Philippa Kaur

InvasionWe used freshwater fish biodiversity data collated by and described in Milardi, et al.47. In summary, the dataset included 3777 sites sampled 1999–2014, recorded a total of 99 different fish species (35 of which were exotic and already established, even if some are restricted to areas with thermal springs), spanned  > 11 degrees of longitude (~ 1200 km) and 10 degrees of latitude (~ 1100 km), covering streams at altitudes -2.7–2500 m above sea level. Community turnover was not a relevant factor in our study, because fish communities are typically stable over these timescales and the data was collected in a restricted timeframe within each area29,39. Furthermore, time elapsed since last introductions was sufficient to analyze distribution patterns after major invasions had already occurred see e.g.23,48.Abundance of each species sampled during the monitoring was recorded with Moyle classes (Moyle and Nichols, 1973), which were weighted according to body-size classes in order to obtain a body-mass-corrected abundance, hereafter referred to simply as abundance. We then calculated an invasion degree, i.e. the share of introduced species in freshwater fish communities, based on the abundance of introduced and native species see e.g.9,49. A high invasion degree equals to a high share of introduced species and a low share of native species.We also selected the top 10 invasive species as further response variables, under the assumption that these would be the main components of the invasion degree, but would respond to different invasion drivers based on each species’ ecology. Invasiveness rank was defined through an index obtained by multiplying colonization (share of sites colonized) and prevalence (average relative abundance in the fish community) of each introduced species. The relative abundance of each of these species in the fish community was used as a response variable, being a measure comparable to invasion degree for single species.Invasion driversWe tested a combination of geographical, climate and anthropogenic impact factors as potential drivers of invasion. To avoid temporal mismatches, we chose time periods that overlapped as much as possible with our biological data.We used basin area, altitude and slope (derived from a seamless digital elevation model of the whole Italian territory at 10 m resolution, Tarquini, et al.50) as geographical variables.We derived climate data from available series of long-term national monitoring (http://www.scia.isprambiente.it/). We used daily air temperature (2000–2009), measured at a total of 2266 sites throughout the country, as a proxy for temperature regimes. We also used cumulated annual precipitations, number of annual dry days (precipitation  More

Harbach, C. J. et al. Delayed senescence in soybean: Terminology, research update, and survey results from growers. Plant Health Progress 17, 76–83 (2016).Article 

Google Scholar 
Hobbs, H. A. et al. Green stem disorder of soybean. Plant Dis. 90, 513–518 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hill, C. B., Hartman, G. L., Esgar, R. & Hobbs, H. A. Field evaluation of green stem disorder in soybean cultivars. Crop Sci. 46, 879–885 (2006).Article 

Google Scholar 
Morita, K. et al. (2006) Effect of green stem on soiled bean index at harvest of soybean by combine harvester. Hokuriku Crop Sci. 41, 107–109 (2006) (in Japanese).
Google Scholar 
Ogiwara, H. Delayed leaf senescence. In: Agriculture, Forestry and Fisheries Research Council of Japan, ed. Soybean-technical development for improving national food self-sufficiency ratio. Annotated bibliography of Agriculture, Forestry, and Fisheries Research, vol. 27, 291–294 (2002). (in Japanese).Crafts-Brandner, S. J. & Egli, D. B. Sink removal and leaf senescence in soybean. Plant Physiol. 85, 662–666 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crafts-Brandner, S. J., Below, F. E., Harper, J. E. & Hageman, R. H. Effects of pod removal on metabolism and senescence of nodulating and nonnodulating soybean isolines. Plant Physiol. 75, 311–317 (1984).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Egli, D. B. & Bruening, W. P. Depodding causes green-stem syndrome in soybean. Crop Manag. 5(1), 1–9. https://doi.org/10.1094/CM-2006-0104-01-RS (2006).Article 

Google Scholar 
Htwe, N. M. P. S. et al. Leaf senescence of soybean at reproductive stage is associated with induction of autophagy-related genes, GmATG8c, GmATG8i and GmATG4. Plant Prod. Sci. 14, 141–147 (2011).CAS 
Article 

Google Scholar 
Leopold, A. C., Niedergang-Kamien, E. & Janick, J. Experimental modification of plant senescence. Plant Physiol. 34, 570–573 (1959).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mondal, M. H., Brun, W. A. & Brenner, M. L. Effects of sink removal on photosynthesis and senescence in leaves of soybean (Glycine max L.) plants. Plant Physiol. 61, 394–397 (1978).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Effect of pod removal on leaf senescence in soybean. Plant Physiol. 70, 1544–1548 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Effect of pod removal on leaf photosynthesis and soluble protein composition of field-grown soybeans. Plant Physiol. 73, 121–124 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wittenbach, V. A. Purification and characterization of a soybean leaf storage glycoprotein. Plant Physiol. 73, 125–129 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Staswick, P. E. Developmental regulation and the influence of plant sinks on vegetative storage protein gene expression in soybean leaves. Plant Physiol. 89, 309–315 (1989).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sato, J., Shiraiwa, T., Sakashita, M., Tsujimoto, Y. & Yoshida, R. The occurrence of delayed stem senescence in relation to trans-zeatin riboside level in the xylem exudate in soybeans grown under excess-wet and drought soil conditions. Plant Prod. Sci. 10, 460–467 (2007).Article 

Google Scholar 
Takehara, T. et al. Occurrence of delayed leaf senescence of soybean caused by Rhizoctonia aerial blight in Japan. Jpn. Agric. Res. Q. 50, 201–208 (2016).Article 

Google Scholar 
Boethel, D. J. et al. Delayed maturity associated with southern green stink bug (Heteroptera: Pentatomidae) injury at various soybean phenological stages. J. Econ. Entomol. 93, 707–712 (2000).CAS 
PubMed 
Article 

Google Scholar 
Islam, M. M. et al. Nitrogen manipulation affects leaf senescence during late seed filling in soybean. Acta Physiol. Plant. 39, 42 (2017).Article 
CAS 

Google Scholar 
Yamazaki, R., Katsube-Tanaka, T. & Shiraiwa, T. Effect of thinning and shade removal on green stem disorder in soybean. Plant Prod. Sci. 21, 83–92 (2018).CAS 
Article 

Google Scholar 
Yamazaki, R., Katsube-Tanaka, T., Kawasaki, Y., Katayama, K. & Shiraiwa, T. Effect of thinning on cultivar differences of green stem disorder in soybean. Plant Prod. Sci. 22, 311–318 (2019).CAS 
Article 

Google Scholar 
Board, J. E. & Tan, Q. Assimilatory capacity effects on soybean yield components and pod number. Crop Sci. 35, 846–851 (1995).Article 

Google Scholar 
Egli, D. B. Soybean reproductive sink size and short-term reductions in photosynthesis during flowering and pod set. Crop Sci. 50, 1971–1977 (2010).Article 

Google Scholar 
Wells, R., Schulze, L. L., Ashley, D. A., Boerma, H. R. & Brown, R. H. Cultivar differences in canopy apparent photosynthesis and their relationship to seed yield in soybean. Crop Sci. 22, 886–890 (1982).Article 

Google Scholar 
Islam, M. M. et al. Nitrogen redistribution and its relationship with the expression of GmATG8c during seed filling in soybean. J. Plant Physiol. 192, 71–74 (2016).CAS 
PubMed 
Article 

Google Scholar 
Zhao, X., Zheng, S. H. & Arima, S. Influence of nitrogen enrichment during reproductive growth stage on leaf nitrogen accumulation and seed yield in soybean. Plant Prod. Sci. 17, 209–217 (2014).CAS 
Article 

Google Scholar 
Brown, A. W. & Hudson, K. A. Transcriptional profiling of mechanically and genetically sink-limited soybeans. Plant Cell Environ. 40, 2307–2318 (2017).CAS 
PubMed 
Article 

Google Scholar 
Tranbarger, T. J., Franceschi, V. R., Hildebrand, D. F. & Grimes, H. D. The soybean 94-kilodalton vegetative storage protein is a lipoxygenase that is localized in paraveinal mesophyll cell vacuoles. Plant Cell 3, 973–987 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
Melo, B. P. et al. Revisiting the soybean GmNAC superfamily. Front. Plant Sci. 9, 1864 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, H. J. et al. Gene regulatory cascade of senescence-associated NAC transcription factors activated by ETHYLENE-INSENSITIVE2-mediated leaf senescence signaling in Arabidopsis. J. Exp. Bot. 65, 4023–4036 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Tucker, M. L., Burke, A., Murphy, C. A., Thai, V. K. & Ehrenfried, M. L. Gene expression profiles for cell wall-modifying proteins associated with soybean cyst nematode infection, petiole abscission, root tips, flowers, apical buds, and leaves. J. Exp. Bot. 58, 3395–3406 (2007).CAS 
PubMed 
Article 

Google Scholar 
Turner, G. W. et al. Experimental sink removal induces stress responses, including shifts in amino acid and phenylpropanoid metabolism, in soybean leaves. Planta 235, 939–954 (2012).CAS 
PubMed 
Article 

Google Scholar 
Roach, T. & Krieger-Liszkay, A. The role of the PsbS protein in the protection of photosystems I and II against high light in Arabidopsis thaliana. Biochim. Biophys. Acta Bioenerg. 1817, 2158–2165 (2012).CAS 
Article 

Google Scholar 
Horton, P., Ruban, A. V. & Walters, R. G. Regulation of light harvesting in green plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 47, 655–684 (1996).CAS 
PubMed 
Article 

Google Scholar 
Hutin, C. et al. Early light-induced proteins protect Arabidopsis from photooxidative stress. Proc. Natl. Acad. Sci. U.S.A. 100, 4921–4926 (2003).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Wang, H. et al. Functional characterization of dihydroflavonol-4-reductase in anthocyanin biosynthesis of purple sweet potato underlies the direct evidence of anthocyanins function against abiotic stresses. PLoS ONE 8, e78484 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Saravitz, D. M. & Siedow, J. N. The differential expression of wound-inducible lipoxygenase genes in soybean leaves. Plant Physiol. 110, 287–299 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pimenta, M. R. et al. The stress-induced soybean NAC transcription factor GmNAC81 plays a positive role in developmentally programmed leaf senescence. Plant Cell Physiol. 57, 1098–1114 (2016).CAS 
PubMed 
Article 

Google Scholar 
Fujimoto, M. et al. Transcriptional switch for programmed cell death in pith parenchyma of sorghum stems. Proc. Natl. Acad. Sci. U.S.A. 115, 8783–8792 (2018).Article 
CAS 

Google Scholar 
Egli, D. B. Variation in leaf starch and sink limitations during seed filling in soybean. Crop Sci. 39, 1361–1368 (1999).CAS 
Article 

Google Scholar 
Board, J. E. & Harville, B. G. Late-planted soybean yield response to reproductive source/sink stress. Crop Sci. 38, 763–771 (1998).Article 

Google Scholar 
Fatichin, Zheng, S. H., Narasaki, K. & Arima, S. Genotypic adaptation of soybean to late sowing in southwestern Japan. Plant Prod. Sci. 16, 123–130 (2013).CAS 
Article 

Google Scholar 
Wakasugi, K. & Fujimori, S. Subsurface Water Level Control System “FOEAS” that promotes the full use of paddy fields. J. Jpn. Soc. Irrig. Drain. Rural Eng. 77, 705–708 (2009) (in Japanese).
Google Scholar 
Fehr, W. R. & Caviness, C. E. Stages of soybean development. Spec. Rep. 80. Iowa Agric. Home Econ. Exp. Stn. Iowa State Univ., Ames. (1977).Furuya, T. & Umezaki, T. Simplified distinction method of degree of delayed stem maturation of soybean plants. Jpn. J. Crop Sci. 62, 126–127 (1993) (in Japanese with English abstract).Article 

Google Scholar 
Kim, D. et al. TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

Experimental design and crop managementField experiments were conducted at Chengyang Agricultural Experimental Station, Qingdao, China (36°18′ 11″/N, 120°21′ 13″/E) in 2019 and 2020. The soil type in the field was brown loam that contained 22.76 g kg−1 organic matter, 82.39 mg kg−1 alkali-hydrolysable N, 25.10 mg kg−1 Olsen-P and 94.89 mg kg−1 exchangeable K. The test cultivars of maize were Jinhai5 with strong lodging resistance and Xundan20 with weak lodging resistance, which were repeated four times in plots laying out in randomized block designs. Plant density was 7.5 plants / m2 with the row spacing of 60 cm. the plot consisted of 8 rows length of 15 m. Two–three seeds per hole were manually sowed at 5 cm on 20 April 2019 and 24 April 2020, and the seedlings were thinned to the target planting density at V2, and harvested on 10 September and 14 September, respectively. Fertilization and irrigation management followed local production practices in maize.Sampling and measurementPlant samples were taken at V8, V12, R1, R2 and R6. Ten typical plants of each tested cultivars were selected to be subjected to mechanical and above-ground morphological measurements at each sampling. The other three maize plants were used to measure morphological traits of roots. Xundan20 was seriously damaged due to the storm in the late stage of maize growth in 2020, resulting in the missing data for physiological maturity.Determination of leaf area vertical distributionLeaf area of expanded leaves each was computed by the coefficient method: Single leaf area = length * width * 0.75. Leaf area for unexpanded leaves was estimated by the leaf weight method. Leaf area per plant was the sum of all individual green leaf areas. Leaf height is the height from the ground to the leaf collar position of maize.Determination of max root side-pulling resistanceSample plants were surrounded with water-proof steel devices inserted into underground, and watered to soil moisture over saturation at one day before mechanical testing. When measured, due to the limited space, all leaves of sample plants are removed in order to improve the measurement accuracy. The defoliated stalks were immobilized by a pair of lengthwise steel clamps to prevent stalks from bending (Fig. 7). After the digital pole dynamometer18 with a 1.5 m long slider and a main unit was linked to the stalks at a height of 80 cm away from the ground, the operator by hand pulled at a slow and uniform speed until the roots were pulled out. Records of load force, declination angle and sensor position were automatically stored in main unit during this operation. The peak value of forces, extracted from records, was taken as the max root side-pulling resistance.Figure 7Schematic diagram for measuring max root side-pulling resistance.Full size imageRoot anti-lodging indexBased on the method of Cui et al.6, the force value comparison is changed to the moment value comparison to calculate root anti-lodging index:$${text{AL}}_{root} = M_{root} / , M_{wind} = F_{root} / , F_{wind}$$
(1)
where M root is the root failure moment, M wind is the wind resultant moment. Root anti-lodging index indicates the ability of plants to resist root lodging. The larger its value is, the stronger the resistance is, and vice versa.$${text{M}}_{root} = F , *d$$
(2)
where F is the max root side-pulling resistance, d is moment arm, i.e., the length of force arm. As a component of root anti-lodging index, the root failure moment represents the ability of the root system to resist lateral pulling. The greater its value is, the better the resistance is, and vice versa.With the base of the stem as the fulcrum,$${text{M}}_{wind} = sum 0.{5}CA_{i} rho V^{2} h_{i}$$
(3)
where C is coefficient of air resistance, ρ is air mass density ,V is the wind speed , Ai is the area of a single leaf , hi is the height of leaf, ∑ represents to sum up over all leaves. C value is set to be 0.219. When encountering wind speed at grade 6 or higher, maize is more prone to lodging. Unless stated explicitly, the following analysis was limited to the upper wind speed for grade 6 wind20.Root morphological traitsThe number and length of all primary nodal roots were measured. Root-soil balls each of two or three tested plants were obtained after lateral root-pulling testing. The images of the three frontal sides, 120 degrees apart from each other, of the root-soil balls were taken using a digital camera. Ball volumes were then evaluated by considering them to be rotationally symmetric. Average volumes were used for further analysis.Single root tensile resistanceRoots after counting the number of nodal roots were used to measure the single root tensile resistance. First, clean the dust off roots. Then, diameters of roots were determined with a vernier caliper. Single root tensile resistance was measured by HF-500 digital push–pull apparatus. Fixed the upper and lower ends of the root, then one end moved slowly and uniformly, the other end was still until the root breaks. The peak tension force displayed by the instrument was taken as the single root tensile resistance.Statistical analysisBased on variance analysis, the Tukey method was used to compare the differences among means. The logarithmic transformation of variables was carried out to improve the homogeneity of error variance if appropriate.The substantive effect or influence of various factors on the response variable can be expressed by effect size of factors, which can be calculated under the framework of variance analysis. Effect size is the proportion of the effect of a certain factor in the total effect, which is a dimensionless number21,22,23.The formula for calculating effect size of factors is:$$omega^{2} = frac{{df_{effect} times left( {MS_{effect} – MS_{error} } right)}}{{SS_{total} + MS_{error} }}$$
(4)
where df is the degree of freedom, MS represents mean square.Two conceptual models were used when dealing with effect size. One model was of components, i.e., taking the logarithm of both sides of Eq. (1):$${text{LOG}}left( {{text{AL}}_{{{text{root}}}} } right) , = {text{ LOG}}left( {{text{M}}_{{{text{root}}}} } right) , + {text{ LOG}}left( {{text{M}}_{{{text{wind}}}} } right)$$
(5)
where LOG denotes logarithmic transformation.The other was the factorial model, i.e.,$${text{factors affecting AL}}_{{{text{root}}}} = {text{ wind grade }} + {text{ cultivar }} + {text{ growth stage}}$$
(6)
Experimental research and field studies on plants including the collection of plant materialThe authors declare that the cultivation of plants and carrying out study in Chengyang Agricultural Experimental Station complies with all relevant institutional, national and international guidelines and treaties.Statement of permissions and/or licenses for collection of plant or seed specimensThe authors declare that the seed specimens used in this study are publicly accessible seed materials and we were given explicit written permission to use them for this research. More

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Insects and plantsLarvae of fall armyworm (FAW, Spodoptera frugiperda) were cultured at the Department of Entomology at the Max Planck Institute for Chemical Ecology, and reared on a semi-artificial diet based on pinto bean59, and maintained under controlled light and temperature conditions (12:12 h light/dark, 21 °C).Feeding experiments3rd–4th instar FAW larvae were utilized for all experiments. Insects were starved overnight prior to feeding experiments. The following day insects were fed with a semi-artificial, pinto bean-based diet or put on maize leaves in small plastic cups and allowed to feed on the respective diets for a day. Insects were dissected in cold phosphate buffered saline (PBS, pH = 7.4) to harvest larval tissues (guts, Malphigian tubules, fat bodies, cuticle), which were stored at − 80 °C until further use. For droplet feeding, 12.5 mM DIMBOA was prepared by dissolving the compound in DMSO. This DIMBOA solution was further diluted in 10% aqueous sucrose solution. The larvae were stimulated with forceps to encourage regurgitation, and 2 μL DIMBOA-sucrose solution was administered directly to the larval mouthparts. Insects were then fed on semi-artificial diet for up to 6 h; following which gut tissue was dissected using cold phosphate buffer and the tissue samples were stored at − 80 °C until further use.Insect cell culturesSpodoptera frugiperda Sf9 cells and Trichoplusia ni Hi5 cells were cultured in Sf-900 II serum-free medium (Gibco) and ExpressFive serum-free medium (Gibco), respectively. Adherent cultures were maintained at 27 °C, and sub-cultured every 3–4 days.Cell treatmentsInsect cells were seeded in 6 well culture plates (Corning) and left at 27 °C overnight. For transcript stability tests, a fresh cycloheximide (CHX) stock (50 mg/mL) was prepared in ethanol and added to the cultured cells at a concentration of 50 µg/mL. Incubations with CHX were performed up to 6 h. For testing substrate specificity, cells were then treated with the following compounds for 1 h—DIMBOA (25–100 μM), indole (50–100 μM), quercetin (50–100 μM), and esculetin (50–100 μM). All the stocks were prepared in DMSO and cells treated with the corresponding volume of pure DMSO served as a control. The range of concentrations used for the substrates was based on previous work38.RNA extraction, reverse transcription and real time-PCR analysisTissue samples from the larvae were homogenized and total RNA extracted using the innuPREP RNA Mini Kit (Analytik Jena). Cell cultures used for RNA extraction were obtained during sub-culturing at full confluency, and centrifuged at 500×g for 5 min. The culture medium was discarded, and the fresh pellets were directly used for RNA extraction. RNA concentrations were measured with the NanoDrop 2000 UV–Vis Spectrophotometer (Thermo Scientific). First strand cDNA was synthesized from 1 μg total RNA using SuperScript III Reverse Transcriptase and OligodT primers from Invitrogen. Sequences were successfully amplified using Phusion High Fidelity DNA Polymerase (New England Biolabs) (PCR protocol: 30 s at 98 °C; 35 cycles of 10 s at 98 °C, 20 s at 55 °C, 45 s at 72 °C; and 5 min at 72 °C). The PCR products were purified with a PCR cleanup kit (Qiagen) and cloned into pCR-Blunt II-TOPO vector (Life Technologies) and transformed into NEB cells (Life Technologies), which were plated on selective LB agar medium containing 100 μg/mL ampicillin and incubated overnight at 37 °C. Positive colonies were identified by PCR using vector-specific M13 primers. Positive clones were confirmed by sequencing. Real time PCR analyses were carried out using Brilliant III SYBR Master Mix, employing SYBR Green chemistry. Relative quantification of the transcript levels was done using the 2−∆∆Ct method60. SfRPL10 was used as reference gene for all analyses. The primer pairs used for distinguishing between the variants are listed in Supplementary Table 1. As the expression of full-length and variants of SfUGT33F28 differed according to the strains, tissues, and treatments being analyzed, variant expression is reported as ratios relative to the canonical transcript to facilitate comparisons.Preparation of minigenes for alternative splicing studiesGenomic DNA was isolated from S. frugiperda larvae using the cetyl trimethyl ammonium bromide (CTAB) protocol61. DNA concentration was measured with the NanoDrop 2000 UV–Vis Spectrophotometer (Thermo Scientific). The minigene was amplified using Phusion High Fidelity DNA Polymerase (New England Biolabs) (PCR protocol: 30 s at 98 °C; 35 cycles of 10 s at 98 °C, 30 s at 55–60 °C, 1 min 30 s at 72 °C; and 10 min at 72 °C), cloned into a pCR-Blunt II-TOPO vector (Life Technologies) and sequenced using M13 primers. The confirmed sequence was eventually cloned into a pIB/V5-His-TOPOvector (Life Technologies) and transformed into NEB cells (Life Technologies). Positive colonies were identified by colony PCR using vector-specific OpIE2 primers, sub-cultured overnight at 37 °C in liquid LB medium containing 100 μg/mL ampicillin and used for plasmid DNA purification with the NucleoSpin Plasmid kit (Macherey-Nagel). Concentration and purity of the obtained construct was assessed by the NanoDrop 2000 UV–Vis Spectrophotometer (Thermo Scientific) and the correct orientation of the PCR products was confirmed by DNA sequencing.Nuclear protein isolationNuclear proteins were isolated from insect cells62 using the protocol originally described with few modifications. Cells grown to concentrations of up to 1 × 106 cells/well were harvested and washed with PBS (pH 7.4). The extracts were centrifuged at 12,000×g for 10 min and pellets were re-suspended in 400 μL cell lysis buffer (10 mM HEPES, pH 7.5, 10 mM KCl, 0.1 mM EDTA pH 8.0, 1 mM DTT, 0.5% Nonidet-40 and 10 μL protease inhibitor cocktail). Cells were allowed to swell on ice for 20 min with intermittent mixing. Suspensions were vortexed to disrupt the cell membranes and then centrifuged at 12,000×g for 10 min at 4 °C. Pelleted nuclei were washed thrice with cell lysis buffer, re-suspended in 50 μL nuclear extraction buffer (20 mM HEPES pH 7.5, 400 mM KCl, 1 mM EDTA pH 8.0, 1 mM DTT, 10% glycerol and protease inhibitor) and incubated on ice for 30 min. Nuclear fractions were collected by centrifugation at 12,000g for 15 min at 4 °C. Protein concentrations were measured by Bradford and extracts were stored at − 80 °C until further use.Electrophoretic mobility shift assay (EMSA)EMSA was performed using the LightShift Chemiluminescent EMSA kit (Thermo Scientific) following the manufacturer’s instructions. Genomic DNA fragments of 20–25 bp corresponding to the 5′ flanking region of UGT33F28 exon 1 (with and without AhR-ARNT motif deletion) were synthesized with covalently linked biotin (Sigma). The DNA probes used in the experiment are listed in Supplementary Table 6. EMSA was performed in 20 µL reactions containing 20 fmol biotinylated DNA probe with 3.5–4 µg nuclear protein extracted from insect cells, according to manufacturer’s instructions. A reaction comprising the above along with the excess of unlabeled canonical DNA probe (200 molar excess) was further employed as a control. The reaction was assembled at room temperature and incubated for 30 min. The reactions were separated on a 5% TBE gel in 0.5X TBE at 100 V for 60 min. The samples were then transferred to a positively charged nylon membrane (Hybond N+, Amersham Bioscience) using semi-dry transfer at 15 V for 30 min. The membrane was cross-linked for 1 min using the auto cross-link function on the UV cross-linker (Stratagene). The biotinylated DNA–protein complex was detected by the streptavidin–horseradish peroxidase conjugated antibody provided in the kit. The membrane was washed and incubated with the chemiluminescence substrate for 5 min and the signals were developed by exposing the membrane to an X-ray film for 1 min.Streptavidin affinity purificationStreptavidin agarose (Sigma-Aldrich) was employed for protein purification. Briefly, 50–100 μL of agarose was packed into a 1.5 mL Eppendorf tube for each sample. The agarose was allowed to settle with a short centrifugation (500×g, 5 min) and the supernatant was discarded. The agarose was washed 4–5 times with binding buffer (PBS containing 1 mM EDTA, 1 mM DTT, 4 µg poly dI. dC as non-specific competitor DNA and protease inhibitor). Simultaneously, the binding reaction with the nuclear protein fraction and the DNA probe was assembled as described above. A 100 μg amount of total nuclear protein was incubated with 4 μg of biotinylated DNA probe at room temperature for 20 min. The reaction was loaded onto the streptavidin column equilibrated with the binding buffer and incubated for another 1 h at room temperature with gentle shaking. Subsequently, the agarose was washed 4–5 times with the binding buffer. After the final wash, the supernatant was aspirated and 10 μL was left above the beads. For protein separation, 20–30 μL pf the SDS loading buffer was added onto the agarose, boiled at 95 °C for 5 min and the sample thus obtained was utilized for electrophoresis.Deletion mutagenesisFor deletion mutagenesis, a pair of primers flanking the sequence to be deleted (non-overlapping) was designed. The pCR-Blunt II-TOPO vector (Life Technologies) clone for the SfUGT33F28 exon 1–2 minigene was utilized as a template. Sequence was successfully amplified using Phusion High Fidelity DNA Polymerase (New England Biolabs) (PCR protocol: 30 s at 98 °C; 20 cycles of 10 s at 98 °C, 30 s at 55–60 °C, 4 min at 72 °C; and 10 min at 72 °C). A DpnI digest was performed to remove the background DNA, followed by ligation and transformation into fresh cells. The sequence of the mutant TOPO clone was then confirmed and utilized as a template for cloning into pIB/V5-His-TOPO vector (Life Technologies) for transfection into insect cells.Cloning and heterologous expression of SfUGTsSequences were amplified from S. frugiperda gut cDNA samples using Phusion High Fidelity DNA Polymerase (New England Biolabs) (PCR protocol: 30 s at 98 °C; 35 cycles of 10 s at 98 °C, 20 s at 55–60 °C, 45 s at 72 °C; and 5 min at 72 °C). The resulting amplified products were purified with a PCR cleanup kit (Qiagen) and incubated with GoTaq DNA polymerase (Promega) for 15 min at 72 °C in order to add A overhangs. The products were cloned into the pIB/V5-His-TOPO vector (Life Technologies) and transformed into NEB cells (Life Technologies), which were plated on selective LB agar medium containing 100 μg/mL ampicillin and incubated overnight at 37 °C. Positive colonies were identified by PCR using vector-specific OpIE2 primers, sub-cultured overnight at 37 °C in liquid LB medium containing 100 μg/mL ampicillin and used for plasmid DNA purification with the NucleoSpin Plasmid kit (Macherey-Nagel). Concentration and purity of the obtained constructs were assessed by NanoDrop 2000 UV–Vis Spectrophotometer (Thermo Scientific) and the correct orientation of the PCR products was confirmed by DNA sequencing.Insect cell transfectionFor transfection, Sf9 cells and Hi5 cells were sub-cultured at full confluency in a 6-well plate in a 1:3 dilution and left overnight to adhere to the flask surface. The medium was replaced, and transfections were carried out using FuGENE HD Transfection Reagent (Promega) in a 1:3 plasmid/lipid ratio (1.7 μg plasmid and 5.0 μL lipid for 3 mL medium). Cells were incubated for 48–72 h at 27 °C and re-suspended in fresh medium containing 50 μg/mL blasticidin for 2 weeks. Stable cell cultures were subsequently maintained at 10 μg/mL blasticidin.Cell lysate preparationCells were obtained from cultures 2 weeks post transfection growing stably on 50 μg/mL blasticidin. A 1 mL quantity of cells was harvested for each construct and re-suspended into 100 µL buffer. Protein concentrations were measured using the Bradford reagent, and 1–2 μg of the cell lysate was used for enzyme assays.Microsome preparationFor microsome extraction, confluent, stably transfected cells from five T-75 flasks (10 mL culture) per recombinant plasmid were harvested by scraping the cells off the bottom using a sterile cell scraper (Sarstedt AG, Nuembrecht, Germany). The obtained cell suspensions were combined into a 50 mL falcon tube and centrifuged at 1000×g for 15 min at 4 °C (AvantiTM J-20 XP Centrifuge, Beckman Coulter, Krefeld, Germany). The supernatant was discarded, the cells were washed twice with ice-cold PBS buffer (pH 7.4) and centrifuged at 1000×g for 15 min. The resulting cell pellet was re-suspended in 10 mL hypotonic buffer (20 mM Tris, 5 mM EDTA, 1 mM DTT, 20% glycerol, pH 7.5), containing 0.1% BenzonaseR nuclease and 100 μL Protease Inhibitor Cocktail (Serva) followed by incubation on ice for 30 min. After cell lysis, the cells were homogenized by 20–30 strokes in a Potter–Elvehjem tissue grinder (Kontes Glass Co., Vineland, USA) and were subsequently mixed with an equal volume of sucrose buffer (20 mM Tris, 5 mM EDTA, 1 mM DTT, 500 mM sucrose, 20% glycerol, pH 7.5). The homogenate was centrifuged at 1200×g and 4 °C for 10 min (AvantiTM J-20 XP Centrifuge, Beckman Coulter), and the supernatant was transferred into Beckman polycarbonate ultracentrifugation bottles (25 × 89 mm) (Beckman Coulter) and centrifuged at 100,000×g and 4 °C for 1.5 h in a fixed angle Type 70 Ti rotor (OptimaTM L-90K Ultracentrifuge, Beckman Coulter). After ultracentrifugation, the clear supernatant, containing the cytosolic fraction, was aliquoted into 1.5 mL Eppendorf tubes. The pellet, containing the microsomal fractions, was re-suspended in 1 mL of phosphate buffer (100 mM K2HPO4, pH 7.0), containing 10 μL Protease Inhibitor Cocktail (Serva) and stored at − 80 °C until further use. Typically, 5–10 μg of the microsome fraction so obtained was utilized for the enzyme assays.Cross-linking assaysCross-linking assays were performed using dimethyl suberimidate (DMS) as the cross-linking agent. A fresh stock of DMS (5 mg/mL) was prepared in 0.2 M triethanolamine (pH 8.0) at the start of each assay. DMS was added to a final concentration of 2.5 mg/mL to insect cell microsomes with gentle shaking up to 3 h, and samples were subsequently stored at − 20 °C until further use. All protein samples were electrophoresed using a 12% Mini-PROTEAN tris glycine gel, blotted onto PVDF membrane using wet transfer at 70 V for 30–45 min, followed by detection using the V5-HRP conjugate.V5-based affinity purificationAnti-V5 agarose affinity gel (Sigma-Aldrich) was employed for protein purification. Briefly, 50–75 μL of the agarose was packed into a 1.5 mL Eppendorf tube for each sample. The agarose was allowed to settle with a short centrifugation and the supernatant was discarded. The agarose was washed 4–5 times with PBS (pH 7.4). Samples to be purified were incubated with 5% digitonin on ice for 20 min and subject to centrifugation at 16,000×g for 30 min. Clarified cell lysate or microsomal extract was added onto the resin (up to 200 μL, volume adjusted by addition of PBS) and incubated for 1.5 h on a shaker. Subsequently, the agarose was washed 4–5 times with PBS. After the final wash, the supernatant was aspirated and 10 μL was left above the beads. This fraction was used for both protein electrophoresis and enzyme assays (separate purifications). For SDS-PAGE, 20–30 μL pf the SDS loading buffer was added onto the agarose, boiled at 95 °C for 5 min and sample thus obtained was utilized for electrophoresis.LC–MS/MS peptide analysisProtein bands of Coomassie Brilliant blue R250 stained gels were cut from the gel matrix and tryptic digestion was carried out63. For LC–MS/MS analysis of the resulting peptides, samples were reconstituted in 20 μL aqueous 1% formic acid, and 1 μL was injected onto an UPLC M-class system (Waters, Manchester, UK) coupled to a Synapt G2-si mass spectrometer (Waters, Manchester, UK). Samples were first pre-concentrated and desalted using a Symmetry C18 trap column (100 Å, 180 µm × 20 mm, 5 µm particle size) at a flow rate of 15 µL/min (0.1% aqueous formic acid). Peptides were eluted onto a ACQUITY UPLC HSS T3 analytical column (100 Å, 75 µm × 200 mm, 1.8 µm particle size) at a flow rate of 350 nL/min with the following gradient: 3–15% over 3 min, 15–20% B over 7 min, 20–40% B over 30 min, 40–50% B over 5 min, 50–70% B over 5 min, 70–95% B over 3 min, isocratic at 95% B for 1 min, and a return to 1% B over 1 min. Phases A and B were composed of 0.1% formic acid and 100% acetonitrile in 0.1% formic acid, respectively). The analytical column was re-equilibrated for 10 min prior to the next injection. The eluted peptides were transferred into the mass spectrometer operated in V-mode with a resolving power of at least 20,000 full width at half height FWHM. All analyses were performed in a positive ESI mode. A 100 fmol/μL sample of human Glu-Fibrinopeptide B in 0.1% formic acid/acetonitrile (1:1 v/v) was infused at a flow rate of 1 μL/min through the reference sprayer every 45 s to compensate for mass shifts in MS and MS/MS fragmentation mode. Data were acquired using data-dependent acquisition (DDA). The acquisition cycle for DDA analysis consisted of a survey scan covering the range of m/z 400–1800 Da followed by MS/MS fragmentation of the ten most intense precursor ions collected at 0.5 s intervals in the range of 50–2000 m/z. Dynamic exclusion was applied to minimize multiple fragmentations for the same precursor ions. MS data were collected using MassLynx v4.1 software (Waters, Manchester, UK).Data processing and protein identificationDDA raw data were processed and searched against a sub-database containing common contaminants (human keratins and trypsin) using ProteinLynx Global Server (PLGS) version 2.5.2 (Waters, Manchester, UK). Spectra remaining unmatched by database searching were interpreted de novo to yield peptide sequences and subjected to homology-based searching using the MS BLAST program64 installed on a local server. MS BLAST searches were performed against a Spodoptera frugiperda database obtained by in silico translation of the S. frugiperda transcriptome37 and against arthropoda database (NCBI). PKL-files of MS/MS spectra were generated and searched against Spodoptera frugiperda database combined with NCBI nr (downloaded on May 24, 2020) using MASCOT software version 2.6.2. The following searching parameters were applied: fixed precursor ion mass tolerance of 15 ppm for the survey peptide, fragment ion mass tolerance of 0.1 Da, 1 missed cleavage, fixed carbamidomethylation of cysteines and possible oxidation of methionine.Enzymatic assaysFor UGT assays, samples from insect cell cultures (transient or stable) were prepared in phosphate buffer (pH 7.0, 100 mM). Typical enzyme reactions included 5–10 µg cell microsomal extracts, 2 μL of 12.5 mM DIMBOA in DMSO (25 nmol), 4 μL of 12.5 mM UDP-glucose in water (50 nmol), and phosphate buffer (pH 7.0, 100 mM) to give an assay volume of 50 μL. Controls containing either boiled enzymatic preparation, or only the protein suspension and buffer were included. After incubation at 30 °C for 60 min, the enzyme reactions were interrupted by adding 50 μL of 1:1 (v:v) methanol/formic acid solution. For enzyme assays involving resin purified microsomal extracts, equal amounts of extracts were employed for resin purification and the enzyme assay (buffer + substrate) was pipetted directly onto the resin. Post incubation, samples were centrifuged, supernatant was collected, and reaction was stopped by addition of methanol/formic acid solution. Assays were centrifuged at 5000g for 5 min and the obtained supernatant was collected and analyzed by LC–MS/MS.Chromatographic methodsFor all analytical chromatography procedures, formic acid (0.05%) in water and acetonitrile were used as mobile phases A and B, respectively, and the column temperature was maintained at 25 °C. Analyses of enzymatic assays and plant samples used a Zorbax Eclipse XDB-C18 column (50 × 4.6 mm, 1.8 μm, Agilent Technologies) with a flow rate of 1.1 mL/min and with the following elution profile: 0–0.5 min, 95% A; 0.5–6 min, 95–67.5% A; 6.02–7 min, 100% B; 7.1–9.5 min, 95% A. LC–MS/MS analyses were performed on an Agilent 1200 HPLC system (Agilent Technologies) coupled to an API 6500 tandem spectrometer (AB Sciex) equipped with a turbospray ion source operating in negative ionization mode. Multiple reaction monitoring (MRM) was used to monitor analyte parent ion to product ion conversion with parameters from the literature for DIMBOA65 and DIMBOA-Glc16. Analyst (version 1.6.3, Applied Biosystems) software was used for data acquisition and processing.Statistical analysisAll statistical analyses were carried out using SigmaPlot 12.0 and R studio (version 3.6.3). Data were tested for homogeneity of variance and normality and were appropriately transformed to meet these criteria where required. The specific statistical method used for each data set is described in the figure legends. More

Aissi A, Beghami Y, Heuertz M (2019) Le chêne faginé (Quercus faginea, Fagaceae) en Algérie: potentiel germinatif et variabilité morphologique des glands et des semis. Plant Ecol Evol 152:437–449Article 

Google Scholar 
Aissi A, Beghami Y, Lepais O, Véla E (2021) Morphological and taxonomic analysis of Quercus faginea (Fagaceae) complex in Algeria. Botany 99:99–113Article 

Google Scholar 
Alberto F, Niort J, Derory J, Lepais O, Vitalis R, Galop D et al. (2010) Population differentiation of sessile oak at the altitudinal front of migration in the French Pyrenees. Mol Ecol 19:2626–2639CAS 
PubMed 
Article 

Google Scholar 
Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell PubDe Barba M, Miquel C, Lobréaux S, Quenette PY, Swenson JE, Taberlet P (2016) High-throughput microsatellite genotyping in ecology: Improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Mol Ecol Resour 17:492–507PubMed 
Article 
CAS 

Google Scholar 
Barthe S, Gugerli F, Barkley NA, Maggia L, Cardi C, Scotti I (2012) Always look on both sides: phylogenetic information conveyed by simple sequence repeat allele sequences. PLoS One 7:e40699CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162:2025–35PubMed 
PubMed Central 
Article 

Google Scholar 
Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2004) GENETIX 4.05, logiciel sous Windows TM pour la génétique des populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5171, Université de Montpellier II, Montpellier (France)
Google Scholar 
Bradbury IR, Wringe BF, Watson B, Paterson I, Horne J, Beiko R et al. (2018) Genotyping-by-sequencing of genome-wide microsatellite loci reveals fine-scale harvest composition in a coastal Atlantic salmon fishery. Evol Appl 11:918–930CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buschbom J, Yanbaev Y, Degen B (2011) Efficient long-distance gene flow into an isolated relict oak stand. J Hered 102:464–472PubMed 
Article 

Google Scholar 
Chapuis M, Raynal L, Plantamp C, Meynard CN, Blondin L, Marin J et al. (2020) A young age of subspecific divergence in the desert locust inferred by ABC Random Forest. Mol Ecol 29:4542–4558PubMed 
Article 

Google Scholar 
Cornuet J-M, Ravigné V, Estoup A (2010) Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0). BMC Bioinforma 11:401Article 
CAS 

Google Scholar 
Crow JF, Aoki K (1984) Group selection for a polygenic behavioral trait: Estimating the degree of population subdivision. Proc Natl Acad Sci USA 81:6073–6077CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Curto M, Winter S, Seiter A, Schmid L, Scheicher K, Barthel LMF et al. (2019) Application of a SSR-GBS marker system on investigation of European Hedgehog species and their hybrid zone dynamics. Ecol Evol 9:2814–2832PubMed 
PubMed Central 
Article 

Google Scholar 
Darby BJ, Erickson SF, Hervey SD, Ellis-Felege SN (2016) Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing. Ecol Evol 6:4502–4512PubMed 
PubMed Central 
Article 

Google Scholar 
Dickey AM, Hall PM, Shatters RG, Mckenzie CL (2013) Evolution and homoplasy at the Bem6 microsatellite locus in three sweetpotato whitefly (Bemisia tabaci) cryptic species. BMC Res Notes 6:249PubMed 
PubMed Central 
Article 

Google Scholar 
Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2013) NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214PubMed 
Article 

Google Scholar 
Durand J, Bodenes C, Chancerel E, Frigerio JM, Vendramin G, Sebastiani F et al. (2010) A fast and cost-effective approach to develop and map EST-SSR markers: oak as a case study. BMC Genomics 11:570PubMed 
PubMed Central 
Article 

Google Scholar 
Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361Article 

Google Scholar 
Estoup A, Jarne P, Cornuet J-M (2002) Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Mol Ecol 11:1591–1604CAS 
PubMed 
Article 

Google Scholar 
Estoup A, Raynal L, Verdu P, Marin J-M (2018) Model choice using Approximate Bayesian Computation and Random Forests: analyses based on model grouping to make inferences about the genetic history of Pygmy human populations. J la Société Fr Stat 159:167–190
Google Scholar 
Excoffier L, Dupanloup I, Huerta-Sánchez E, Sousa VC, Foll M (2013) Robust demographic inference from genomic and SNP data. PLoS Genet 9:e1003905PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567PubMed 
Article 

Google Scholar 
Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–87CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Feliner GN (2014) Patterns and processes in plant phylogeography in the Mediterranean Basin. A review. Perspect Plant Ecol, Evol Syst 16:265–278Article 

Google Scholar 
Flagel L, Brandvain Y, Schrider DR (2019) The unreasonable effectiveness of convolutional neural networks in population genetic inference. Mol Biol Evol 36:220–238CAS 
PubMed 
Article 

Google Scholar 
Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180:977–993PubMed 
PubMed Central 
Article 

Google Scholar 
Gaggiotti OE, Chao A, Peres-Neto P, Chiu CH, Edwards C, Fortin MJ et al. (2018) Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales. Evol Appl 11:1176–1193PubMed 
PubMed Central 
Article 

Google Scholar 
García Murillo P., Harvey-Brown Y (2017) Quercus canariensis. In: The IUCN Red List of Threatened Species,, p e.T78809256A80570536GBIF Secratariat (2021a) Quercus faginea Lam. In: GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omeiaccessed via GBIF.org on 2022-05-10GBIF Secratariat (2021b). Quercus canariensis Willd. In: GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omeiaccessed via GBIF.org on 2022-05-10Gómez A, Lunt DH (2007) Refugia within refugia: Patterns of phylogeographic concordance in the Iberian peninsula. In: Weiss S, Ferrand N (eds) Phylogeography of Southern European Refugia. Springer Netherlands, Dordrecht, p 155–188Chapter 

Google Scholar 
Gorener V, Harvey-Brown Y, Barstow M (2017) Quercus canariensis. IUCN red List Threat species e.T7880925Goudet J (1995) FSTAT (Version 1.2): A computer program to calculate F-statistics. J Hered 86:485–486Article 

Google Scholar 
Hampe A, Petit RJ (2005) Conserving biodiversity under climate change: the rear edge matters. Ecol Lett 8:461–7PubMed 
Article 

Google Scholar 
Hardy OJ, Charbonnel N, Fréville H, Heuertz M (2003) Microsatellite allele sizes: a simple test to assess their significance on genetic differentiation. Genetics 163:1467–82CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620Article 
CAS 

Google Scholar 
Harvey-Brown Y, García Murillo PG, Buira A (2017) Quercus faginea. IUCN Red List Threat Species: e.T78916251A80570540.Henriques R, von der Heyden S, Matthee CA (2016) When homoplasy mimics hybridization: a case study of Cape hakes (Merluccius capensis and M. paradoxus). PeerJ 4:e1827PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hewitt GM (1999) Post-glacial re-colonization of European biota. Biol J Linn Soc 68:87–112Article 

Google Scholar 
Hey J, Won YJ, Sivasundar A, Nielsen R, Markert JA (2004) Using nuclear haplotypes with microsatellites to study gene flow between recently separated Cichlid species. Mol Ecol 13:909–919CAS 
PubMed 
Article 

Google Scholar 
Hoban S, Bruford M, D’Urban Jackson J, Lopes-Fernandes M, Heuertz M, Hohenlohe PA et al. (2020) Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol Conserv 248:108654Article 

Google Scholar 
Hoogenboom J, de Knijff P, Laros JFJ, de Leeuw RH, van der Gaag KJ, Sijen T (2016) FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise. Forensic Sci Int Genet 27:27–40PubMed 
Article 
CAS 

Google Scholar 
Hothorn T, Hornik K, van de Wiel MA, Zeileis A (2008) Implementing a class of permutation tests: the coin package. J Stat Softw 28:1–23Article 

Google Scholar 
Jamieson IG, Allendorf FW (2012) How does the 50/500 rule apply to MVPs? Trends Ecol Evol 27:578–584PubMed 
Article 

Google Scholar 
Jerome D, Vasquez F (2018) Quercus faginea. IUCN Red List Threat Species e.T7891625Kalinowski ST (2005) HP-RARE 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5:187–189CAS 
Article 

Google Scholar 
Kampfer S, Lexer C, Glössl J, Steinkellner H (1998) Characterization of (GA)n microsatellite loci from Quercus robur. Hereditas 129:183–186CAS 
Article 

Google Scholar 
Kivelä M, Arnaud-Haond S, Saramäki J (2015) EDENetworks: A user-friendly software to build and analyse networks in biogeography, ecology and population genetics. Mol Ecol Resour 15:117–122PubMed 
Article 

Google Scholar 
Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour 15:1179–1191CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Layton KKS, Dempson B, Snelgrove PVR, Duffy SJ, Messmer AM, Paterson IG et al. (2020) Resolving fine‐scale population structure and fishery exploitation using sequenced microsatellites in a northern fish. Evol Appl: eva.12922.Lepais O, Chancerel E, Boury C, Salin F, Manicki A, Taillebois L et al. (2020) Fast sequence-based microsatellite genotyping development workflow. PeerJ 8:e9085PubMed 
PubMed Central 
Article 

Google Scholar 
Lepais O, Leger V, Gerber S (2006) Short note: high throughput microsatellite genotyping in oak species. Silvae Genet 55:238Article 

Google Scholar 
Lepais O, Muller SD, Ben Saad-Limam S, Benslama M, Rhazi L, Belouahem-Abed D et al. (2013) High genetic diversity and distinctiveness of rear-edge climate relicts maintained by ancient tetraploidisation for Alnus glutinosa. PLoS One 8:e75029CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leroy T, Roux C, Villate L, Bodénès C, Romiguier J, Paiva JAP et al. (2017) Extensive recent secondary contacts between four European white oak species. N. Phytol 214:865–878CAS 
Article 

Google Scholar 
Lye GC, Lepais O, Goulson D (2011) Reconstructing demographic events from population genetic data: the introduction of bumblebees to New Zealand. Mol Ecol 20:2888–900CAS 
PubMed 
Article 

Google Scholar 
Magri D, Fineschi S, Bellarosa R, Buonamici A, Sebastiani F, Schirone B et al. (2007) The distribution of Quercus suber chloroplast haplotypes matches the palaeogeographical history of the western Mediterranean. Mol Ecol 16:5259–66CAS 
PubMed 
Article 

Google Scholar 
Marin J, Pudlo P, Estoup A, Robert C (2018) Likelihood-free model choice. In: Sisson S A, Fan Y, Beaumont M (eds) Handbook of Approximate Bayesian Computation, CRC Press, pp. 153.Médail F, Diadema K (2009) Glacial refugia influence plant diversity patterns in the Mediterranean Basin. J Biogeogr 36:1333–1345Article 

Google Scholar 
Moracho E, Moreno G, Jordano P, Hampe A (2016) Unusually limited pollen dispersal and connectivity of Pedunculate oak (Quercus robur) refugial populations at the species’ southern range margin. Mol Ecol 25:3319–3331CAS 
PubMed 
Article 

Google Scholar 
Mountain JL, Knight A, Jobin M, Gignoux C, Miller A, Lin AA et al. (2002) SNPSTRs: Empirically derived, rapidly typed, autosomal haplotypes for inference of population history and mutational processes. Genome Res 12:1766–1772CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Muir G, Lowe AJ, Fleming CC, Vogl C (2004) High nuclear genetic diversity, high levels of outcrossing and low differentiation among remnant populations of Quercus petraea at the margin of its range in Ireland. Ann Bot 93:691–697CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Neophytou C, Gärtner SM, Vargas-Gaete R, Michiels H-G (2015) Genetic variation of Central European oaks: shaped by evolutionary factors and human intervention? Tree Genet Genomes 11:1–15Article 

Google Scholar 
Payseur BA, Cutter AD (2006) Integrating patterns of polymorphism at SNPs and STRs. Trends Genet 22:424–429CAS 
PubMed 
Article 

Google Scholar 
Petit RJ, Brewer S, Bordács S, Burg K, Cheddadi R, Coart E et al. (2002) Identification of refugia and post-glacial colonisation routes of European white oaks based on chloroplast DNA and fossil pollen evidence. Ecol Manag 156:49–74Article 

Google Scholar 
Petit RJ, Hampe A, Cheddadi R (2005) Climate changes and tree phylogeography in the Mediterranean. Taxon 54:877–885Article 

Google Scholar 
Press MO, Hall AN, Morton EA, Queitsch C (2019) Substitutions are boring: Some arguments about parallel mutations and high mutation rates. Trends Genet 35:253–264CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Press MO, Mccoy RC, Hall AN, Akey JM, Queitsch C (2018) Massive variation of short tandem repeats with functional consequences across strains of Arabidopsis thaliana. Genome Res 28:1169–1178CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–59CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pudlo P, Marin J-M, Estoup A, Cornuet J-M, Gautier M, Robert CP (2016) Reliable ABC model choice via random forests. Bioinformatics 32:859–866CAS 
PubMed 
Article 

Google Scholar 
Ramakrishnan U, Mountain JL (2004) Precision and accuracy of divergence time estimates from STR and SNPSTR variation. Mol Biol Evol 21:1960–1971CAS 
PubMed 
Article 

Google Scholar 
Raynal L, Marin J-M, Pudlo P, Ribatet M, Robert CP, Estoup A (2019) ABC random forests for Bayesian parameter inference. Bioinformatics 35:1720–1728CAS 
PubMed 
Article 

Google Scholar 
Rodríguez-Sánchez F, Hampe A, Jordano P, Arroyo J (2010) Past tree range dynamics in the Iberian Peninsula inferred through phylogeography and palaeodistribution modelling: A review. Rev Palaeobot Palynol 162:507–521Article 

Google Scholar 
Šarhanová P, Pfanzelt S, Brandt R, Himmelbach A, Blattner FR (2018) SSR-seq: Genotyping of microsatellites using next-generation sequencing reveals higher level of polymorphism as compared to traditional fragment size scoring. Ecol Evol 8:10817–10833PubMed 
PubMed Central 
Article 

Google Scholar 
Scotti-Saintagne C, Mariette S, Porth I, Goicoechea PG, Barreneche T, Bodénès C et al. (2004) Genome scanning for interspecific differentiation between two closely related oak species [Quercus robur L. and Q. petraea (Matt.) Liebl.]. Genetics 168:1615–26CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vartia S, Villanueva-Cañas JL, Finarelli J, Farrell ED, Collins PC, Hughes GM et al. (2016) A novel method of microsatellite genotyping-by-sequencing using individual combinatorial barcoding. R Soc Open Sci 3:150565PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Viruel J, Haguenauer A, Juin M, Mirleau F, Bouteiller D, Boudagher-Kharrat M et al. (2018) Advances in genotyping microsatellite markers through sequencing and consequences of scoring methods for Ceratonia siliqua (Leguminosae). Appl Plant Sci 6:e01201PubMed 
PubMed Central 
Article 

Google Scholar 
Wang J (2016) Individual identification from genetic marker data: developments and accuracy comparisons of methods. Mol Ecol Resour 16:163–175CAS 
PubMed 
Article 

Google Scholar 
Waples RS, Do C (2010) Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol Appl 3:244–262PubMed 
Article 

Google Scholar 
Xie KT, Wang G, Thompson AC, Wucherpfennig JI, Reimchen TE, MacColl ADC et al. (2019) DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science (80-) 363:81–84CAS 
Article 

Google Scholar  More

Six biological replicates each were sampled from four fruit species (blueberries, cherries, raspberries, and strawberries) at four developmental stages. Developmental stages were based on fruit pigmentation ranging from unripe (green) to fully ripe (red/purple/navy; Fig. S1) throughout June to September in 2018. Ten fruits (except blueberries N = 20) were collected for each species per replicate, and this was replicated six times for each ripening stage for each fruit at different sites.Quantitative analysis of fungal communitiesMetabarcoding analysis is generally not quantitative, but the addition of 265 P. cucumerina cells to sub-samples prior to DNA extraction served as an internal standard to attempt an estimation of the size of fungal populations. One replicate spiked with the internal standard of the strawberry stage 3 samples was removed due to poor sequence quality leaving 96 non-spiked and 95 spiked samples which produced a total of 38,445,395 reads that clustered into 1712  > 97% identity Amplicon Sequence Variants (ASV), which from here-in we call phylotypes (Table S1). Blast searches across all phylotypes for matches to the P. cucumerina internal standard’s ITS sequence generated from Sanger sequencing revealed one phylotype that matched with 100% identity. Plectosphaerella cucumerina was naturally present in 21 of the 95 non-spiked samples and comprised of a total of 444 reads. Cherry was the only fruit where the internal standard was reliably recovered: 23 of 24 spiked samples and only one of 24 non-spiked samples contained the internal standard phylotype. After internal standard DNA read normalisation, the mean (± SE) number of fungal cells from each of the useable 23 pairs of cherry replicates was 307,323 (± 39,090) cells. The range of phylotype cell abundance across all cherry samples was 3.9 million for an Aureobasidium phylotype to 3 cells for a phylotype taxonomically assigned no higher level than kingdom. There was no significant change in total fungal cell numbers across cherry maturation stage (Kruskal–Wallis, chi-squared = 2.63, P = 0.45; Fig. S2), but fruit surface areas also increased significantly (Kruskal–Wallis, chi-squared = 19.70, P = 0.0002, Fig. S2). When cell numbers were normalised for surface area this revealed that absolute fungal population sizes remained static across cherry maturation stages (Kruskal–Wallis, chi-squared = 2.49, P = 0.48; Fig. 1A). However, there was a significant change in absolute Saccharomycetales cell numbers when normalised for cherry surface area across maturation (Kruskal–Wallis, chi-squared = 15.30, P = 0.002): stage 1 had significantly greater absolute Saccharomycetales cell numbers than stage 4 (P = 0.0007; Fig. 1B). Six individual Saccharomycetales yeast phylotypes from the genera Debaryomyces, Saccharomyces, Kodamaea, one from the family Pichiaceae, and phylotypes with  > 97% homology to M. pulcherrima and Metschnikowia gruessii, had significantly greater abundances on ripening stage 1 than 4 (P values span 0.045 to 0.006).Figure 1Absolute fungal cell abundances on cherry epicarp. Number of total fungal (A) and Saccharomycetales yeasts (B) cells per mm2 of cherry epicarp (N = 6 except, stage 3 and 4, N = 5) at four ripening stages (1, unripe/green fruit; 2, de-greening fruit; 3, ripening fruit; and 4, fully ripe/harvest fruit) estimated from DNA read abundances normalised to DNA abundances from the deliberate addition of 265 live Plectosphaerella cucumerina cells prior to DNA extraction. Different lower-case letters above bars show significant differences between ripening stages at P  > 0.05, Dunn’s comparisons post-hoc with Benjamini–Hochberg multiple comparison correction.Full size imageOverview of fungal diversity across all fruit samplesThe P. cucumerina internal standard phylotype was removed from all samples, and the sequence data normalised and analysed. A total of 1712 fungal phylotypes was revealed, comprising seven phyla, 25 classes, 96 orders, 197 families, and 280 genera. The most abundant and diverse phylum was Ascomycota, comprising 92.2% of reads and 57.3% of phylotypes, followed by Basidiomycota (7.7% reads and 33.6% phylotypes), Zygomycota (0.1% and 1.1%), Chytridiomycota ( > 0.1% and 0.7%), Mucoromycota ( > 0.1% and 0.3%), Glomeromycota and Rozellomycota (both  > 0.1% and 0.1%; Fig. S3A). A phylotype from the Cladosporium genus was the most common phylotype across all samples, comprising 60.8% of reads. A total of 87 phylotypes from the order Saccharomycetales (budding yeasts) was detected, comprising 1,792,782 DNA reads (4.7% of the total) spanning 10 families and 25 genera. Metschnikowia was the most abundant Saccharomycetales genus (40.0% of Saccharomycetales reads), followed by Hanseniaspora (38.2%), then Pichia (5.2%), with the remaining genera contributing fewer than 3% each. Candida was the most diverse genus within the order Saccharomycetales accounting for 21.8% of phylotypes, despite only comprising 2.4% of reads, followed by Metschnikowia (11.5%), Hanseniaspora (8.0%) and Pichia (6.9%), with each of the remaining genera contributing fewer than 3.5% of phylotypes each (Fig. S3B). The most common Saccharomycetales yeast across all samples was a phylotype from the genus Hanseniaspora with  > 97% homology to H. uvarum and comprised 38.2% of the total Saccharomycetales reads (Fig. S3B).The effect of fruit species and ripening stage on epicarp fungal communitiesWe analysed differences in three biodiversity metrics to evaluate the effect of fruit species and maturation stage on fungal communities: differences in the absolute numbers of phylotypes (richness); differences in the types of phylotypes (i.e. presences/absences); and differences in the relative abundances of phylotypes (community composition) following Morrison-Whittle et al.14 and Morrison‐Whittle and Goddard37.
Fungal phylotype richnessPhylotype richness was not normally distributed (Shapiro-Wilks, P = 0.008) but square root transformation allowed the data to conform to the assumptions of ANOVA. There was a significant effect of both fruit type and ripening stage on the number of fungal phylotypes, including an interaction between the two (F3,175 = 18.58, P = 1.65 × 10–10; F3,175 = 5.00, P = 0.002 and F9,175 = 6.69, P = 3.25 × 10–8 respectively). Comparisons of effect sizes revealed fruit type (ω2 = 0.30) had a 4.4 times greater effect than ripening stage (ω2 = 0.068) on fungal phylotype richness. Disregarding ripening stage, cherry (mean ± SE number of phylotypes = 98 ± 4.1) had significantly more fungal phylotypes than blueberry (68 ± 3.7), raspberry (72 ± 2.9) and strawberry (76 ± 3.2) (Tukey’s HSD, P  0.05) and there was a significant effect of ripening stage on the number of fungal phylotypes for cherry, raspberry, and strawberry (one-way ANOVA: F3,44 = 4.33, P = 0.0093; F3,44 = 13.56, P = 2.11 × 10–6 and F3,44 = 13.86, P = 1.84 × 10–6, respectively, Fig. 2), but not blueberry (F3,44 = 2.27, P = 0.055). On cherries phylotype numbers increased during ripening, but raspberry and strawberry had greater numbers at intermediate stages of fruit maturation (Fig. 2).Figure 2Number of observed phylotypes across fruit types and maturation stages. Number of fungal phylotypes across four ripening stages (1, unripe/green fruit; 2, de-greening fruit; 3, ripening fruit; and 4, fully ripe/harvest fruit) for blueberry, cherry, raspberry and strawberry (N = 12 except N = 11 for strawberry stage 3). Numbers of fungal phylotypes differ across ripening stages for cherry, raspberry and strawberry but not blueberry (ANOVA, P values shown). Where significant, different lowercase letters represent significant differences in phylotype numbers within each fruit (P  97% homology to Metschnikowia kunwiensis and H. uvarum on raspberry; and phylotypes with  > 97% homology to Kalmanozyma fusiformata (Ustilaginaceae smut fungi) and Podosphaera aphanis on strawberry.Twenty-four of the 195 indicator phylotypes belonged to the Saccharomycetales budding yeasts (Table S13). There were no Saccharomycetales indicator phylotypes for cherry, and just one for blueberry, a fungal phylotype with  > 97% homology to Metschnikowia koreensis. Raspberry had 15 Saccharomycetales indicator phylotypes: three with  > 97% homology to the Metschnikowia and, Candida genera, two Pichia and Schwanniomyces, and one each from Hanseniaspora, Barnettozyma, Debaryomyces, Candida, Geotrichum and Martiniozyma. There were eight indicator phylotypes for strawberry; two Candida and one from each of the Metschnikowia, Starmerella, Kodamaea and Hyphopichia genera and the Pichiaceae family, and a phylotype assigned to the no higher level than fungal kingdom (with  > 97% homology to deposit from Candida genus). The dynamics of Saccharomycetales yeast indicator phylotypes abundances across maturation for raspberry and strawberry is shown in Fig. 6.Figure 6Dynamics of changes in the proportion of budding yeast indicator phylotypes. Mean proportion of reads for the Saccharomycetales budding yeast indicator phylotypes that are significantly overrepresented on (A) raspberry and (B) strawberry (P  97% homology identified by manual Blast searches.Full size imageDifferences of yeast known to be attractive to D. suzukii
Yeast from the Hanseniaspora, Pichia, Saccharomyces, Candida and Metschnikowia genera and their combinations are attractive to D. suzukii27,28,30,31, and phylotypes belonging to these genera were recovered here. The combined relative read abundances of all phylotypes assigned to these genera were significantly different between fruit types and ripening stages (Kruskal–Wallis chi-squared = 60.54, P = 4.51 × 10–13; chi-squared = 10.11, P = 0.018, respectively). Raspberry had the highest relative abundance of yeast genera known to be attractive to D. suzukii (mean ± SE = 21,539 ± 4339) and this was significantly greater than on the other fruits (P  97% homology to H. uvarum as over-represented on raspberry generally, and especially at later stages (Fig. 6A).Differences of Botrytis cinerea, known to be repulsive to D. suzukii
The relative read abundances of B. cinerea were significantly different between fruit types and ripening stages (Kruskal–Wallis chi-squared = 73.45, P = 7.80 × 10–16; Kruskal–Wallis chi-squared = 23.81, P = 2.74 × 10–5, respectively). Raspberry had the lowest relative abundance of B. cinerea (mean ± SE = 800 ± 136) and this was significantly lower than strawberry (1994 ± 292) and blueberry (5990 ± 1305) (P  More

Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 1–11 (2015).CAS 
Article 

Google Scholar 
Abatzoglou, J. T., Williams, A., Boschetti, L., Zubkova, M. & Kolden, C. A. Global patterns of interannual climate-fire relationships. Glob. Change Biol. 24, 5164–5175 (2018).ADS 
Article 

Google Scholar 
Giorgi, F. Climate change hot-spots. Geophys. Res. Lett. 33, L08707 (2006).ADS 
Article 

Google Scholar 
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
IPCC In Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge University Press, 2021).
Google Scholar 
Dupuy, J. et al. Climate change impact on future wildfire danger and activity in southern Europe: A review. Ann. For. Sci. 77, 35 (2020).Article 

Google Scholar 
Turco, M. et al. Decreasing fires in mediterranean Europe. PLoS ONE 11, e0150663 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Turco, M. et al. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nat. Commun. 9, 1–9 (2018).Article 
CAS 

Google Scholar 
Ruffault, J. et al. Increased likelihood of heat-induced large wildfires in the Mediterranean Basin. Sci. Rep. 10, 13790 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moreira, F. et al. Wildfire management in Mediterranean-type regions: Paradigm change needed. Environ. Res. Lett. 15, 011001 (2020).ADS 
Article 

Google Scholar 
Di Giuseppe, F. et al. Fire Weather Index: The skill provided by the European Centre for Medium-Range Weather Forecasts ensemble prediction system. Nat. Hazards Earth Syst. Sci. 20, 2365–2378 (2020).ADS 
Article 

Google Scholar 
Van Wagner, C. E. Development and structure of the Canadian forest fireweather index system. Canadian Forestry Service, Forestry Technical Report 35 (1987).de Groot, W. J. et al. Development of the Indonesian and Malaysian fire danger rating systems. Mitig. Adapt. Strat. Global Change. 12, 165–180 (2007).Article 

Google Scholar 
Venäläinen, A. et al. Temporal variations and change in forest fire danger in Europe for 1960–2012. Nat. Hazards Earth Syst. Sci. 14, 1477–1490 (2014).ADS 
Article 

Google Scholar 
Bowman, D. M. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 1–6 (2017).Article 

Google Scholar 
Abatzoglou, J. T. et al. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).ADS 
Article 

Google Scholar 
Jain, P. et al. Observed increases in extreme fire weather driven by atmospheric humidity and temperature. Nat. Clim. Change 12, 63–70 (2022).ADS 
Article 

Google Scholar 
Calheiros, T. et al. Recent evolution of spatial and temporal patterns of burnt areas and fire weather risk in the Iberian Peninsula. Agr. For. Meteorol. 287, 107923 (2020).Article 

Google Scholar 
Abatzoglou, J. T. et al. Increasing synchronous fire danger in forests of the western United States. Geophys. Res. Lett. 48, e2020GL091377 (2021).ADS 

Google Scholar 
Kaiser, J. W. et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554 (2012).ADS 
CAS 
Article 

Google Scholar 
Peuch, V. H. et al. The use of satellite data in the Copernicus atmosphere monitoring service. In IEEE International Geoscience and Remote Sensing Symposium (ed Moreno, J.) 1594–1596 (IEEE, 2018).Carnicer, J. et al. Regime shifts of Mediterranean forest carbon uptake and reduced resilience driven by multidecadal ocean surface temperatures. Glob. Change Biol. 25, 2825–2840 (2019).ADS 
Article 

Google Scholar 
Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Fut. 7, 892–910 (2019).ADS 
Article 

Google Scholar 
Rogers, B. M. et al. Focus on changing fire regimes: Interactions with climate, ecosystems, and society. Environ. Res. Lett. 15, 030201 (2020).ADS 
Article 

Google Scholar 
Duane, A. et al. Towards a comprehensive look at global drivers of novel extreme wildfire events. Clim. Change 165, 1–21 (2021).ADS 
Article 

Google Scholar 
Ellis, T. M. et al. Global increase in wildfire risk due to climate-driven declines in fuel moisture. Glob. Change Biol. 28, 1544–1559 (2022).Article 

Google Scholar 
Grassi, G. et al. On the realistic contribution of European forests to reach climate objectives. Carbon Balance Manag. 14, 1–5 (2019).CAS 
Article 

Google Scholar 
Pilli, R., Alkama, R., Cescatti, A., Kurz, W. A. & Grassi, G. The European forest Carbon budget under future climate conditions and current management practices. Biogeosci. Discuss. 1, 33 (2022).
Google Scholar 
Migliavacca, M. et al. Modeling biomass burning and related carbon emissions during the 21st century in Europe. J. Geophys. Res. Biogeosci. 118, 1732–1747 (2013).CAS 
Article 

Google Scholar 
Resco de Dios, V. et al. Climate change induced declines in fuel moisture may turn currently fire-free Pyrenean mountain forests into fire-prone ecosystems. Sci. Total Environ. 797, 149104 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pausas, J. G. & Keeley, J. E. Wildfires and global change. Front. Ecol. Environ. 19, 387–395 (2021).Article 

Google Scholar 
Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).PubMed 
Article 

Google Scholar 
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Carnicer, J. et al. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. Proc. Natl. Acad. Sci. 108, 1474–1478 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seidl, R., Schelhaas, M. J., Rammer, W. & Verkerk, P. J. Increasing forest disturbances in Europe and their impact on carbon storage. Nat. Clim. Change 4, 806–810 (2014).ADS 
CAS 
Article 

Google Scholar 
Forzieri, G. et al. Vulnerability of European forests to climate risks. Geophys. Res. Abstr. 21, 1 (2019).
Google Scholar 
Senf, C. & Seidl, R. Mapping the forest disturbance regimes of Europe. Nat. Sustain. 4, 63–70 (2021).Article 

Google Scholar 
Carnicer, J. et al. Forest resilience to global warming is strongly modulated by local-scale topographic, microclimatic and biotic conditions. J. Ecol. 109, 3322–3339 (2021).Article 

Google Scholar 
Sanginés de Cárcer, P. et al. Vapor–pressure deficit and extreme climatic variables limit tree growth. Glob. Change Biol. 24, 1108–1122 (2018).ADS 
Article 

Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Carnicer, J., Barbeta, A., Sperlich, D., Coll, M. & Peñuelas, J. Contrasting trait syndromes in angiosperms and conifers are associated with different responses of tree growth to temperature on a large scale. Front. Plant Sci. 4, 409 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Lee, H. et al. Implementing land-based mitigation to achieve the Paris Agreement in Europe requires food system transformation. Environ. Res. Lett. 14, 104009 (2019).ADS 
CAS 
Article 

Google Scholar 
Bednar-Friedl, B. et al. Europe. In Climate Change 2022: Impacts, Adaptation and Vulnerability. IPCC-WMO.Luyssaert, S. et al. Trade-offs in using European forests to meet climate objectives. Nature 562, 259–262 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nabuurs, G. J. et al. By 2050 the mitigation effects of EU forests could nearly double through climate smart forestry. Forests 8, 484 (2017).Article 

Google Scholar 
Vizzarri, M., Pilli, R., Korosuo, A., Frate, L. & Grassi, G. The role of forests in climate change mitigation: The EU context. In Climate-Smart Forestry in Mountain Regions (eds Tognetti, R. et al.) 507–520 (Springer, 2022).Chapter 

Google Scholar 
Tognetti, R., Smith, M. & Panzacchi, P. Climate-Smart Forestry in Mountain Regions 574 (Springer, 2022).Book 

Google Scholar 
Ali, E. et al. Mediterranean Region. In Climate Change 2022: Impacts, Adaptation and Vulnerability. IPCC-WMO.IPCC, 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press) (in press).Boer, M. M. et al. Changing weather extremes call for early warning of potential for catastrophic fire. Earth’s Fut. 5, 1196–1202 (2017).ADS 
Article 

Google Scholar 
Drobyshev, I. et al. Trends and patterns in annually burned forest areas and fire weather across the European boreal zone in the 20th and early 21st centuries. Agric. For. Meteorol. 306, 108467 (2021).ADS 
Article 

Google Scholar 
Chen, Y., Morton, D. C., Andela, N., Giglio, L. & Randerson, J. T. How much global burned area can be forecast on seasonal time scales using sea surface temperatures?. Environ. Res. Lett. 11, 045001 (2016).ADS 
Article 

Google Scholar 
McCarty, J. L., Smith, T. E. & Turetsky, M. R. Arctic fires re-emerging. Nat. Geosci. 13, 658–660 (2020).ADS 
CAS 
Article 

Google Scholar 
Witze, A. The Arctic is burning like never before—And that’s bad news for climate change. Nature 585, 336–338 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Scholten, R. C., Jandt, R., Miller, E. A., Rogers, B. M. & Veraverbeke, S. Overwintering fires in boreal forests. Nature 593, 399–404 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Smith, T., McCarty, J., Turetsky, M. & Parrington, M. Geospatial analysis of Arctic fires in the MODIS era: 2003–2020. In EGU General Assembly Conference Abstracts (2021).Lehtonen, I., Venäläinen, A., Kämäräinen, M., Peltola, H. & Gregow, H. Risk of large-scale fires in boreal forests of Finland under changing climate. Nat. Hazards Earth Syst. Sci. 16, 239–253 (2016).ADS 
Article 

Google Scholar 
Fernandes, P. M., Pereira Pacheco, A., Almeida, R. & Claro, J. The role of fire-suppression force in limiting the spread of extremely large forest fires in Portugal. Eur. J. For. Res. 135, 253–262 (2016).Article 

Google Scholar 
Vitolo, C. et al. ERA5-based global meteorological wildfire danger maps. Sci. Data 7, 216 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
San-Miguel-Ayanz, M. et al. In Comprehensive Monitoring of Wildfires in Europe: The European Forest Fire Information System (EFFIS) (ed. Tiefenbacher, J.) 87–108 (InTech, Croatia, 2012).
Google Scholar 
Harvey, D. A., Alexander, M. E. & Janz, B. A comparison of fire-weather severity in northern Alberta during the 1980 and 1981 fire seasons. For. Chron. 62, 507–513 (1986).Article 

Google Scholar 
Copernicus Climate Change Service. Fire Danger Indicators for Europe from 1970 to 2098 Derived from Climate Projections (2020). https://doi.org/10.24381/CDS.CA755DE7.Flannigan, M. D. et al. Fuel moisture sensitivity to temperature and precipitation: Climate change implications. Clim. Change 134, 59–71 (2016).ADS 
CAS 
Article 

Google Scholar 
Fargeon, H. et al. Projections of fire danger under climate change over France: Where do the greatest uncertainties lie?. Clim. Change 160, 479–493 (2020).ADS 
Article 

Google Scholar 
Rovithakis, A. et al. Future climate change impact on wildfire danger over the Mediterranean: The case of Greece. Environ. Res. Lett. 17, 045022 (2022).ADS 
Article 

Google Scholar 
Iturbide, M. et al. An update of IPCC climate reference regions for subcontinental analysis of climate model data: Definition and aggregated datasets. Earth Syst. Sci. Data 12, 2959–2970 (2020).ADS 
Article 

Google Scholar  More