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    Plant–microbiome interactions: from community assembly to plant health

    1.
    Vandenkoornhuyse, P., Quaiser, A., Duhamel, M., Le Van, A. & Dufresne, A. The importance of the microbiome of the plant holobiont. New Phytol. 206, 1196–1206 (2015).
    PubMed  Google Scholar 
    2.
    Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012). This is one of the first studies to use high-throughput sequencing to profile the plant-associated microbiota, suggesting compartment-specific assembly of microbial communities.
    CAS  PubMed  PubMed Central  Google Scholar 

    3.
    Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828–840 (2012).
    CAS  PubMed  Google Scholar 

    4.
    Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc. Natl Acad. Sci. USA 110, 6548–6553 (2013).
    CAS  PubMed  Google Scholar 

    5.
    Ofek-Lalzar, M. et al. Niche and host-associated functional signatures of the root surface microbiome. Nat. Commun. 5, 4950 (2014).
    CAS  PubMed  Google Scholar 

    6.
    Bulgarelli, D. et al. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17, 392–403 (2015). In this study, shotgun metagenome analysis was used to elucidate the microbial traits involved in the bacterium–bacteriophage, interbacterial and host–bacterium interactions that govern plant colonization.
    CAS  PubMed  PubMed Central  Google Scholar 

    7.
    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  Google Scholar 

    8.
    Zarraonaindia, I. et al. The soil microbiome influences grapevine-associated microbiota. mBio 6, e02527–14 (2015).
    PubMed  PubMed Central  Google Scholar 

    9.
    Coleman-Derr, D. et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol. 209, 798–811 (2016).
    CAS  PubMed  Google Scholar 

    10.
    De Souza, R. S. C. et al. Unlocking the bacterial and fungal communities assemblages of sugarcane microbiome. Sci. Rep. 6, 28774 (2016).
    PubMed  PubMed Central  Google Scholar 

    11.
    Fonseca-García, C. et al. The cacti microbiome: interplay between habitat-filtering and host-specificity. Front. Microbiol. 7, 150 (2016).
    PubMed  PubMed Central  Google Scholar 

    12.
    Fitzpatrick, C. R. et al. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl Acad. Sci. USA 115, E1157–E1165 (2018).
    CAS  PubMed  Google Scholar 

    13.
    Hamonts, K. et al. Field study reveals core plant microbiota and relative importance of their drivers. Environ. Microbiol. 20, 124–140 (2018).
    CAS  PubMed  Google Scholar 

    14.
    Niu, B., Paulson, J. N., Zheng, X. & Kolter, R. Simplified and representative bacterial community of maize roots. Proc. Natl Acad. Sci. USA 114, E2450–E2459 (2017).
    CAS  PubMed  Google Scholar 

    15.
    Xu, J. et al. The structure and function of the global citrus rhizosphere microbiome. Nat. Commun. 9, 4894 (2018). This study presents one of the most comprehensive investigations on the structure and functional features of the microbiome associated with a particular plant species, identifying the core microbiota and functions that are persistently present at a global scale.
    PubMed  PubMed Central  Google Scholar 

    16.
    Bergelson, J., Mittelstrass, J. & Horton, M. W. Characterizing both bacteria and fungi improves understanding of the Arabidopsis root microbiome. Sci. Rep. 9, 24 (2019).
    PubMed  PubMed Central  Google Scholar 

    17.
    Wagner, M. R. et al. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat. Commun. 7, 12151 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    18.
    Roman-Reyn, V. et al. The rice leaf microbiome has a conserved community structure controlled by complex host-microbe. Preprint at bioRxiv https://doi.org/10.1101/615278 (2019).

    19.
    Cregger, M. A. et al. The Populus holobiont: dissecting the effects of plant niches and genotype on the microbiome. Microbiome 6, 31 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    20.
    Lemanceau, P., Blouin, M., Muller, D. & Moënne-Loccoz, Y. Let the core microbiota be functional. Trends Plant Sci. 22, 583–595 (2017).
    CAS  PubMed  Google Scholar 

    21.
    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  Google Scholar 

    22.
    Leach, J. E., Triplett, L. R., Argueso, C. T. & Trivedi, P. Communication in the phytobiome. Cell 169, 587–596 (2018).
    Google Scholar 

    23.
    Levy, A. et al. Genomic features of bacterial adaptation to plants. Nat. Genet. 50, 138–150 (2018). In this study, comparative genomics is used to identify the genes involved in bacterial adaptation to plants, including genes associated with plant colonization, microorganism–microorganism competition and host–microorganism interactions.
    CAS  Google Scholar 

    24.
    Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl Acad. Sci. USA 106, 16428–16433 (2009).
    CAS  PubMed  Google Scholar 

    25.
    Liu, Z. et al. A genome-wide screen identifies genes in rhizosphere-associated Pseudomonas required to evade plant defenses. mBio 9, e00433-–18 (2018).
    PubMed  PubMed Central  Google Scholar 

    26.
    Cole, B. J. et al. Genome-wide identification of bacterial plant colonization genes. PLoS Biol. 15, e2002860 (2018).
    Google Scholar 

    27.
    Richardson, A. E. & Simpson, R. J. Soil microorganisms mediating phosphorus availability update on microbial phosphorus. Plant Physiol. 156, 989–996 (2011).
    CAS  PubMed  PubMed Central  Google Scholar 

    28.
    Pieterse, C. M. et al. Induced systemic resistance by beneficial microbes. Ann. Rev. Phytopathol. 52, 347–375 (2014).
    CAS  Google Scholar 

    29.
    Trivedi, P., Trivedi, C., Grinyer, J., Anderson, I. C. & Singh, B. K. Harnessing host-vector microbiome for sustainable plant disease management of phloem-limited bacteria. Front. Plant Sci. 7, 1423 (2016).
    PubMed  PubMed Central  Google Scholar 

    30.
    Backer, R. et al. Plant growth-promoting rhizobacteria: context, mechanisms of action, and roadmap to commercialization of biostimulants for sustainable agriculture. Front. Plant Sci. 9, 1473 (2018).
    PubMed  PubMed Central  Google Scholar 

    31.
    Gouda, S. et al. Revitalization of plant growth promoting rhizobacteria for sustainable development in agriculture. Microbiol. Res. 206, 131–140 (2018).
    PubMed  Google Scholar 

    32.
    Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100 (2011). This study identifies vital bacterial groups and functional traits that are involved in building disease-suppressive soils, thus demonstrating that selective enrichment of microbial groups in response to pathogen attack protects plants against infections.
    CAS  PubMed  Google Scholar 

    33.
    Santhanam, R., Weinhold, A., Goldberg, J., Oh, Y. & Baldwin, I. T. Native root-associated bacteria rescue a plant from a sudden-wilt disease that emerged during continuous cropping. Proc. Natl Acad. Sci. USA 112, E5013–E5020 (2015).
    CAS  PubMed  Google Scholar 

    34.
    Trivedi, P. et al. Keystone microbial taxa regulate the invasion of a fungal pathogen in agro-ecosystems. Soil. Biol. Biochem. 111, 10–14 (2017).
    CAS  Google Scholar 

    35.
    Ravanbakhsh, M., Kowalchuk, G. A. & Jousset, A. Root-associated microorganisms reprogram plant life history along the growth–stress resistance tradeoff. ISME J. 13, 3093–3101 (2019).
    CAS  PubMed  Google Scholar 

    36.
    Xue, C. et al. Manipulating the banana rhizosphere microbiome for biological control of Panama disease. Sci. Rep. 5, 11124 (2015).
    CAS  PubMed  PubMed Central  Google Scholar 

    37.
    Castrillo, G. et al. Root microbiota drive direct integration of phosphate stress and immunity. Nature 543, 513–518 (2017). In this study, a synthetic microbial community is used to define the molecular interactions that activate a microbiome-mediated response under nutrient-deficient conditions while repressing host immune output, allowing selective microbial colonization.
    CAS  PubMed  PubMed Central  Google Scholar 

    38.
    Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019). This study demonstrates that slight variation in single plant genes can result in differential recruitment and enrichment of selected microbial groups and functions that correlate with higher nitrogen use efficiency of indica than of japonica varieties of rice.
    CAS  PubMed  Google Scholar 

    39.
    Durán, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983 (2018). This study demonstrates that biocontrol traits of root-associated bacteria modulate interkingdom interactions between bacterial and filamentous eukaryotic microorganisms, resulting in a balanced plant–microbiome interaction that favours plant growth and survival against root-derived fungi and/or oomycetes.
    PubMed  PubMed Central  Google Scholar 

    40.
    Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).
    PubMed  PubMed Central  Google Scholar 

    41.
    Maignien, L., DeForce, E. A., Chafee, M. E., Eren, A. M. & Simmons, S. L. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. mBio 5, e00682-13 (2014).
    PubMed  PubMed Central  Google Scholar 

    42.
    Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015). This study demonstrates a significant overlap between bacterial isolates from plant environments and their representation in culture-independent surveys, suggesting that a substantial proportion of the plant-associated microbiota is culturable.
    CAS  PubMed  Google Scholar 

    43.
    Edwards, J. A. et al. Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLoS Biol. 16, e2003862 (2018).
    PubMed  PubMed Central  Google Scholar 

    44.
    Morella, N. M. et al. Successive passaging of a plant-associated microbiome reveals robust habitat and host genotype-dependent selection. Proc. Natl Acad. Sci. USA 117, 1148–1159 (2019).
    PubMed  Google Scholar 

    45.
    Moissl-Eichinger, C. et al. Archaea are interactive components of complex microbiomes. Trends Microbiol. 26, 70–85 (2018).
    CAS  PubMed  Google Scholar 

    46.
    Taffner, J. et al. What is the role of Archaea in plants? New insights from the vegetation of alpine bogs. MSphere 3, e00122-–18 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    47.
    Taffner, J., Cernava, T., Erlacher, A. & Berg, G. Novel insights into plant-associated archaea and their functioning in arugula (Eruca sativa Mill.). J. Adv. Res. 19, 39–48 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    48.
    Pratama, A. A. & van Elsas, J. D. The ‘neglected’ soil virome — potential role and impact. Trends Microbiol. 26, 649–662 (2018).
    CAS  PubMed  Google Scholar 

    49.
    Trubl, G. et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 3, e00076-18 (2018).
    PubMed  PubMed Central  Google Scholar 

    50.
    Morella, N. M., Gomez, A. L., Wang, G., Leung, M. S. & Koskella, B. The impact of bacteriophages on phyllosphere bacterial abundance and composition. Mol. Ecol. 27, 2025–2038 (2018).
    PubMed  Google Scholar 

    51.
    Castillo, J. D., Vivanco, J. M. & Manter, D. K. Bacterial microbiome and nematode occurrence in different potato agricultural soils. Microb. Ecol. 74, 888–900 (2017).
    PubMed  Google Scholar 

    52.
    Elhady, A. et al. Microbiomes associated with infective stages of root-knot and lesion nematodes in soil. PLoS ONE 12, e0177145 (2017).
    PubMed  PubMed Central  Google Scholar 

    53.
    Treonis, A. M. et al. Characterization of soil nematode communities in three cropping systems through morphological and DNA metabarcoding approaches. Sci. Rep. 8, 2004 (2018).
    PubMed  PubMed Central  Google Scholar 

    54.
    Gao, Z., Karlsson, I., Geisen, S., Kowalchuk, G. & Jousset, A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 24, 165–176 (2018).
    PubMed  Google Scholar 

    55.
    Larousse, M. & Galiana, E. Microbial partnerships of pathogenic oomycetes. PLoS Pathog. 13, e1006028 (2017).
    PubMed  PubMed Central  Google Scholar 

    56.
    Ploch, S. & Thines, M. Obligate biotrophic pathogens of the genus Albugo are widespread as asymptomatic endophytes in natural populations of Brassicaceae. Mol. Ecol. 20, 3692–3699 (2015).
    Google Scholar 

    57.
    Benhamou, N. et al. Pythium oligandrum: an example of opportunistic success. Microbiol 158, 2679–2694 (2012).
    CAS  Google Scholar 

    58.
    Sapp, M., Ploch, S., Fiore-Donno, A. M., Bonkowski, M. & Rose, L. E. Protists are an integral part of the Arabidopsis thaliana microbiome. Environ. Microbiol. 20, 30–43 (2018).
    CAS  PubMed  Google Scholar 

    59.
    Astudillo-García, C. et al. Evaluating the core microbiota in complex communities: a systematic investigation. Environ. Microbiol. 19, 1450–1462 (2017).
    PubMed  Google Scholar 

    60.
    Yeoh, Y. K. et al. Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence. Nat. Commun. 8, 215 (2017).
    PubMed  PubMed Central  Google Scholar 

    61.
    Garrido-Oter, R. et al. Modular traits of the rhizobiales root microbiota and their evolutionary relationship with symbiotic rhizobia. Cell Host Microbe 24, 155–167 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    62.
    Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016). This study demonstrates the presence of highly interconnected ‘hub species’ in microbial networks that act as mediators between a host and its associated microbiome.
    PubMed  PubMed Central  Google Scholar 

    63.
    Muller, E. E. et al. Using metabolic networks to resolve ecological properties of microbiomes. Curr. Opin. Sys. Biol. 8, 73–80 (2018).
    Google Scholar 

    64.
    Röttjers, L. & Faust, K. From hairballs to hypotheses — biological insights from microbial networks. FEMS Microbiol. Rev. 42, 761–780 (2018).
    PubMed  PubMed Central  Google Scholar 

    65.
    Shade, A., Jacques, M. A. & Barret, M. Ecological patterns of seed microbiome diversity, transmission, and assembly. Curr. Opin. Microbiol. 37, 15–22 (2017).
    PubMed  Google Scholar 

    66.
    Gloria, T. C. et al. Functional microbial features driving community assembly during seed germination and emergence. Front. Plant Sci. 9, 902 (2018).
    Google Scholar 

    67.
    Wei, Z. et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci. Adv. 5, eaaw0759 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    68.
    Tian, B. et al. Beneficial traits of bacterial endophytes belonging to the core communities of the tomato root microbiome. Agric. Ecosys. Env. 247, 149–156 (2017).
    Google Scholar 

    69.
    Lau, J. A. & Lennon, J. T. Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc. Natl Acad. Sci. USA. 109, 14058–14062 (2012).
    CAS  PubMed  Google Scholar 

    70.
    Gehring, C. A., Sthultz, C. M., Flores-Rentería, L., Whipple, A. V. & Whitham, T. G. Tree genetics defines fungal partner communities that may confer drought tolerance. Proc. Natl Acad. Sci. USA 114, 11169–11174 (2017).
    CAS  PubMed  Google Scholar 

    71.
    Zhang, Y. et al. Huanglongbing impairs the rhizosphere-to-rhizoplane enrichment process of the citrus root-associated microbiome. Microbiome 5, 97 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    72.
    Knief, C. et al. Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. ISME J. 6, 1378–1390 (2012).
    CAS  PubMed  Google Scholar 

    73.
    Jiménez Bremont, J. F. et al. Physiological and molecular implications of plant polyamine metabolism during biotic interactions. Front. Plant Sci. 5, 95 (2014).
    PubMed  PubMed Central  Google Scholar 

    74.
    Busk, P. K. & Lange, L. Classification of fungal and bacterial lytic polysaccharide monooxygenases. BMC Genomics 16, 368 (2015).
    PubMed  PubMed Central  Google Scholar 

    75.
    Trivedi, P., Anderson, I. C. & Singh, B. K. Microbial modulators of soil carbon storage: integrating genomic and metabolic knowledge for global prediction. Trends Microbiol. 21, 641–651 (2013).
    CAS  PubMed  Google Scholar 

    76.
    Jiang, X. et al. Impact of spatial organization on a novel auxotrophic interaction among soil microbes. ISME J. 12, 1443–1456 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    77.
    Blair, P. M. et al. Exploration of the biosynthetic potential of the Populus microbiome. mSystems 3, e00045–18 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    78.
    Sessitsch, A. et al. Functional characteristics of an endophyte community colonizing rice roots as revealed by metagenomic analysis. Mol. Plant Microbe Interact. 25, 28–36 (2012).
    CAS  PubMed  Google Scholar 

    79.
    Han, G. Z. Origin and evolution of the plant immune system. New Phytol. 222, 70–83 (2019).
    PubMed  Google Scholar 

    80.
    Eitas, T. K. & Dangl, J. L. NB-LRR proteins: pairs, pieces, perception, partners, and pathways. Curr. Opin. Plant Biol. 13, 472–477 (2010).
    CAS  PubMed  PubMed Central  Google Scholar 

    81.
    Hardoim, P. R. et al. The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol. Mol. Biol. Rev. 79, 293–320 (2015).
    PubMed  PubMed Central  Google Scholar 

    82.
    McCann, H. C. et al. Origin and evolution of the kiwifruit canker pandemic. Genome Biol. Evol. 9, 932–944 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    83.
    Berendsen, R. L. et al. Disease-induced assemblage of a plant-beneficial bacterial consortium. ISME J. 12, 1496–1507 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    84.
    Kwak, M. J. et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat. Biotechnol. 36, 1100–1109 (2018). This study demonstrates that the disease resistance traits of plant varieties are conferred by selective assembly of a native microbiota to rescue a plant from fungal invasion.
    CAS  Google Scholar 

    85.
    Mendes, L. W., Raaijmakers, J. M., de Hollander, M., Mendes, R. & Tsai, S. M. Influence of resistance breeding in common bean on rhizosphere microbiome composition and function. ISME J. 12, 212–224 (2019).
    Google Scholar 

    86.
    Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019). This study demonstrates a microbiome-mediated, multitiered defence system against fungal pathogens, in which the first defence layer is formed by the rhizosphere microbiota; any subsequent attempt to colonize the plant root activates a second layer of defence through plant endophytes that produce antifungal compounds, including effectors, enzymes and antibiotics.
    PubMed  Google Scholar 

    87.
    Helfrich, E. J. et al. Bipartite interactions, antibiotic production and biosynthetic potential of the Arabidopsis leaf microbiome. Nat. Microbiol. 3, 909–919 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    88.
    Rutherford, S. T. & Bassler, B. L. Bacterial quorum sensing: its role in virulence and possibilities for its control. Cold Spring Harb. Perspect. Med. 2, a012427 (2012).
    PubMed  PubMed Central  Google Scholar 

    89.
    Hartmann, A. & Schikora, A. Plant responses to bacterial quorum sensing molecules. Front. Plant Sci. 6, 643 (2015).
    PubMed  PubMed Central  Google Scholar 

    90.
    Mousa, W. K. et al. Root-hair endophyte stacking in finger millet creates a physicochemical barrier to trap the fungal pathogen Fusarium graminearum. Nat. Microbiol. 1, 16167 (2016).
    CAS  PubMed  Google Scholar 

    91.
    Trivedi, P., Spann, T. & Wang, N. Isolation and characterization of beneficial bacteria associated with citrus roots in Florida. Microb. Ecol. 62, 324–336 (2011).
    CAS  PubMed  Google Scholar 

    92.
    Chagas, F. O. et al. Chemical signaling involved in plant–microbe interactions. Chem. Soc. Rev. 47, 1652–1704 (2018).
    CAS  PubMed  Google Scholar 

    93.
    Schmidt, R. et al. Fungal volatile compounds induce production of the secondary metabolite Sodorifen in Serratia plymuthica PRI-2C. Sci. Rep. 7, 862 (2017).
    PubMed  PubMed Central  Google Scholar 

    94.
    Korenblum, E. et al. Rhizosphere microbiome mediates systemic root metabolite exudation by root-to-root signaling. Proc. Natl Acad. Sci. USA 117, 3874–3883 (2020).
    CAS  PubMed  Google Scholar 

    95.
    Russell, A. B., Peterson, S. B. & Mougous, J. D. Type VI secretion system effectors: poisons with a purpose. Nat. Rev. Microbiol. 12, 137–148 (2014).
    CAS  PubMed  PubMed Central  Google Scholar 

    96.
    Bernal, P., Llamas, M. A., Filloux, A. & Type, V. I. Secretion systems in plant-associated bacteria. Environ. Microbiol. 201, 15–72 (2018).
    Google Scholar 

    97.
    Speare, L. et al. Bacterial symbionts use a type VI secretion system to eliminate competitors in their natural host. Proc. Natl Acad. Sci. USA 115, E8528–E8537 (2018).
    CAS  PubMed  Google Scholar 

    98.
    Vorholt, J. A., Vogel, C., Carlström, C. I. & Mueller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2019).
    Google Scholar 

    99.
    Cordovez, V., Dini-Andreote, F., Carrión, V. J. & Raaijmakers, J. M. Ecology and evolution of plant microbiomes. Ann. Rev. Microbiol. 73, 69–88 (2019).
    CAS  Google Scholar 

    100.
    Hestrin, R., Hammer, E. C., Mueller, C. W. & Lehmann, J. Synergies between mycorrhizal fungi and soil microbial communities increase plant nitrogen acquisition. Commun. Biol. 2, 233 (2019).
    PubMed  PubMed Central  Google Scholar 

    101.
    Averill, C., Bhatnagar, J. M., Pearse, W. D. & Kivlin, S. N. Global imprint of mycorrhizal fungi on whole-plant nutrient economics. Proc. Natl Acad. Sci. USA 116, 23163–23168 (2019).
    CAS  PubMed  Google Scholar 

    102.
    Lu, T. et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome 6, 231 (2018).
    PubMed  PubMed Central  Google Scholar 

    103.
    Bodenhausen, K. et al. Petunia- and Arabidopsis-specific root microbiota responses to phosphate supplementation. Phytobiomes J. 3, 112–124 (2019).
    Google Scholar 

    104.
    Almario, J. et al. Root-associated fungal microbiota of nonmycorrhizal Arabis alpina and its contribution to plant phosphorus nutrition. Proc. Natl Acad. Sci. USA 114, E9403–E9412 (2017).
    CAS  PubMed  Google Scholar 

    105.
    Hacquard, S. et al. Survival trade-offs in plant roots during colonization by closely related beneficial and pathogenic fungi. Nat. Commun. 7, 1–13 (2016).
    Google Scholar 

    106.
    Voges, M. J., Bai, Y., Schulze-Lefert, P. & Sattely, E. S. Plant-derived coumarins shape the composition of an Arabidopsis synthetic root microbiome. Proc. Natl Acad. Sci. USA 116, 12558–12565 (2019). This study demonstrates that the production of secondary metabolites produced by plants under stress conditions acts as a signalling mechanism to sculpt the rhizosphere microbiome.
    PubMed  Google Scholar 

    107.
    Stringlis, I. A. et al. MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health. Proc. Natl Acad. Sci. USA 115, E5213–E5222 (2018).
    CAS  PubMed  Google Scholar 

    108.
    Martínez-Medina, A., Van Wees, S. C. & Pieterse, C. M. Airborne signals from Trichoderma fungi stimulate iron uptake responses in roots resulting in priming of jasmonic acid-dependent defenses in shoots of Arabidopsis thaliana and Solanum lycopersicum. Plant Cell Env. 40, 2691–2705 (2017).
    Google Scholar 

    109.
    Penton, C. R. et al. Fungal community structure in disease suppressive soils assessed by 28S LSU gene sequencing. PLoS ONE 9, e93893 (2014).
    PubMed  PubMed Central  Google Scholar 

    110.
    Cha, J. Y. et al. Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J. 10, 119–129 (2016).
    CAS  PubMed  Google Scholar 

    111.
    Hol, W. G. et al. Non-random species loss in bacterial communities reduces antifungal volatile production. Ecology 96, 2042–2048 (2015).
    PubMed  Google Scholar 

    112.
    Carrión, V. J. et al. Involvement of Burkholderiaceae and sulfurous volatiles in disease-suppressive soils. ISME J. 12, 2307–2321 (2018).
    PubMed  PubMed Central  Google Scholar 

    113.
    Chialva, M. et al. Native soils with their microbiotas elicit a state of alert in tomato plants. New Phytol. 220, 1296–1308 (2018).
    CAS  PubMed  Google Scholar 

    114.
    Peralta, A. L., Sun, Y., McDaniel, M. D. & Lennon, J. T. Crop rotational diversity increases disease suppressive capacity of soil microbiomes. Ecosphere 9, e02235 (2018).
    Google Scholar 

    115.
    Kesten, C. et al. Pathogen-induced pH changes regulate the growth–defense balance of plants. EMBO J. 16, e101822550491 (2019).
    Google Scholar 

    116.
    Yuan, J. et al. Root exudates drive the soil-borne legacy of aboveground pathogen infection. Microbiome 6, 56 (2018).
    Google Scholar 

    117.
    Hu, L. et al. Root exudate metabolites drive plant-soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 9, 2738 (2018).
    PubMed  PubMed Central  Google Scholar 

    118.
    Kong, H. G., Song, G. C. & Ryu, C. M. Inheritance of seed and rhizosphere microbial communities through plant–soil feedback and soil memory. Environ. Microbiol. Rep. 11, 479–486 (2019).
    PubMed  Google Scholar 

    119.
    Fitzpatrick, C. R., Mustafa, Z. & Viliunas, J. Soil microbes alter plant fitness under competition and drought. J. Evol. Biol. 32, 438–450 (2019).
    PubMed  Google Scholar 

    120.
    Eida, A. A. et al. Desert plant bacteria reveal host influence and beneficial plant growth properties. PLoS ONE 13, e0208223 (2018).
    PubMed  PubMed Central  Google Scholar 

    121.
    Naylor, D. & Coleman-Derr, D. Drought stress and root-associated bacterial communities. Front. Plant Sci. 8, 2223 (2018).
    PubMed  PubMed Central  Google Scholar 

    122.
    Xu, L. et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc. Natl Acad. Sci. USA 115, E4284–E4293 (2018). Using a multi-‘omics’ approach, this study demonstrates selective enrichment of monoderms (bacteria with a thick cell wall) that possess transporters connected with specialized metabolites produced by plants under drought stress.
    CAS  PubMed  Google Scholar 

    123.
    Timm, C. M. et al. Abiotic stresses shift belowground Populus-associated bacteria toward a core stress microbiome. mSystems 3, e00070-17 (2018).
    PubMed  PubMed Central  Google Scholar 

    124.
    Wagner, M. R. et al. Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecol. Lett. 17, 717–726 (2014).
    PubMed  PubMed Central  Google Scholar 

    125.
    Ravanbakhsh, M., Sasidharan, R., Voesenek, L. A., Kowalchuk, G. A. & Jousset, A. Microbial modulation of plant ethylene signaling: ecological and evolutionary consequences. Microbiome 6, 52 (2018).
    PubMed  PubMed Central  Google Scholar 

    126.
    Giauque, H., Connor, E. W. & Hawkes, C. V. Endophyte traits relevant to stress tolerance, resource use and habitat of origin predict effects on host plants. New Phytol. 221, 2239–2249 (2019).
    CAS  PubMed  Google Scholar 

    127.
    Kudjordjie, E. N., Sapkota, R., Steffensen, S. K., Fomsgaard, I. S. & Nicolaisen, M. Maize synthesized benzoxazinoids affect the host associated microbiome. Microbiome 7, 59 (2019).
    PubMed  PubMed Central  Google Scholar 

    128.
    Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470 (2018).
    CAS  PubMed  Google Scholar 

    129.
    Huang, A. C. et al. A specialized metabolic network selectively modulates Arabidopsis root microbiota. Science 364, eaau6389 (2019).
    CAS  PubMed  Google Scholar 

    130.
    McCann, H. C., Nahal, H., Thakur, S. & Guttman, D. S. Identification of innate immunity elicitors using molecular signatures of natural selection. Proc. Natl Acad. Sci. USA 109, 4215–4220 (2012).
    CAS  PubMed  Google Scholar 

    131.
    Hacquard, S., Spaepen, S., Garrido-Oter, R. & Schulze-Lefert, P. Interplay between innate immunity and the plant microbiota. Ann. Rev. Phytopathol. 55, 565–589 (2017).
    CAS  Google Scholar 

    132.
    Chen, H. et al. One-time nitrogen fertilization shifts switchgrass soil microbiomes within a context of larger spatial and temporal variation. PLoS ONE 14, e0211310 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    133.
    Trivedi, P. et al. Soil aggregation and associated microbial communities modify the impact of agricultural management on carbon content. Environ. Microbiol. 19, 3070–3086 (2017).
    CAS  PubMed  Google Scholar 

    134.
    Hartman, K. et al. Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming. Microbiome 6, 14 (2018).
    PubMed  PubMed Central  Google Scholar 

    135.
    Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).
    PubMed  PubMed Central  Google Scholar  More

  • in

    Colony co-founding in ants is an active process by queens

    1.
    Bourke, A. F. G. & Heinze, J. The ecology of communal breeding: The case of multiple-queen Leptothoracine ants. Philos. Trans. R. Soc. Lond. B Biol. Sci.345, 359–372 (1994).
    ADS  Google Scholar 
    2.
    Bourke, A. F. G. Principles of Social Evolution. Oxford Series in Ecology and Evolution (2011).

    3.
    Cockburn, A. Evolution of helping behavior in cooperatively breeding birds. Annu. Rev. Ecol. Evol. Syst.29, 141–177 (1998).
    Google Scholar 

    4.
    Jennions, M. Cooperative breeding in mammals. Trends Ecol. Evol.9, 89–93 (1994).
    CAS  PubMed  Google Scholar 

    5.
    Lukas, D. & Clutton-Brock, T. Life histories and the evolution of cooperative breeding in mammals. Proc. R. Soc. B279, 4065–4070 (2012).
    PubMed  Google Scholar 

    6.
    Purcell, J. Geographic patterns in the distribution of social systems in terrestrial arthropods. Biol. Rev.86, 475–491 (2011).
    PubMed  Google Scholar 

    7.
    Wong, M. & Balshine, S. The evolution of cooperative breeding in the African cichlid fish, Neolamprologus pulcher. Biol. Rev.86, 511–530 (2011).
    PubMed  Google Scholar 

    8.
    Dugatkin, L. Animal cooperation among unrelated individuals. Naturwissenschaften89, 533–541 (2002).
    ADS  CAS  PubMed  Google Scholar 

    9.
    Emlen, S. The evolution of helping. An ecological constraints model. Am. Nat.119, 29–39 (1982).
    Google Scholar 

    10.
    Nichols, H. J. et al. Food availability shapes patterns of helping effort in a cooperative mongoose. Anim. Behav.83, 1377–1385 (2012).
    Google Scholar 

    11.
    Riehl, C. & Strong, M. J. Stable social relationships between unrelated females increase individual fitness in a cooperative bird. Proc. R. Soc. B285, 20180130 (2018).
    PubMed  Google Scholar 

    12.
    Sharp, S. P., English, S. & Clutton-Brock, T. H. Maternal investment during pregnancy in wild meerkats. Evol. Ecol.27, 1033–1044 (2012).
    Google Scholar 

    13.
    Taborsky, M. Broodcare helpers in the cichlid fish Lamprologus brichardi: Their costs and benefits. Anim. Behav.32, 1236–1252 (1984).
    Google Scholar 

    14.
    Hamilton, W. D. The genetical evolution of social behaviour. J. Theor. Biol.7, 1–52 (1964).
    CAS  PubMed  Google Scholar 

    15.
    Bshary, R. Cooperation between unrelated individuals—a game theoretic approach. In Animal Behaviour: Evolution and Mechanisms (ed. Kappeler, P.) 213–240 (Springer, Berlin, 2010).
    Google Scholar 

    16.
    Dugatkin, L. A. & Mesterton-Gibbons, M. Cooperation among unrelated individuals: Reciprocal altruism, by-product mutualism and group selection in fishes. Biosystems37, 19–30 (1996).
    CAS  PubMed  Google Scholar 

    17.
    Keller, L. Queen Number and Sociality in Insects (Oxford University Press, Oxford, 1993).
    Google Scholar 

    18.
    Matsuura, K., Fujimoto, M., Goka, K. & Nishida, T. Cooperative colony foundation by termite female pairs: Altruism for survivorship in incipient colonies. Anim. Behav.64, 167–173 (2002).
    Google Scholar 

    19.
    Mesterton-Gibbons, M. & Dugatkin, L. A. Cooperation among unrelated individuals: Evolutionary factors. Q. Rev. Biol.67, 267–281 (1992).
    Google Scholar 

    20.
    Bernasconi, G. & Strassmann, J. E. Cooperation among unrelated individuals: The ant foundress case. Trends Ecol. Evol.14, 477–482 (1999).
    CAS  PubMed  Google Scholar 

    21.
    Itô, Y. Behaviour and Social Evolution of Wasps (Oxford University Press, Oxford, 1993).
    Google Scholar 

    22.
    Packer, L. Multiple-foundress associations in sweat bees. In Queen Number and Sociality in Insects (ed. Keller, L.) 215–233 (Oxford University Press, Oxford, 1993).
    Google Scholar 

    23.
    Schwarz, M. P., Bull, N. J. & Hogendoorn, K. Evolution of sociality in the allodapine bees: A review of sex allocation, ecology and evolution. Insectes Soc.45, 349–368 (1998).
    Google Scholar 

    24.
    Shellman-Reeve, J. S. The spectrum of eusociality in termites. In The Evolution of Social Behavior in Insects and Arachnids (eds Choe, J. C. & Crespi, B. J.) 52–93 (Cambridge University Press, Cambridge, 1997).
    Google Scholar 

    25.
    Thorne, B. L. Evolution of eusociality in termites. Annu. Rev. Ecol. Evol. Syst.28, 27–54 (1997).
    Google Scholar 

    26.
    Hölldobler, B. & Wilson, E. O. The Ants (Springer, Berlin, 1990).
    Google Scholar 

    27.
    Schmid-Hempel, P. Parasites in Social Insects (Princeton University Press, Princeton, 1998).
    Google Scholar 

    28.
    Tschinkel, W. R. The Fire Ants (Harvard University Press, Cambridge, 2006).
    Google Scholar 

    29.
    Cole, B. J. The ecological setting of social evolution. In Organization of Insect Societies (eds Gadau, J. & Fewell, J.) 74–104 (Harvard University Press, Cambridge, 2009).
    Google Scholar 

    30.
    Johnson, R. A. Colony founding by pleometrosis in the semi-claustral seed-harvester ant Pogonomyrmex calfornicus (Hymenoptera: Formicidae). Anim. Behav.68, 1189–1200 (2004).
    Google Scholar 

    31.
    Tschinkel, W. R. An experimental study of pleometrotic colony founding in the fire ant, Solenopsis invicta: What is the basis for association?. Behav. Ecol. Sociobiol.43, 247–257 (1998).
    Google Scholar 

    32.
    Jerome, C. A., McInnes, D. A. & Adams, E. S. Group defense by colony-founding queens in the fire ant Solenopsis invicta. Behav. Ecol.9, 301–308 (1998).
    Google Scholar 

    33.
    Helms Cahan, S. & Julian, G. E. Fitness consequences of cooperative colony founding in the desert leaf-cutter ant Acromyrmex versicolor. Behav. Ecol.10, 585–591 (1999).
    Google Scholar 

    34.
    Adams, E. S. & Tschinkel, W. R. Effects of foundress number on brood raids and queen survival in the fire ant Solenopsis invicta. Behav. Ecol. Sociobiol.37, 233–242 (1995).
    Google Scholar 

    35.
    Clark, R. M. & Fewell, J. H. Social dynamics drive selection in cooperative associations of ant queens. Behav. Ecol.25, 117–123 (2014).
    Google Scholar 

    36.
    Offenberg, J., Peng, R. & Nielsen, M. Development rate and brood production in haplo- and pleometrotic colonies of Oecophylla smaragdina. Insectes Soc.59, 307–311 (2012).
    Google Scholar 

    37.
    Rissing, S. W. & Pollock, G. B. An experimental analysis of pleometric advantage in the desert seed-harvester ant Messor pergandei (Hymenoptera; Formicidae). Insectes Soc.38, 205–211 (1991).
    Google Scholar 

    38.
    Sasaki, K., Jibiki, E., Satoh, T. & Obara, Y. Queen phenotype and behaviour during cooperative colony founding in Polyrhachis moesta. Insectes Soc.52, 19–25 (2005).
    Google Scholar 

    39.
    Waloff, N. The effect of the number of queens of the ant Lasius flavus (Fab.) (Hym. Formicidae) on their survival and on the rate of development of the first brood. Insectes Soc.4, 391–408 (1957).
    Google Scholar 

    40.
    Bartz, S. H. & Hölldobler, B. Colony founding in Myrmecocystus mimicus Wheeler (Hymenoptera, Formicidae) and the evolution of foundress associations. Behav. Ecol. Sociobiol10, 137–147 (1982).
    Google Scholar 

    41.
    Helms Cahan, S. Ecological variation across a transition in colony-founding behavior in the ant Messor pergandei. Oecologia129, 629–635 (2001).
    ADS  Google Scholar 

    42.
    Sommer, K. & Hölldobler, B. Colony founding by queen association and determinants of reduction in queen number in the ant Lasius niger. Anim. Behav.50, 287–294 (1995).
    Google Scholar 

    43.
    Tschinkel, W. R. & Howard, D. F. Colony founding by pleometrosis in the fire ant, Solenopsis invicta. Behav. Ecol. Sociobiol12, 103–113 (1983).
    Google Scholar 

    44.
    Herbers, J. M. Nest site limitation and facultative polygyny in the ant Leptothorax longispinosus. Behav. Ecol. Sociobiol19, 115–122 (1986).
    Google Scholar 

    45.
    Nonacs, P. Queen condition and alate density affect pleometrosis in the ant Lasius pallitarsis. Insectes Soc.39, 3–13 (1992).
    Google Scholar 

    46.
    Masoni, A. et al. Pleometrotic colony foundation in the ant Crematogaster scutellaris (Hymenoptera: Formicidae): Better be alone than in bad company. Myrmecol. News25, 51–59 (2016).
    Google Scholar 

    47.
    Sommer, K. & Hölldobler, B. Pleometrosis in Lasius niger. In Biology and Evolution of Social Insects (ed. Billen, J.) 47–50 (Leuven University Press, Leuven, 1992).
    Google Scholar 

    48.
    Pfennig, D. W. Absence of joint nesting advantage in desert seed harvester ants: Evidence from a field experiment. Anim. Behav.49, 567–575 (1995).
    Google Scholar 

    49.
    Tschinkel, W. R. Brood raiding and the population dynamics of founding and incipient colonies of the fire ant Solenopsis invicta. Ecol. Entomol.17, 179–188 (1992).
    Google Scholar 

    50.
    Helms Cahan, S. & Fewell, J. H. Division of labor and the evolution of task sharing in queen associations of the harvester ant Pogonomyrmex californicus. Behav. Ecol. Sociobiol.56, 9–17 (2004).
    Google Scholar 

    51.
    Helmkampf, M., Mikheyev, A. S., Kang, Y., Fewell, J. & Gadau, J. Gene expression and variation in social aggression by queens of the harvester ant Pogonomyrmex californicus. Mol. Ecol.25, 3716–3730 (2016).
    PubMed  Google Scholar 

    52.
    Overson, R. P., Gadau, J., Clark, R. M., Pratt, S. C. & Fewell, J. H. Behavioral transitions with the evolution of cooperative nest founding by harvester ant queens. Behav. Ecol. Sociobiol.68, 21–30 (2014).
    Google Scholar 

    53.
    Shaffer, Z. et al. The foundress’s dilemma: Group selection for cooperation among queens of the harvester ant, Pogonomyrmex californicus. Sci. Rep.6, 29828 (2016).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    54.
    Aron, S., Steinhauer, N. & Fournier, D. Influence of queen phenotype, investment and maternity apportionment on the outcome of fights in cooperative foundations of the ant Lasius niger. Anim. Behav.77, 1067–1074 (2009).
    Google Scholar 

    55.
    Brütsch, T., Avril, A. & Chapuisat, M. No evidence for social immunity in co-founding queen associations. Sci. Rep.7, 16262 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    56.
    Chérasse, S. & Aron, S. Measuring inotocin receptor gene expression in chronological order in ant queens. Horm. Behav.96, 116–121 (2017).
    PubMed  Google Scholar 

    57.
    Dreier, S. & d’Ettorre, P. Social context predicts recognition systems in ant queens. J. Evol. Biol.22, 644–649 (2009).
    CAS  PubMed  Google Scholar 

    58.
    Holman, L., Dreier, S. & d’Ettorre, P. Selfish strategies and honest signalling: Reproductive conflicts in ant queen associations. Proc. R. Soc. B277, 2007–2015 (2010).
    CAS  PubMed  Google Scholar 

    59.
    Pull, C. D. & Cremer, S. Co-founding ant queens prevent disease by performing prophylactic undertaking behaviour. BMC Evol. Biol.17, 219 (2017).
    PubMed  PubMed Central  Google Scholar 

    60.
    Pull, C. D., Hughes, W. H. O. & Brown, M. J. F. Tolerating an infection: An indirect benefit of co-founding queen associations in the ant Lasius niger. Naturwissenschaften100, 1125–1136 (2013).
    ADS  CAS  PubMed  Google Scholar 

    61.
    Bernasconi, G. & Keller, L. Phenotype and individual investment in cooperative foundress associations of the fire ant, Solenopsis invicta. Behav. Ecol9, 478–485 (1998).
    Google Scholar 

    62.
    Bernasconi, G. & Keller, L. Effect of queen phenotype and social environment on early queen mortality in incipient colonies of the fire ant, Solenopsis invicta. Anim. Behav.57, 371–377 (1999).
    CAS  PubMed  Google Scholar  More

  • in

    Sap flow of Amorpha fruticosa: implications of water use strategy in a semiarid system with secondary salinization

    1.
    Tian, F. Q., Hu, H. C., Zhang, Z. & Hu, H. P. Secondary salinization and evapotranspiration under mulched drip irrigation condition in Tarim River basin of northwestern China. EGU Gen. Assembly Conf. Abstr. 15, EGU2013–8341 (2013).
    2.
    Liu, T. et al. Differentially improved soil microenvironment and seedling growth of Amorpha fruticosa by plastic, sand and straw mulching in a saline wasteland in northwest China. Ecol. Eng.122, 126–134 (2018).
    Article  Google Scholar 

    3.
    Rewald, B., Rachmilevitch, S., McCue, M. D. & Ephrath, J. E. Influence of saline drip-irrigation on fine root and sap-flow densities of two mature olive varieties. Environ. Exp. Bot.72, 107–114 (2011).
    Article  Google Scholar 

    4.
    Wullshleger, S. D., Meinzer, F. C. & Vertessy, R. A. A review of whole-plant water use studies in tree. Tree Physiol.18, 499–512 (1998).
    Article  Google Scholar 

    5.
    Forster, M. A. How significant is nocturnal sap flow?. Tree Physiol.34, 757–765 (2014).
    Article  Google Scholar 

    6.
    Chu, C. R., Hsieh, C. I., Wu, S. Y. & Phillips, N. G. Transient response of sap flow to wind speed. J. Exp. Bot.60, 249–255 (2009).
    CAS  Article  Google Scholar 

    7.
    Ma, C. K. et al. Environmental controls on sap flow in black locust forest in Loess Plateau, China. Sci. Rep.7, 13160 (2017).
    ADS  Article  Google Scholar 

    8.
    Zhao, C. Y., Si, J. H., Feng, Q., Yu, T. F. & Li, P. D. Comparative study of daytime and nighttime sap flow of Populus euphratica. J. Plant Growth Regul.82, 1–10 (2017).
    ADS  Article  Google Scholar 

    9.
    Zhang, Q. Y. et al. Sap flow of black locust in response to short-term drought in southern Loess Plateau of China. Sci. Rep.8, 6222 (2018).
    ADS  Article  Google Scholar 

    10.
    Rosado, B. H. P., Oliveira, R. S., Joly, C. A., Aidar, M. P. M. & Burgess, S. S. O. Diversity in nighttime transpiration behavior of woody species of the Atlantic Rain Forest, Brazil. Agric. For. Meteorol.159, 13–20 (2012).
    ADS  Article  Google Scholar 

    11.
    Doronila, A. I. & Forster, M. A. Performance measurement via sap flow monitoring of three eucalyptus species for mine site and dryland salinity phytoremediation. Int. J. Phytoremediat.17, 101–108 (2015).
    CAS  Article  Google Scholar 

    12.
    Fang, W. W., Lu, N., Zhang, Y., Jiao, L. & Fu, B. J. Responses of nighttime sap flow to atmospheric and soil dryness and its potential roles for shrubs on the Loess Plateau of China. J. Plant Ecol.11, 717–729 (2018).
    Article  Google Scholar 

    13.
    Daley, M. J. & Phillips, N. G. Interspecific variation in nighttime transpiration and stomatal conductance in a mixed New England deciduous forest. Tree Physiol.26, 411–419 (2006).
    Article  Google Scholar 

    14.
    Phillips, N. G., Lewis, J. D., Logan, B. A. & Tissue, D. T. Inter- and intra-specific variation in nocturnal water transport in Eucalyptus. Tree Physiol.30, 586–596 (2010).
    Article  Google Scholar 

    15.
    Caspari, H. W., Green, S. R. & Edwards, W. R. N. Transpiration of well-watered and water-stressed Asian pear trees as determined by lysimetry, heat-pulse, and estimated by a Penman-Monteith model. Agric. For. Meteorol.67, 13–27 (1993).
    ADS  Article  Google Scholar 

    16.
    Pfautsch, S. & Adams, M. A. Water flux of Eucalyptus regnans: Defying summer drought and a record heatwave in 2009. Oecologia172, 317–326 (2013).
    ADS  Article  Google Scholar 

    17.
    Chang, X. X., Zhao, W. Z. & He, Z. B. Radial pattern of sap flow and response to microclimate and soil moisture in Qinghai spruce (Picea crassifolia) in the upper Heihe River Basin of arid northwestern China. Agric. For. Meteorol.187, 14–21 (2014).
    ADS  Article  Google Scholar 

    18.
    Wang, Y. N. et al. Response of the daily transpiration of a larch plantation to variation in potential evaporation, leaf area index and soil moisture. Sci. Rep.9, 4697 (2019).
    ADS  Article  Google Scholar 

    19.
    Prieto, I., Kikvidze, Z. & Pugnaire, F. I. Hydraulic lift: soil processes and transpiration in the Mediterranean leguminous shrub Retama sphaerocarpa (L.) Boiss. Plant Soil329, 447–456 (2010).

    20.
    Shen, Q., Gao, G. Y., Fu, B. J. & Lü, Y. H. Sap flow and water use sources of shelter-belt trees in an arid inland river basin of Northwest China. Ecohydrology8, 1446–1458 (2014).
    Article  Google Scholar 

    21.
    Carter, J. L., Veneklaas, E. J., Colmer, T. D., Eastham, J. & Hatton, T. J. Contrasting water relations of three coastal tree species with different exposure to salinity. Physiol. Plantarum.127, 360–373 (2006).
    CAS  Article  Google Scholar 

    22.
    Ma, J. X., Huang, X., Li, W. H. & Zhu, C. G. Sap flow and trunk maximum daily shrinkage (MDS) measurements for diagnosing water status of Populus euphratica in an inland river basin of Northwest China. Ecohydrology6, 994–1000 (2013).
    CAS  Article  Google Scholar 

    23.
    Zhao, W. Z. & Liu, B. The response of sap flow in shrubs to rainfall pulses in the desert region of China. Agric. For. Meteorol.150, 1297–1306 (2010).
    ADS  Article  Google Scholar 

    24.
    Berbigier, P. et al. Transpiration of a 64-year-old maritime pine stand in Portugal. Oecologia107, 33–42 (1996).
    ADS  Article  Google Scholar 

    25.
    Chen, D. Y., Wang, Y. K., Liu, S. Y., Wei, X. G. & Wang, X. Response of relative sap flow to meteorological factors under different soil moisture conditions in rainfed jujube (Ziziphus jujuba Mil) plantations in semiarid Northwest China. Agric. Water. Manag.136, 23–33 (2014).
    Article  Google Scholar 

    26.
    Shen, Q., Gao, G. Y., Fu, B. J. & Lü, Y. H. Responses of shelterbelt stand transpiration to drought and groundwater variations in an arid inland river basin of Northwest China. J. Hydrol.531, 738–748 (2015).
    ADS  Article  Google Scholar 

    27.
    Sperry, J. S., Alder, N. N. & Eastlack, S. E. The effect of reduced hydraulic conductance on stomatal conductance and xylem cavitation. J. Exp. Bot.44, 1075–1082 (1993).
    Article  Google Scholar 

    28.
    Barbeta, A., Ogaya, R. & Peñuelas, J. Comparative study of diurnal and nocturnal sap flow of Quercus ilex and Phillyrea latifolia in a Mediterranean holm oak forest in Prades (Catalonia, NE Spain). Trees26, 1651–1659 (2013).
    Article  Google Scholar 

    29.
    Knapp, A. K. & Yavitt, J. B. Gas exchange characteristics of Typha latifolia L. from nine sites across North America. Aquat. Bot.49, 203–215 (1995).

    30.
    Wood, S. A. et al. Retraction notice to impacts of fire on forest age and runoff in mountain ash forests. Funct. Plant Biol.35, 483–492 (2008).
    Article  Google Scholar 

    31.
    Pfautsch, S. et al. Diurnal patterns of water use in Eucalyptus victrix indicate pronounced desiccation-rehydration cycles despite unlimited water supply. Tree Physiol.31, 1041–1051 (2011).
    Article  Google Scholar 

    32.
    Wang, Y. B. et al. The characteristics of nocturnal sap flow and stem water recharge pattern in growing season for a Larix principis-rupprechtii plantation. Acta Ecol. Sin.33, 1375–1385 (2013) ((in Chinese with English Abstract)).
    CAS  Article  Google Scholar 

    33.
    Campbell, G. S. & Norman, J. M. An Introduction to Environmental Biophysics. (Springer, New York, 1998). ISBN 978-0-387-94937-6.

    34.
    Granier, A. Evaluation of transpiration in a Douglas-fir stand by means of sap flow measurements. Tree Physiol.3, 309–320 (1987).
    CAS  Article  Google Scholar 

    35.
    Tie, Q., Hu, H. C., Tian, F. Q., Guan, H. D. & Lin, H. Environmental and physiological controls on sap flow in a subhumid mountainous catchment in north China. Agric. For. Meteorol.240–241, 46–57 (2017).
    ADS  Article  Google Scholar 

    36.
    R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2016). https://www.R-project.org. More

  • in

    Background choice and immobility as context dependent tadpole responses to perceived predation risk

    Study site
    Experiments were conducted in the Reserva Particular do Patrimônio Natural (RPPN) Santuário do Caraça, a private conservation unit in the southern portion of the Espinhaço Mountain range, Minas Gerais state, Brazil. The climate is seasonal, with a rainy period from October to March, and a dry period from April to September. Mean air temperatures vary between 13 and 29oC26.
    The focal species of this study was Ololygon machadoi. The tadpoles of this treefrog have been previously shown to react to both visual and chemical predator cues (from Belostoma testaceopallidum; Melo et al.18) by positioning themselves preferentially on yellow backgrounds where they are disruptive17,18. Ololygon machadoi breeds year-round in many streams in the RPPN, and we used one of these streams (20o05′37″S, 43o29′59″W; 1293 m above sea level), where its tadpoles are abundant, to conduct the experiments. It is a small first order stream27 with sandy or rocky bottoms. Stream bank vegetation is dense, composed of herbs, shrubs and trees. In the vicinities of the point where we conducted the experiments (and up to 150 m upstream) stream width ranges from 2 to 6 m, and stream depth, from just a few centimeters to about 1 m28.
    Response to predator cues in the natural habitat
    We built three enclosures measuring 40 × 35 cm with a plastic mesh around a metal frame that limited two compartments, one measuring 35 × 35 cm, and another contiguous to it measuring 5 × 35 cm. Both compartments were 15 cm high and were open both below and above. We set the enclosures at a stream section of shallow water and flat rocky bottom, where water filled the enclosures up to about 5 cm (Fig. 4). We placed the enclosures with the small compartment upstream, so that the tadpole in the larger compartment would be exposed to both visual and chemical cues of the predator to be introduced in the smaller compartment. We collected three water bugs (Belostoma testaceopallidum) to be used in the experiments in a nearby stream from the same water basin (20o06′40″S, 43o28′48″W; 1,254 m above sea level). We collected the tadpoles very close to the enclosures (up to 5 m upstream).
    Figure. 4

    Experimental design showing the water bug predators (Belostoma testaceopallidum, (A) and tadpoles of Ololygon machadoi (B) inside enclosures placed in a stream (C) at the RPPN Santuário do Caraça, Southeastern Brazil, where both species occur. A schematic representation of the experimental enclosures is also shown (D).

    Full size image

    Before we started each trial, we inspected the area covered by the enclosures to make sure no tadpoles or other animals remained inside. We then sealed the bottom carefully with sand from the same stream and placed one tadpole within each cage, in the larger compartment. We waited 3 min, sufficient for the tadpole to return to normal activity levels after translocation to the enclosures17, and then we recorded whether each tadpole was moving or standing still in 30 s intervals, during 15 min for a total of 30 observations. When tadpoles moved, they always moved on the bottom, never through the water column. After this, we waited another 3 min and repeated the 30 movement records for the next 15 min. We then removed the water bug and repeated another observation turn (waited 3 min, then made 30 movement observations separated by 30 s intervals). After each individual tadpole was tested, we released it downstream, to avoid using the same individual more than once. Tadpoles were all in developmental stage 2529 and measured 20.2 ± 2.4 mm (n = 33 tadpoles measured). We maintained the water bugs in individual recipients with clean stream water and used them randomly in the three enclosures. After all the experiments we collected them for identification.
    We tested 3 tadpoles simultaneously, then restarted the whole experiment with other 3 tadpoles, and so on, until we tested 54 tadpoles during three consecutive days (3–5 October, 2018). The weather was sunny with some clouds and short periods of light rain, during which we did not conduct experiments.
    Defensive responses based on previous experiences
    For this experiment, we collected stage 25 tadpoles at the same stream section and kept them for no more than 2 h in polystyrene boxes with stream water by the nearby (about 1.3 km) lodging of the RPPN Santuário do Caraça, where we conducted the experiments to test the influence of previous experience on tadpole background choice and immobility. We placed individual tadpoles in plastic trays measuring 43 cm length, 30 cm width, 9 cm height, with half the bottom covered by a picture of a natural yellow background (rocks in its natural habitat). The other half was covered with the same picture manipulated digitally to match the hues and luminance of natural dark backgrounds, as in17. We filled the trays with tap water that comes straight from the main stream at the reserve, replacing the water at every trial. For each trial, we waited 3 min. after tadpole placement in the center of the tray, then we observed tadpoles for 30 min, recording their background every minute. After that, we applied one of three treatments during a 5-min interval: (1) an aversive stimulus was applied to the tadpole every time it positioned itself on the yellow background, or (2) on the dark background or (3) no stimulus was applied (control). The aversive stimulus consisted in one person approaching a wood stick slowly towards the tadpole until it reacted fleeing. After the treatments, we conducted another 30 min of observations recording tadpole background every minute. The tadpoles were all returned to their original stream after the experiments. We tested 2 tadpoles in each treatment simultaneously and then repeated the trials until tests of 30 tadpoles for each treatment (total 90 tadpoles) were completed. Experiments were performed from 4 to 6 October 2016. Experiments were conducted in the shade with natural light, and all days were sunny.
    All the procedures were performed in accordance with relevant guidelines/regulations adopted by the responsible institutions: Sisbio/ICMBio (45302-1, 62316-1) authorized animal manipulation in situ and the Ethical Committee of the Pontifícia Universidade Católica de Minas Gerais (032/2016, 003/2018) approved the experimental procedures in accordance with animal welfare guidelines. The water bugs were identified as Belostoma testaceopallidum Latreille, 1807, and the collected specimens were deposited in the collection of aquatic insects of the Parasitology Department of the Institute of Biological Sciences (DPIC) of the Federal University of Minas Gerais, Belo Horizonte, Minas Gerais state, Brazil, under the accession number 9498.
    Statistical analyses
    We compared the level of activity of tadpoles (given by the number of instant positive records of movement) before, during, and after the presence of the water bug in the enclosures in the stream. We also evaluated a possible effect of direct sun incidence or shade on the enclosures30, and its interaction with tadpole activity levels. We built Generalized Linear Mixed Models (GLMM) with the packages “car”31 and “MASS”32 in R33. We considered number of movement records + 1 (to adjust to distributions that must be non-zero) as the dependent variable, phase (before, during, or after the presence of the water bug) and light (sun or shade) as explanatory variables, and individual tadpole as a random variable. Tadpoles might present different reactions to predators based on their previous experiences34. Considering individual as a random variable would also account for possible differences among times of the day and cages on individual behaviour. We built models including each one or both explanatory variables, with or without their interaction. We compared these models with a null model that included only the random variable, in order to identify the variable(s) with the strongest explanatory power.
    We also used GLMMs to test for the ability of tadpoles to avoid a background colour after an aversive experience on it. Since tadpoles had to choose between dark and yellow, we arbitrarily used the number of records on dark backgrounds as dependent variable, because the records on yellow would represent the alternative situation (not on dark). We used treatment and phase (before and after the treatments were applied) as fixed variables and tadpole as a random variable. In order to test for the expression of immobility after the aversive stimuli, we considered the number of times tadpoles changed background colour in consecutive observations as a surrogate for tadpole movement (our dependent variable), treatment and phase as fixed variables and tadpole as a random variable.
    We used the package MuMIn35 for R33 to select the best models, a procedure recommended to control the overall type I error rate36. We conducted Tukey post hoc tests with the package emmeans37. More

  • in

    Human practices promote presence and abundance of disease-transmitting mosquito species

    1.
    Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science348, 1217 (2015).
    ADS  PubMed  Google Scholar 
    2.
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature574, 671–674 (2019).
    ADS  CAS  Article  Google Scholar 

    3.
    Johnson, P. T. J., de Roode, J. C. & Fenton, A. Why infectious disease research needs community ecology. Science (80).349, 1259504 (2015).
    Article  Google Scholar 

    4.
    Lambin, E. F., Tran, A., Vanwambeke, S. O., Linard, C. & Soti, V. Pathogenic landscapes: interactions between land, people, disease vectors, and their animal hosts. Int. J. Health Geogr.9, 54 (2010).
    Article  Google Scholar 

    5.
    Muturi, E. J., Costanzo, K., Kesavaraju, B., Lampman, R. & Alto, B. W. Interaction of a pesticide and larval competition on life history traits of Culex pipiens. Acta Trop.116, 141–146 (2010).
    CAS  Article  Google Scholar 

    6.
    Krol, L. et al. Eutrophication mediates consequences of predator-prey interactions and temperature for Aedes aegypti. Parasit. Vectors12, 179 (2019).
    Article  Google Scholar 

    7.
    Samuel, M., Brooke, B. D. & Oliver, S. V. Effects of inorganic fertilizer on larval development, adult longevity and insecticide susceptibility in the malaria vector Anopheles arabiensis (Diptera: Culicidae). Pest Manag. Sci. https://doi.org/10.1002/ps.5676 (2020).
    Article  PubMed  Google Scholar 

    8.
    Jeanrenaud, A. C. S. N., Brooke, B. D. & Oliver, S. V. The effects of larval organic fertiliser exposure on the larval development, adult longevity and insecticide tolerance of zoophilic members of the Anopheles gambiae complex (Diptera: Culicidae). PLoS ONE https://doi.org/10.1371/journal.pone.0215552 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    9.
    Roche, B., Rohani, P., Dobson, A. P. & Guégan, J.-F. The impact of community organization on vector-borne pathogens. Am. Nat.181, 1–11 (2012).
    Article  Google Scholar 

    10.
    Lord, J. S. et al. Sampling design influences the observed dominance of Culex tritaeniorhynchus: considerations for future studies of japanese encephalitis virus transmission. PLoS Negl. Trop. Dis. https://doi.org/10.1371/journal.pntd.0004249 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    11.
    Takahashi, M. The effects of environmental and physiological conditions of Culex tritaeniorhynchus on the pattern of transmission of Japanese encephalitis virus. J. Med. Entomol. https://doi.org/10.1093/jmedent/13.3.275 (1976).
    Article  PubMed  Google Scholar 

    12.
    Hauser, G., Thiévent, K. & Koella, J. C. Consequences of larval competition and exposure to permethrin for the development of the rodent malaria Plasmodium berghei in the mosquito Anopheles gambiae. Parasit. Vectors https://doi.org/10.1186/s13071-020-3983-9 (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    13.
    Reiskind, M. H. & Lounibos, L. P. Effects of intraspecific larval competition on adult longevity in the mosquitoes Aedes aegypti and Aedes albopictus. Med. Vet. Entomol. https://doi.org/10.1111/j.1365-2915.2008.00782.x (2009).
    Article  PubMed  PubMed Central  Google Scholar 

    14.
    Franklinos, L. H. V., Jones, K. E., Redding, D. W. & Abubakar, I. The effect of global change on mosquito-borne disease. Lancet Infect. Dis.19, 302–312 (2019).
    Article  Google Scholar 

    15.
    Li, R. et al. Climate-driven variation in mosquito density predicts the spatiotemporal dynamics of dengue. Proc. Natl. Acad. Sci.116, 3624–3629 (2019).
    CAS  Article  Google Scholar 

    16.
    Barrera, R., Amador, M. & MacKay, A. J. Population dynamics of aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl. Trop. Dis.5, (2011).

    17.
    Patz, J. A. & Olson, S. H. Malaria risk and temperature: influences from global climate change and local land use practices. Proc. Natl. Acad. Sci. USA103, 5635–5636 (2006).
    ADS  CAS  Article  Google Scholar 

    18.
    Afrane, Y. A., Githeko, A. K. & Yan, G. The ecology of Anopheles mosquitoes under climate change: case studies from the effects of deforestation in East African highlands. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/j.1749-6632.2011.06432.x (2012).
    Article  PubMed  PubMed Central  Google Scholar 

    19.
    Murdock, C. C., Evans, M. V., McClanahan, T. D., Miazgowicz, K. L. & Tesla, B. Fine-scale variation in microclimate across an urban landscape shapes variation in mosquito population dynamics and the potential of Aedes albopictus to transmit arboviral disease. PLoS Negl. Trop. Dis.11, e0005640 (2017).
    Article  Google Scholar 

    20.
    Leta, S. et al. Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus. Int. J. Infect. Dis.67, 25–35 (2018).
    Article  Google Scholar 

    21.
    Beck-Johnson, L. M. et al. The importance of temperature fluctuations in understanding mosquito population dynamics and malaria risk. R. Soc. Open Sci.4, 160969 (2017).
    ADS  Article  Google Scholar 

    22.
    Johnson, M. F., Gómez, A. & Pinedo-Vasquez, M. Land use and mosquito diversity in the Peruvian amazon. J. Med. Entomol. https://doi.org/10.1603/0022-2585(2008)45[1023:luamdi]2.0.co;2 (2008).
    Article  PubMed  Google Scholar 

    23.
    Versteirt, V. et al. Nationwide inventory of mosquito biodiversity (Diptera: Culicidae) in Belgium Europe. Bull. Entomol. Res. https://doi.org/10.1017/S0007485312000521 (2013).
    Article  PubMed  Google Scholar 

    24.
    Ferraguti, M. et al. Effects of landscape anthropization on mosquito community composition and abundance. Sci. Rep.6, 29002 (2016).
    ADS  CAS  Article  Google Scholar 

    25.
    Ibañez-Justicia, A., Stroo, A., Dik, M., Beeuwkes, J. & Scholte, E. J. National mosquito (Diptera: Culicidae) survey in the Netherlands 2010–2013. J. Med. Entomol. https://doi.org/10.1093/jme/tju058 (2015).
    Article  PubMed  Google Scholar 

    26.
    Johnson, T., Braack, L., Guarido, M., Venter, M. & Gouveia Almeida, A. P. Mosquito community composition and abundance at contrasting sites in northern South Africa, 2014–2017. J. Vector Ecol. https://doi.org/10.1111/jvec.12378 (2020).
    Article  PubMed  Google Scholar 

    27.
    Cornel, A. J. et al. Mosquito community composition in South Africa and some neighboring countries. Parasit. Vectors https://doi.org/10.1186/s13071-018-2824-6 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    28.
    Sedda, L. et al. Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance. J. R. Soc. Interface https://doi.org/10.1098/rsif.2018.0941 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    29.
    Thongsripong, P. et al. Mosquito vector diversity across habitats in central thailand endemic for dengue and other arthropod-borne diseases. PLoS Negl. Trop. Dis.7, e2507 (2013).
    Article  Google Scholar 

    30.
    Meyer Steiger, D. B., Ritchie, S. A. & Laurance, S. G. W. Mosquito communities and disease risk influenced by land use change and seasonality in the Australian tropics. Parasit. Vectors9, (2016).

    31.
    Gorsich, E. E. et al. A comparative assessment of adult mosquito trapping methods to estimate spatial patterns of abundance and community composition in southern Africa. Parasit. Vectors12, 462 (2019).
    Article  Google Scholar 

    32.
    Braack, L., Gouveia De Almeida, A. P., Cornel, A. J., Swanepoel, R., & De Jager, C. Mosquito-borne arboviruses of African origin: review of key viruses and vectors. Parasit. Vectors11, (2018).

    33.
    Do Manh, C. et al. Vectors and malaria transmission in deforested, rural communities in north-central Vietnam. Malar. J. https://doi.org/10.1186/1475-2875-9-259 (2010).
    Article  PubMed Central  Google Scholar 

    34.
    Burkett-Cadena, N. D. & Vittor, A. Y. Deforestation and vector-borne disease: forest conversion favors important mosquito vectors of human pathogens. Basic Appl. Ecol. https://doi.org/10.1016/j.baae.2017.09.012 (2018).
    Article  Google Scholar 

    35.
    Barros, F. S. M. & Honório, N. A. Deforestation and malaria on the amazon frontier: larval clustering of anopheles darlingi (Diptera: Culicidae) determines focal distribution of malaria. Am. J. Trop. Med. Hyg. https://doi.org/10.4269/ajtmh.15-0042 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    36.
    Keesing, F. et al. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature468, 647–652 (2010).
    ADS  CAS  Article  Google Scholar 

    37.
    MacDonald, A. J. & Mordecai, E. A. Amazon deforestation drives malaria transmission, and malaria burden reduces forest clearing. Proc. Natl. Acad. Sci. USA.116, 22212–22218 (2019).
    CAS  Article  Google Scholar 

    38.
    Wiens, J. A. Ecological Flows Across Landscape Boundaries: A Conceptual Overview. In Landscape boundaries (eds. Hansen, A. J. & di Castri, F.) 217–235 (Springer, 1992).

    39.
    Jiang, Z., Huete, A. R., Didan, K. & Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2008.06.006 (2008).
    Article  Google Scholar 

    40.
    Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology https://doi.org/10.1890/07-1206.1 (2008).
    Article  PubMed  Google Scholar 

    41.
    Mabidi, A., Bird, M. S. & Perissinotto, R. Distribution and diversity of aquatic macroinvertebrate assemblages in a semiarid region earmarked for shale gas exploration (Eastern Cape Karoo, South Africa). PLoS ONE12, e0178559 (2017).
    Article  Google Scholar 

    42.
    Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol.18, 117–143 (1993).
    Article  Google Scholar 

    43.
    Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–2. Cran R (2019).

    44.
    Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).
    Article  Google Scholar  More

  • in

    Within-individual phenotypic plasticity in flowers fosters pollination niche shift

    Field sampling design
    To determine if there was within-individual plasticity in floral traits between spring and summer conditions, 50 plants of each of four populations from SE Spain (Supplementary Table 1) were marked at the onset of the flowering period in late February–early March 2018. The phenotype of two flowers per individual was quantified (see below). We revisited each population during summer (June 2018) and the same floral traits were quantified in the summer flowers of those plants still flowering (117 plants; Supplementary Table 1).
    Experimental design
    We performed an experiment testing the effect of temperature and photoperiod in floral plasticity. It included three treatments: (1) Treatment 1, where 30 plants flowered first in conditions mimicking the spring temperature and photoperiod of Mediterranean Spain (day/night = 10/14 h, temperature = 20/10 °C, average daily temperature = 14.2 °C; see Supplementary Table 1), and afterwards in conditions mimicking a mild summer (day/night = 16/8 h, temperature = 30/20 °C, average daily temperature = 23.8 °C). (2) Treatment 2, where 30 plants flowered first in spring conditions and afterwards in hot summer conditions (day/night = 16/8 h, temperature = 35/25 °C, average daily temperature = 28.8 °C). (3) Treatment 3 (control) where 15 plants flowered first in spring conditions, and afterwards they flowered again in spring conditions. For all treatments, we removed flowers before starting the second round of flowering.
    We experimentally tested the occurrence of reverse plasticity by performing a Treatment 4 in which 15 plants from Treatment 1 that flowered both during spring and summer conditions were again submitted to a period mimicking spring conditions (Supplementary Table 1).
    Floral traits
    We measured, both in field and experimental conditions, three floral traits during spring (or under experimental spring conditions) and in summer (or under experimental hot summer conditions). These traits were corolla size, corolla shape and corolla colour.
    Corolla size of each studied flower was estimated by means of two traits: (1) corolla diameter, estimated as the distance in mm between the edge of two opposite petals. (2) Corolla tube length, the distance in mm between the corolla tube aperture and the base of the sepals. These variables were measured by using a digital calliper with ±0.1 mm of error.
    Corolla shape variation was studied using geometric morphometric tools based on a landmark-based methodology43. For this, in each of the two selected flowers per individual plant studied in each of the four populations, we took a digital photo of the front view and planar position. We defined 32 co-planar landmarks covering the corolla shape and using midrib, primary and secondary veins and petal extremes and connections21,44. From the two-dimensional coordinates of landmarks, we extracted shape information and computed the generalized orthogonal least-squares Procrustes averages using the generalized procrustes analysis (GPA) superposition method. Due to the intrinsic symmetry pattern exhibited by Brassicaceae flowers, we did the analyses considering both the symmetric and asymmetric components of the shape45,46,47. We performed a principal component analysis (PCA) on the GPA-aligned specimens, and afterwards, we did a canonical variate analysis (CVA) to explore the difference in shape between season and populations43,47. Geometric morphometric analyses were performed in the R packages ‘geomorph’48, ‘Morpho’47 and ‘shapes’49,50.
    To explore the relative position of the corolla shape of spring and summer flowers in the morphospace created by the species most related phylogenetically with M. arvensis, we performed a phylomorphospace. This analysis creates a plot of the main principal dimensions (the three first principal components in this case) of a tangent space for the Procrustes shape variables of the pool of species considered in the analysis and superimposed the phylogenetic tree relating this species in this plot51,52. By doing this, this analysis reveals how the shape evolves. To perform this analysis, we collected information on the corolla shape of 72 additional species belonging to the Brassicaceae tribe Brassiceae, the tribe to which M. arvensis belongs (Supplementary Table 3). We followed the same procedure as with M. arvensis, using the same number of landmarks and computing the generalized orthogonal least-squares Procrustes averages using GPA superposition method. In this analysis, we kept separate the spring and summer flowers of M. arvensis. The phylogenetic relationship between these 72 species was obtained by making a supertree using Brassicaceae trees hosted in the repository TreeBASE Web (TreeBase.org)53. We first downloaded individual phylogenetic trees from TreeBASE. Second, we concatenated all these individual trees and made a skeleton supertree. Finally, we pruned this supertree, keeping only the species included in the geometric morphometric analysis, and insert the two ‘pseudospecies’ of M. arvensis (spring and summer) as sister species. Afterwards, we projected the value of the three first components of each species on a 3D phylogenetically explicit plot. The phylogenetic analysis was performed in the R packages ‘treeman’54, ‘phangorn’55, ‘phytools’56 and ‘treebase’53, whereas the phylomorphospace analysis was performed in the R packages ‘geomorph’48.
    The corolla colour of M. arvensis is produced by the accumulation of flavonoids57,58. Anthocyanin and non-anthocyanin flavonoids present in the petals of M. arvensis were analysed by ultra-performance liquid chromatography (UPLC) (ACQUITY System I-Class, Waters) coupled with quadrupole time-of-flight mass spectrometry (SYNAPT G2 HDMS Q-TOF, Waters). Analytical separation of flavonoids was performed on an Acquity HSST33 analytical column (150 mm × 2.1 mm internal diameter, 1.8 μm). A mobile phase with a gradient programme combining deionized water with 0.5% of acetic acid as solvent A and acetonitrile with 0.5% of acetic acid as solvent B was used. The initial conditions were 95% A and 5% B and a linear gradient was then established to reach 95% (v/v) of B. The total run time was 15 min and the post-delay time was 5 min. The mobile phase flow rate was 0.4 mL min−1. After chromatographic separation, a high-resolution mass spectrometry analysis was carried out in positive electrospray ionization (ESI+). The ionization source parameters using high-purity nitrogen were set at 600 L h−1 for desolvation gas flow and 30 L h−1 for cone gas flow. Spectra were recorded over the mass/charge (m/z) range of 50–1500. Data were recorded and processed using MassLynx software. The flavonoids present in the petal extracts were characterized according to their retention times, mass spectra and molecular formula, and compared with published data when available. We calculated the relative abundance of each compound in both lilac and white petal samples (N = 5 and 2, respectively) using peak intensities.
    Quantification of flavonoids present in flowers of M. arvensis was performed spectrophotometrically. Two flowers of each plant used in field and experimental studies were analysed in each blooming period. We collected the four petals of a flower. Flavonoids were extracted in 1.5 ml of MeOH:HCl (99:1% v-v) and stored at −80 °C in the dark, following the procedure described in ref. 34. Two replicas of 200 μL for each sample were measured in a Multiskan GO microplate spectrophotometer (Thermo Fisher Scientific Inc., MA, USA). Main flavonoid classes present in the petals of M. arvensis are anthocyanins (cyanidin derivatives) and flavonols (kaempferol, quercetin and isorhamnetin derivatives; Supplementary Table 4)57,58. Thus, total anthocyanins and flavonols were quantified as absorbance at 520 and 350 nm, respectively. Their concentrations were calculated using five-point calibration curves of cyanidin-3-glucoside chloride (Sigma-Aldrich, Steinheim, Germany) and kaempferol-3-glucoside standards (Extrasynthese, Genay, France) and expressed as cyanidin-3-glucoside and kaempferol-3-glucoside equivalents in fresh weight (mg g−1 FW), respectively.
    Objective quantification of petal colour of lilac and white petals of M. arvensis was performed by measuring their UV–Vis spectral reflectance. A petal of a flower of each colour morph (N = 10) were measured with a Jaz portable spectrometer (Ocean Optics Inc., Dunedin, FL, USA) equipped with a deuterium–tungsten halogen light source (200–2000 nm) and a black metal probe holder (6 mm diameter opening at 45°). Reflectance, relative to a white standard (WS-1-SL), was analysed with SpectraSuite v.10.7.1 software (Ocean Optics). To maximize the amount of light used in reflectance measurements and to reduce occasionally erratic reflectance values at individual nm, we set an integration time of 2 s and smoothing boxcar width of 12, respectively59.
    Foliar traits
    We measured, both in field and experimental conditions, five leaf traits during spring (or under experimental spring conditions) and in summer (or under experimental hot summer conditions). These traits were the specific leaf area (SLA, m2 kg−1), the leaf dry matter content (LDMC, mg g−1), the carbon-to-nitrogen content of leaves (C:N ratio), the isotopic signature of 13C in leaves (δ13C, ‰), and the CO2 compensation point and the slope of the A–Ci curve.
    SLA and LDMC were measured following standard protocols60. For SLA and LDMC we collected three fully expanded and mature leaves without any visible damage (e.g., herbivory, pathogen attack) from the base, midsection and apical part of outer stems (that is, leaves were not shaded by other leaves) and at random aspects. Leaves were rehydrated overnight in the dark and subsequently weighted and scanned. Leaf area was measured using the Midebmp software (Almería, Spain). Leaves were dried in the oven at 60 °C and weighted after 72 h. From these measurements, we calculated the SLA as the one-sided area of the fully rehydrated fresh leaf divided by its dry mass, while the LDMC is the ratio between the leaf dry mass and the fully rehydrated fresh mass.
    Carbon isotopic signature (δ13C), as well as the C and N relative content in leaves, were analysed in a couple of fully expanded leaves per plant without any visible damage. Oven-dry leaves were ground in a ball mill MM400 (Retsch GmbH, Haan, Germany) at 3000 rpm for 1 min to obtain a fine powder, which was stored in Eppendorf tubes. We wrapped 0.003 g of each sample in tin capsules D1008 (Elemental Microanalysis, United Kingdom). Leaf δ13C and leaf C and N relative content (in mass percentage) were determined at the Stable Isotope Analysis Lab—Centro de Instrumentación Científica (CIC) of the University of Granada (Spain) with a GC IsoLink—MS—Delta V continuous flow mass spectrometer (MS) system that includes a ISQ-QD single quadrupole MS and a gas chromatographer Trace 1310 (Thermo Fisher Scientific™, Spain). The isotopic abundance was expressed in parts per thousand (‰) as

    $$delta = left( {{R}_{{mathrm{sample}}}/{R}_{{mathrm{standard}}}-1} right) times 1000$$
    (1)

    where Rsample and Rstandard are the molar ratios of heavy (13C) to light (12C) stable isotopes of the sample (Rsample) and an international standard (Rstandard). MS precision was 0.15‰ for carbon, based on replicate analyses of standard reference materials.
    We measured responses of CO2 assimilation rate (A) versus calculated substomatal or intercellular CO2 concentration (Ci) (henceforth, A–Ci curves) to determine the instantaneous photosynthetic metabolism of plants of the intermediate C3–C4 species M. arvensis on plants grown under the two experimental conditions (N= 22 plants, spring and hot summer conditions). Gas exchange measurements were performed on one to two mature, fully expanded leaves per plant and experimental condition using a LICOR 6400 (LI-COR Biosciences, Lincoln, USA) and following the standard recommendations to correct leakage errors61,62,63. Cuvette conditions were maintained at a constant photosynthetic photon flux density (PPFD) of 1500 µmol m−2 s−1, a vapour pressure deficit (VPD) that ranged from 1.0 to More