POX enzyme and the highest enzymatic changes were related to the CAT enzyme. Moreover, it was observed that the highest amount of catalase enzymatic activity was in H genotype. In a way that in plants under drought stress CAT activity was increased up to I3 drought level; but, after this level with increasing drought (I4 drought level) CAT enzymatic activity was decreased. CAT and POX are among important plants enzymes which can protect plant cells against free radicals63. In this study, in drought period, enzymatic activity of CAT and POX was increased, this means that lavender genotypes, in the face of stress produce antioxidant enzymes to protect themselves. While in H genotype compared to other genotypes, in high drought stress, CAT activity was decreased which this response indicates the different function of this genotype in dealing with ROS. Enzymatic response to drought condition was different in various lavender genotypes. Generally, the negative effect of drought is shown by production of reactive oxygen species (ROS). Increased enzymatic activity of antioxidant enzymes, particularly CAT and POX can reduce the negative effects of drought64, 65. In this regard, increased activity of antioxidant enzymes in different genotypes of Calendula officinalis plant was reported to56.Malondialdehyde (MDA) contentReaction of different lavender genotypes under drought stress was different in terms of malondialdehyde (MDA) production and accumulation (Table 2). With increasing drought, MDA content was significantly increased in M and H genotypes. The highest amount of MDA in these genotypes was 14.34 and 9.50 nmolg − 1 FM respectively, which was observed in drought level of 30–40% of field capacity. This indicates a significant increase in MDA content with increasing drought (Fig. 8). While the process of production and accumulation of MDA in S genotype was different at various drought levels. For S genotype, in first level of drought (I2), MDA content was increased which showed the vulnerability of the cell membrane at this drought level. But with increasing drought, gradually, the S genotype plants adapted to the dry environment, which in this level cell membrane damage was not obvious. Then, increasing in drought stress resulted in increased MDA content. Generally, in I2 and I3 drought levels, lavender genotypes underwent varying degrees of damage, which in M and H genotypes followed by increasing enzymatic activity, and in S genotype it resulted in decreased enzymatic activity. But in the highest level of drought (I4), the cell membrane was seriously damaged and in all three genotypes and MDA content was significantly increased (Fig. 8).Figure 8The effect of drought stress on MDA content in different lavender genotypes.Full size imageMembrane lipid peroxidation due to the accumulation of active oxygen species leads to cell damage and death. In plants this lipid peroxidation happens under drought stress66. MDA is the final product of membrane peroxidation and membrane processes. Simultaneously with peroxidation, the MDA content increases significantly67. So the MDA content can be considered as an indicator of drought-resistance in plants. Among lavender genotypes, in the highest level of drought, MDA content in M genotype was significantly increased compared to others genotypes; whish suggests that M genotype is more vulnerable in comparison with the two other genotypes. An increase in MDA content under drought stress, was reported in Thymus species as well66.Quantity and quality of essential oilMutual interaction between drought stress and genotype had a significant effect on percentage and yield of essential oil in lavender plants (Table 2). Our findings suggested a different essential oil percentage for each genotype in various levels of drought stress. With increasing drought to I3 level, the essential oil percentage was increased in M and H genotypes, but after that with increasing drought to a higher level (I4), essential oil percentage in these genotypes was decreased. While in S genotype, increasing essential oil percentage totally had an upward trend (Fig. 9).Figure 9The effect of drought stress on essential oil percent in different lavender genotypes.Full size imageEvaluation of essential oil percentage in different levels of drought, showed that in I2 drought level, the highest amount of essential oil (0.81%) was observed in H genotype; and in I3 and I4 drought levels, the highest amounts of essential oil were 1.29% and 1.68% respectively, which were observed in S genotype. Moreover, our results suggest that the highest difference in essential oil percentage in the studied genotypes compared to the control, was related to S genotype (Fig. 9). Totally, the highest percentage of essential oil was observed in S genotype in I4 drought level. This shows the high capacity of this genotype to produce essential oil under drought stress.Essential oil yield was significantly affected by genotype and drought. The results showed that the essential oil yield in S genotype was different from the others. So that the highest yield of essential oil (0.055 g per plant) was observed in this genotype in I3 drought level. While in H and M genotypes the highest amounts were 0.068 g and 0.065 g respectively, which were gained in I2 drought level (Fig. 10). Results of comparing average data showed that the highest yield of essential oil at I2 and I3 levels was obtained with 151/85% and 122.22% difference compared to the control, respectively, and they gained from H genotype. This indicates the high potential of H genotype to maintain biomass and produce essential oil in drought stress. Also our results suggest that in the highest drought level (I4), the highest essential oil yield (0.046 g per plant) was observed in M genotype (Fig. 10).Figure 10The effect of drought stress on essential oil yield in different lavender genotypes.Full size imagePrincipal component analysis (PCA)PCA analysis was performed to identify susceptibility of genotypes in irrigation regimes. According to physiological traits in the PCA analysis (Fig. 11a, b), the first factor (PC1) explains about 90% of the total variance of variables, and the second factor (PC2) about 8%.Figure 11Principal component analysis (PCA) for genotypes (a) and physiological traits (b) based on water status calculated for physiological traits. (R 4.0.4 packages, https://rstudio.com/products/rstudio/).Full size imageThe results of PCA analysis of different irrigation regimes showed that in the first component, which shows 89.91% of changes, the best traits are antioxidant enzymes CAT, SOD, APX, while in the second component, with 8.10% changes, only the trait Catalase is the best trait. Also, in total, the first and second components, which show 98.01% of the changes, show CAT as the most effective trait (Fig. 11a).The results of PCA analysis in lavender genotypes showed that the first and second main components could explain 98.91% of the existing changes. So that the first main component with 91.13% and the second component with 7.78% had a share in the total variation. Therefore, using these two components and ignoring other components will only cause the loss of a small part of about 1.09% of the data changes (Fig. 11b). These two principal components include peroxidase, ascorbate peroxidase, and superoxide. Physiological responses of Lavandula genotypes (L. angustifolia cv. Hidcote, L. angustifolia cv. Munstead, and L. stricta) submitted to drought stress were evaluated through principal component analysis (PCA), and the results are illustrated in Fig. 11a. Lavandula stricta presents higher levels of CAT activity than L. angustifolia cv. Hidcote and L. angustifolia cv. Munstead. In addition, APX and CAT increase in stress-treated in 30–40% FC. This result shows that L. stricta exhibits the most affected physiological changes while trying to adjust to changes in the water status of the environment, under the imposed conditions and shows the highest resistance.The results of analysis of essentials oils from H, S and M genotypes is shown is Tables 4, 5 and 6. The trend of changes in essential oils composition is described in all three genotypes. By studying the mass spectra and the Kovats retention index, 23 compounds were identified in the H genotype’s essential oil (Table 4). The yield of H genotype essential oil from I1 to I4 drought levels was 99.89%, 82.78%, 81.09% and 82.85%, respectively. The main components of H genotype essential oil in I1 to I4 drought levels, include 1.8-Cineol compounds (5.94%, 7.73%, 4.24% and 3.50%), Linalool (23.20%, 16.30%, 11.90% and 10.57%), Camphor (3.41%, 4.65%, 2.32% and 2.87%), Borneol (4.89%, 3.34%, 3.65% and 3.01%), Bornyl formate (27.32%, 16.04%, 19.45% and 20.03%), Lavandulyl acetate (1.40%, 4.21%, 6 and 8.35%), Caryophyllene oxide (10.92%, 11.77%, 12.16% and 19.91%), α-Muurolene (4.38%, 3.20%, 1.20% and 0%) (Table 4). The results of grouping the essential oil compounds showed that the amount of hydrocarbon monoterpenes from I1 to I4 drought level were 12.88%, 8.86%, 8.53% and 6.06%, respectively. The amount of oxygen monoterpenes was 64.76%, 50.70%, 43.32% and 42.45%; and hydrocarbon sesquiterpene compounds were 13.12%, 11.45%, 13.03% and 13.96%. The amount of oxygen sesquiterpene compounds were 10.92%, 11.77%, 16.21%, and 19.91%; which shows that increasing drought level, result in decreasing monoterpene compounds, and increasing sesquiterpene compounds.Table 4 Chemical composition of essential oils extracted from Lavandula angustifolia cv. Hidcote plants under different irrigation regime.Full size tableTable 5 Chemical composition of essential oils extracted from Lavandula stricta plants under different irrigation regime.Full size tableTable 6 Chemical composition of essential oils extracted from Lavandula angustifolia cv. Munstead plants different irrigation regime.Full size tableHeat map for the essential oil profile in Lavandula angustifolia cv. Hidcote corresponding to the different irrigation regime The similar discrimination was also supported by the heatmap constructed for essential compounds. Accordingly, 22 rows and 4 columns were achieved. α- pinene, β-Pinene, δ-3-Carene, type of Cymene, 1,8-Cineol, Camphor and Linalool from the main compounds, peaked at control. Moreover, lavandulyl acetate, Myrtenyl acetate, caryophyllene oxide, camphene and γ-Cadinene revealed highest percentage at 30–40% FC, Some compounds, such as Camphor and Linalyl acetate, are at the levels of the intermediate irrigation regime (Fig. 12). It is remarkable that as the water limit increases, the amount of monoterpene compounds decreases and the amount of sesquiterpene compounds increases.Figure 12Heatmap for the essential oil profile in aerial parts of Lavandula angustifolia cv. Hidcote corresponding to irrigation regimes (CIMminer, https://discover.nci.nih.gov/cimminer/oneMatrix.do).Full size imageWith evaluation of the essential oil from S genotype, 18 compounds were identified (Table 5). The amount of essential oil in I1 to I4 drought levels was 99.41%, 98.48%, 99.53% and 99.93% respectively (Table 5). Among identified compounds in S genotype the followings were accounted for the highest amount of components in the essential oil in I1 to I4 levels respectively; Linalool (32.60%, 28.45%, 20.12% and 19.12%), decanal (10.26%, 15.21%, 18.56% and 19.27%), 1-Decanol (8.01%, 10.31%, 17.88% and 21.34%), Kessane (2.44%, 4.43%, 9.99% and 11.50%), Hexadecane (1.26%, 5.77%, 6.10% and 11.9%), 2-methyl-1-hexadecanol (11.1%, 9.32%, 8.15% and 2.37%) and Hexahydrofarnesyl acetone (6.8%, 6.34%, 3.78% and 1.26%) (Table 5). The most obvious point was the high percentage of Linalool, decanal and 1-Decanol in the S genotype. With increasing drought, Linalool compounds were decreased and decanal and 1-Decanol compounds were increased. The grouping of essential oil components also showed that among the 18 compounds identified, the following were the highest in I1 to I4 drought levels, respectively; 3 hydrocarbon monoterpenes with total of (5.34%, 5.44%, 4.57% and 4.34%), 6 oxygen monoterpenes with total of (60.49%, 61.03%, 59.57% and 60.45%), 3 hydrogen sesquiterpenes with total of (5.69%, 10.27%, 11.85% and 15.24%) and 6 oxygen sesquiterpenes with total of (27.89%, 28.09%, 29.44% and 18.32%). With increasing drought, the amounts of hydrocarbon monoterpenes and oxygen sesquiterpenes were decreased; while the amount of hydrocarbon sesquiterpenes was increased. Also the highest amount of oxygen monoterpenes, by 61.03%, was seen in I2 drought level.Heat map for the essential oil profile in Lavandula stricta corresponding to the different irrigation regime The parallel discrimination was also supported by the heatmap constructed for essential compounds. Accordingly, 18 rows and 4 columns were achieved. α- pinene, Amyl isovalerate, Citronellol, β-Ionone and Linalool from the main compounds, peaked at control. Moreover, α-Thujene, decanal, 1-Decanol, Sesquiphellandrene, Kessane and Hexadecane revealed highest percentage at 30–40% FC (Fig. 13). These results confirm the results obtained from the Lavandula angustifolia cv. Hidcote so that as the water limit increases, the amount of monoterpene compounds decreases and the amount of Sesquiterpene compounds increases.Figure 13Heatmap for the essential oil profile in aerial parts of Lavandula stricta corresponding to irrigation regimes (CIMminer, https://discover.nci.nih.gov/cimminer/oneMatrix.do).Full size imageEssential oil yield in M genotype from I1 to I4 drought levels was obtained 99.90%, 98.38%, 93.08% and 87.04% (Table 6). As it is shown in Table 6, analysis of the essential oil from M genotype included 27 compounds which its major part was consisted of Camphor (16.82%, 16.32%, 17.11% and 18.30%), Borneol (44.96%, 42.80%, 37.54% and 30.99%) and Caryophyllene oxide (14.68%, 15.21%, 15.90% and 17.21%) from I1 to I4 drought levels, respectively. comparison of essential oil components (Table 6) showed that from 27 identified compounds in M genotype, the followings were the most prevalent from I1 to I4 levels respectively, including hydrocarbon monoterpene with total of (17.82%, 17.45%, 13.91% and 9.96%), 12 total oxygen monoterpene compounds with total of (65.95%, 62.05%, 56.96% and 50.42%), 4 hydrocarbon sesquiterpenes with total of (1.58%, 23.23%, 5.42% and 8.09%) and 2 oxygen sesquiterpenes with total of (14.91%, 15.65%, 16.79% and 18.37%). The highest drought level resulted in 31.76% and 17.23% increase in Camphor and Caryophyllene oxide. It also caused 31.07% decrease in Borneol compared to the control (Table 6). Totally, with increasing drought level, monoterpene compounds were decreased and sesquiterpene compounds were increased in lavender genotypes.The major components of essential oil were different in various lavender genotypes in the highest level of drought (I4). In this study in H genotype, the compounds Linalool, Bornyl formate and Caryophyllene oxide; in S genotype the compounds Linalool, decanal, 1-Decanol, Kessane and Hexadecane; and in M genotype the compounds Camphor, Borneol and Caryophyllene oxide, were the most prevalent components of essential oil. In this study, Borneol compound was not observed in S genotype. regarding the fact that essential oil extraction was performed on flowering branches in all three genotypes, and they were studied under similar drought conditions; and also comparing the results of this study with finding of other studies shows that the difference in types and percentage of essential oil’s components can be due to the effect of genetic differences; and to some extent, environmental factors on essential oil in different genotypes.A total comparison of essential oil analysis results for different lavender genotypes under drought stress showed that oxygen monoterpenes are the most prevalent components of the essential oil, which will decrease with increasing drought level. Sarker et al. (2012) reported that the essential oil of lavender (Lavandula angustifolia) contains high amounts of linalool and linalool acetate, along with scares amount of other monoterpenes68. A study by Hassan et al. (2014) showed that the compounds carvacrol, phenol-2-amino-4, 6-bis, trans-2-caren-4-ol, and n-hexadecanoic acid are the main constituents of Lavandula stricta plants which were collected from the Shaza Mountains in southern Saudi Arabia69. Total results from essential oil analysis in this study showed that Linalool was the main ingredient of essential oils in H and S genotypes. This compound is an oxygen monoterpene with a density of 0.85 and a pleasant smell, and is the main component of the essential oil from lavender plant. While in M genotype, Borneol was the main component of the essential oil, which is a circular monoterpene compound with density of Mohammadnejad ganji et al. (2017) suggested that the difference in natural quality of the essential oil from lavender plants is related to intrinsic factors (genetic or heredity capabilities and maturity), and external factors including sunlight, water, heat, pressure, latitude, and soil which affect plant growth and essential oil production70.Heat map for the essential oil profile in Lavandula angustifolia cv. Munstead corresponding to the different irrigation regime The parallel discrimination was also supported by the heatmap constructed for essential compounds. Accordingly, 18 rows and 4 columns were achieved. α- pinene, Tricycle, Camphene, Thuja-2,4(10)-diene, δ-3-Carene, ρ-Cymene, Borneol and limonene from the main compounds, peaked at control. Moreover, Camphor, α-Santalene, γ-Cadinene, δ-Cadinene, Caryophyllene oxide, α-Muurolene and Ledene oxide-(II) revealed highest percentage at 30–40% FC (Fig. 14). The results showed that the composition of the compounds was similar to the previous two genotypes and the water limit increases, the amount of monoterpene compounds decreases and the amount of Sesquiterpene compounds increases.Figure 14Heatmap for the essential oil profile in aerial parts of Lavandula angustifolia cv. Munstead corresponding to irrigation regimes (CIMminer, https://discover.nci.nih.gov/cimminer/oneMatrix.do).Full size imageEssential oils are generally in the group of terpenoids and The structure of terpenoids consists of two main precursors, isopentenyl pyrophosphate (IPP) and its isomer, dimethylallyl pyrophosphate (DMAPP). These compounds are synthesized via the cytosolic pathway of mevalonic acid (MVA) or plasticity of methylerythritol phosphate (MEP)71. The MVA pathway is primarily responsible for the synthesis of Sesquiterpenoids and triterpenoids, while the MEP pathway is used for the biosynthesis of monoterpenoids, diterpenoids and tetraterpenoids72. Monoterpenes and Sesquiterpenes are the main constituents of essential oils that play a role in aroma, flavor, photosynthetic pigments and antioxidant activities73.In drought conditions, the amount of these isoprenes does not decrease in relation to the mediators of the MEP pathway and in contrast sometimes increases. Therefore, sesquiterpene compounds increase in drought conditions because most of these compounds are synthesized through the MVA pathway74. Another reason for the decrease in MEP path flux is the location of this path, which has a significant impact in drought conditions. In this case, plastids are not able to provide the required IPP of this path, so most monoterpene compounds are reduced75.Also, since the quality of the essential oil is due to the presence of linalool and linalyl acetate76. According to the results obtained from heatmaps related to essential oils, three genotypes are identified, the highest amount of linalool amount in S genotype was remained under mind- (I2) till severe-drought (I4) condition. This indicates more compatibility with maintaining the desired quality of drought conditions in this plant than the other two commercial genotypes. And then the H genotype is in the second stage due to the presence of important compounds.Comparing the grouping created in the heat maps related to the essential oil of 3 genotypes, it is clear that the two genotypes S and H were divided into two groups I1, I2 and I3, I4 in the genotype. But in the genotype M, the results were divided into I4 and I3 groups I2 were divided into genotypes. This can be due to differences in the resistance mechanism of plants in different genotypes, so in genotypes S and H of the plant through increasing sesquiterpene compounds showed resistance to drought stress, while in genotype M increased resistance to drought levels through higher monoterpene compounds. Another conclusion that can be drawn from these heat maps is that in genotypes S and H, the rate of drought resistance in the first and second levels of drought with the third and fourth levels has shown more changes in the type of essential oil compounds, while in the third genotype (M) these changes in the last level drought has been most evident.At a glance, it seems Genotype S has a different mechanism in reducing the negative effects of drought compared to genotypes M and H, So that, among the enzymatic and non-enzymatic mechanisms, it tends to use the enzymatic pathway more. In association with the production of “proline”, drought stress index osmolyte, genotype S has a different trend from genotypes H and M and this osmolyte in this plant has a lower production flux compared to other genotypes. Also, due to the fact that the production of soluble sugars in this plant has been moderate compared to other genotypes, it is expected this genotype replace proline with another osmolyte or uses an enzymatic mechanism to deal with drought, as the results of antioxidant enzyme “catalase” related to genotype S had the highest value with a significant difference under drought stress, while, in the H and M genotypes, the SOD enzyme was responsive to drought.On the other hand, the high resistance of genotype S can be attributed to the greater activation of the pathway of essential oil compounds. Because by examining the constituents of the essential oil (monoterpene and sesquiterpene), it can be concluded that genotype H and then M at high drought levels still retain the ability to produce monoterpene compounds, while in genotype S with increasing drought, the amount of semi-heavy compounds (sesquiterpene) has increased significantly (Fig. 15), this can confirm the existence of a different resistance mechanism in the S genotype. Because some structural compounds of the membrane, such as sterols, are made from the mevalonic acid (MVA) pathway of acetyl coenzyme A origin. For this reason it seems that S genotype by setting up terpenoid pathways involved in the production of steroids another solution to drought is by preserving its plasma membrane. Steroids are derivatives of triterpenes that, along with phospholipids, are major components of plasma membranes70. Also, the study of MDA content as the final product of membrane lipid peroxidation in genotypes at the fourth level of drought (the most severe drought) showed the M genotype is most sensitive to drought. In this way, the two genotypes S and H have almost equal MDA content, so that it can be said that with a small difference from genotype S, genotype H has less composition.Figure 15The amount of monoterpenes and sesquiterpene compounds in different genotypes under irrigation regimes.Full size imageContinuous production of isoprene under drought conditions shows that despite the reduction in the synthesis of osmolyte and relative increasing of MDA (with very little difference from genotype H) that occurs under these conditions, the function of this pathway is essential for the S genotype. Isoprene has long been used to protect plants from drought, high temperatures and oxidative stress are recommended77. Of course, it was showed which is possible with increasing drought, sufficient isoprene is not produced to counteract and launch defense pathways and instead used as a general signal to increase drought tolerance78,79.Reasons such as further activation of terpenoid skeletal pathways towards the production of semi-heavy (sesquiterpene) compounds, production of steroids via the MVA pathway could be a reason for lower susceptibility of S genotype and high resistance of this genotype through these mechanisms compared to other genotypes. In contrast, on the one hand, H genotype using proline production, soluble sugar levels and decreased MDA in response to stress caused by drought and on the other hand, the ability to produce substances important monoterpenes, such as Linalool and Linalyl acetate, with the aim of using medicine and aromatherapy76, It (H genotype) can be considered as a cultivar with high commercial value and significant resistance to M genotype. More

1.Gupta A, Sharma VK. Using the taxon-specific genes for the taxonomic classification of bacterial genomes. BMC Genom. 2015;16:1–15.Article 
CAS 

Google Scholar 
2.Gil R, Silva FJ, Pereto J, Moya A. Determination of the Core of a Minimal Bacterial Gene Set. Microbiol Mol Biol Rev. 2004;68:518–37.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Mira A, Martín-Cuadrado AB, D’Auria G, Rodríguez-Valera F. The bacterial pan-genome: a new paradigm in microbiology. Int Microbiol. 2010;13:45–57.CAS 
PubMed 

Google Scholar 
4.Escalas A, Troussellier M, Yuan T, Bouvier T, Bouvier C, Mouchet MA, et al. Functional diversity and redundancy across fish gut, sediment and water bacterial communities. Environ Microbiol. 2017;19:3268–82.PubMed 
Article 

Google Scholar 
5.Jurburg SD, Salles JF. Functional Redundancy and Ecosystem Function — The Soil Microbiota as a Case Study. In: Lo Y-H, Blanco JA, Shovonlal R, editors. Biodiversity in Ecosystems—Linking Structure and Function. BoD–Books on Demand; 2015. p. 29–49.6.Louca S, Polz MF, Mazel F, Albright MBN, Huber JA, O’Connor MI, et al. Function and functional redundancy in microbial systems. Nat Ecol Evol. 2018;2:936–43.PubMed 
Article 

Google Scholar 
7.Polz MF, Hunt DE, Preheim SP, Weinreich DM. Patterns and mechanisms of genetic and phenotypic differentiation in marine microbes. Philos Trans R Soc Lond B Biol Sci. 2006;361:2009–21.PubMed 
PubMed Central 
Article 

Google Scholar 
8.Young JPW. Bacteria Are Smartphones and Mobile Genes Are Apps. Trends Microbiol. 2016;24:931–2.CAS 
PubMed 
Article 

Google Scholar 
9.Boon E, Meehan CJ, Whidden C, Wong DHJ, Langille MGI, Beiko RG. Interactions in the microbiome: communities of organisms and communities of genes. FEMS Microbiol Rev. 2014;38:90–118.CAS 
PubMed 
Article 

Google Scholar 
10.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;5:12000–16.Article 

Google Scholar 
11.Barberán A, Casamayor EO, Fierer N. The microbial contribution to macroecology. Front Microbiol. 2014;5:1–8.Article 

Google Scholar 
12.Shade A, Dunn RR, Blowes SA, Keil P, Bohannan BJM, Herrmann M, et al. Macroecology to Unite All Life, Large and Small. Trends Ecol Evol. 2018;33:731–44.PubMed 
Article 

Google Scholar 
13.Chase AB, Martiny JB. The importance of resolving biogeographic patterns of microbial microdiversity. Microbiol Aust. 2018;1:5–8.Article 

Google Scholar 
14.Shoemaker WR, Locey KJ, Lennon JT. A macroecological theory of microbial biodiversity. Nat Ecol Evol. 2017;1:e1450v4.Article 

Google Scholar 
15.Bachy C, Worden AZ. Microbial ecology: finding structure in the rare biosphere. Curr Biol. 2014;24:R315–R317.16.Lynch MDJ, Neufeld JD. Ecology and exploration of the rare biosphere. Nat Rev Microbiol. 2015;13:217–29.CAS 
PubMed 
Article 

Google Scholar 
17.Pedrós-Alió C. The Rare Bacterial Biosphere. Ann Rev Mar Sci. 2012;4:449–66.PubMed 
Article 

Google Scholar 
18.Rabinowitz D. Seven forms of rarity and their frequency in the flora of the British Isles. In: Soulé ME, editors. Conservation biology: the science of scarcity and diversity. Sinauer Associates; Massachusetts; 1986.19.McGeoch MA, Gaston KJ. Occupancy frequency distributions: patterns, artefacts and mechanisms. Biol Rev Camb Philos Soc. 2002;77:311–31.PubMed 
Article 

Google Scholar 
20.Blackburn TM, Cassey P, Gaston KJ. Variations on a theme: Sources of heterogeneity in the form of the interspecific relationship between abundance and distribution. J Anim Ecol. 2006;75:1426–39.PubMed 
Article 

Google Scholar 
21.Buckley HL, Freckleton RP. Understanding the role of species dynamics in abundance-occupancy relationships. J Ecol. 2010;98:645–58.Article 

Google Scholar 
22.Gaston KJ, Blackburn TM, Greenwood JJD, Gregory RD, Quinn RM, Lawton JH. Abundance-occupancy relationships. J Appl Ecol. 2000;37:39–59.Article 

Google Scholar 
23.Miranda LE, Killgore KJ. Abundance–occupancy patterns in a riverine fish assemblage. Freshw Biol. 2019;64:2221–33.Article 

Google Scholar 
24.Suhonen J, Jokimäki J. Temporally stable species occupancy frequency distribution and abundance-occupancy relationship patterns in urban wintering bird assemblages. Front Ecol Evol. 2019;7:129.Article 

Google Scholar 
25.Webb TJ, Barry JP, McClain CR. Abundance–occupancy relationships in deep sea wood fall communities. Ecography. 2017;40:1339–47.Article 

Google Scholar 
26.Amend AS, Oliver TA, Amaral-Zettler LA, Boetius A, Fuhrman JA, Horner-Devine MC, et al. Macroecological patterns of marine bacteria on a global scale. J Biogeogr. 2013;40:800–11.Article 

Google Scholar 
27.Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51.PubMed 
Article 
CAS 

Google Scholar 
28.Barnes CJ, Burns CA, van der Gast CJ, McNamara NP, Bending GD. Spatio-temporal variation of core and satellite arbuscular mycorrhizal fungus communities in Miscanthus giganteus. Front Microbiol. 2016;7:1–12.
Google Scholar 
29.Fillol M, Auguet JC, Casamayor EO, Borrego CM. Insights in the ecology and evolutionary history of the Miscellaneous Crenarchaeotic Group lineage. ISME J. 2016;10:665–77.PubMed 
Article 

Google Scholar 
30.Jeanbille M, Gury J, Duran R, Tronczynski J, Agogué H, Saïd OBen, et al. Response of core microbial consortia to chronic hydrocarbon contaminations in coastal sediment habitats. Front Microbiol. 2016;7:1–13.
Google Scholar 
31.Lindh MV, Sjöstedt J, Ekstam B, Casini M, Lundin D, Hugerth LW, et al. Metapopulation theory identifies biogeographical patterns among core and satellite marine bacteria scaling from tens to thousands of kilometers. Environ Microbiol. 2017;19:1222–36.CAS 
PubMed 
Article 

Google Scholar 
32.Logares R, Audic SS, Bass D, Bittner L, Boutte C, Christen R, et al. Patterns of Rare and Abundant Marine Microbial Eukaryotes. Curr Biol. 2014;24:813–21.CAS 
PubMed 
Article 

Google Scholar 
33.Michelland R, Thioulouse J, Kyselková M, Grundmann GL. Bacterial Community Structure at the Microscale in Two Different Soils. Micro Ecol. 2016;72:717–24.CAS 
Article 

Google Scholar 
34.Unterseher M, Jumpponen A, Öpik M, Tedersoo L, Moora M, Dormann CF, et al. Species abundance distributions and richness estimations in fungal metagenomics – Lessons learned from community ecology. Mol Ecol. 2011;20:275–85.PubMed 
Article 

Google Scholar 
35.Grime JP. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J Ecol. 1998;86:902–10.Article 

Google Scholar 
36.Grime JP. Dominant and subordinate components of plant communities: implications for succession, sta- bility and diversity. In: Gray AJ, Crawley MJ, editors. Colonization, Succession and Stability. Oxford:Blackwell Scientific Publications; 1984. p. 413–28.37.Hanski I. Dynamics of Regional Distribution: the Core and Satellite Species Hypothesis. Oikos. 1982;38:210.Article 

Google Scholar 
38.Magurran AE, Henderson PA. Explaining the excess of rare species in natural species abundance distributions. Nature. 2003;422:714–6.CAS 
PubMed 
Article 

Google Scholar 
39.Newton R, Shade A. Lifestyles of rarity: understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat Micro Ecol. 2016;78:51–63.Article 

Google Scholar 
40.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:e01371–14.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Shade A, Gilbert JA. Temporal patterns of rarity provide a more complete view of microbial diversity. Trends Microbiol. 2015;23:335–40.CAS 
PubMed 
Article 

Google Scholar 
42.Koch AL. Oligotrophs versus copiotrophs. BioEssays. 2001;23:657–61.CAS 
PubMed 
Article 

Google Scholar 
43.Cobo-Simón M, Tamames J. Relating genomic characteristics to environmental preferences and ubiquity in different microbial taxa. BMC Genom. 2017;18:1–11.Article 
CAS 

Google Scholar 
44.Tu Q, Yu H, He Z, Deng Y, Wu L, Van Nostrand JD, et al. GeoChip 4: a functional gene-array-based high-throughput environmental technology for microbial community analysis. Mol Ecol Resour. 2014;14:914–28.CAS 
PubMed 

Google Scholar 
45.Xu X, Wang N, Lipson D, Sinsabaugh R, Schimel J, He L, et al. Microbial macroecology: in search of mechanisms governing microbial biogeographic patterns. Glob Ecol Biogeogr. 2020;29:1870–86.Article 

Google Scholar 
46.Reich PB, Knops J, Tilman D, Craine J, Ellsworth D, Tjoelker M, et al. Plant diversity enhances ecosystem responses to elevated CO2 and nitrogen deposition. Nature. 2001;410:809–12.CAS 
PubMed 
Article 

Google Scholar 
47.Field CB, Chapin FS, Chiariello NK, Holland EA, Mooney HA. The Jasper Ridge CO2 Experiment: Design and Motivation. In: Mooney HA, Koch GW, (Editors). Carbon Dioxide and Terrestrial Ecosystems. San Diego, California: Academic Press; 1996. p. 121–45.Chapter 

Google Scholar 
48.Luo C, Rodriguez-R LM, Johnston ER, Wu L, Cheng L, Xue K, et al. Soil microbial community responses to a decade of warming as revealed by comparative metagenomics. Appl Environ Microbiol. 2014;80:1777–86.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Mauritz M, Bracho R, Celis G, Hutchings J, Natali SM, Pegoraro E, et al. Nonlinear CO2 flux response to 7 years of experimentally induced permafrost thaw. Glob Chang. Biol. 2017;23:3646–66.
Google Scholar 
50.Natali SM, Schuur EAG, Mauritz M, Schade JD, Celis G, Crummer KG, et al. Permafrost thaw and soil moisture driving CO2 and CH4 release from upland tundra. J Geophys Res Biogeosci. 2015;120:525–37.CAS 
Article 

Google Scholar 
51.Yang Y, Gao Y, Wang S, Xu D, Yu H, Wu L, et al. The microbial gene diversity along an elevation gradient of the Tibetan grassland. ISME J. 2014;8:430–40.CAS 
PubMed 
Article 

Google Scholar 
52.Yang Y, Wu L, Lin Q, Yuan M, Xu D, Yu H, et al. Responses of the functional structure of soil microbial community to livestock grazing in the Tibetan alpine grassland. Glob Chang Biol. 2013;19:637–48.PubMed 
Article 

Google Scholar 
53.Zhang Y, Cong J, Lu H, Li G, Xue Y, Deng Y, et al. Soil bacterial diversity patterns and drivers along an elevational gradient on Shennongjia Mountain, China. Micro Biotechnol. 2015;8:739–46.Article 

Google Scholar 
54.Zhang Y, Cong J, Lu H, Deng Y, Liu X, Zhou J, et al. Soil bacterial endemism and potential functional redundancy in natural broadleaf forest along a latitudinal gradient. Sci Rep. 2016;6:1–8.Article 
CAS 

Google Scholar 
55.Paula FS, Rodrigues JLM, Zhou J, Wu L, Mueller RC, Mirza BS, et al. Land use change alters functional gene diversity, composition and abundance in Amazon forest soil microbial communities. Mol Ecol. 2014;23:2988–99.PubMed 
Article 

Google Scholar 
56.Rodrigues JLM, Pellizari VH, Mueller R, Baek K, Jesus EDC, Paula FS, et al. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc Natl Acad Sci USA. 2013;110:988–93.CAS 
PubMed 
Article 

Google Scholar 
57.He Z, Deng Y, Van Nostrand JD, Tu QC, Xu MY, Hemme CL, et al. GeoChip 3.0 as a high-throughput tool for analyzing microbial community composition, structure and functional activity. Isme J. 2010;4:1167–79.CAS 
PubMed 
Article 

Google Scholar 
58.He Z, Gentry TJ, Schadt CW, Wu L, Liebich J, Chong SC, et al. GeoChip: a comprehensive microarray for investigating biogeochemical, ecological and environmental processes. ISME J. 2007;1:67–77.CAS 
PubMed 
Article 

Google Scholar 
59.Li X, He Z, Zhou J. Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation. Nucleic Acids Res. 2005;33:6114–23.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Tu Q, He Z, Deng Y, Zhou J. Strain/species-specific probe design for microbial identification microarrays. Appl Environ Microbiol. 2013;79:5085–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Wu L, Liu X, Schadt CW, Zhou J. Microarray-based analysis of subnanogram quantities of microbial community DNAs by using whole-community genome amplification. Appl Environ Microbiol. 2006;72:4931–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Wu L, Liu X, Schadt CW, Zhou J. Microarray-based analysis of subnanogram quantities of microbial community DNAs by using whole-community genome amplification. Applied and Environmental Microbiology. 2006;72:4931–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara RB, et al. Package ‘vegan’. Community ecology package, version. 2013;2:1–295.
Google Scholar 
64.Anderson MJ, Bueno AS. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
Google Scholar 
65.Crow EL, Patil GP. Applications in Ecology. In: Cros E, Shimizu K, editors. Lognormal Distributions. New York and Basel:Marcel Dekker; 1988. p. 303–30.66.Ser-Giacomi E, Zinger L, Malviya S, De Vargas C, Karsenti E, Bowler C, et al. Ubiquitous abundance distribution of non-dominant plankton across the global ocean. Nat Ecol Evol. 2018;2:1243–9.PubMed 
Article 

Google Scholar 
67.Wu L, Ning D, Zhang B, Li Y, Zhang P, Shan X, et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat Microbiol. 2019;4:1183–95.CAS 
PubMed 
Article 

Google Scholar 
68.Locey KJ, Lennon JT. Scaling laws predict global microbial diversity. Proc Natl Acad Sci. 2016;113:5970–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Louca S, Mazel F, Doebeli M, Parfrey LW. A census-based estimate of Earth’s bacterial and archaeal diversity. PLoS Biol. 2019;2:1–30.
Google Scholar 
70.Tokeshi M. Dynamics of distribution in animal communities: theory and analysis. Res Popul Ecol (Kyoto). 1992;34:249–73.Article 

Google Scholar 
71.Logares R, Deutschmann IM, Junger PC, Giner CR, Krabberød AK, Schmidt TSB, et al. Disentangling the mechanisms shaping the surface ocean microbiota. Microbiome. 2020;8:55.PubMed 
PubMed Central 
Article 

Google Scholar 
72.Azovsky A, Mazei Y. Do microbes have macroecology? Large-scale patterns in the diversity and distribution of marine benthic ciliates. Glob Ecol Biogeogr. 2013;22:163–72.Article 

Google Scholar 
73.Noguez AM, Arita HT, Escalante AE, Forney LJ, García-Oliva F, Souza V. Microbial macroecology: highly structured prokaryotic soil assemblages in a tropical deciduous forest. Glob Ecol Biogeogr. 2005;14:241–8.Article 

Google Scholar 
74.Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Papp L, Izsák J, Papp L, Izsak J. Bimodality in Occurrence Classes: a Direct Consequence of Lognormal or Logarithmic Series Distribution of Abundances- A Numerical Experimentation. Oikos. 1997;79:191.Article 

Google Scholar 
76.Verberk WCEP, van der Velde G, Esselink H. Explaining abundance-occupancy relationships in specialists and generalists: A case study on aquatic macroinvertebrates in standing waters. J Anim Ecol. 2010;79:589–601.PubMed 
Article 

Google Scholar 
77.Liao J, Cao X, Zhao L, Wang J, Gao Z, Wang MC, et al. The importance of neutral and niche processes for bacterial community assembly differs between habitat generalists and specialists. FEMS Microbiol Ecol. 2016;92:fiw174.PubMed 
Article 
CAS 

Google Scholar 
78.Slatyer RA, Hirst M, Sexton JP. Niche breadth predicts geographical range size: a general ecological pattern. Ecol Lett. 2013;16:1104–14.PubMed 
Article 

Google Scholar 
79.Fierer N, Barberán A, Laughlin DC. Seeing the forest for the genes: using metagenomics to infer the aggregated traits of microbial communities. Front Microbiol. 2014;5:1–6.Article 

Google Scholar 
80.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 
81.Wertz S, Degrange V, Prosser JI, Poly F, Commeaux C, Guillaumaud N, et al. Decline of soil microbial diversity does not influence the resistance and resilience of key soil microbial functional groups following a model disturbance. Environ Microbiol. 2007;9:2211–9.PubMed 
Article 

Google Scholar 
82.Wertz S, Degrange V, Prosser JI, Poly F, Commeaux C, Freitag T, et al. Maintenance of soil functioning following erosion of microbial diversity. Environ Microbiol. 2006;8:2162–9.CAS 
PubMed 
Article 

Google Scholar 
83.Mendes LW, Tsai SM, Navarrete AA, de Hollander M, van Veen JA, Kuramae EE. Soil-Borne microbiome: linking diversity to function. Micro Ecol. 2015;70:255–65.CAS 
Article 

Google Scholar 
84.Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome – SM. Science. 2015;348:1261359–1261359.PubMed 
Article 
CAS 

Google Scholar 
85.Wohl DL, Arora S, Gladstone JR. Functional redundancy supports biodiversity and ecosystem function in a cloased and constant environment. Ecology. 2008;85:1534–40.Article 

Google Scholar 
86.Kurm V, Geisen S, Gera Hol WH. A low proportion of rare bacterial taxa responds to abiotic changes compared with dominant taxa. Environ Microbiol. 2019;21:750–8.PubMed 
Article 

Google Scholar 
87.Bergkessel M, Basta DW, Newman DK. The physiology of growth arrest: Uniting molecular and environmental microbiology. Nat Rev Microbiol. 2016;14:549–62.CAS 
PubMed 
Article 

Google Scholar 
88.Hofer U. Life in the slow lane. Nat Rev Microbiol. 2019;26:266–7.Article 
CAS 

Google Scholar 
89.Baho DL, Peter H, Tranvik LJ. Resistance and resilience of microbial communities – Temporal and spatial insurance against perturbations. Environ Microbiol. 2012;9:2283–92.Article 

Google Scholar 
90.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 
91.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 
92.Lawson CE, Strachan BJ, Hanson NW, Hahn AS, Hall ER, Rabinowitz B, et al. Rare taxa have potential to make metabolic contributions in enhanced biological phosphorus removal ecosystems. Environ Microbiol. 2015;17:4979–93.CAS 
PubMed 
Article 

Google Scholar 
93.Zhou J, He Z, Yang Y, Deng Y, Tringe SG, Alvarez-Cohen L. High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. MBio. 2015;6:e02288–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
94.Zhou J, Wu L, Deng Y, Zhi X, Jiang YH, Tu Q, et al. Reproducibility and quantitation of amplicon sequencing-based detection. ISME J. 2011;5:1303–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Shi Z, Yin H, Van Nostrand JD, Voordeckers JW, Tu Q, Deng Y, et al. Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities. mSystems. 2019;4:99–117.
Google Scholar  More

Morphology and light-emitting behavior of the undescribed Japanese Terebellidae wormIn 2016, some of the present authors were exploring shallow coastal waters (depth less than 1 m) to observe the ecological behaviors of marine animals in the Noto Peninsula, when they discovered unknown violet-light-emitting worms. At the sampling point, the worms were living in small holes (a few centimeters in diameter) or in cracks in rocks covered by sand at the shallow sea bottom (Supplementary Fig. S1). We successfully video-recorded their emission of violet light from the whole tentacle stretching into sea water when stimulated by air bubbling at night (Fig. 1A–C; Supplementary Videos S1 and S2). The violet-light emission consisted of rapid flashes with variable duration in the order of milliseconds (Supplementary Video S3), as observed for the worm P. perplexus in response to stimulation17. From our morphological observation, we identified the violet-light-emitting worm as a member of Polycirrus on the basis of the following characteristics18: (1) a sheetlike prostomium covering the upper lip; (2) avicular unicini on some neuropodia; (3) no branchiae. The specimens also have the following characteristics: (1) neurochaetae beginning on last notochaetigerous segment, chaetiger 14; (2) uncini with a long neck and concave base; (3) notopodial pre- and post-chaetal lobes both similar shape. These characters are also found in Polycirrus disjunctus Hutchings and Glasby18; however some of the characters in parapodial lobes and chaetae have differentiation. Thus, we concluded that this species should be treated as an undescribed species. Further comparative observation is needed to describe the species. At this time we treated the Polycirrus species observed in this study as Polycirrus sp. ISK. Application of an electric pulse also caused clear light emission from the tentacles of the living worm (Fig. 1D; Supplementary Video S3), and the luminescence spectrum showed that its λmax was 444 nm or slightly longer, depending on the individual (Fig. 2A). We also found that light emission was efficiently induced by the addition of KCl solution and observed the time course of light emission with rapid fluctuations with variable duration in the order of milliseconds for up to 30 s (Fig. 2B). The flash pattern was similar to that observed in a study of P. perplexus17. In the genus Polycirrus, P. medius and P. nervosus in Japan have been described18,19. However, the morphological features of the species in the present study differed from these species on the basis of our observations described above.Figure 1Photographs of Polycirrus sp. ISK. (A) Polycirrus sp. ISK in its natural habitat with bright-field illumination. (B) Bioluminescence of Polycirrus sp. ISK in its natural habitat without bright-field illumination. The worms were stimulated by air bubbling from SCUBA gear. (C) A single worm with stretched tentacles. Tentacles are indicated by white arrows. (D) The worm with light emission at the tentacles. This worm was stimulated by an electric shock. Scale bars = 100 mm for A and B, 10 mm for C and D. Each photograph was extracted from the videos recorded with the following settings: sensitivity, ISO 51200 or 11 lx; white balance, 4300 K or 5800 K; shutter speed, 1/30 s or 1/60 s; iris, F1.8-3.5; frame rate, 29.97 fps or 60 i; frame size, 1920 × 1080 pixels. Original high quality videos are available at https://youtu.be/KEsU0kWAEfg and https://youtu.be/24dxvPlBDB0Full size imageFigure 2Luminescence spectra and KCl-induced light emission of Polycirrus sp. ISK. (A) Spectrum analysis of Polycirrus sp. ISK using a living worm stimulated by an electric shock. The luminescence spectra were obtained from two different individuals. The λmax represented in closed circles and open circles were 444 nm and 446 nm, respectively. (B) Typical light-emission signal of a living worm soaked in 667 mM KCl. The black line indicates luminescence intensity after adding KCl solution, and the gray line indicates luminescence intensity before adding KCl solution.Full size imageJapanese Polycirrus spp. have not been described as luminous worms according to our review of the literature and web pages. In addition, the number of reports for new Polycirrus spp. from all over the world has been increasing, but a limited number of species are known to emit light13,17,18. Our finding of KCl-induced light emission from Polycirrus sp. ISK suggested that we can easily test the light-emitting ability of Polycirrus spp. by luminescence measurement just after adding KCl solution. A spectrum pattern has been reported for only one species, P. perplexus collected in California17, and it would be necessary for further understanding of these species to examine the light-emitting abilities and to compare light-emitting behaviors and spectrum patterns. The color of bioluminescence is often related to habitat, and light in the blue range is typical for pelagic species20. Thus, one of the points to be focused on is the ecological function of the violet-light emission of this worm inhabiting in a shallow coastal water environment. In P. perplexus, deterring predation is a possible function of luminescence based on that species’ habitat and its violet-light emission17,21. As shown in Supplementary Videos S1, S2, which are the first video records of in situ light emission of a Polycirrus species, the air bubble-stimulated luminescence of Polycirrus sp. ISK in its natural habitat also seemed to deter predation, but this explanation is still speculative.Differentially expressing genes between the tentacles and the rest of bodyA few years after discovering this worm, we found it difficult to collect enough of them to conduct common biochemical and chemical analyses because we did not find a place densely inhabited by hundreds of the worms whose wet weight was a few tens of milligrams (e.g. 16.5, 29.8, or 31.8 mg). Next, we conducted RNA-Seq analysis. In luminous animals with strong light emission, such as firefly or syllid polychaetes (Syllidae), luciferase expression is high especially at the luminous organ or in the whole body22,23. On the other hand, the light emission of Polycirrus sp. ISK was not so strong compared to that of fireflies, and the light-emitting area was limited to the tentacles. In addition, the genetic information related to the tentacles responsible for various ecological functions is still limited. Thus, in the present study we decided to purify RNA from the tentacles and the rest of body separately (Fig. 1C) and performed RNA-Seq analysis followed by a computational analysis using the MASER pipeline24. By de novo assembly, 110,775 contigs were predicted; 26.1% of them showed more than twice the expression level in the tentacles than in the rest of body, whereas 20.8% showed more than twice the expression in the rest of body than in the tentacles. When we performed a blastX search to the NCBI nr database for the contigs longer than 300 bp, 35.6% showed significant homology with registered genes with e-values of less than 1e−10. The average length for these contigs was 1384 bp, and half of them were in the range of 463–1863 bp (Supplementary Fig. S2). In the assembled sequence, we found the cytochrome oxidase subunit I (COI) gene and tried to construct a phylogenetic tree. However, the obtained phylogenetic tree was unreliable due to the low bootstrap values as shown in Supplementary Fig. S3.To focus on the tissue-specific genes, we first picked up genes with high expression levels based on high fpkm values (over 1000) and then ranked these genes based on the tissue-specificity judged by the comparison of fpkm values in tentacles and the rest of body. In tentacle-specific genes, we found that some genes coding for lectin(-like) domains were ranked in the top eight as shown in Supplementary Table S1. Of the top eight genes in the rest of body-specific genes (Supplementary Table S2), seven exhibited no similarity to any genes, and the remaining gene exhibited significant similarity to a hypothetical protein of Capitella teleta, which is a Polychaetes species with whole-genome information available25. Recently, TPM is preferably used to normalize expression level, and the value is used for statistical differential expression analysis26, and we also calculated TPM for tissue-specific genes (Supplementary Table S3).As we were unable to conduct statistical differential expression analysis due to no biological/technical replication resulted from difficulties in the sample collection, we simply compared TPM value between the tentacle and the rest of body samples. The ratio of TPM (tentacle/rest of body) was calculated, and then top 100 genes (Fig. 3A), which were highly expressing in the tentacle, were selected. Similarly, top 100 genes highly expressing in the rest of body were selected using the ratio of TPM (rest of body/tentacle) (Fig. 3B). These gene lists were annotated by gene ontology (GO) terms and analyzed using WEGO program27. WEGO results showed different expression patterns for the tentacle and the rest of body. In the tentacle, GO terms including cell adhesion, biological adhesion, small molecular binding, positive regulation of biological process, regulation of response to stimulus, carbohydrate binding, and immune response were significantly higher (Fig. 3C, D). In the rest of body, GO terms including hydrolase activity, catalytic activity, localization, and establishment of localization were significantly higher. In the top 100 genes highly expressing in the tentacle, we found 21 genes annotated as a gene coding for fucolectin by blast search (Supplementary Table S4). Fucolectin is a fucose-binding lectin involved in the innate immunity of diverse invertebrate species28. However, its function in invertebrates remains unclear, and no information is available for Terebellidae, including sequence information. Fucolectin was first identified in eel with mRNA distribution mainly in liver and gill28. In sea cucumber, expression of the fucolectin gene is confirmed in respiratory trees, muscle, and tentacle29. We were not able to see whether this gene was expressed in the respiratory organ of Polycirrus sp. ISK because a characteristic of the genus Polycirrus is the absence of branchiae18. Nevertheless, the tentacle-specific expression of fucolectin was consistent with the observation in sea cucumber, and the high expression of such proteins involved in innate immunity seemed reasonable because tentacles stretching out of their bodies can be damaged by attack of predators and thus are threatened by infectious bacteria and other pathogens11, as is the respiratory organ. In addition, localization of antimicrobial compounds in Terebellidae worms is suggested to be of antiseptic importance in damage by predation14. This study would provide indispensable information about the ecological meaning of Polycirrus sp. ISK’s life in future genetic studies.Figure 3WEGO analysis of highly expressing genes in the tentacle and the rest of body. (A) Box plot graph for the distribution of TPM value for top 100 genes highly expressing in the tentacle. Corresponding genes in each part are colored in the same gradation color according to the TPM value (red to blue form higher to lower value). (B) Box plot graph for the distribution of TPM value for top 100 genes highly expressing in the rest of body. Each gene is colored as in (A). (C) WEGO analysis of top 100 genes highly expressing in the tentacle (orange bar) and the rest of the body (blue bar). (D) P-values from Chi-square tests obtained by WEGO analysis. CC cellular component, MF molecular function, BP biological process.Full size imageTranscripts coding for luciferase-like genes in the wormTo find genes similar to the known luciferase, which is an enzyme oxidizing a specific compound called luciferin to emit light, from related species in polychaetes, we performed a blastX analysis against the Odontosyllis luciferase sequence using our RNA-Seq data. We found a gene coding for a protein that exhibited similarity to Odontosyllis luciferase, but the e-value was more than 1e−10 (Supplementary Fig. S4). In addition, the top hit for this gene analyzed by blastX was annotated to code an uncharacterized protein of Saccoglossus kowalevskii (Hemichordata), and its specific function was not predicted. Other hits were for genes from Chordata, Mollusca, and other phyla but there was no hit from Annelida. This result would suggest that the light-emission system of Polycirrus sp. ISK differs from that of the genus Odontosyllis, although further experiments using high purity Odontosyllis luciferase and the substrate will be necessary to confirm this. In further blastX analyses of representative luciferases, photoproteins, and a putative luciferase [luciferases from the ostracod Cypridina noctiluca (Accession number: BAD08210.1), the copepod Gaussia princeps (AAG54095.1), the deep-sea shrimp Oplophorus gracilirostris (BAB13775.1 and BAB13776.1), the firefly Photinus pyralis (AAA29795.1), the sea pansy Renilla reniformis (AAA29804.1); photoproteins from the hydrozoan jellyfish Aequorea victoria (AAA27720.1), the hydroid Clytia gregaria (CAA49754.1), the hydroid Obelia geniculate (AAL86372.1); a putative luciferase from the tunicate Pyrosoma atlanticum30 sequences using our RNA-Seq data], we found some tissue-nonspecific genes whose sequences exhibited similarity to firefly luciferase (FLuc) or Renilla luciferase-like protein (RLuc-like) sequences with an e-values of less than 1e−10 and percent identity of more than 50%. FLuc is a member of the acyl-adenylate-forming superfamily of enzymes responsible for firefly luciferin-dependent bioluminescence, which is found in terrestrial luminous beetles emitting light ranging from green to red31. Previously, a putative acyl-CoA synthetase protein was found in the luminous organ of firefly squid emitting blue light32, but there is no clear biochemical evidence that such protein is responsible for firefly squid’s bioluminescence. On the other hand, RLuc is responsible for coelenterazine-dependent bioluminescence, which is found in marine luminous organisms belonging to various taxa. An RLuc-like protein is found to be localized in luminous organs of the brittle star Amphiura filiformis, as revealed by taking advantage of the cross reactivity of anti-RLuc antibody to A. filiformis RLuc-like protein33. A recent study reported that recombinant RLuc-like protein found in P. atlanticum exhibited luciferase activity to coelenterazine30. However, an RLuc-like protein from sea urchin Strongylocentroutus purpuratus is confirmed to exhibit dehalogenase activity to various substrates but no luciferase activity to coelenterazine34. Therefore, it is suspected that Polycirrus sp. ISK possesses a luminescence system using an RLuc-like enzyme.Coelenterazine content in the wormTo investigate whether Polycirrus sp. ISK possesses not only a Renilla luciferase homologous gene but also coelenterazine, we analyzed an ethanolic extract of Polycirrus sp. ISK by UPLC with a UV–visible detector (Fig. 4). The obtained UPLC chromatogram did not show a peak corresponding to that of authentic coelenterazine. When further checking the chromatogram, we found the peak at a retention time similar to those of authentic coelenteramide and coelenteramine, which can be formed from coelenterazine. However, the absorption spectrum obtained by UPLC analysis and the mass spectrum obtained by MS/MS analysis were not identical to those of authentic coelenteramide or coelenteramine (Fig. 4 and Supplementary Figs. S5 and S6). In addition, when the worm extract was mixed with a recombinant RLuc, we did not detect luminescence using a luminometer. These results suggested that the luminescence system in the worm was independent of coelenterazine, although a RLuc homologous gene was found. Similarly, the existence of an RLuc homologous gene was reported in P. atlanticum, which has been suggested to use a coelenterazine-independent luminescence system relying on bacterial bioluminescent symbionts30,35. We also mixed the worm extract with a recombinant cypridinid luciferase, but we did not detect luminescence using a luminometer. This result was consistent with Harvey’s observation for P. caliendrum16. To examine whether the luminescence system is based on luciferin–luciferase reaction, which is found in various luminous animals including some syllid Odontosyllis spp.23,36,37,38,39, we prepared two different extracts of the worm using 100 mM HEPES–NaOH buffer (pH 7.4) and methanol, and subsequently subjected a mixture of the two to luminescent measurement. As a result, no light emission was detected from the mixture of the buffer and methanolic extracts of the worm. This result was also consistent with Harvey’s observation for P. caliendrum16. However, there is still a possibility that the light emission is based on luciferin–luciferase reaction, because luciferin–luciferase reaction found in fireflies or luminous mushrooms requires a cofactor such as ATP or NADPH, and we did not test all possible conditions due to the limitation of the number of collected specimens. In addition, extraction of luciferin and luciferase in the active form is sometimes difficult, as shown in previous studies37. Further studies using hundreds or more of the specimens must be performed to elucidate the mechanism underlying the violet-light emission.Figure 4Comparison of the ethanolic extract of Polycirrus sp. ISK with CTZ, CTMD, and CTM. (A) UPLC analysis of (a) the extract, (b) authentic CTZ, (c) authentic CTMD, and (d) authentic CTM using a multiwavelength detector. The black solid line indicates detection at 333 nm, and the blue solid line indicates detection at 435 nm. The compound between the red vertical dashed lines was collected for MS/MS analysis. (B) Absorption spectra of the compound from the extract, CTZ, CTMD, and CTM obtained at retention times of (a) 9.65, (b) 10.89, (c) 9.47, and (d) 9.27 shown in (A). CTZ coelenterazine, CTMD coelenteramide, CTM coelenteramine. These chemical structures are shown in Supplementary Fig. S5.Full size image More

1.Hansen, L. J. & Hoffman, J. R. Climate Savvy: Adapting Conservation and Resource Management to a Changing World, Austral Ecology (Island Press, 2011).Book 

Google Scholar 
2.Huang, Q., Fleming, C., Robb, B., Lothspeich, A. & Songer, M. How different are species distribution model predictions? Application of a new measure of dissimilarity and level of significance to giant panda Ailuropoda melanoleuca. Ecol. Inform. 46, 114–124 (2018).Article 

Google Scholar 
3.Giorgi, F. & Mearns, L. O. Probability of regional climate change based on the Reliability Ensemble Averaging (REA) method. Geophys. Res. Lett. 30, 1–4 (2003).Article 

Google Scholar 
4.Palmer, T. N., Doblas-Reyes, F. J., Hagedorn, R. & Weisheimer, A. Probabilistic prediction of climate using multi-model ensembles: From basics to applications. Philos. Trans. R. Soc. London B Biol. Sci. 360, 1991–1998 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Dormann, C. F. et al. Prediction uncertainty of environmental change effects on temperate European biodiversity. Ecol. Lett. 11, 235–244 (2008).PubMed 
Article 

Google Scholar 
6.Spence, M. A. et al. A general framework for combining ecosystem models. Fish Fish. 19, 1031–1042 (2018).Article 

Google Scholar 
7.Janssen, P. H. M., Petersen, A. C., van der Sluijs, J. P., Risbey, J. S. & Ravetz, J. R. A guidance for assessing and communicating uncertainties. Water Sci. Technol. 52, 125–131 (2005).CAS 
PubMed 
Article 

Google Scholar 
8.Frewer, L. The public and effective risk communication. Toxicol. Lett. 149, 391–397 (2004).CAS 
PubMed 
Article 

Google Scholar 
9.Leiserowitz, A. A., Malbach, E. W., Roser-Renough, C., Smith, N. & Dawson, E. Climategate, public opinion, and the loss of trust. Am. Behav. Sci. 57, 818–837 (2012).Article 

Google Scholar 
10.Hyder, K. et al. Making modelling count: Increasing the contribution of shelf-seas community and ecosystem models to policy development and management. Mar. Policy 61, 291–302 (2015).Article 

Google Scholar 
11.Cartwright, S. J. et al. Communicating complex ecological models to non-scientist end users. Ecol. Model. 338, 51–59 (2016).Article 

Google Scholar 
12.Doyle, E. E. H., Johnston, D. M., Smith, R. & Paton, D. Communicating model uncertainty for natural hazards: A qualitative systematic thematic review. Int. J. Disaster Risk Reduct. 33, 449–476 (2019).Article 

Google Scholar 
13.Kamal, A. et al. Recent advances and challenges in uncertainty visualization: A survey. J. Vis. https://doi.org/10.1007/s12650-021-00755-1 (2021).Article 

Google Scholar 
14.Spiegelhalter, D. J. & Riesch, H. Don’t know, can’t know: Embracing deeper uncertainties when analysing risks. Philos. Trans. R. Soc. A 369, 4730–4750 (2011).ADS 
MathSciNet 
Article 
MATH 

Google Scholar 
15.Brodlie, K., Osorio, R. A. & Lopes, A. A review of uncertainty in data visualization. In Expanding the Frontiers of Visual Analytics and Visualization (eds Dill, J. et al.) 81–109 (Springer, 2012).Chapter 

Google Scholar 
16.MacEachren, A. M. et al. Visualizing geospatial information uncertainty: what we know and what we need to know. Cartogr. Geogr. Inf. Sci. 32, 139–160 (2005).Article 

Google Scholar 
17.Ibrekk, H. & Morgan, M. G. Graphical communication of uncertain quantities to nontechnical people. Risk Anal. 7, 519–529 (1987).Article 

Google Scholar 
18.Hawkins, E. The cascade of uncertainty in climate projections. https://www.climate-lab-book.ac.uk/2014/cascade-of-uncertainty/. 6 Feb 2014.19.Wilby, R. L. & Dessai, S. Robust adaptation to climate change. Weather 65, 180–185 (2010).ADS 
Article 

Google Scholar 
20.Daron, J., Lorenz, S., Taylor, A. & Dessai, S. Communicating future climate projections of precipitation change. Clim. Change. 166, 23 (2021).ADS 
Article 

Google Scholar 
21.Kinkeldey, C., MacEachren, A. M. & Schiewe, J. How to assess visual communication of uncertainty? A systematic review of geospatial uncertainty visualisation user studies. Cartogr. J. 51, 372–386 (2014).Article 

Google Scholar 
22.van Vuuren, D. P. et al. The representative concentration pathways: An overview. Clim. Change 109, 5–31 (2011).ADS 
Article 

Google Scholar 
23.Breslow, N. E. & Clayton, D. G. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88, 9–25 (1993).MATH 

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

Google Scholar 
25.Christensen, R.H.B. ordinal—Regression models for ordinal data. R package version 2018.4-19. https://CRAN.R-project.org/package=ordinal Deposited 19 Apr 2018.26.Peterson, B. & Harrell, F. E. Partial proportional odds models for ordinal response variables. Appl. Stat. 39, 205–217 (1990).Article 
MATH 

Google Scholar 
27.Momeni, A., Pincus, M. & Libien, J. Introduction to Statistical Methods in Pathology (Springer, 2018).Book 

Google Scholar 
28.Turner, H. & Firth, D. Bradley-Terry models in R: The BradleyTerry2 package. J. Stat. Softw. 48, 1–21 (2012).Article 

Google Scholar 
29.Akaike, H. Information theory and an extension of maximum likelihood principle. In Proceedings of the 2nd International Symposium on Information Theory. Tsahkadsor, Armenia, USSR, 2–8 September (1973).30.Rubinstein, R. The cross-entropy method for combinatorial and continuous optimization. Methodol. Comput. Appl. Probab. 1, 127–190 (1999).MathSciNet 
Article 

Google Scholar 
31.Kendall, M. G. A new measure of rank correlation. Biometrika 30, 81–93 (1938).Article 
MATH 

Google Scholar 
32.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).Book 
MATH 

Google Scholar 
33.Firth, D., qvcalc: Quasi variances for factor effects in statistical statistical models. R package version 0.9-1. https://CRAN.R-project.org/package=qvcalc. Deposited 19 Sept 2017.34.Pihur, V., Datta, S. & Datta, S. RankAggreg: Weighted rank aggregation (2018). R package version 0.6-4. https://CRAN.R-project.org/package=RankAggreg. Deposited 18 Mar 2018.35.Belia, S., Fidler, F., Williams, J. & Cumming, G. Researchers misunderstand confidence intervals and standard error bars. Psychol. Methods 10, 389 (2005).PubMed 
Article 

Google Scholar 
36.Cumming, G., Fidler, F. & Vaux, D. L. Error bars in experimental biology. J. Cell Biol. 177, 7–11 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Hullman, J., Resnick, P. & Adar, E. Hypothetical outcome plots outperform error bars and violin plots for inferences about reliability of variable ordering. PLoS ONE 10, e0142444 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Correll, M. & Gleicher, M. Error bars considered harmful: Exploring alternate encodings for mean and error. IEEE Trans. Vis. Comput. Graph. 20, 2142–2151 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Lipkus, I. M. & Hollands, J. G. The visual communication of risk. JNCI Monogr. 25, 149–163 (1999).Article 

Google Scholar 
40.Spiegelhalter, D., Pearson, M. & Short, I. Visualizing uncertainty about the future. Science 333, 1393–1400 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
41.Few, S. Solutions to the problem of over-plotting in graphs. Visual Business Intelligence Newsletter (2008).42.Daron, J. D., Lorenz, S., Wolski, P., Blamey, R. C. & Jack, C. Interpreting climate data visualisations to inform adaptation decisions. Clim. Risk Manag. 10, 17–26 (2015).Article 

Google Scholar 
43.Heer, J. & Agrawala, M. Multi-scale banking to 450. IEEE Trans. Vis. Comput. Graph. 12, 701–708 (2006).PubMed 
Article 

Google Scholar 
44.Lorenz, S., Dessai, S., Paavola, J. & Forster, P. M. The communication of physical science uncertainty in European National Adaptation Strategies. Clim. Change 132, 143–155 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
45.Quispel, A., Maes, A. & Schilperoord, J. Graph and chart aesthetics for experts and laymen in design: The role of familiarity and perceived ease of use. Inf. Vis. 15, 238–252 (2016).Article 

Google Scholar 
46.Saary, M. J. Radar plots: A useful way for presenting multivariate health care data. J. Clin. Epidemiol. 61, 311–317 (2008).PubMed 
Article 

Google Scholar 
47.Vaughan, N. E. & Gough, C. Expert assessment concludes negative emissions scenarios may not deliver. Environ. Res. Lett. 11, 95003 (2016).Article 
CAS 

Google Scholar 
48.Peltier, J. Excel Charting Dos and Don’ts (Peltier Technical Services, 2013).
Google Scholar 
49.Lohse, G. L. The role of working memory on graphical information processing. Behav. Inf. Technol. 16, 297–308 (1997).Article 

Google Scholar 
50.Grainger, S., Mao, F. & Buytaert, W. Environmental data visualisation for non-scientific contexts: Literature review and design framework. Environ. Model. Softw. 85, 299–318 (2016).Article 

Google Scholar 
51.Few, S. Heatmaps: to bin or not to bin? Visual Business Intelligence Newsletter (2017).52.Moreland, K. Diverging color maps for scientific visualization. In Advances in Visual Computing (eds Bebis, G. et al.) 92–103 (Springer, 2009).Chapter 

Google Scholar 
53.Alrehiely, M., Eslambolchilar, P. & Borgo, R. Evaluating different visualization designs for personal health data. In Proceedings of the 32nd International BCS Human Computer Interaction Conference, Belfast, UK, 4–6 July (2018).54.Saket, B., Endert, A. & Demiralp, C. Task-based effectiveness of basic visualizations. IEEE Trans. Visual Comput. Graph. 25, 2505–2512 (2019).Article 

Google Scholar 
55.Dahshan, M., Polys, N. F., Jayne, R. S. & Pollyea, R. M. Making sense of scientific simulation ensembles with semantic interaction. Comput. Graph. Forum. 39, 325–343 (2020).Article 

Google Scholar  More