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Figure 3 in Seasonal abundance of Tetranychus urticae and Amblyseius swirskii (Acari: Tetranychidae and Phytoseiidae) on four strawberry cultivars
Figure 3. Overall mean numbers of Tetranychus urticae and Amblyseius swirskii on four strawberry cultivars during (a) 2017/2018 and (b) 2018/2019 seasons.
Figure 1 in Seasonal abundance of Tetranychus urticae and Amblyseius swirskii (Acari: Tetranychidae and Phytoseiidae) on four strawberry cultivars
Figure 1. Mean numbers of Tetranychus urticae and Amblyseius swirskii populations on four strawberry cultivars during 2017/2018 season.
Figure 2 in Seasonal abundance of Tetranychus urticae and Amblyseius swirskii (Acari: Tetranychidae and Phytoseiidae) on four strawberry cultivars
Figure 2. Mean numbers of Tetranychus urticae and Amblyseius swirskii populations on four strawberry cultivars during 2018/2019 season.
Figure 2 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 2 Abundances (number of individuals per beating sample) of phytoseiid mite species on seedlings in August. A – mean Amblyseius swirskii abundance with and without pollen provisioning. B – The relationship betweenTyphlodromus athiasae andA. swirskii abundances on different cultivars. The order of cultivars appearing in the legend corresponds to the magnitudes of their fitted intercepts (Pomello> Volka> …> Shamouti). Error bars are ± 1 SE
Figure 1 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 1 Phytoseiid species abundances (number of individuals per beating sample) on different cul- tivars in April, 5 weeks post release, on seedlings where Euseius stipulatus was released, with pollen provisioning (white bars), on seedlings where Euseius scutalis was released, with pollen provision- ing (gray bars), and on seedlings where no predator was released, without pollen provisioning (black bars). A – Euseius stipulatus abundances. B –Iphiseius degeneransabundances. C –Amblyseius swirskii abundances. Error bars are ± 1 SE.
Figure 3 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 3 Mean daily reproductive output per female (panels A and B) and survival rate (of both sexes, panels C and D), ofA. swirskii and E. stipulatus on Pomelo and Shamouti leaf discs in climate-controlled chambers. Panels A and C – Temperature regime 1 (simulating spring temperatures). Panels B and D – Temperature regime 2 (simulating summer temperatures). See Table 2 for the daily temperature schedule of each regime. Note the different scales of reproductive output between the two temperature regimes. Error bars are ± 1 SE.
Novel Genomic Regions linked to Ascochyta blight Resistance in two differentially resistant cultivars of chickpea
<p><em>Ascochyta</em> blight (AB) caused by the fungal pathogen <em>Ascochyta rabiei</em> is a devastating foliar disease of chickpea (<em>Cicer arietinum</em> L.). Genotyping-by-sequencing (GBS) has been used in the current study for the identification of AB associated quantitative trait loci (QTLs) and their gene(s). We evaluated genotyping-by-sequencing (GBS)-based approach for mapping QTLs associated with AB resistance in chickpea using two recombinant inbred lines populations (AB<sub>3279</sub> and AB<sub>482</sub>) derived from two crosses ILC 1929 X ILC 3279 and ILC 1929 X ILC 482, under six different environments. In total, twenty-one different genomic regions were identified on linkage groups CalG02 and CalG04 pertaining to AB resistance in both populations AB<sub>3279</sub> and AB<sub>482</sub>. Four genomic regions were detected on CalG02 in the population AB<sub>3279</sub> and nine major genomic regions were associated with AB resistance on CaLG04, five out of them were common to both resistant parents ‘ILC3279’ and ‘ILC482’, and eight minor genomic regions with two out of them common between both populations. These regions contain 1,118 SNPs significantly associated (p ≤ 0.001) with AB resistance. Gene ontology (GO) assigned these QTLs to 319 genes, many of which were associated with stress and disease resistance, with most important genes belonging to resistance gene families including Leucine-Rich Repeat (LRR), and transcription factors families. Our results may refer to the flowering-associated gene GIGANTEA as a possible key factor in AB resistance in chickpea. The results have narrowed the AB resistance associated regions on the chickpea physical map and the associated markers will help in breeding programs for chickpea improvement.</p>
Data from: "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat"
<p><em><strong>Data and code used for the study : "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat".</strong></em></p> <p>The script "Manuscript_Analyses.R" contains all code for the statistical analysis presented in the manuscript (main text & supplementary information). This script uses files produced in the folder "Locus-by-locus analysis" as inputs, and "manhattan_custom.R" as a source function ("manhattan_custom.R" is used to highlight SNPs in a given interval and to write specified SNPs name on manhattan plots). The file "Traits_monocultures.csv" contains the 20 functional traits measured on the 179 monoculture plots (see Supplementary Methods for more information on trait measurement). This file is used as an input in the script "Manuscript_Analyses.R".</p> <p>The "Locus-by-locus analysis" folder contains all analyses conducted to test the effect of allelic richness on the four variables of interest: Grain Yield (GY, g/m²), Spike Number per m² (SNb, nb spikes/m²), Thousand Kernel Weight (TKW, g), and Septoria tritici blotch (STB) severity. The locus-by-locus analysis is performed with the script "Allelic_richness_locus_by_locus_analysis.R". This analysis generates a list of .csv files with one file per chromosome. Each file contains the pvalues and estimated effect sizes of the tested SNPs for the given chromosome. These output files are stored in folders named after the variables for which the effect of allelic richness was tested ("RAW_GY", "RAW_SNb", "RAW_TKW", and "RAW_severity"). The script "Allelic_richness_locus_by_locus_output_processing.R" combines all .csv files into a single dataframe and produces three diagnostic plots: Manahattan plots, histograms of p-value distributions, and p-value q-q plots. p-value thresholds were computed based on a Family-Wise Error Rate of 5% using the Galwey correction. This is done in the "pvalue_thresholds" folder with the "Meff_computation.R" script. "Meff_computation.R" uses the "Meff_function.R" as a source function and generates "GY_thresholds.csv" and "STB_thresholds.csv" as outputs (these files contains different thresholds computed according to different methods but we only retained the Galwey method (most recent) for the analyses. Since GY, SNb, and TKW were analyzed with the same number of SNPs (~19K), we used the same significance threshold for the three variables ("GY_thresholds.csv"), whereas we computed a different thresholds for STB ("STB_thresholds.csv") for which we could only include ~6K SNPs in the analysis. The "geno_pos.csv" file contains the physical positions of the SNPs.</p> <p>Upstream the locus-by-locus analysis, phenotypic and genotypic files are prepared in the "Phenoytpic file preparation" and "Genotypic file preparation" folders, respecively.</p> <p>The phenotypic file preparation includes the correction of yield-related variables (GY, SNb, and TKW) for spatial auto-correlation in the "Spatial_analyses_YLD_variables" folder, and the computation of plot-level variables from individual-level variables with the "Allelic_richness_phenotypic_file_prep.R" script. In this script, we compute both absolute plot values (termed "RAW_...) and relative plot values (termed "RYT_..., only for mixture plots). All phenotypic files have the same structure with the same first 6 columns: "focal" = identity of the focal genotype (the one for which the variable is measured, only relevant for variables measured at the individual-level), "neighbor" = identity of the neighbor genotype (the neighbor of the genotype for which the variable is measured, only relevant for variables measured at the individual-level), "pair" = identity of the genotypic pair (combines the identity of the focal and the neighbor genotypes), "assoc" = type of plot ("M" = monoculture or pure stand plot, "P" = mixture plot), "row" = position of the plot along the smallest dimension of the grid (see Figure 1), "column" = position of the plot along the largest dimension of the grid (see Figure 1).</p> <p>The genotypic file preparation is done with the "Allelic_richness_genotypic_file_prep.R" script and includes SNP filtering, computation of matrices of allelic richness, and computation of matrices of genetic similarity between genotypic pairs. The analysis is done separatly for yield-related variables and for STB severity since the two types of variable were not measured on the same set of plots.</p>
Figure 1 in First record, current status, symptoms, infested cultivars and potential impact of the blueberry bud mite, Acalitus vaccinii (Keifer) (Prostigmata: Eriophyidae) in South Africa
Figure 1 Acalitus vaccinii (Keifer, 1939) in South Africa: A – colony at the base of a symptomatic flower bud bract of Vaccinium corymbosum 'Berkeley'; B – enlarged part of the colony shown in Figure 1A; C – relatively small colony between corolla and calyx ofV. corymbosum 'Elliott' flower with callus-like tissue caused by the mites. Symptoms caused byA. vaccinii in South Africa: D – flower galls on V. virgatum 'Centurion' which are more compact than those on V. corymbosum 'Berkeley'in Figure 1E; E – rosette-like flower galls on V. corymbosum 'Berkeley'; F – hypertrophic red "roughened" callus-like tissue of a flower gall on V. corymbosum 'Ivanhoe'; G – red callus-like tissue on outside of corolla ofV. corymbosum 'Elliott' flower.
RNA-seq data of "Transcriptome analyses of leaves reveal that hexanoic acid priming differentially regulate gene expression in contrasting Coffea arabica cultivars"
<p>This dataset represent FASTQ gziped files from the study "Transcriptome analyses of leaves reveal that hexanoic acid priming differentially regulate gene expression in contrasting <em>Coffea arabica</em> cultivars" (<a href="https://doi.org/10.3389/fsufs.2021.735893">https://doi.org/10.3389/fsufs.2021.735893</a>). Sequencing was done using an Illumina Novaseq 6000 instrument, paired-sequencing (2 X150 bp). Sample details are also available at https://www.ebi.ac.uk/ena/browser/view/ERA6282544.</p> <p> </p> <p>All filenames have the following naming scheme:</p> <p>LCS7609_DS_AAA_leafBBB_(R1 or R2).fq.gz</p> <p>AAA stands for the abbreviations:</p> <p>- CC (Coffea arabica cv Catuai control)</p> <p>- CHx (Coffea arabica cv Catuai exposed to Hexanoic acid)</p> <p>- OC (Coffea arabica cv Obatã control)</p> <p>- OHx (Coffea arabica cv Obatã exposed to Hexanoic acid)</p> <p>BBB stands for the number of biological replicate (1, 2 or 3).</p> <p> </p> <p> </p> <p> </p>
Agronomic performance of cultivar mixtures and pure stands of 8 winter wheat varieties, obtained from mixture field trials in Switzerland from 2021 to 2023, together with associated functional traits measurements
<p>This dataset contains agronomic performance data for 8 Swiss winter wheat cultivars, grown in pure stands and in mixtures at 3 locations in Switzerland during 3 growing seasons (2021-2023). The dataset has been used to analyse the effects of cultivar mixtures on agronomic performance and stability, which is published in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.eja.2024.127504" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.eja.2024.127504</a>. </p> <p>The dataset contains notably grain yield, protein content, thousand kernel weight, specific weight, and Zeleny sedimentation value, as well as functional traits measured at flowering for each mixture and pure stand plot. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h3>Methods </h3> <p> <em>Field trials </em></p> <div>Field trials were set up over the course of three growing seasons – 2020/2021, 2021/2022 and 2022/2023 – in three sites across the Swiss Central Plateau. The experimental sites were located in Changins (46°19′ N 6°14′ E, 455m a.s.l), Delley (46°55′ N 6°58′ E, 494m a.s.l) and Utzenstorf (47°97′ N 7°33′ E, 483m a.s.l.). </div> <div>Experimental communities consisted of pure stand plots, 2-cultivars mixtures, and one plot with the 8 cultivars mixed. We sowed every possible combination of 2-cultivar mixtures, amounting to a total of 28 2-cultivar mixtures treatments, to which we added the 8-cultivar mixture. Each community was grown in a plot of 7.1 m<sup>2</sup> (1.5m∗4.7m). We used a complete randomized block design, with 3 replicates, the plots being randomized at each site within each block. Sowing was performed with a small plot drill (Wintersteiger plotseed TC). Density of sowing was 350 viable seeds/m<sup>2</sup>. For the mixtures, seeds were mixed beforehand at a 2 × 50 % mass ratio for 2-cultivars mixtures and 8 × 12.5 % for the 8-cultivar mixture. We chose this method of mixing as this is what is commonly done by farmers in Switzerland. Plots were sowed mechanically each autumn and fertilized with ammonium nitrate at a rate of 140 N/ha in 3 applications (40 N/ha at tillering stage/BBCH 22–29; 60 N/ha at the beginning of stem elongation/BBCH 30–31; 40 N/ha at booting stage/BBCH 45–47). The trials were grown according to the Swiss <em>Extenso</em> scheme, i.e. without any fungicide, insecticide, and growth regulator. Weeds were regulated twice or thrice per season with the application of herbicides commonly used in Switzerland.</div> <div> </div> <div><em>Ear density</em></div> <div> </div> <div>Before harvest, we manually harvested horizontal bands of 1.5 × 0.3 square meters per plot. The location of the band was randomly chosen but we avoided plot edges (i.e. the band was located at more than 0.5 m from the lower and upper edge of each plot). We counted the heads, and obtained ear density from the head counts.</div> <div> </div> <div><em>Trait measurements </em></div> <div> </div> <div>At flowering time, we randomly sampled 6 healthy leaves per plot. We immediately wrapped this leaf in moist cotton; this was stored overnight at room temperature in open plastic bags. The following day, we removed excess surface water on the leaf and weighted it to obtain its water saturated weight. This leaf was then scanned with a flatbed scanner (Perfection V39II, Epson), oven-dried in a paper envelope at 80°C for 72 hours, and subsequently weighed again to obtain its dry weight. Leaf Dry Matter Content (LDMC) was calculated as the ratio of leaf dry mass (g) to water saturated leaf mass (g). Using the leaf scans, we measured leaf area with the image processing software ImageJ. Specific Leaf Area (SLA) was calculated as the ratio of leaf area (cm2) to leaf dry mass (g).</div> <div> </div> <div><em>Phenology and height </em></div> <div> </div> <div>For each plot, we recorded the heading date as the day of the year, in which 50 % of the ears of the plot had fully emerged from the flag leaf. Plant height was measured in each plot at BBCH 59–75, by taking the average height in centimeters from the ground to the top of five random ears, excluding awns.</div> <div> </div> <p><em>Harvest and post harvest measurements</em></p> <p>At maturity, we harvested each plot with a combine harvester (Zürn 150, Schontal-Westernhausen, Switzerland). The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured hectoliter weight (test weight, HLW, kg/hl) and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15 % of humidity. Protein content (% of dry matter) was measured at the site level with a near-infrared instrument (ProxiMate™, Büchi instruments). Thousand kernel weight (TKW, g) was measured at the plot level with a Marvin seed analyzer (GTA Sensorik, Neubrandenburg, Germany). Zeleny sedimentation value was measured by the laboratory of Delley Seeds and Plants. </p> <p> </p>
Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network
<p>This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h2>Methods </h2> <p><em>Field trials </em></p> <p>The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566).</p> <p>Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m<sup>2</sup> plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m<sup>2</sup>, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator. </p> <p>The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the <em>extenso</em> conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.</p> <p> </p> <p><em>Data collection </em></p> <p>For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured.</p> <p>The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then.</p> <p>At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).</p>
MicroCT scans of sun and shade grown leaves of Cabernet Sauvignon and Blaufränkisch grapevine (Vitis vinifera L.) cultivars
<p>Image data set of sun and shade-grown leaves of Cabernet Sauvignon (CS) and Blaufränkisch (BF) grapevine (Vitis vinifera L.) cultivars.</p> <p>When using this dataset, please cite:</p> <blockquote> <p>Théroux-Rancourt G, Herrera C, Voggeneder K, Luijken N, Nocker L, Savi T, Scheffknecht S, Schneck M, Tholen D. (accepted) Analyzing anatomy over three dimensions unpacks the differences in mesophyll diffusive area between sun and shade Vitis vinifera leaves. AoB Plants</p> </blockquote> <p> </p> <p><strong>Data acquisition methodology</strong></p> <p>Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland) in pots (Cabernet Sauvignon (CS)) or as cut shoots with the cut end placed in water (Blaufränkisch (BF)). Before scanning, a leaf was cut from the stem and a thin strip of ~1.5 mm width and 1.5 cm length was cut in between apparent higher-order veins, immediately wrapped in polyimide tape and inserted into a styrofoam block glued with wax onto a holder. Three (CS) or two (BF) strips were cut at different locations on the leaf surface to ensure within-leaf replications and to get better leaf-level averages. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and magnified using a 40x (CS) or 20x (BF) objective, yielding respective final voxel sizes of 0.1625 µm (field of view: ~416x416x312 µm) and 0.325 µm (field of view: ~832x832x624 µm). Scanned projections were reconstructed to cross-sectional view using both absorption (gridrec; Marone <em>et al.</em> 2012) and phase contrast enhancement (Paganin <em>et al.</em> 2002) reconstructions.</p> <p><br> <br> <strong>Dataset description</strong></p> <p>After aligning the stacks to be parallel to the image edges using ImageJ (Schneider <em>et al.</em> 2012), at least nine slices were hand labeled using a graphics pen display tablet to precisely segment the background, the epidermis, and the vasculature. The mesophyll cells and the intercellular airspace were segmented by thresholding each absorption and phase contrast scan to maximize airspace volume (background) and taking care to avoid false segmentation within the cells (i.e. false segmentation of airspace). The hand-labeled slices were then used to automatically segment the whole stack using a Python-based random-forest machine learning approach (Théroux-Rancourt, Jenkins, <em>et al.</em> 2020).</p> <p> </p> <p><strong>File naming convention</strong></p> <p>For all stacks, files start with:<br> <em>Cultivar_Treatment_Plantn_leaf_N_</em></p> <ul> <li>Cultivar: <em>CS</em> for Cabernet Sauvignon, <em>BF</em> for Blaufränkisch</li> <li>Treatment: <em>Sun</em> for plants grown under high light, <em>Shade</em> for plants grown under low light</li> <li>Plantn: Plant number; 1-6 for CS, 1-5 for BF</li> <li>leaf: Leaf number; 1-4</li> </ul> <p><em>N</em> specifies the type of stack:</p> <ul> <li><em>GRID</em> (gridrec reconstruction)</li> <li><em>PAGANIN</em> (phase contrast enhancement reconstruction)</li> <li><em>labelled-stack</em> (hand labeled slices / ground truth; stacks have been hand labeled in cross-sectional view.)</li> <li><em>SEGMENTED</em> (automatically segmented stack using random-forest machine learning approach)</li> <li><em>STOMATAL_REGIONS_BBOX_CROPPED</em> (Stack with individually segmented stomatal vaporsheds, i.e. airspace closest to a stoma. The stack has been cropped in paradermal view around the stomata closest to the stack's edges, i.e. a bounding box (BBOX) with stomatal vaporsheds fully enclosed).</li> </ul> <p>All stacks are provided as 8-bit grayscale TIF files. Stacks have been hand labeled in cross-sectional view.</p> <p> </p> <p><strong>Plant material and growth conditions </strong></p> <p>The experiment was carried out over two consecutive years, in 2018 and 2019, at the facilities of BOKU UFT (Tulln, Austria). In the first year, rooted grafts of <em>Vitis vinifera </em>‘Cabernet Sauvignon’ (clone 191E) on 101-14 rootstock (hereafter named CS) were acquired from a local nursery (<em>Reben Iby</em>, Neckenmarkt, Austria). In the second year, rooted grafts of <em>Vitis vinifera </em>‘Blaufränkisch’ (clone 13-3 GM) on 5 BB rootstock (hereafter named BF) were acquired from the same nursery. Blaufränkisch is known to have originated from Lower Styria (present day Styria, Slovenia, Maul <em>et al. </em>2016) and to be genetically different from Cabernet Sauvignon (Magris <em>et al. </em>2021), which originated in the Bordeaux region in France.</p> <p>The rooted grafts were planted in 7-L pots and allowed to grow in a glasshouse without any environmental control. For CS, nutrient-rich, sieved vineyard soil mixed with perlite (3:1 ratio) was used, while for BF pots were filled with commercial pot substrate containing slow release fertilizer (10 g pot-1, 15-5-20 NPK “Entec vino”). Pots were watered to pot capacity automatically every day. Clones were planted June 1 and April 1 in the first and second year, respectively. When all the plants had at least three mature leaves on one shoot, plants were pruned so that only one dominant shoot remained. To ensure that only leaves fully developed under different light conditions were used for further analyses, the last developing leaf below the tip was marked before moving half of the plants to the shaded environment (on June 29, 2018 for CS and on April 18, 2019 for BF). For the shade treatment, a tent of about 2 m (height) x 1.5 m (width) x 1.5 m (depth) was made from black polypropylene cloth (HaGa-Welt GmbH & Co. KG, Elze, Germany), resulting in a 60% reduction in photosynthetic photon flux density (PPFD). A spectrometer (FLAME-S-VIS- ES, Ocean Optics Inc. Largo, USA) was used to confirm that under both light conditions, relative differences in the contribution of red, green blue and far-red light to the total PPFD were below 5% (i.e. spectrally neutral shade).</p> <p>During the week before synchrotron microCT scanning (last week of August in 2018 and first week of September in 2019), one mature leaf per plant was selected at least three leaves above the previously mentioned mark indicating the last developing leaf at the start of the shade treatment. The measured leaves were estimated to be about one month old, resulting in an average daily light integral (DLI) of 30 (CS sun), 12 (CS shade), 24 (BF sun), and 10 (BF shade) mol m<sup>-2</sup> day<sup>-1</sup>. These estimates were computed using solar radiation measured at a weather station a few meters from the glasshouse, and using PPFD values measured inside the glasshouse. Average daily PPFD was below 700 μmol m<sup>-2</sup> s<sup>-1</sup> under full light, with maximum recorded values at leaf level of ~1200 μmol m<sup>-2</sup> s<sup>-1</sup> under full light and ~500 μmol m<sup>-2 </sup>s<sup>-1</sup> under shade, i.e. ~60% reduction.</p> <p> </p> <p><strong>References</strong></p> <p><strong>Marone F, Stampanoni M</strong>. <strong>2012</strong>. Regridding reconstruction algorithm for real-time tomo- graphic imaging. <em>J. Synchrotron Radiat. </em><strong>19</strong>: 1029–1037.</p> <p><strong>Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW</strong>. <strong>2002</strong>. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. <em>J. Microsc. </em><strong>206</strong>: 33–40.</p> <p><strong>Schneider CA, Rasband WS, Eliceiri KW</strong>. <strong>2012</strong>. NIH Image to ImageJ: 25 years of image analysis. <em>Nat. Methods </em><strong>9</strong>: 671–675.</p> <p><strong>Théroux-Rancourt G, Jenkins MR, Brodersen CR, McElrone A, Forrestel EJ, Earles JM</strong>. <strong>2020</strong>. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning. <em>Appl. Plant Sci. </em><strong>8</strong>: e11380.</p>
Model inputs and outputs for: Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
<p>Files used in initial submission of manuscript to <em>Geoscientific Model Development</em>. Files with names beginning sdates and gdds were used as model inputs for some experiments. File with name beginning hdates was used in postprocessing. ZIP archives are model experimental outputs.</p>
Reciprocal nutritional provisioning between leafcutter ants and their fungal cultivar mediates performance of symbiotic farming systems
<ol> <li>Optimized food acquisition is challenging because foraged diet items are chemically complex and often nutritionally imbalanced. These challenges are likely magnified when foraged foods are used to provision others (e.g., offspring, nestmates, symbionts) with different nutritional requirements.</li> <li>We used a theoretical framework of nutritional niches to study these provisioning challenges in leafcutter ants that cultivate a fungal symbiont with nutrients derived from freshly foraged plant fragments. While the leaf-cutting behaviours of free-ranging foragers are well studied, little is known about how colonies use these plant fragments to produce their fungal crop within underground nest chambers.</li> <li>For instance, gardener ants are known to convert vegetation into a nutritional mulch that they plant on the fungus garden. However, it remains poorly understood how the ants use this mulch to target the specific nutritional needs of their fungal crop, and whether the cultivar signals if provisioned mulch meets its nutritional needs. Towards answers, we performed three experiments to assess the precision and specificity of nutritional regulation in farming systems of the Panamanian leafcutter ant <em>Acromyrmex</em> <em>echinatior</em>.</li> <li>A laboratory feeding experiment with nutritionally defined diets showed that ant farmers collect a specific intake target for protein and carbohydrates and then linked strict protein regulation by foragers to the cultivar's fundamental niche for protein. </li> <li>An in vitro experiment with the fungal cultivar in isolation did not detect a signal of protein stress that could be used by the ants to regulate their provisioning behaviour, but it did identify an elevated fatty acid that may reinforce optimal nutritional provisioning if detected by gardening ants.</li> <li>A feeding experiment with isotopically labelled diets then revealed nutrient-specific and caste-specific allocation timelines, with nitrogen being assimilated into the cultivar's nutritional rewards before being exclusively consumed by developing brood. In turn, these combined results help resolve the integrated behaviours that give rise to resilient leafcutter farming productivity. </li> <li>These results show how nutritional niches can help disentangle reciprocal provisioning dynamics between symbionts while providing a framework to explore the nutritional transactions that mediate symbiotic stability (e.g., sanctioning, screening, policing).</li> </ol>
Bottom-up effects of apple cultivars on parasitoids via aphid hosts
<p>Variability of intraspecific host plant quality for phytophagous insects may have consequences on the structure and functioning of associated food webs. The quality of host plants can affect aphids fitness, influencing their life history traits and altering the nutritional resources available to higher trophic levels, potentially affecting the development of solitary parasitoids. Here, we assessed the potential bottom-up effects of intraspecific variability amongst three cultivars (Gala, Ariane and Greensleeves) of the domesticated apple tree (<em>Malus domestica</em>) with putative resistance towards the rosy apple aphid (<em>Dysaphis plantaginea</em>) on the aphid’s performance, and its cascading effects on the parasitoid <em>Ephedrus cerasicola</em>. We measured aphid pre-reproductive period, lipid and water contents, and recorded their feeding behavior using the electropenetrography technique. Parasitoid developmental duration, sex ratio, hind tibia size and female egg load were measured and used to evaluate <em>E. cerasicola</em> performance according to the cultivar on which their aphid hosts had been reared. Only the development time of parasitoids was found to be longer on Ariane and Green Sleeves cultivars than on the Gala cultivar. Aphid feeding behavior variables related to phloem consumption were negatively impacted on apple tree cultivars on which the development time of parasitoids had been reduced. We discuss in what way cultivar quality can be an important component of tritrophic interactions: the resistant Ariane and Green Sleeves cultivars negatively impacted the aphids but appeared to have limited bottom-up effects on the parasitoids. </p>
Reciprocal nutritional provisioning between leafcutter ants and their fungal cultivar mediates performance of symbiotic farming systems
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Figure 1 in Life table parameters of Tetranychus urticae (Trombidiformes: Tetranychidae) on four strawberry cultivars
Figure 1. Age-stage survival rate (Sxj) of Tetranychus urticae on four strawberry cultivars.
Identification of candidate SNPs in the encoding region of two sugarcane cultivars as to resistance to water stress
<p>Identification of candidate SNPs in the encoding region of two sugarcane cultivars as to resistance to water stress</p>
Improved control of Septoria tritici blotch in durum wheat using cultivar mixtures
<p>Mixtures of cultivars with contrasting levels of resistance can suppress infectious diseases in wheat, as demonstrated in numerous field experiments. Most studies focused on airborne pathogens in bread wheat, while splash-dispersed pathogens have received less attention, and no studies have been conducted in durum wheat. We conducted a two-year field experiment in Tunisia, to evaluate the performance of cultivar mixtures with varying proportions of resistance (0–100%) in controlling the polycyclic, splash-dispersed disease Septoria tritici blotch (STB) in durum wheat. To measure STB severity, we used a high-throughput method based on digital image analysis of 3074 infected leaves collected from 42 and 40 experimental plots during the first and second years, respectively. This allowed us to quantify pathogen reproduction on wheat leaves and to acquire a large dataset that exceeds previous studies with respect to accuracy and precision. Our analyses show that introducing only 25% of a disease-resistant cultivar into a pure stand of a susceptible cultivar provides a substantial reduction of almost 50% in disease severity compared to the susceptible pure stand. However, comprising the resistant component of two cultivars instead of one did not further improve disease control, contrary to predictions of epidemiological theory. Susceptible cultivars can be agronomically superior to resistant cultivars or be better accepted by growers for other reasons. Hence, if mixtures with only a moderate proportion of the resistant cultivar provide a similar degree of disease control as resistant pure stands, as our analysis indicates, such mixtures are more likely to be accepted by growers.</p>
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