Ecology

Current scientific definitions of pondsWe compiled existing scientific definitions of ponds by conducting a backwards and forwards search of papers referenced in or subsequently referencing three seminal pond papers8,17,18 (see “Methods”). We ultimately compiled 54 pond definitions from scientific literature (data available19). The variables most often included in definitions were surface area (91% of definitions), depth (48%), permanence (48%), origin (i.e., natural or human-made; 33%), and standing water (33%; Fig. 2a). When surface area or depth were included in definitions, they were often mentioned qualitatively (e.g., “small” and “shallow”). Of the 61% of definitions that included a maximum pond surface area, the range was 0.1 to 100 ha, the median was 2 ha, and all but two definitions were ≤ 10 ha (Fig. 2b). For depth, only 17% of studies provided a maximum depth cutoff, which ranged 2 to 8 m (Fig. 2c). Of the 26 definitions mentioning permanence, 22 stated that ponds could be temporary or permanent and only three indicated that ponds are exclusively permanent waterbodies. Of the 18 definitions mentioning origin, 17 mentioned that ponds could be natural or human-made with the remaining study indicating ponds can have diverse origins.Figure 2Summary of “pond” definitions from scientific literature including (a) presence of various morphological, biological, and physical characteristics in the definition as blue bars (n = 54 definitions total). Bold black lines indicate the number of definitions with surface area and depth values. Histograms of the upper limits from “pond” definitions for (b) surface area and (c) maximum depth.Full size imageOther important factors included in definitions related to morphometry. For example, 30% of definitions mentioned the potential for plants to colonize the entire basin, which relates to high light penetration (mentioned in 11% of definitions) and/or shallow depths. For example, Wetzel11 defines ponds as having enough light penetration that macrophyte photosynthesis can occur over the entire waterbody. As such, these conditions may be comparable to the littoral region of lakes (11% of definitions). Lastly, 7% of pond definitions mentioned mixing versus stratification, whereby ponds mix more than lakes20 yet less than shallow lakes due to a smaller fetch16.To assess if there was agreement in pond definitions among papers, we examined the number of times each definition was cited. Across the 54 definitions, there were 89 citations of 48 unique papers. Ultimately, most papers (75%) were only cited only once, indicating no consensus in pond definition. The most cited paper was Biggs et al.21, which accounted for 15% of citations. The next two most cited papers were Oertli et al.17 and Sondergaard et al.18, which were seminal papers included in our backwards-forwards search, and each comprised 8% of citations.International definitionsAt an international level, there is no consensus on how to discriminate among ponds, lakes, and wetlands. In North America, wetlands are generally considered to be shallow:  More

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

Wink, M. Evolution of secondary metabolites from an ecological and molecular phylogenetic perspective. Phytochemistry 64, 3–19 (2003).CAS 
PubMed 
Article 

Google Scholar 
Khare, S. et al. Plant secondary metabolites synthesis and their regulations under biotic and abiotic constraints. J. Plant Biol. 63, 203–216 (2020).CAS 
Article 

Google Scholar 
Gajger, I. T. & Dar, S. A. Plant allelochemicals as sources of insecticides. Insects 12, 189 (2021).Article 

Google Scholar 
Vig, A. P., Rampal, G., Thind, T. S. & Arora, S. Bio-protective effects of glucosinolates: A review. LWT Food Sci. Technol. 42, 1561–1572 (2009).CAS 
Article 

Google Scholar 
Sikorska-Zimny, K. & Beneduce, L. The glucosinolates and their bioactive derivatives in Brassica: A review on classification, biosynthesis and content in plant tissues, fate during and after processing, effect on the human organism and interaction with the gut microbiota. Crit. Rev. Food Sci. Nutr. 61, 2544–2571. https://doi.org/10.1080/10408398.2020.1780193 (2020).CAS 
Article 
PubMed 

Google Scholar 
Radojčić Redovniković, I., Glivetić, T., Delonga, K. & Vorkapić-Furač, J. Glucosinolates and their potential role in plant. Period. Biol. 110, 297–309 (2008).
Google Scholar 
Wittstock, U., Kliebenstein, D. J., Lambrix, V., Reichelt, M. & Gershenzon, J. Chapter five glucosinolate hydrolysis and its impact on generalist and specialist insect herbivores. Recent Adv. Phytochem. 37, 101–125 (2003).CAS 
Article 

Google Scholar 
Noret, N. et al. Palatability of Thlaspi caerulescens for snails: Influence of zinc and glucosinolates. New Phytol. 165, 763–772 (2005).CAS 
PubMed 
Article 

Google Scholar 
Hopkins, R. J., Van Dam, N. M. & Van Loon, J. J. A. Role of glucosinolates in Insect-plant relationships and multitrophic interactions. Annu. Rev. Entomol. 54, 57–83 (2008).Article 
CAS 

Google Scholar 
Guleria, S. & Tiku, A. K. Botanicals in pest management: Current status and future perspectives. Integr. Pest Manag. 1, 317–329 (2009).
Google Scholar 
Clay, N. K., Adio, A. M., Denoux, C., Jander, G. & Ausubel, F. M. Glucosinolate metabolites required for an Arabidopsis innate immune response. Science 323, 95–101 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yang, B. et al. Inhibitory effect of allyl and benzyl isothiocyanates on ochratoxin a producing fungi in grape and maize. Food Microbiol. 100, 103865 (2021).CAS 
PubMed 
Article 

Google Scholar 
Agrawal, A. A. & Kurashige, N. S. A role for isothiocyanates in plant resistance against the specialist herbivore Pieris rapae. J. Chem. Ecol. 296(29), 1403–1415 (2003).Article 

Google Scholar 
Müller, C. et al. The role of the glucosinolate-myrosinase system in mediating greater resistance of Barbarea verna than B. vulgaris to Mamestra brassicae Larvae. J. Chem. Ecol. 44, 1190–1205 (2018).PubMed 
Article 
CAS 

Google Scholar 
Kegley, S. E., Hill, B. R., Orme, S. & Choi, A. H. PAN Pesticide Database (Pesticide Action Network, 2000).
Google Scholar 
Worfel, R. C., Schneider, K. S. & Yang, T. C. S. Suppressive effect of allyl isothiocyanate on populations of stored grain insect pests. J. Food Process. Preserv. 21, 9–19 (1997).CAS 
Article 

Google Scholar 
Wu, H., Zhang, G. A., Zeng, S. & Lin, K. C. Extraction of allyl isothiocyanate from horseradish (Armoracia rusticana) and its fumigant insecticidal activity on four stored-product pests of paddy. Pest Manag. Sci. 65, 1003–1008 (2009).CAS 
PubMed 
Article 

Google Scholar 
Bhushan, S., Gupta, S., Kaur Sohal, S., Arora, S. & Saroj Arora, C. Assessment of insecticidal action of 3-Isothiocyanato-1-propene on the growth and development of Spodoptera litura (Fab.) (Lepidoptera: Noctuidae). J. Entomol. Zool. Stud. 4, 1068–1073 (2016).
Google Scholar 
Dhillon, M. K., Naresh, J. S., Singh, R. & Sharma, N. K. Reaction of different bitter gourd (Momordica charantia L.) genotypes to melon fruit fly, Bactrocera cucurbitae (Coquillett). Int. J. Plant Prot. 33, 55–59 (2005).
Google Scholar 
Ekesi, S., Nderitu, P. W. & Chang, C. L. Adaptation to and small-scale rearing of invasive fruit fly Bactrocera invadens (Diptera: Tephritidae) on artificial diet. Ann. Entomol. Soc. Am. 100, 562–567 (2007).Article 

Google Scholar 
Jakhar, S. et al. Estimation losses due to fruit fly, Bactrocera cucurbitae (Coquillett) on long melon in semi-arid region of Rajasthan. J. Entomol. Zool. Stud. 8, 632–635 (2020).MathSciNet 

Google Scholar 
Ladania, M. S. Physiological Disorders and Their Management. Citrus Fruit: Biology, Technology and Evaluation 451–463 (Academic press, 2008).
Google Scholar 
Du, Y., Grodowitz, M. J. & Chen, J. Insecticidal and enzyme inhibitory activities of isothiocyanates against red imported fire ants, Solenopsis invicta. Biomolecules 10, 716 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Tsao, R., Reuber, M., Johnson, L. & Coats, J. R. Insecticidal toxicities of glucosinolate· containing extracts from crambe seeds. J. Agric. Urban Entomol. 13, 109–120 (1996).CAS 

Google Scholar 
Li, Q., Eigenbrode, S. D., Stringam, G. R. & Thiagarajah, M. R. Feeding and growth of Plutella xylostella and Spodoptera eridania on Brassica juncea with varying glucosinolate concentrations and myrosinase activities. J. Chem. Ecol. 26, 2401–2419 (2000).CAS 
Article 

Google Scholar 
Noble, R. R., Harvey, S. G. & Sams, C. E. Toxicity of Indian mustard and allyl isothiocyanate to masked chafer beetle larvae. Plant Health Prog. 3, 9 (2002).Article 

Google Scholar 
Sousa, A. H., Faroni, L. R. A., Pimentel, M. A. G. & Freitas, R. S. Relative toxicity of mustard essential oil to insect-pests of stored products. Rev. Caatinga 27, 222–226 (2014).
Google Scholar 
de Souza, L. P., Faroni, L. R. D. A., Lopes, L. M., de Sousa, A. H. & Prates, L. H. F. Toxicity and sublethal effects of allyl isothiocyanate to Sitophilus zeamais on population development and walking behavior. J. Pest Sci. 91, 761–770 (2018).Article 

Google Scholar 
Freitas, R. C. P., Faroni, L. R. D. A., Haddi, K., Jumbo, L. O. V. & Oliveira, E. E. Allyl isothiocyanate actions on populations of Sitophilus zeamais resistant to phosphine: Toxicity, emergence inhibition and repellency. J. Stored Prod. Res. 69, 257–264 (2016).Article 

Google Scholar 
Jabeen, A., Zaitoon, A., Lim, L. T. & Scott-Dupree, C. Toxicity of five plant volatiles to adult and egg stages of Drosophila suzukii matsumura (Diptera: Drosophilidae), the spotted-wing Drosophila. J. Agric. Food Chem. 69, 9511–9519 (2021).CAS 
PubMed 
Article 

Google Scholar 
Wu, H., Liu, X. R., Yu, D. D., Zhang, X. & Feng, J. T. Effect of allyl isothiocyanate on ultra-structure and the activities of four enzymes in adult Sitophilus zeamais. Pestic. Biochem. Physiol. 109, 12–17 (2014).CAS 
PubMed 
Article 

Google Scholar 
Zhang, C., Wu, H., Zhao, Y., Ma, Z. & Zhang, X. Comparative studies on mitochondrial electron transport chain complexes of Sitophilus zeamais treated with allyl isothiocyanate and calcium phosphide. Pestic. Biochem. Physiol. 126, 70–75 (2016).CAS 
PubMed 
Article 

Google Scholar 
Jeschke, V. et al. How glucosinolates affect generalist lepidopteran larvae: Growth, development and glucosinolate metabolism. Front Plant Sci. 8, 1995. https://doi.org/10.3389/fpls.2017.01995 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Agnihotri, A. R., Hulagabali, C. V., Adhav, A. S. & Joshi, R. S. Mechanistic insight in potential dual role of sinigrin against Helicoverpa armigera. Phytochemistry 145, 121–127. https://doi.org/10.1016/j.phytochem.2017.10.014 (2018).CAS 
Article 
PubMed 

Google Scholar 
Jeschke, V. et al. So much for glucosinolates: A generalist does survive and develop on Brassicas, but at what cost?. Plants 10, 962. https://doi.org/10.3390/plants10050962 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Benrey, B. & Denno, R. F. The slow-growth-high-mortality hypothesis: A test using the cabbage butterfly. Ecology 78, 987–999 (1997).
Google Scholar 
Shroff, R., Vergara, F., Muck, A., Svatoš, A. & Gershenzon, J. Nonuniform distribution of glucosinolates in Arabidopsis thaliana leaves has important consequences for plant defense. Proc. Natl. Acad. Sci 05, 6196–6201 (2008).ADS 
Article 
CAS 

Google Scholar 
Bai, P. P. et al. Inhibition of phenoloxidase activity delays development in Bactrocera dorsalis (Diptera: Tephritidae). Fla. Entomol. 97, 477–485. https://doi.org/10.1653/024.097.0218 (2014).Article 

Google Scholar 
Datta, R., Kaur, A., Saraf, I., Singh, I. P. & Kaur, S. Effect of ethyl acetate extract and purified compounds of Alpinia galanga (L.) on Immune Response of a Polyphagous Lepidopteran pest, Spodoptera litura (Fabricius). Asian J. Adv. Basic Sci. 6, 16–21 (2018).CAS 

Google Scholar 
Hartzer, K. L., Zhu, K. Y. & Baker, J. E. Phenoloxidase in larvae of Plodia interpunctella (Lepidoptera: Pyralidae): Molecular cloning of the proenzyme cDNA and enzyme activity in larvae paralyzed and parasitized by Habrobracon hebetor (Hymenoptera: Braconidae). Arch. Insect Biochem. Physiol. 59, 67–79 (2005).CAS 
PubMed 
Article 

Google Scholar 
Silva, C. J. M. et al. Immune response triggered by the ingestion of polyethylene microplastics in the dipteran larvae Chironomus riparius. J. Hazard. Mater. 414, 125401. https://doi.org/10.1016/j.jhazmat.2021.125401 (2021).CAS 
Article 
PubMed 

Google Scholar 
Aucoin, R. R., Philogène, B. J. R. & Arnason, J. T. Antioxidant enzymes as biochemical defenses against phototoxin induced oxidative stress in three species of herbivorous Lepidoptera. Arch. Insect Biochem. Physiol. 16, 139–152 (1991).CAS 
Article 

Google Scholar 
Wang, Y., Branicky, R., Noë, A. & Hekimi, S. Superoxide dismutases: Dual roles in controlling ROS damage and regulating ROS signaling. Int. J. Cell Biol. 217, 1915–1928. https://doi.org/10.1083/jcb.201708007 (2018).CAS 
Article 

Google Scholar 
Zhang, C., Ma, Z., Zhang, X. & Wu, H. Transcriptomic alterations in Sitophilus zeamais in response to allyl isothiocyanate fumigation. Pest. Biochem. Physiol. 137, 62–70. https://doi.org/10.1016/j.pestbp.2016.10.001 (2017).CAS 
Article 

Google Scholar 
Felton, G. W. & Summers, C. B. Antioxidant systems in insects. Arch. Insect Biochem. Physiol. 29, 187–197. https://doi.org/10.1002/arch.940290208 (1995).CAS 
Article 
PubMed 

Google Scholar 
Cadenas, E. Mechanisms of oxygen activation and reactive oxygen species detoxification. In Oxidative Stress and Antioxidant Defenses in Biology (ed. Ahmad, S.) 1–46 (Chapman & Hall, 1995). https://doi.org/10.1007/978-1-4615-9689-9_1.Chapter 

Google Scholar 
Schramm, K., Vassão, D. G., Reichelt, M., Gershenzon, J. & Wittstock, U. Metabolism of glucosinolate- derived isothiocyanates to glutathione conjugates in generalist lepidopteran herbivores. Insect Biochem. Mol. Biol. 42, 174–182. https://doi.org/10.1016/j.ibmb.2011.12.002 (2012).CAS 
Article 
PubMed 

Google Scholar 
Falk, K. L. et al. The role of glucosinolates and the jasmonic acid pathway in resistance of Arabidopsis thaliana against molluscan herbivores. Mol. Ecol. 23, 1188–1203. https://doi.org/10.1111/mec.12610 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gloss, A. D. et al. Evolution in an ancient detoxification pathway is coupled with a transition to herbivory in the Drosophilidae. Mol. Biol. Evol. 31, 2441–3245. https://doi.org/10.1093/molbev/msu201 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bhatt, P., Zhou, X., Huang, Y., Zhang, W. & Chen, S. Characterization of the role of esterases in the biodegradation of organophosphate, carbamate, and pyrethroid pesticides. J. Hazard. Mater. 1, 125026. https://doi.org/10.1016/j.jhazmat.2020.125026 (2021).CAS 
Article 

Google Scholar 
Murfadunnisa, S. et al. Larvicidal and enzyme inhibition of essential oil from Spheranthus amaranthroids (Burm.) against lepidopteran pest Spodoptera litura (Fab.) and their impact on non-target earthworms. Biocatal. Agric. Biotechnol. 21, 101324. https://doi.org/10.1016/j.bcab.2019.101324 (2019).Article 

Google Scholar 
Sengottayan, S. N. Physiological and biochemical effect of neem and other Meliaceae plants secondary metabolites against Lepidopteran insects. Front. Physiol. 4, 359. https://doi.org/10.3389/fphys.2013.00359 (2013).Article 

Google Scholar 
Augustyniak, M., Gladysz, M. & Dziewięcka, M. The Comet assay in insects: Status, prospects and benefits for science. Mutat. Res. Rev. Mutat. Res. 767, 67–76. https://doi.org/10.1016/j.mrrev.2015.09.001Get (2016).CAS 
Article 
PubMed 

Google Scholar 
Foster, E. R. & Downs, J. A. Histone H2A phosphorylation in DNA double strand break repair. FEBS J. 272, 3231–3240. https://doi.org/10.1111/j.1742-4658.2005.04741.x (2005).CAS 
Article 
PubMed 

Google Scholar 
Porichha, S. K., Sarangi, P. K. & Prasad, R. Genotoxic effect of chlorpyrifosin Channa punctatus. Cytol. Genet. 9, 631–638 (1998).
Google Scholar 
Kalita, M. K., Haloi, K. & Devi, D. Larval exposure to chlorpyrifos affects nutritional physiology and induces genotoxicity in silkworm Philosamia ricini (Lepidoptera: Saturniidae). Front. physiol. 7, 1–14. https://doi.org/10.3389/fphys.2016.00535 (2016).Article 

Google Scholar 
Datta, R. et al. Assessment of genotoxic and biochemical effects of purified compounds of Alpinia galanga on a polyphagous lepidopteran pest Spodoptera litura (Fabricius). Phytoparasitica 48, 501–511. https://doi.org/10.1007/s12600-020-00813-8 (2020).CAS 
Article 

Google Scholar 
Afify, A. & Negm, A. A. K. H. Genotoxic effect of insect growth regulators on different stages of peach fruit fly, Bactrocera zonata (Saunders)(Diptera: Tephritidae). Afr. Entomol. 26, 154–161 (2018).Article 

Google Scholar 
Gupta, J. N., Verma, A. N. & Kashyap, R. K. An improved method for mass rearing for melon fruit fly Dacus cucurbitae Coquillett. Indian J. Entomol. 40, 470–471 (1978).
Google Scholar 
Srivastava, B. G. A chemically defined diet for Dacus cucurbitae (Coq.) larvae under aseptic conditions. Entomol. News Lett. 5, 24 (1975).
Google Scholar 
Kumar, A., Sood, S., Mehta, V., Nadda, G. & Shanker, A. Biology of Thysanoplusia orichalcea (Fab.) in relation to host preference and suitability for insect culture and bioefficacy. Indian J. Appl. Entomol. 18, 16–21 (2004).
Google Scholar 
Martinez, S. S. & Emden, H. F. V. Growth disruption, abnormalities and mortality of Spodoptera littoralis (Boisduval) (Lepidoptera: Noctuidae) caused by azadirachtin. Neotrop. Entomol. 30, 113–125 (2001).CAS 
Article 

Google Scholar 
Khan, Z. R. & Saxena, R. C. Behavioural and physiological responses of Sogatella furcifera (Homoptera: Delphacidae) to selected resistant and susceptible rice cultivars. J. Econ. Entomol. 78, 1280–1286 (1985).Article 

Google Scholar 
Zimmer, M. Phenol oxidation. In Methods to Study Litter Decomposition (eds Graça, M. A. et al.) (Springer, 2005).
Google Scholar 
Kono, Y. Generation of superoxide radical during auto-oxidation of hydroxylamine and an assay for superoxide dismutase. Arch. Biochem. Biophys. 186, 189–195. https://doi.org/10.1016/0003-9861(78)90479-4 (1978).CAS 
Article 
PubMed 

Google Scholar 
Bergmeyer, H. U. Reagents for enzymatic analysis. In Methods of Enzymatic Analysis (eds Bergmeyer, H. U. & Gawehn, K.) 438 (Verlag Chemie, 1974).
Google Scholar 
Chien, C. & Dauterman, W. C. Studies on glutathione S-transferases in Helicoverpa (=Heliothis) zea. Insect Biochem. 21, 857–864. https://doi.org/10.1016/0020-1790(91)90092-S (1991).CAS 
Article 

Google Scholar 
Katzenellenbogen, B. & Kafatos, F. C. General esterases of silk worm moth moulting fluid: Preliminary characterization. J. Insect Physiol. 17, 1139–1151. https://doi.org/10.1016/0022-1910(71)90016-3 (1971).CAS 
Article 

Google Scholar 
Mac Intyre, R. J. A method for measuring activities of acid phosphatases separated by acrylamide gel electrophoresis. Biochem. Genet. 5, 45–56 (1971).CAS 
Article 

Google Scholar 
Singh, N. P., McCoy, M. T., Tice, R. R. & Schneider, E. L. A simple technique for quantitation of low levels of DNA damage in individual cells. Exp. Cell Res. 175, 184–191 (1988).CAS 
PubMed 
Article 

Google Scholar  More

Infection properties of clade A and clade B T7-like cyanophagesWe set out to test the hypothesis that the phylogenetic separation of T7-like cyanophages into two major clades reflects differences in their infection physiology. To do this we investigated a suite of infection properties of three pairs of clade A and B phages, each pair infecting the same Synechococcus host (Table 1) to allow us to control for variability in host genetics and physiology. These six cyanophages are representatives of 3 clade A and 2 clade B cyanophage subclades (SI Appendix, Table S1).Table 1 Summary of infection physiology of three pairs of clade A and clade B cyanophages infecting the same Synechococcus hosts.Full size tableWe began by investigating adsorption kinetics and the length of time taken to produce new phages in the infection cycle, the latent period, from phage growth curve experiments. In all three pairs of phages, adsorption was 7–15-fold more rapid in the clade A phage versus the clade B phage (Fig. 1, Table 1). Furthermore, the clade A phage had a faster infection cycle with a latent period that was 3-5-fold shorter than the clade B phage on the same host (Fig. 1a–c) (Table 1). To determine how representative these findings are for a greater diversity of T7-like cyanophages we report the latent period of nine additional non-paired phages that infect a variety of hosts and span the diversity of this cyanophage genus, measured here and taken from the literature (SI Appendix, Table S1). These phages showed the same pattern as observed between phage pairs, although one clade A phage had a relatively long latent period (see SI Appendix, Table S1). Overall, the 5 clade A phages representative of 5 subclades had a significantly shorter latent period (3.3 ± 3.6 h, n = 5 phages (mean ± SD) than the 10 clade B phages from 7 subclades (7.7 ± 2.0 h, n = 10 phages) (Kruskal-Wallis: χ2 = 4.72, df = 1; p = 0.029, n = 15). No significant differences in the length of the latent period were found for clade B phages that infected Synechococcus and Prochlorococcus (Kruskal-Wallis: χ2 = 1.13, df = 1; p = 0.29, n = 10).Fig. 1: Comparison of the infection physiology between pairs of clade A and clade B T7-like cyanophage infecting the same Synechococcus host.a–c Cyanophage growth curves, d–f burst sizes, g–i virulence as the percentage of lysed host cells, j–l decay as loss of infectivity, m–o plaque sizes. a, d, g, j, m Clade A Syn5 phage and clade B S-TIP37 phage infecting WH8109. b, e, h, k, n Clade A S-CBP42 phage and clade B S-RIP2 phage infecting WH7803. c, f, i, l, o Clade A S-TIP28 phage and clade B S-TIP67 phage infecting CC9605. The host strain is shown at the right of the panels. Red and blue lines or bars show results for clade A and clade B phages, respectively. a–c, g–I Error bars indicate standard deviations. d–f Burst size results are for single cells. j–l The solid line shows the fitted multi-level linear model. m–o The time after infection at which plaques were photographed appears above the images. *p value  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. 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

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