Philippa Kaur

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

1.Parmesan, C. & Yohe, G. Nature 421, 37–42 (2003).CAS 
Article 

Google Scholar 
2.Charmantier, A. & Gienapp, P. Evol. Appl. 7, 15–28 (2014).Article 

Google Scholar 
3.Merilä, J. & Hendry, A. P. Evol. Appl. 7, 1–14 (2014).Article 

Google Scholar 
4.Gienapp, P., Teplitsky, C., Alho, J. S., Mills, J. A. & Merilä, J. Mol. Ecol. 17, 167–178 (2008).CAS 
Article 

Google Scholar 
5.Bonamour, S., Chevin, L.-M., Charmantier, A. & Teplitsky, C. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180178 (2019).Article 

Google Scholar 
6.Visser, M. E., Caro, S. P., van Oers, K., Schaper, S. V. & Helm, B. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3113–3127 (2010).CAS 
Article 

Google Scholar 
7.Caro, S. P., Schaper, S. V., Hut, R. A., Ball, G. F. & Visser, M. E. PLoS Biol. 11, e1001517 (2013).CAS 
Article 

Google Scholar 
8.Visser, M. E. & Gienapp, P. Nat. Ecol. Evol. 3, 879–885 (2019).Article 

Google Scholar 
9.Cole, E. F., Regan, C. E. & Sheldon, B. C. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01140-4 (2021).Article 

Google Scholar 
10.Radchuk, V. et al. Nat. Commun. 10, 3109 (2019).Article 

Google Scholar 
11.Charmantier, A. et al. Science 320, 800–803 (2008).CAS 
Article 

Google Scholar 
12.Dewitt, T. J., Sih, A. & Wilson, D. S. Trends Ecol. Evol. 13, 77–81 (1998).CAS 
Article 

Google Scholar 
13.Díaz, S. et al. (eds) Summary for Policymakers of the Global Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019). More

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

Google Scholar 
2.Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).CAS 

Google Scholar 
3.Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).
Google Scholar 
4.Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).
Google Scholar 
5.Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
6.Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14 (2014).
Google Scholar 
7.Chevin, L. M. & Hoffmann, A. A. Evolution of phenotypic plasticity in extreme environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 372, 20160138 (2017).
Google Scholar 
8.Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180174 (2019).
Google Scholar 
9.Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Change Biol. 16, 3304–3313 (2010).
Google Scholar 
10.Kharouba, H. M. et al. Global shifts in the phenological synchrony of species interactions over recent decades. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).CAS 

Google Scholar 
11.Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).
Google Scholar 
12.Visser, M. E. & Gienapp, P. Evolutionary and demographic consequences of phenological mismatches. Nat. Ecol. Evol. 3, 879–885 (2019).
Google Scholar 
13.Kharouba, H. M. & Wolkovich, E. M. Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Change 10, 406–415 (2020).
Google Scholar 
14.Samplonius, J. M. et al. Strengthening the evidence base for temperature-mediated phenological asynchrony and its impacts. Nat. Ecol. Evol. 5, 155–164 (2021).
Google Scholar 
15.Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008).CAS 

Google Scholar 
16.Tomotani, B. M. et al. Climate change leads to differential shifts in the timing of annual cycle stages in a migratory bird. Glob. Change Biol. 24, 823–835 (2018).
Google Scholar 
17.Moyes, K. et al. Advancing breeding phenology in response to environmental change in a wild red deer population. Glob. Change Biol. 17, 2455–2469 (2011).
Google Scholar 
18.Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).CAS 

Google Scholar 
19.Todd, B. D., Scott, D. E., Pechmann, J. H. K. & Whitfield Gibbons, J. Climate change correlates with rapid delays and advancements in reproductive timing in an amphibian community. Proc. R. Soc. Lond. B Biol. Sci. 278, 2191–2197 (2011).
Google Scholar 
20.Taylor, S. G. Climate warming causes phenological shift in pink salmon, Oncorhynchus gorbuscha, behavior at Auke Creek, Alaska. Glob. Change Biol. 14, 229–235 (2008).
Google Scholar 
21.Mills, L. S. et al. Camouflage mismatch in seasonal coat color due to decreased snow duration. Proc. Natl Acad. Sci. USA 110, 7360–7365 (2013).CAS 

Google Scholar 
22.Lameris, T. K. et al. Arctic geese tune migration to a warming climate but still suffer from a phenological mismatch. Curr. Biol. 28, 2467–2473.e4 (2018).CAS 

Google Scholar 
23.Singer, M. C. & Parmesan, C. Phenological asynchrony between herbivorous insects and their hosts: signal of climate change or pre-existing adaptive strategy? Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3161–3176 (2010).
Google Scholar 
24.Charmantier, A. & Gienapp, P. Climate change and timing of avian breeding and migration: evolutionary versus plastic changes. Evol. Appl. 7, 15–28 (2014).
Google Scholar 
25.Keogan, K. et al. Global phenological insensitivity to shifting ocean temperatures among seabirds. Nat. Clim. Change 8, 313–317 (2018).
Google Scholar 
26.Both, C. & Visser, M. E. Adjustment to climate change is constrained by arrival date in a long-distance migrant bird. Nature 411, 296–298 (2001).CAS 

Google Scholar 
27.Both, C., van Asch, M., Bijlsma, R. G., van den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations? J. Anim. Ecol. 78, 73–83 (2009).
Google Scholar 
28.Cresswell, W. & McCleery, R. How great tits maintain synchronization of their hatch date with food supply in response to long-term variability in temperature. J. Anim. Ecol. 72, 356–366 (2003).
Google Scholar 
29.Visser, M. E., Van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. Lond. B Biol. Sci. 265, 1867–1870 (1998).
Google Scholar 
30.Sanz, J. J., Potti, J., Moreno, J., Merino, S. & Frías, O. Climate change and fitness components of a migratory bird breeding in the Mediterranean region. Glob. Change Biol. 9, 461–472 (2003).
Google Scholar 
31.Marrot, P., Charmantier, A., Blondel, J. & Garant, D. Current spring warming as a driver of selection on reproductive timing in a wild passerine. J. Anim. Ecol. 87, 754–764 (2018).
Google Scholar 
32.Burgess, M. D. et al. Tritrophic phenological match–mismatch in space and time. Nat. Ecol. Evol. 2, 970–975 (2018).
Google Scholar 
33.Visser, M. E., Holleman, L. J. M. & Gienapp, P. Shifts in caterpillar biomass phenology due to climate change and its impact on the breeding biology of an insectivorous bird. Oecologia 147, 164–172 (2006).
Google Scholar 
34.Pearce-Higgins, J. W., Yalden, D. W. & Whittingham, M. J. Warmer springs advance the breeding phenology of golden plovers Pluvialis apricaria and their prey (Tipulidae). Oecologia 143, 470–476 (2005).CAS 

Google Scholar 
35.Nussey, D. H., Clutton-Brock, T. H., Elston, D. A., Albon, S. D. & Kruuk, L. E. B. Phenotypic plasticity in a maternal trait in red deer. J. Anim. Ecol. 74, 387–396 (2005).
Google Scholar 
36.Husby, A. et al. Contrasting patterns of phenotypic plasticity in reproductive traits in two great tit (Parus major) populations. Evolution 64, 2221–2237 (2010).
Google Scholar 
37.Matthysen, E., Adriaensen, F. & Dhondt, A. A. Multiple responses to increasing spring temperatures in the breeding cycle of blue and great tits (Cyanistes caeruleus, Parus major). Glob. Change Biol. 17, 1–16 (2011).
Google Scholar 
38.Fisher, J. I., Mustard, J. F. & Vadeboncoeur, M. A. Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens. Environ. 100, 265–279 (2006).
Google Scholar 
39.Duparc, A. et al. Co-variation between plant above-ground biomass and phenology in sub-alpine grasslands. Appl. Veg. Sci. 16, 305–316 (2013).
Google Scholar 
40.Hinks, A. E. et al. Scale-dependent phenological synchrony between songbirds and their caterpillar food source. Am. Nat. 186, 84–97 (2015).
Google Scholar 
41.Lambrechts, M. M., Blondel, J., Maistre, M. & Perret, P. A single response mechanism is responsible for evolutionary adaptive variation in a bird’s laying date. Proc. Natl Acad. Sci. USA 94, 5153–5155 (1997).CAS 

Google Scholar 
42.Dawson, A. Control of the annual cycle in birds: endocrine constraints and plasticity in response to ecological variability. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 1621–1633 (2008).
Google Scholar 
43.Visser, M. E. et al. Phenology, seasonal timing and circannual rhythms: towards a unified framework. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3113–3127 (2010).CAS 

Google Scholar 
44.Caro, S. P., Schaper, S. V., Hut, R. A., Ball, G. F. & Visser, M. E. The case of the missing mechanism: how does temperature influence seasonal timing in endotherms? PLoS Biol. 11, e1001517 (2013).CAS 

Google Scholar 
45.Bourgault, P., Thomas, D., Perret, P. & Blondel, J. Spring vegetation phenology is a robust predictor of breeding date across broad landscapes: a multi-site approach using the Corsican blue tit (Cyanistes caeruleus). Oecologia 162, 885–892 (2010).
Google Scholar 
46.Bison, M. et al. Best environmental predictors of breeding phenology differ with elevation in a common woodland bird species. Ecol. Evol. 10, 10219–10229 (2020).
Google Scholar 
47.Bernhardt, J. R., O’Connor, M. I., Sunday, J. M. & Gonzalez, A. Life in fluctuating environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190454 (2020).
Google Scholar 
48.Gienapp, P., Reed, T. E. & Visser, M. E. Why climate change will invariably alter selection pressures on phenology. Proc. R. Soc. Lond. B Biol. Sci. 281, 20141611 (2014).
Google Scholar 
49.Lönnstedt, O. M., McCormick, M. I., Chivers, D. P. & Ferrari, M. C. O. Habitat degradation is threatening reef replenishment by making fish fearless. J. Anim. Ecol. 83, 1178–1185 (2014).
Google Scholar 
50.Pellerin, F., Cote, J., Bestion, E. & Aguilée, R. Matching habitat choice promotes species persistence under climate change. Oikos 128, 221–234 (2019).
Google Scholar 
51.Firth, J. A., Verhelst, B. L., Crates, R. A., Garroway, C. J. & Sheldon, B. C. Spatial, temporal and individual-based differences in nest-site visits and subsequent reproductive success in wild great tits. J. Avian Biol. 49, e01740 (2018).
Google Scholar 
52.Naef-Daenzer, B. & Keller, L. F. The foraging performance of great and blue tits (Parus major and P. caeruleus) in relation to caterpillar development, and its consequences for nestling growth and fledging weight. J. Anim. Ecol. 68, 708–718 (1999).
Google Scholar 
53.Naef-Daenzer, B. Patch time allocation and patch sampling by foraging great and blue tits. Anim. Behav. 59, 989–999 (2000).CAS 

Google Scholar 
54.Bouwhuis, S., Sheldon, B. C., Verhulst, S. & Charmantier, A. Great tits growing old: selective disappearance and the partitioning of senescence to stages within the breeding cycle. Proc. R. Soc. Lond. B Biol. Sci. 276, 2769–2777 (2009).CAS 

Google Scholar 
55.Cole, E. F. & Sheldon, B. C. The shifting phenological landscape: within- and between-species variation in leaf emergence in a mixed-deciduous woodland. Ecol. Evol. 7, 1135–1147 (2017).
Google Scholar 
56.Wint, W. The role of alternative host-plant species in the life of a polyphagous moth, Operophtera brumata (Lepidoptera: Geometridae). J. Anim. Ecol. 52, 439–450 (1983).
Google Scholar 
57.Keller, L. F. & van Noordwijk, A. J. Effects of local environmental conditions on nestling growth in the great tit Parus major L. Ardea 82, 349–362 (1994).
Google Scholar 
58.Wilkin, T. A., Garant, D., Gosler, A. G. & Sheldon, B. C. Density effects on life-history traits in a wild population of the great tit Parus major: analyses of long-term data with GIS techniques. J. Anim. Ecol. 75, 604–615 (2006).
Google Scholar 
59.Wilkin, T. A. & Sheldon, B. C. Sex differences in the persistence of natal environmental effects on life histories. Curr. Biol. 19, 1998–2002 (2009).CAS 

Google Scholar 
60.Gagen, M. et al. The tree ring growth histories of UK native oaks as a tool for investigating chronic oak decline: an example from the Forest of Dean. Dendrochronologia 55, 50–59 (2019).
Google Scholar 
61.Sturrock, R. N. et al. Climate change and forest diseases. Plant Pathol. 60, 133–149 (2011).
Google Scholar 
62.MacColl, A. D. C. The ecological causes of evolution. Trends Ecol. Evol. 26, 514–522 (2011).
Google Scholar 
63.Grant, P. R. & Price, T. D. Population variation in continuously varying traits as an ecological genetics problem. Integr. Comp. Biol. 21, 795–811 (1981).
Google Scholar 
64.Hereford, J. A quantitative survey of local adaptation and fitness trade-offs. Am. Nat. 173, 579–588 (2009).
Google Scholar 
65.Hadfield, J. D. The spatial scale of local adaptation in a stochastic environment. Ecol. Lett. 19, 780–788 (2016).
Google Scholar 
66.Porlier, M. et al. Variation in phenotypic plasticity and selection patterns in blue tit breeding time: between- and within-population comparisons. J. Anim. Ecol. 81, 1041–1051 (2012).
Google Scholar 
67.Hidalgo Aranzamendi, N., Hall, M. L., Kingma, S. A., van de Pol, M. & Peters, A. Rapid plastic breeding response to rain matches peak prey abundance in a tropical savanna bird. J. Anim. Ecol. 88, 1799–1811 (2019).
Google Scholar 
68.Caro, S. P., Lambrechts, M. M., Balthazart, J. & Perret, P. Non-photoperiodic factors and timing of breeding in blue tits: impact of environmental and social influences in semi-natural conditions. Behav. Process. 75, 1–7 (2007).CAS 

Google Scholar 
69.Bourret, A., Bélisle, M., Pelletier, F. & Garant, D. Multidimensional environmental influences on timing of breeding in a tree swallow population facing climate change. Evol. Appl. 8, 933–944 (2015).
Google Scholar 
70.Nussey, D. H., Wilson, A. J. & Brommer, J. E. The evolutionary ecology of individual phenotypic plasticity in wild populations. J. Evol. Biol. 20, 831–844 (2007).CAS 

Google Scholar 
71.Morris, D. W. Toward an ecological synthesis: a case for habitat selection. Oecologia 136, 1–13 (2003).
Google Scholar 
72.Long, R. A. et al. Linking habitat selection to fitness-related traits in herbivores: the role of the energy landscape. Oecologia 181, 709–720 (2016).
Google Scholar 
73.Morris, D. W. Spatial scale and the cost of density-dependent habitat selection. Evol. Ecol. 1, 379–388 (1987).
Google Scholar 
74.Patten, M. A. & Kelly, J. F. Habitat selection and the perceptual trap. Ecol. Appl. 20, 2148–2156 (2010).
Google Scholar 
75.Ponchon, A., Garnier, R., Grémillet, D. & Boulinier, T. Predicting population responses to environmental change: the importance of considering informed dispersal strategies in spatially structured population models. Divers. Distrib. 21, 88–100 (2015).
Google Scholar 
76.Nilsson, A. L. K. et al. Hydrology influences breeding time in the white-throated dipper. BMC Ecol. 20, 70 (2020).
Google Scholar 
77.Nilsson, A. L. K. et al. Location is everything, but climate gets a share: analyzing small-scale environmental influences on breeding success in the white-throated dipper. Front. Ecol. Evol. 8, 542846 (2020).
Google Scholar 
78.Martin, R. O., Cunningham, S. J. & Hockey, P. A. R. Elevated temperatures drive fine-scale patterns of habitat use in a savanna bird community. Ostrich 86, 127–135 (2015).
Google Scholar 
79.Bailey, L. D. et al. Habitat selection can reduce effects of extreme climatic events in a long-lived shorebird. J. Anim. Ecol. 88, 1474–1485 (2019).
Google Scholar 
80.Kirby, K. J. et al. Changes in the tree and shrub layer of Wytham Woods (southern England) 1974–2012: local and national trends compared. Forestry 87, 663–673 (2014).
Google Scholar 
81.Perrins, C. & McCleery, R. Laying dates and clutch size in the great tit. Wilson Bull. 101, 236–253 (1989).
Google Scholar 
82.Wilkin, T. A., Perrins, C. M. & Sheldon, B. C. The use of GIS in estimating spatial variation in habitat quality: a case study of lay-date in the great tit Parus major. Ibis 149, 110–118 (2007).
Google Scholar 
83.Perrins, C. M. Population fluctuations and clutch-size in the great tit, Parus major L. J. Anim. Ecol. 34, 601–647 (1965).
Google Scholar 
84.Wesołowski, T. & Rowiński, P. Timing of bud burst and tree-leaf development in a multispecies temperate forest. For. Ecol. Manage. 237, 387–393 (2006).
Google Scholar 
85.Gibson, C. W. D. in Woodland Conservation and Research in the Clay Vale of Oxfordshire and Buckinghamshire (eds Kirby, K. J. & Write, F. J.) 32–40 (JNCC, 1988).86.Dawkin, H. C. & Field, D. R. B. A Long-Term Surveillance System for British Woodland Vegetation. Commonwealth Forestry Institute, Oxford, Occasional Paper No. 1. (1978).87.Horsfall, A. S. & Kirby, K. J. The Use of Permanent Quadrats to Record Changes in the Structure and Composition of Wytham Woods, Oxfordshire Research and Survey in Nature Conservation No. 1 (JNCC, 1992).88.Wilkin, T. A., King, L. E. & Sheldon, B. C. Habitat quality, nestling diet, and provisioning behaviour in great tits Parus major. J. Avian Biol. 40, 135–145 (2009).
Google Scholar 
89.Van Noordwijk, M. & Purnomosidhi, P. Root architecture in relation to tree–soil–crop interactions and shoot pruning in agroforestry. Agrofor. Syst. 30, 161–173 (1995).
Google Scholar 
90.Bailey, L. D. & van de Pol, M. climwin: an R toolbox for climate window analysis. PLoS ONE 11, e0167980 (2016).
Google Scholar 
91.van de Pol, M. et al. Identifying the best climatic predictors in ecology and evolution. Methods Ecol. Evol. 7, 1246–1257 (2016).
Google Scholar 
92.Simmonds, E. G., Cole, E. F. & Sheldon, B. C. Cue identification in phenology: a case study of the predictive performance of current statistical tools. J. Anim. Ecol. 88, 1428–1440 (2019).
Google Scholar 
93.Oksanen, J. et al. vegan: Community Ecology Package: R Package v.2.5-6 (2019); https://CRAN.R-project.org/package=vegan94.Sturges, H. A. The choice of a class interval. J. Am. Stat. Assoc. 21, 65–66 (1926).
Google Scholar 
95.Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. https://doi.org/10.18637/jss.v033.i02 (2010).96.R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020); http://www.R-project.org/ More

Summary of sequencing dataWe obtained 168 million and 180 million 250 bp reads from Asian Badger and Northern Hog Badger, respectively. After removing transcripts and unigenes below 200 bp, we obtained 335,772 transcripts and 285,159 unigenes belonging to Asian Badger and 413,917 transcripts and 362,075 unigenes belonging to Northern Hog Badger (Table 1). Next, we analysed the length distribution of the unigenes and transcripts in these two species (Fig. 1). Their N50 of transcript length is longer than 1000 bp, and their N50 of unigene length is longer than 600 bp. The average GC content of the transcriptome data of Asian Badge was 52.71%, a value slightly higher than that of the Northern Hog Badger, which was 52.12% (Table 1).Table 1 Summary of the transcriptome of Asian Badgers and Northern Hog Badgers.Full size tableFigure 1Length and quantity distribution of transcripts and unigenes.Full size imageFunctional annotation and classification of the assembled unigenesThe success rate of annotation of these research data in the seven databases is shown in Table 2. In total, 34,150 (ZH) and 31,632 (GH) unigenes had GO terms (Table 2). Among them, there were three GO items related to digestion: positive regulation of the digestive system process (GH and ZH both have one gene), digestive tract development (GH and ZH both have four genes), and digestion (GH has five genes, ZH has three). Next, we compared the GO terms of Asian Badger and Northern Hog Badger transcriptomes and found that the distributions pattern of gene functions from these two species were particularly similar (Fig. 2). This predictable result indicates that there is no bias in the construction of the libraries from the Asian Badger and Northern Hog Badger. For both species, in the three main partitions (cellular component, molecular function, and biological process) of the GO classification, ‘Cellular process’, ‘Binding’ and ‘Metabolic process’, terms were principal individually (Fig. 2). In total, 8915 (ZH) and 10,203 (GH) unigenes had KOG terms (Table 2). In addition, 15,667 (ZH) and 17,823 (GH) were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Table 2) and grouped into 32 subclasses. Interestingly, the digestive system subcategory contains 695 and 611 unigenes in Asian Badger and Northern Hog Badger, respectively, involving 9 pathways, namely, bile, gastric acid, pancreatic, salivary secretion, carbohydrate, protein, vitamin, fat digestion and absorption, and mineral absorption.Table 2 Gene annotation success rate statistics.Full size tableFigure 2GO term Top20 for GH and ZH.Full size imageAnalysis of orthologous genesThe transcriptome evolution of different species can be understood by comparing transcriptome data. We analysed the possible orthologous genes between the transcriptome of Asian Badger and Northern Hog Badger obtained in this study. We selected a total of 5227 homologous gene pairs from these four species. After 5227 pairs of homologous genes were optimized and screened, 943 orthologous gene pairs were obtained (Supplementary Table S1).To explore whether the genes related to small intestinal digestion in Asian Badger and Northern Hog Badger have undergone adaptive evolution. We can predict the genes that affect the evolution of the two species through selection pressure on orthologous genes12. We selected 473 orthologous gene pairs with Ka/Ks  > 1 called divergent orthologous genes from the Ka/ks analysis results. We obtained 1263 orthologous gene pairs with Ka/Ks  More

1.Eitzel, M. V. et al. Citizen science terminology matters: Exploring key terms. Citiz. Sci. 2(1), 1 (2017).Article 

Google Scholar 
2.European Citizen Science Association (ECSA). ECSA’s Characteristics of Citizen Science. Apr 2020 [cited 3 July 2020]. In ECSA Our Documents [Internet]. https://ecsa.citizen-science.net/wp-content/uploads/2020/05/ecsa_characteristics_of_citizen_science_-_v1_final.pdf.3.Shirk, J. L. et al. Public participation in scientific research: A framework for deliberate design. Ecol. Soc. 17(2), 29 (2012).Article 

Google Scholar 
4.Follett, R. & Strezov, V. An analysis of citizen science based research: Usage and publication patterns. PLoS One 10(11), e0143687 (2015).Article 

Google Scholar 
5.Tauginienė, L. et al. Citizen science in the social sciences and humanities: The power of interdisciplinarity. Palgrave Commun. 6, 89 (2020).Article 

Google Scholar 
6.European Citizen Science Association (ECSA). Ten Principles of Citizen Science. Sept 2015 [cited 1 Dec 2020]. In ECSA Our Documents [Internet]. https://ecsa.citizen-science.net/wp-content/uploads/2020/02/ecsa_ten_principles_of_citizen_science.pdf.7.Vayena, E. & Tasioulas, J. Adapting standards: Ethical oversight of participant-led health research. PLoS Med. 10(3), e1001402 (2013).Article 

Google Scholar 
8.Resnik, D. B., Elliott, K. C. & Miller, A. K. A framework for addressing ethical issues in citizen science. Environ. Sci. Policy 54, 475–481 (2015).Article 

Google Scholar 
9.Riesch, H. & Potter, C. Citizen science as seen by scientists: Methodological, epistemological and ethical dimensions. Public Underst. Sci. 23(1), 107–120 (2014).Article 

Google Scholar 
10.Rothstein, M. A., Wilbanks, J. T. & Brothers, K. B. Citizen science on your smartphone: An ELSI research agenda. J. Law Med. Ethics 43(4), 897–903 (2015).Article 

Google Scholar 
11.Guerrini, C. J., Majumder, M. A., Lewellyn, M. J. & McGuire, A. L. Citizen science, public policy. Science 361(6398), 134–136 (2018).ADS 
CAS 
Article 

Google Scholar 
12.Resnik, D. B. Citizen scientists as human subjects: Ethical issues. Citiz. Sci. 4(1), 11 (2019).
Google Scholar 
13.Woolley, J. P. et al. Citizen science or scientific citizenship? Disentangling the uses of public engagement rhetoric in national research initiatives. BMC Med. Ethics 17, 33 (2016).Article 

Google Scholar 
14.Rasmussen, L. M. Research ethics in citizen science. In The Oxford Handbook of Research Ethics (eds Iltis, A. S. & MacKay, D.) (Oxford University Press, 2021).
Google Scholar 
15.Chesser, S., Poster, M. M. & Tuckett, A. G. Cultivating citizen science for all: Ethical considerations for research projects involving diverse and marginalized populations. Int. J. Soc. Res. Methodol. 23(5), 497–508 (2020).Article 

Google Scholar 
16.Vayena, E. & Tasioulas, J. “We the scientists”: A human right to citizen science. Philos. Technol. 28, 479–485 (2015).Article 

Google Scholar 
17.Rasmussen, L. M. “Filling the ‘Ethics Gap’ in Citizen Science Research”: A Workshop Report. 2017 [cited 15 July 2020]. In NIEHS Partnerships for Environmental Public Health [Internet]. https://www.niehs.nih.gov/research/supported/translational/peph/webinars/ethics/rasmussen_508.pdf.18.Louviere, J. J., Flynn, T. N. & Marley, A. A. J. Best-Worst Scaling: Theory, Methods and Applications (Cambridge University Press, 2015).Book 

Google Scholar 
19.Mühlbacher, A. C., Kaczynski, A., Zweifel, P. & Johnson, F. R. Experimental measurement of preferences in health and healthcare using best-worst scaling: An overview. Health Econ. Rev. 6, 2 (2016).Article 

Google Scholar 
20.Marti, J. A best-worst scaling survey of adolescents’ level of concern for health and non-health consequences of smoking. Soc. Sci. Med. 75, 87–97 (2012).Article 

Google Scholar 
21.Erdem, S. & Rigby, D. Investigating heterogeneity in the characterization of risks using best worst scaling. Risk Anal. 33(9), 1728–1748 (2013).Article 

Google Scholar 
22.Peay, H. L., Hollin, I. L. & Bridges, J. F. P. Prioritizing parental worry associated with Duchenne muscular dystrophy using best-worst scaling. J. Genet. Couns. 25, 305–313 (2016).Article 

Google Scholar 
23.Flynn, T. N., Louviere, J. J., Peters, T. J. & Coast, J. Best-worst scaling: What it can do for health care research and how to do it. J. Health Econ. 26, 171–189 (2007).Article 

Google Scholar 
24.Finn, A. & Louviere, J. J. Determining the appropriate response to evidence of public concern: The case of food safety. J. Public Policy Mark. 11(2), 12–25 (1992).Article 

Google Scholar 
25.Louviere, J. J. & Flynn, T. N. Using best-worst scaling choice experiments to measure public perceptions and preferences for healthcare reform in Australia. Patient 3(4), 275–283 (2010).Article 

Google Scholar 
26.Janssen, E. M., Benz, H. L., Tsai, J.-H. & Bridges, J. F. P. Identifying and prioritizing concerns associated with prosthetic devices for use in a benefit-risk assessment: A mixed methods approach. Expert Rev. Med. Devices 15(5), 385–398 (2018).CAS 
Article 

Google Scholar 
27.Auger, P., Devinney, T. M. & Louvier, J. J. Using best-worst scaling methodology to investigate consumer ethical beliefs across countries. J. Bus. Ethics 70, 299–326 (2007).Article 

Google Scholar 
28.Bridges, J. F. P. et al. Conjoint analysis applications in health—A checklist: A report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health 14, 403–413 (2011).Article 

Google Scholar 
29.Coast, J. et al. Using qualitative methods for attribute development for discrete choice experiments: Issues and recommendations. Health Econ. 21, 730–741 (2012).Article 

Google Scholar 
30.Johnson, F. R. et al. Constructing experimental designs for discrete-choice experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. Value Health 16, 3–13 (2013).Article 

Google Scholar 
31.Hauber, A. B. et al. Statistical methods for the analysis of discrete choice experiments: A report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value Health 19, 300–315 (2016).Article 

Google Scholar 
32.Flynn, T. N. Valuing citizen and patient preferences in health: Recent developments in three types of best-worst scaling. Expert Rev. Pharmacoecon. Outcomes Res. 10(3), 259–267 (2010).Article 

Google Scholar 
33.Louviere, J., Lings, I., Islam, T., Gudergan, S. & Flynn, T. An introduction to the application of (case 1) best-worst scaling in marketing research. Int. J. Res. Mark. 30, 292–303 (2013).Article 

Google Scholar 
34.Jannsen, E. M., Marshall, D. A., Hauber, A. B. & Bridges, J. F. P. Improving the quality of discrete-choice experiments in health: How can we assess validity and reliability?. Expert Rev. Pharmacoecon. Outcomes Res. 17(6), 531–542 (2017).Article 

Google Scholar 
35.Gallego, G., Bridges, J. F. P. & Flynn, T. Using best-worst scaling in horizon scanning for hepatocellular carcinoma technologies. Int. J. Technol. Assess. Health Care 28(3), 339–346 (2012).Article 

Google Scholar 
36.Bowser, A., Shilton, K., Preece, J. & Warrick, E. Accounting for privacy in citizen science: Ethical research in a context of openness. In CSCW ’17: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing 2124–2136 (Association for Computing Machinery, 2017).37.Pandya, R. E. A framework for engaging diverse communities in citizen science in the US. Front Ecol. Environ. 10(6), 314–317 (2012).Article 

Google Scholar 
38.Citizen Science Association (CSA). Who We Are; Who We Serve. n.d. [cited 1 Sept 2021]. In CSA About [Internet]. https://www.citizenscience.org/about/.39.Sawtooth Software. The MaxDiff System Technical Paper v. 9. Oct. 2020 [cited 25 Jan 2021] In Technical Papers [Internet]. https://sawtoothsoftware.com/resources/technical-papers/maxdiff-technical-paper.40.English, P. B., Richardson, M. J. & Garzon-Galvis, C. From crowdsourcing to extreme citizen science: Participatory research for environmental health. Annu. Rev. Public Health 39, 335–350 (2018).CAS 
Article 

Google Scholar 
41.Conrad, C. C. & Hilchey, K. G. A review of citizen science and community-based environmental monitoring: Issues and opportunities. Environ. Monit. Assess. 176, 273–291 (2011).Article 

Google Scholar 
42.Cashman, S. B. et al. The power and the promise: Working with communities to analyze data, interpret findings, and get to outcomes. Am. J. Public Health 98(8), 1407–1417 (2008).Article 

Google Scholar 
43.Citizen Science Association (CSA). Trustworthy Data Practices. n.d. [cited 5 Sept 2020]. In CSA Events [Internet]. https://www.citizenscience.org/data-ethics-study/.44.Cheung, K. L. et al. Using best-worst scaling to investigate preferences in health care. Pharmacoecon. 34, 1195–1209 (2016).Article 

Google Scholar 
45.Hastings, J. J. A. When citizens do science. Narrat. Inq. Bioeth. 9(1), 33–34 (2019).Article 

Google Scholar 
46.Guerrini, C. J., Trejo, M., Canfield, I. & McGuire, A. L. Core values of genomic citizen science: Results from a qualitative interview study. BioSocieties https://doi.org/10.1057/s41292-020-00208-2 (2020).Article 

Google Scholar 
47.Sharon, T. Self-tracking for health and the Quantified Self: Re-articulating autonomy, solidarity, and authenticity in the age of personalized medicine. Philos. Technol. 30, 93–121 (2017).Article 

Google Scholar  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