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Density independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops - Dataset
<p>Materials and Methods</p> <p>Fieldwork</p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were the two most common families present in these field surveys, so were prioritised for collection. Spiders were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018. Surveys and sampling were conducted five days per week across this period. Each transect was adjacent to a randomly selected tramline and they were distributed across the entire field. The areas searched were 4 m<sup>2</sup> quadrats at least 10 m apart and all observed linyphiids and lycosids were collected in approximately 15-minute searches. The spiders included in this study were taken from 64 locations across 24 days (Supplementary Table 3) along the aforementioned transects. Spiders were individually placed into 1.5 ml microcentrifuge tubes containing 100 % ethanol using an aspirator, regularly changing meshing, at least every five spiders, to limit potential cross-contamination between spiders (spiders were also subsequently washed during transferral to fresh ethanol at the identification and, separately, dissection stages). Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground and its approximate dimensions were recorded, the latter calculated as approximate web area. Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until subsequent DNA extraction. To obtain data on local prey density, 4 m<sup>2</sup> of ground and crop stems were suction sampled using a ‘G-vac’ for 30 seconds at each quadrat from which spiders were collected, with the collected material emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab.</p> <p>All invertebrates were identified to family level due to the restriction of many of the metabarcoding-derived dietary data to this level, and the difficulty associated with finer taxonomic resolution of many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage).</p> <p> </p> <p>Extraction and high-throughput sequencing of spider gut DNA</p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key <sup>1</sup>. Abdomens were removed from spiders and again washed in and transferred to fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens <sup>2</sup>. At least one extraction negative (blank tubes treated identically to samples) was included per 12 spiders (each extraction typically contained 24 spiders, thus two extraction negatives), which was included in subsequent PCR and high-throughput sequencing to detect instances of lab/reagent contamination.</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR <sup>3</sup> amplified a broad range of invertebrates including spiders, and TelperionF-LaureR, amplified a range of invertebrates but fewer spiders (modified from TelperionF-LaurelinR <sup>3</sup> via one base-pair change from Laurelin; 5’-ggrtawacwgttcawccagt-3’). Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively.</p> <p>Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet <em>et al. </em>(2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions (Supplementary Table 1) and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed (Drake et al., 2021; Supplementary Information 1).</p> <p> </p> <p>Statistical analysis</p> <p>All analyses were conducted in R v4.0.0 <sup>6</sup>. Initial multivariate analyses used binary data (i.e., presence/absence) given the various problems inherent to quantifying metabarcoding data <sup>7,8</sup>. Prey species that occurred only once across all of the dietary samples were removed before further analyses to prevent outliers skewing the results, which is particularly problematic for non-metric multidimensional scaling. Spider diets were compared between variables using multivariate generalized linear models (MGLMs) via ‘manyglm’ in the ‘mvabund’ package <sup>9</sup> with a binomial error family and Monte Carlo resampling. Model independent variables included spider genus, spider life stage (juvenile or adult, the latter defined by fully developed genitalia), spider sex and all two-way interactions between these variables. Pairwise two-way interactions were also included between the aforementioned variables and Julian day to account for how seasonality may affect these relationships.</p> <p>Coarse dietary differences were visualised by non-metric multidimensional scaling (NMDS) via metaMDS in the ‘vegan’ package <sup>10</sup> with Jaccard distance in two dimensions and 999 tries. For NMDS, outliers (usually samples containing rare taxa) were identified by plotting and subsequently removed to facilitate separation of samples and achieve minimum stress. For visualisation of the effect of categorical variables against the dietary NMDS, spider plots were created using ‘ordispider’ with ‘ggplot’ and the ‘RColorBrewer’ ‘Accent’ colour palette <sup>11</sup>. Spider diet was compared against web characteristics for spiders for which both data were available using the MGLM process outlined above, but with starting models containing web height, web area, an interaction between the two, and pairwise interactions between genus, life stage and sex with the two web variables. This model used the same binomial error family as above, but with a ‘cloglog’ link function. For visualisation of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the function “ordisurf” of the “ggplot” package in R.</p> <p>All prey taxa were classified as agricultural pests, natural enemies or excluded from subsequent analyses of intraguild predation and biocontrol (Supplementary Table 2). Intraguild predation and biocontrol variables were created by counting the number of natural enemy taxa, and, separately, of agriculturally relevant “pest” taxa (taxa containing species that commonly adversely affect agricultural productivity; Supplementary Table 2) in each spider’s diet. These resultant count data (effectively the diversity of pests and natural enemies predated by each individual spider) were separately analysed against spider genus, life stage and sex via GLM. “Site” (denoting the 4 m<sup>2</sup> area from which spiders were collected within fields) was initially included as a random effect in generalized linear mixed-models, but no significant effect was observed when comparing this model against a standard GLM via a likelihood ratio test of nested models using the ‘lrtest’ command in the ‘lmtest’ package <sup>12</sup>. Standard GLMs were thus used to avoid issues relating to singularity in the mixed models. The assumptions for the resultant Poisson error family GLMs were tested using the “testResiduals” function of the ‘DHARMa’ package <sup>13</sup>. Intraguild predation and biocontrol differences between significant terms were visualised using violin plots with the quartiles, median and 95 % upper limit annotated using the ‘geom_violin’ function in ‘ggplot2’.</p> <p><em>In situ</em> spider prey choice was analysed using network-based null models in the ‘econullnetr’ package <sup>14</sup> with the ‘generate_null_net’ command, visually represented with the ‘plot_preferences’ command. Binary dietary data were used alongside suction sample count data to represent prey availability. These suction sample data, as described above, were collected at the same sites as the spiders three days after spider collection. Prior to the taxonomic prey choice analysis, an hemipteran identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty. Standardised effect sizes (SES) were extracted for all comparisons for each individual spider and compared between genera, life stages and sexes using permutational multivariate analysis of variance (PerMANOVA) using the ‘adonis’ function of the ’vegan’ package with 9999 permutations and a Euclidean distance matrix to determine overall differences in prey choice.</p> <p> </p> <p>References</p> <p>1. Roberts, M. J. <em>The Spiders of Great Britain and Ireland (Compact Edition)</em>. (Harley Books, 1993).</p> <p>2. Krehenwinkel, H., Kennedy, S., Pekár, S. & Gillespie, R. G. A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. <em>Methods Ecol. Evol.</em> <strong>8</strong>, 126–134 (2017).</p> <p>3. Cuff, J. P. <em>et al.</em> Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecol. Entomol.</em> <strong>46</strong>, 249–261 (2021).</p> <p>4. Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. <em>Environmental DNA</em>. (Oxford University Press, 2018).</p> <p>5. Drake, L. E. <em>et al.</em> An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods Ecol. Evol.</em> <strong>in press</strong>, (2021).</p> <p>6. R Core Team. R: A language and environment for statistical computing. (2020).</p> <p>7. Deagle, B. E., Thomas, A. C., Shaffer, A. K. & Trites, A. W. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? <em>Mol. Ecol. Resour.</em> <strong>13</strong>, 620–633 (2013).</p> <p>8. Deagle, B. E. <em>et al.</em> Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? <em>Mol. Ecol.</em> <strong>28</strong>, 391–406 (2019).</p> <p>9. Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund – an R package for model-based analysis of multivariate abundance data. <em>Methods Ecol. Evol.</em> <strong>3</strong>, 471–474 (2012).</p> <p>10. Oksanen, J. <em>et al.</em> vegan: Community Ecology Package. (2016).</p> <p>11. Neuwirth, E. RColorBrewer: ColorBrewer palettes. (2014).</p> <p>12. Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. <em>R News</em> <strong>2</strong>, 7–10 (2002).</p> <p>13. Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. (2020).</p> <p>14. Vaughan, I. P. <em>et al.</em> econullnetr: an r package using null models to analyse the structure of ecological networks and identify resource selection. <em>Methods Ecol. Evol.</em> <strong>9</strong>, 728–733 (2018).</p>
Data from: Synergistic effects of grass competition and insect herbivory on the weed Rumex obtusifolius in an inundative biocontrol approach
<p>Data are from a field experiment to test for synergistic interactions between grass competition and herbivory on <i>Rumex obtusifolius</i>, a prominent weed in temperate grasslands worldwide.</p><p><i>Rumex obtusifolius</i> was grown in the presence and absence of competition from the grass <i>Lolium perenne</i> and subjected to herbivory through targeted inoculation with root-boring <i>Pyropteron</i> spp.</p><p>To explore whether the interactive effects of competition and herbivory were size-dependent, <i>R. obtusifolius</i> was planted covering a large range of plant sizes found in managed grasslands.</p><p>The experimental layout followed a split-split plot design. Main-level factor was <i>L. perenne</i> competition, split-level factor was herbivory application, split-split-level factor was initial root mass of <i>R. obtusifolius</i>. Main-plots were arranged according to a randomized complete block design on the site (8 blocks, each containing a <i>L. perenne</i> competition and a no competition treatment).</p>
Dataset from the analysis of biological activity of endophytic strain Serratia quinivorans KP32, the expression of biocontrol-related genes and the activity of antioxidant enzymes in bacterial cells treated with pathogenic fungi filtrates
<p>This dataset contains the data from the analyses published in the article entitled "Genetic Determinants of Antagonistic Interactions and the Response of New Endophytic Strain <i>Serratia quinivorans</i> KP32 to Fungal Phytopathogens" in the International Journal of Molecular Sciences (https://doi.org/10.3390/ijms232415561). The data consist of results collected for studies on the antifungal activity of KP32 strain towards four fungal phytopathogens, results of primer efficiency determination and studies on the expression of genes potentially involved in biocontrol after treatment of KP32 strain with the fungal phytopathogens filtrates. Additionally, absorbances from activity tests for catalase (CAT) and superoxide dismutase (SOD) in the strain treated with fungal pathogens are included.</p>
F I G U R E 2 Fitted logistic curves with 95 in Circadian and seasonal flight activity differences between the sexes of the biocontrol agent Eadya daenerys (Hymenoptera: Braconidae) and the impact of host size on adult emergence
F I G U R E 2 Fitted logistic curves with 95% confidence intervals for the effect of Paropsisterna agricola beetle prepupal weight (mg) for three post-beetle prepupal outcomes (dead beetle prepupa, beetle or E. daenerys wasp).
F I G U R E 3 in Circadian and seasonal flight activity differences between the sexes of the biocontrol agent Eadya daenerys (Hymenoptera: Braconidae) and the impact of host size on adult emergence
F I G U R E 3 Host beetle prepupal weight (mg) (using both Paropsisterna agricola <80 mg and Paropsis charybdis>80 mg) and the head capsule width (mm) of laboratory-reared Eadya daenerys across both host species (n = 96).
F I G U R E 1 in Circadian and seasonal flight activity differences between the sexes of the biocontrol agent Eadya daenerys (Hymenoptera: Braconidae) and the impact of host size on adult emergence
F I G U R E 1 Weekly adult total malaise trap counts for three paropsine leaf beetle hosts of E. daenerys at Runnymede for the 2015/2016 season.
F I G U R E 4 in Circadian and seasonal flight activity differences between the sexes of the biocontrol agent Eadya daenerys (Hymenoptera: Braconidae) and the impact of host size on adult emergence
F I G U R E 4 Head capsule width (mm) of adult Eadya daenerys (left) reared in the laboratory on Paropsisterna agricola (n = 179) or (right) collected in the field (n = 253).
Figure 1 in Microorganisms from corn stigma with biocontrol potential of Fusarium verticillioides
Figure 1. Percentage of mycelial growth inhibition of Fusarium verticillioides by endophytic and epiphytic microorganisms from maize silks collected in different Brazilian regions.
Fig. 1 in The red imported fire ant (Hymenoptera: Formicidae) in the West Indies: distribution of natural enemies and a possible test bed for release of self-sustaining biocontrol agents
Fig. 1. Distribution of 2 fire ant microsporidian pathogens (Kneallhazia solenopsae, Vairimorpha invictae) and 2 fire ant viruses (SINV-1, SiDNV) among collections of the red imported fire ant, Solenopsis invicta, from islands in the West Indies. The fire ant RNA viruses SINV-2 and SINV-3 were not detected in any of the collections. The number of collections from monogyne colonies is shown over the total number of collections for each island or island group (Tortola [1/5], St. John [0/1], and St. Thomas [3/4]).
Fig. 1 in Field host range of Apanteles opuntiarum (Hymenoptera: Braconidae) in Argentina, a potential biocontrol agent of Cactoblastis cactorum (Lepidoptera: Pyralidae) in North America
Fig. 1. Distribution of Apanteles opuntiarum (circles) and Apanteles alexanderi (triangles) that emerged from species of Pyralidae collected in Argentina, Aug 2007–Mar 2014.
Fig. 2 in Biocontrol bites biocontrol: potential interference of the Brazilian peppertree biological control thrips Pseudophilothrips ichini (Thysanoptera: Phlaeothripidae) by Montandoniola confusa (Hemiptera: Anthocoridae)
Fig. 2. Cases of predation on Pseudophilothrips ichini by Montandoniola confusa: (A) adult M. confusa feeding on an adult P. ichini in a garden plot on 30 Dec 2019; (B) adult M. confusa feeding on larval P. ichini in a laboratory colony on 24 Aug 2021; (C) nymphal M. confusa feeding on an adult P. ichini in a laboratory colony on 16 Aug 2021; and (D) nymphal M. confusa feeding on a larval P. ichini in a laboratory colony on 22 Nov 2021. Photo credit for plate B: Jenna Owens; photo credit for plate D: Carly Cogan.
Fig. 1 in Biocontrol bites biocontrol: potential interference of the Brazilian peppertree biological control thrips Pseudophilothrips ichini (Thysanoptera: Phlaeothripidae) by Montandoniola confusa (Hemiptera: Anthocoridae)
Fig. 1. (A) Dorsal view of a point-mounted adult specimen of Montandoniola confusa collected on Brazilian peppertree in an outdoor garden plot in Davie, Broward County, Florida, USA, on 2 Feb 2020; (B) dorsal view of a point-mounted late nymphal instar specimen of M. confusa collected in an indoor thrips colony rearing cage on 20 Aug 2021.
Fig. 2 in Biocontrol of citrus blackfly, Aleurocanthus woglumi Ashby (Homoptera: Aleyrodidae), by spraying Aschersonia sp. conidia collected from infected nymphs in Quintana Roo, Mexico
Fig. 2. (a) Epizootic caused by Aschersonia sp. on citrus blackfly nymphs afer conidia application on 30 ha in Jose Maria Morelos, Quintana Roo, Mexico (late Nov 2018); (b) presence of Aschersonia sp. on citrus blackfly nymphs (Sep to Nov 2020) without conidia application in Jose Maria Morelos, Quintana Roo, Mexico. Photographs provided by Cipriano Villarreal-Rizo.
Fig. 1 in Biocontrol of citrus blackfly, Aleurocanthus woglumi Ashby (Homoptera: Aleyrodidae), by spraying Aschersonia sp. conidia collected from infected nymphs in Quintana Roo, Mexico
Fig. 1. (a) Aschersonia sp. conidia application site, Jose Maria Morelos, Quintana Roo, Mexico; (b) selection of citrus blackfly nymphs infected with Aschersonia sp. from citrus leaves; (c) conidia extraction with a needle from infected nymphs; (d) conidia suspension; (e) selected citrus leaves with citrus blackfly nymphs uninfected with Aschersonia sp. for conidia application; (f) citrus blackfly nymph infection by Aschersonia sp. and mycelia development afer fungus application in 30 leaves infested with citrus blackfly nymphs in Jose Maria Morelos locality at 7 d afer application; (g) at about 55 d afer application. Photographs provided by Cipriano Villarreal-Rizo.
Complementary effects of pollination and biocontrol services enable ecological intensification in macadamia orchards
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From ecological menace to roadside attraction: 28 years of evidence support successful biocontrol of purple loosestrife
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Pathways for accidental biocontrol: the human-mediated dispersal of insect predators and parasitoids
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Climate matching models for Ceratapion basicorne (Coleoptera: Apionidae), a biocontrol agent of yellow starthistle
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Return of diversity: wetland plant community recovery following purple loosestrife biocontrol
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Data from: Genome assembly of the ragweed leaf beetle, a step forward to better predict rapid evolution of a weed biocontrol agent to environmental novelties
<p><span>Rapid evolution of weed biological control agents (BCAs) to new biotic and abiotic conditions is poorly understood and so far, only little considered both in pre-release and post-release studies, despite potential major negative or positive implications for risks of non-targeted attacks or for colonizing yet unsuitable habitats, respectively. Provision of genetic resources, such as assembled and annotated genomes, is essential to assess potential adaptive processes by identifying underlying genetic mechanisms. Here, we provide the first sequenced genome of a phytophagous insect used as a BCA, <i>i.e.</i> the leaf beetle <i>Ophraella communa</i>, a promising BCA of common ragweed, recently and accidentally introduced into Europe. A total 33.98 Gb of raw DNA sequences, representing c. 43-fold coverage, were obtained using the PacBio SMRT-Cell sequencing approach. Among the five different assemblers tested, the SMARTdenovo assembly displaying the best scores was then corrected with Illumina short reads. A final genome of 774 Mb containing 7,003 scaffolds was obtained. The reliability of the final assembly was then assessed by benchmarking universal single-copy orthologous genes (> 96.0% of the 1,658 expected insect genes) and by remapping tests of Illumina short reads (average of 98.6% ± 0.7% without filtering). The number of protein-coding genes of 75,642, representing 82% of the published antennal transcriptome, and the phylogenetic analyses based on 825 orthologous genes placing <i>O. communa </i>in the monophyletic group of Chrysomelidae, confirm the relevance of our genome assembly. Overall, the genome provides a valuable resource for studying potential risks and benefits of this BCA facing environmental novelties.</span></p>
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