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In this study, we showed that the expression of cry and cp4-epsps genes in wild cotton altered the secretion of EFN, the associations with different ant species, and the levels of herbivore damage on target plants. Wcry constantly maintained a high production of EFN, regardless of the MeJA treatment, but nectar production was minimal in Wcp4-epsps. These changes in nectar inducibility seem to modify the composition of ant communities, foster the dominance of the generalist and defensive species C. planatus in Bt plants and the presence of ants without defensive role, M. ebeninum, in the herbicide tolerant genotype, while W plants had both defending species (C. planatus, C. rectangularis aulicus and P. gracilis) and invasive ant species (P. longicornis) in the same proportion. Furthermore, herbivore damage and its associated ant community were different according to the introgressed transgene.
Wild and introgressed cotton do not display phenotypic equivalence in natural conditions
In general, it has been assumed that introgressed and wild genotypes should display similar phenotypes in the absence of the selection agents targeted by transgenes. However, when we compared the control group and the three genotypes, we registered different nectar secretion patterns among them (Fig. 1). Similar results have been registered in populations of bt rice and glyphosate-tolerant sunflowers living in natural conditions where introgressed plants are different from their wild relatives5.
Transgene expression modified indirect induced defences in wild cotton
Most plants are able to induce responses after herbivore damage and/or phytohormone exogenous application (i.e. jasmonic acid, JA; methyl jasmonate, MeJA; and salicylic acid, SA)11,28,29. However, unlike wild plants without transgenes, individuals with transgenes were not sensitive to the induction treatment with MeJA for increasing their EFN production (Fig. 1). These results contrast with previous reports on cultivated varieties, such as Bt and glyphosate-resistant (cp4-epsps), in which direct defences such as gossypol terpenoids (160%), hemigossypolone (160%), helicoids 1|4 (213%) and indirect defenses, such as volatile compounds (VOCs) (171.2%) and extrafloral nectar (EFN) (133%), were reported to increase in plants sprinkled with JA and MeJA21,28,29,30.
The inability of plants with transgenes to have the production of extrafloral nectar induced in them was related to different processes dependent on the identity of the transgenes in question. Whereas Wcry control plants had a high EFN production equivalent to the induced state of W plants, EFN production in Wcp4-epsps plants was inhibited. Contrasting these findings with results obtained under controlled conditions (i.e. greenhouse and crop conditions)3,21, we suggest that EFN production is linked to genotypes with transgenes and abiotic stress in the coastal dunes, because transgenes are connected to main metabolic pathways that respond to stressful conditions21.
Wild cotton with cp4-epsps
In the absence of herbicides acting as a selection agent, wild plants with cp4-epsps exhibited large differences compared to wild plants without them. Their low nectar production ( > 8 µg/mL) (Fig. 1) could be linked to the crosstalk between the jasmonate and the salicylate (SA) pathways (Fig. 4, orange and purple section). In G. hirsutum and other species, SA signalling has been proven to negatively affect JA signalling (e.g. Zea mays, Solanum lycopersicum, Nicotiana tabacum and Arabidopsis thaliana)31,32,33: therefore, we suggest an interference between the SA and JA pathways given previous reports that an over-expression of the cp4-epsps gene modifies the second part of the shikimate pathway (post-chorismate), which leads to the synthesis of essential amino acids as phenylalanine, tryptophan, or tyrosine, the latter being a precursor of benzoic acid BE, and SA34,35 (Fig. 4, purple section). This evidence highlights that hidden crosstalk effects among different metabolic pathways can scale up and modify plant phenotypes (e.g. extrafloral nectar production).
Figure 4

A diagram illustrating how the expression of cry (A) and cp4-epsps (B) in absence of their selection agent (pests and glyphosate) can affect the extrafloral nectar production. The extrafloral nectar (EFN) production is an induced defence that can be triggered by foliar herbivory, mechanical damage, and exogenous application of phytohormones (i.e. jasmonic acid, methyl jasmonate, and salicylic acid). These factors activate the octadecanoid pathway, and therefore, the production of extrafloral nectar, (A) aqua rectangle. The (C) section is an example of this reaction in a wild cotton plant (without transgenes). After damage, the key genes (yellow mesh) of the octadecanoid pathway are activated and produce extrafloral nectar. Another scenario is when the wild cotton expresses cry genes (A section), in this case, the key genes of the octadecanoid pathway interact synergistically with the cry transgene (green mesh). This triggers an over-expression of the production of EFN (aqua thick arrow), switching from inducible to constitutive responses. When the plants express cp4-epsps (B section), the production of extrafloral nectar is reduced or inhibited. A possible answer is an over-expression of the epsps gene (gold curve arrow), that increased production of salicylic acid which creates a crosstalk between shikimate and octadecanoid pathways (black cross-talk arrow). When the shikimate pathway is activated, the principal inducible defence is the production of volatile organic compounds (VOCs) (pink rectangle).

Full size image

Wild cotton with cry
Wild cotton plants with cry genes continuously produced EFN as a constitutive defence (Fig. 1), in equivalent quantities as the induced state of W plants. EFN production is regulated by the octadecanoid signalling pathway, which can be activated by herbivore damage, mechanical damage, and phytohormones, such as JA and MeJA21,28 (Fig. 4, green section). However, for cotton, a specific elicitor is not necessary36. Four key genes for the synthesis of JA and MeJA have been described: AOS, AOC, HPL, and COI137. In Bt maize, studies comparing GM corn and its isogenic lines report an increase of 24% in phenols and 63% of DIMBOA (2,4-dihidroxi-7-metoxi-1,4-benzoxazin-3-ona; natural defences against lepidopteran herbivores)11. This is consistent with observations of a synergy between maize direct defences and Bt genes, after exogenous applications of JA (Fig. 4, orange section). Considering the latter, we suggest that Wcry cotton may present a similar response, as the genes activating the JA pathway are GhAOS and GhCOI1 (homologs to maize JA biosynthesis genes: ZmAOS and ZmCOI1), in addition to Ghppo1, which confers natural resistance to lepidopteran pest, such as H. armigera38. The interaction of cry with other genes could modify the production of EFN in Wcry plants.
Effect of the transgenes’ expression on ants associated to wild cotton
We identified eight species of ants harvesting EFN (Table 2), but with distinctive communities as a function of the plant genotype. This result suggests that the change in quantity, and possibly the composition and quality of EFN, can influence the ant community associated with G. hirsutum39,40,41.
Changes in plant reward production could potentially compromise the attraction of natural enemies of herbivores42. In our study, the availability of EFN was modified. Although species richness was the same as in W plants (Table 2), the most abundant ant species associated with Wcp4-epsps plants, M. ebeninum, is considered a generalist species. Moreover, due to the lack of aggressive behaviour, this species does not represent an effective biotic defence43. The high abundance of this non-defensive species could be associated with the greater herbivore damage observed in Wcp4-epsps plants (Fig. 2). In contrast, W or Wcry plants showed a greater abundance of more aggressive ant species such as C. planatus, C. rectangulatus, and P. brunneus and significantly less herbivore damage.
In Wcry cotton, the community of patrolling ants was mainly dominated by C. planatus, in both treatments (control and induction). Interestingly, although the amount of nectar did not vary between treatments, the abundance of ants was significantly different. The dominance of a single ant species could have benefited the plants with increased indirect defence, reducing herbivore damage and promoting a greater seed production per plant, as described in Turnera ulmifolia44, Schomburgkia tibicinis45, and Opuntia stricta42. However, considering the aggressive and dominant behaviour of C. planatus, there may be ecological costs through antagonistic relationships with pollinators. Ants can interrupt pollination and affect plant fitness25,46,47. The outcome of these mutualistic and antagonistic interactions requires further study.
Effects of transgenes on herbivore damage
Considering that the type of mutualism that cotton sustains with ants is defensive, we suggest that the change we observed in the composition of ants is likely to have influenced herbivore damage in the different genotypes, which in turn has the potential to reduce fitness as shown by other studies of cotton48,49,50. However, a study carried out on wild upland cotton reported that plants tolerate intermediate levels of leaf damage inflicted by leaf-chewing insects ( More

Sample collection
Algae were sampled from August 27th to September 21st (2017) from seven populations also collected for Bonthond et al. [28], including three native populations; Akkeshi (Japan), Soukanzan (Japan), Rongcheng (China); and four non-native populations; Pleudihen-sur-Rance (France), Nordstrand (Germany), Cape Charles Beach (Viriginia) and Tomales Bay (California, Fig. 1, Table S1). Individuals fixed to hard substratum (see [30]) were sampled at least a meter apart from one another and stored in separate plastic bags. As A. vermiculophyllum has a complex, haplodiplontic life-cycle only diploids were included in the experiment. Life-cycle stages were identified in the field with a dissecting microscope or post-hoc by microsatellite genotyping [31]. After transport in coolers and storage at 4 °C in the lab, bags with algae were shipped to Germany, arriving within 4–6 days after collection. In the climate room (15 °C), individuals were transferred to separate transparent aquaria with transparent lids, containing 1.75 L artificial seawater (ASW) prepared from tap water and 24 gL−1 artificial sea salt without CaCO3 (high CaCO3 concentrations increase disease risk, Weinberger data unpublished) and exposed to 12 h of light per day (86.0 µmol m−2s−1 at the water surface). Aquaria were moderately aerated with aeration stones. Per population, four diploid individuals were acclimated over 31–32 days to climate room conditions prior to starting the experiment. Water was exchanged weekly with new ASW enriched with 2 mL Provasoli-Enrichment Solution (PES; [32]). At the start of the experiment, wet weight was recorded and individuals were divided into two parts of ~10 g each and placed into two plastic tanks with 1.75 L water and 2 mL PES (Fig. 1).
Fig. 1: Schematic overview of the sampling design and experimental process.

Algae were collected from native populations Rongcheng (ron), Soukanzan (sou) and Akkeshi (akk) and non-native populations Tomales Bay (tmb), Cape Charles Beach (ccb), Pleudihen-sur-Rance (fdm) and Nordstrand (nor). In the climate room algae were acclimated for 5 weeks and divided into two thalli. One of the thalli was treated for three days with an antibiotic mixture after which both groups were monitored for six weeks, during which the treated algae received inoculum with each water change. Microbiota samples were taken in the field (tfield), directly after disturbance (t0) and after 1, 2, 4 and 6 weeks (t1, t2, t4 and t6).

Full size image

Experimental setup
To rigorously disturb the microbial community, one of each of the pairs of aquaria containing the same algal individual was treated with a combination of antibiotics, aiming to increase the effectivity (10 mgL−1 ampicillin, 10 mgL−1 streptomycin, 10 mgL−1 chloramphenicol) and the other (control) remained untreated. All experimental work was conducted with disposable gloves and sterilized equipment, to minimize contamination. After three days, the water was removed from all tanks (treated and control) and the wet weight was recorded for all algae. All individuals were rinsed with one 1.75 L volume ASW and re-incubated in 1.75 L ASW. Subsequently, both groups received new ASW with 2 mL PES weekly and individuals treated with antibiotics received also 2 mL inoculum. The inoculum was prepared from individuals of all 7 populations, following the procedure to remove epibiota as described in Bonthond et al. [28]. Briefly, apical fragments of 1 g were separated from the thallus and transferred to 50 mL tubes containing 15 ± 1 glass beads (3 mm) and 15 mL ASW and vortexed for 6 min to separate epibiota from the algal tissue. In total, 8 samples were prepared from one individual per population. The resulting suspensions were pooled and mixed with glycerol (20% final glycerol concentration), aliquoted in 50 mL tubes and stored at −20 °C. For each water exchange, a new aliquot was defrosted at room temperature and added to the water of treated algae. Wet weight was recorded weekly with water exchanges. Before weighing the individual on aluminum foil, it was dipped twice on a separate aluminum foil sheet, to reduce attached water in a systematic way. Endo- and epiphytic microbiota were sampled in the field (tfield, [28]), at the start of the experiment (t0), after one week (t1), two weeks (t2), four weeks (t4) and six weeks (t6, Fig. 1). To equalize acclimation times across populations the experiment was stacked into five groups (Table S2). At each sampling moment, 0.5 or 1 g of tissue was separated from all individuals with sterilized forceps and epibiota were extracted similarly to the preparation of the inoculum. The resulting suspension was filtered through 0.2 µm pore size PCTA filters. Both the filters and the remaining tissue were preserved at −20 °C.
DNA extraction and amplicon sequencing
Tissue samples were defrosted, rinsed with absolute ethanol and DNA free water to remove hydro- and moderately lipophilic cells and molecules from the surface and cut to fragments with sterilized scissors. DNA was then extracted from these fragments (endobiota) and from preserved filters (epibiota) using the ZYMO Fecal/soil microbe kit (D6102; ZYMO-Research, Irvine, CA, USA), following the manufacturer’s protocol. Although this method to separate endo- and epibiota was shown to resolve distinct communities [28], tightly attached epiphytic cells may not be completely removed from the surface and detectable in endophytic samples as well. Two 16S-V4 amplicon libraries, over which the samples were divided in a balanced manner, were prepared as in Bonthond et al. [28], following the two-step PCR strategy from Gohl et al. [33], using the same set of 16S-V4 target primers and indexing primers. The libraries were sequenced on the Illumina MiSeq platform (2×300 PE) at the Max-Planck-Institute for Evolutionary Biology (Plön, Germany), including four negative DNA extraction controls and four negative and positive PCR controls (mock communities; ZYMO-D6311). The fastq files were de-multiplexed (0 mismatches). Relevant field samples from Bonthond et al. [28] were combined with the new dataset and assembled, quality filtered and classified altogether with Mothur v1.43.0 [34] using the SILVA-alignment release 132 [35]. Sequences were clustered within 3% dissimilarity into OTUs using the opticlust algorithm. Mitochondrial, chloroplast, eukaryotic and unclassified sequences were removed. To prepare the community matrix we discarded singleton OTUs (in the full dataset), samples with More

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Department of Geography, Texas A&M University, College Station, TX, USA
J. Loisel

Department of Geography, University of Exeter, Exeter, UK
A. V. Gallego-Sala, M. J. Amesbury, D. J. Charman & T. P. Roland

Ecosystems and Environment Research Programme, University of Helsinki, Helsinki, Finland
M. J. Amesbury, A. Korhola, M. Väliranta, S. Juutinen, K. Minkkinen & S. Piilo

Department of Geography and Geotop Research Center, University of Quebec at Montreal, Montreal, Quebec, Canada
G. Magnan & M. Garneau

Magister of Environment and Soil Science Department, Tanjungpura University, Pontianak, Indonesia
G. Anshari

Department of Geography and Environment, University of Hawaii at Manoa, Honolulu, HI, USA
D. W. Beilman

Department of Ecology and Territory, Pontificial Xavierian University, Bogota, Colombia
J. C. Benavides

Organic Geochemistry Unit, School of Chemistry, and School of Earth Sciences, University of Bristol, Bristol, UK
J. Blewett & B. D. A. Naafs

Environmental Studies Program and Earth and Oceanographic Science Department, Bowdoin College, Brunswick, ME, USA
P. Camill

Department of Geology, Chulalongkorn University, Bangkok, Thailand
S. Chawchai

Department of Geography, University of California, Los Angeles, Los Angeles, CA, USA
A. Hedgpeth

Max Planck Institute for Meteorology, Hamburg, Germany
T. Kleinen & V. Brovkin

Faculty of Engineering, Chemical and Environmental Engineering, University of Nottingham, Nottingham, UK
D. Large

Centro de Investigación GAIA Antártica, University of Magallanes, Punta Arenas, Chile
C. A. Mansilla

Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
J. Müller & F. Joos

Consortium Érudit, Université de Montréal, Montreal, Quebec, Canada
S. van Bellen

Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, USA
J. B. West

Department of Earth and Environmental Sciences, Lehigh University, Bethlehem, PA, USA
Z. Yu

Institute for Peat and Mire Research, School of Geographical Sciences, Northeast Normal University, Changchun, China
Z. Yu

Department of Environmental Studies, Mount Holyoke College, South Hadley, MA, USA
J. L. Bubier

Department of Geography, McGill University, Montreal, Quebec, Canada
T. Moore

Department of Physical Geography, Stockholm University, Stockholm, Sweden
A. B. K. Sannel

School of Geography, Geology and the Environment, University of Leicester, Leicester, UK
S. Page

Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
M. Bechtold & W. Swinnen

School of Geography & Sustainable Development, University of St Andrews, St Andrews, UK
L. E. S. Cole

Department of Earth, Ocean & Atmospheric Science, Florida State University, Tallahassee, FL, USA
J. P. Chanton

Department of Bioscience, Aarhus University, Roskilde, Denmark
T. R. Christensen

Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada
M. A. Davies & S. A. Finkelstein

Instituto Franco-Argentino para el Estudio del Clima y sus Impactos, Buenos Aires, Argentina
F. De Vleeschouwer

Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
S. Frolking & C. Treat

Department of Geobotany and Plant Ecology, University of Lodz, Lodz, Poland
M. Gałka

Laboratoire d’Ecologie Fonctionnelle et Environnement, UMR 5245, CNRS-UPS-INPT, Toulouse, France
L. Gandois

Cranfield Soil and Agrifood Institute, Cranfield University, Cranfield, UK
N. Girkin

Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
L. I. Harris

Stockholm Environment Institute, University of York, York, UK
A. Heinemeyer

Max Planck Institute for Biogeochemistry, Jena, Germany
A. M. Hoyt

Lawrence Berkeley National Laboratory, Berkeley, CA, USA
A. M. Hoyt

Florence Bascom Geoscience Center, United States Geological Survey, Reston, VA, USA
M. C. Jones

Department of Marine and Coastal Environmental Science, Texas A&M University at Galveston, Galveston, TX, USA
K. Kaiser

Department of Biology, University of Victoria, Victoria, British Columbia, Canada
T. Lacourse

Faculty of Geographical and Geological Sciences, Climate Change Ecology Research Unit, Adam Mickiewicz University, Poznań, Poland
M. Lamentowicz

Natural Resources Institute Finland (Luke), Helsinki, Finland
T. Larmola

Agroscope, Zurich, Switzerland
J. Leifeld

Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland
A. Lohila

Finnish Meteorological Institute, Climate System Research, Helsinki, Finland
A. Lohila

Department of Geography, Royal Holloway, University of London, Egham, UK
A. M. Milner

Department of Forest Sciences, University of Helsinki, Helsinki, Finland
K. Minkkinen

School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
P. Moss

Lamont-Doherty Earth Observatory, Palisades, NY, USA
J. Nichols

National Park Service, Washington DC, WA, USA
J. O’Donnell

Department of Environment & Geography, University of York, York, UK
R. Payne

Department of Chemistry, and Department of Geological and Environmental Science, Hope College, Holland, MI, USA
M. Philben

Department of Geography and Environmental Science, University of Reading, Reading, UK
A. Quillet

Department of Applied Earth Sciences, Uva Wellassa University, Badulla, Sri Lanka
A. S. Ratnayake

School of Biosciences, University of Nottingham, Nottingham, UK
S. Sjögersten

Département de Géographie, Université de Montréal, Montréal, Québec, Canada
O. Sonnentag & J. Talbot

Geography, School of Natural and Built Environment, Queen’s University Belfast, Belfast, UK
G. T. Swindles

Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, USA
A. C. Valach

Department of Environment and Sustainability, Grenfell Campus, Memorial University, Corner Brook, Newfoundland, Canada
J. Wu More