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9 results for “parasitoid network”
Forest structure and heterogeneity increase diversity and alter the composition of host-parasitoid networks
<p>Antagonistic host-parasitoid interactions can be quantified using bipartite and meta networks, which have the potential to reveal how habitat structural elements relate to this important ecosystem function. Here, we analysed the host-parasitoid interactions of cavity-nesting bees and wasps, as well as their abundance, diversity, and species richness with forest structural elements from 127 forest research plots in southwestern Germany. We found that parasitoid abundance, diversity, and species richness all increase with host abundance, a potential mediator between parasitoids and forest structure. Both parasitoid abundance and diversity increased with stand structural complexity, possibly mediated by the abundance of hosts. Additionally, parasitoid abundance increased with increasing standing deadwood and herb cover. The bipartite networks of host-parasitoid interactions showed higher connectance with increasing standing deadwood, herb cover, and host abundance. Analyses of interactions within the host-parasitoid metanetwork revealed that increasing host abundance and decreasing canopy cover diversify the suites of interactions present at the plot level. These results demonstrate that forest structural elements can improve the stability and resilience of host-parasitoid networks by promoting parasitoids and diversifying interactions in ecological networks.</p>
Forest structure and heterogeneity increase diversity and alter the composition of host-parasitoid networks
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Shifts in host-parasitoid networks across community garden management and urban landscape gradients
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Data from: Differential effects of fertilisers on pollination and parasitoid interaction networks
Grassland fertilisation drives non-random plant loss resulting in areas dominated by perennial grass species. How these changes cascade through linked trophic levels, however, is not well understood. We studied how grassland fertilisation propagates change through the plant assemblage into the plant-flower visitor, plant-leaf miner and leaf miner-parasitoid networks using a year's data collection from a long-term grassland fertiliser application experiment. Our experiment had three fertiliser treatments each applied to replicate plots 15 m2 in size: mineral fertiliser, farmyard manure, and mineral fertiliser and farmyard manure combined, along with a control of no fertiliser. The combined treatment had the most significant impact, and both plant species richness and floral abundance decreased with the addition of fertiliser. While insect species richness was unaffected by fertiliser treatment, fertilised plots had a significantly higher abundance of leaf miners and parasitoids and a significantly lower abundance of bumblebees. The plant-flower visitor and plant-herbivore networks showed higher values of vulnerability and lower modularity with fertiliser addition, while leaf miner-parasitoid networks showed a rise in generality. The different groups of insects were impacted by fertilisers to varying degrees: while the effect on abundance was the highest for leaf miners, the vulnerability and modularity of flower visitor networks was the most affected. The impact on the abundance of leaf miners was positive and three times higher than the impact on parasitoids, and the impact on bumblebee abundance was negative and double the magnitude of impact on flower abundance. Overall our results show that while insect species richness was unaffected by fertilisers, network structure changed significantly as the replacement of forbs by grasses resulted in changes in relative abundance across trophic levels, with the direction of change depending on the type of network. By studying multiple networks simultaneously, we were able to rank the relative impact of habitat change on the different groups of species within the community. This provided a more holistic picture of the impact of agricultural intensification and provides useful information when deciding on priorities for mitigation. 01-Oct-2020
Defensive symbiont genotype distributions are linked to parasitoid attack networks - Dataset and scripts
<p>This R project includes data and scripts for paper - Defensive symbiont genotype distributions are linked to parasitoid attack networks</p> <p>Script for analysis:</p> <p> Run all the scripts in order, to get all analyses and results in this paper. </p> <p> Script_0_package_install_load.R: <br> Installs and loads the necessary R packages required for all subsequent scripts.<br> This R project</p> <p> Script_1_Extract_from_Table_S2.R<br> Extracts information from:<br> Table_S2.csv (located in /Rawdata/) Data_4_Aphid_sequences.fas<br> Generates 10 files for further analyses:<br> Aphid relatedness distance:<br> Aphid_phylogenetic_relatedness_16species.csv<br> Aphid_phylogenetic_relatedness_22species.csv<br> Aphid_phylogenetic_relatedness_31species.csv<br> Hamiltonella - Aphid Matrices:<br> Hamiltonella_Aphid_matrix_16species.csv<br> Hamiltonella_Aphid_matrix_22species.csv<br> Hamiltonella_Aphid_matrix_31species.csv<br> Parasitoid - Aphid Matrices:<br> Para_Aphid_matrix_16species.csv<br> Para_Aphid_matrix_22species.csv<br> Plant - Aphid Matrices:<br> Plant_Aphid_matrix_16species.csv<br> Plant_Aphid_matrix_31species.csv</p> <p><br> Script_2_BarPlot_Fig1.R: <br> Generates Fig. 1 using: <br> Data_11_Parasitoid_genus_aphid_22_species_Fig.1.csv Data_12_Plant_genus_aphid_31_species_Fig.1.csv<br> The bar order in Fig. 1 is arranged by proportion, which is not directly supported in the ggplot2 package in R. Therefore, we need manually reordered if using the first plot code; or, manually colour it using the second plot code. </p> <p><br> Script_3_MMRR_analysis.R<br> Uses 10 distance/distribution matrices of Parasitoid, Plant, Hamiltonella, and Aphid relationships to compute matrix correlations with the MMRR (Multiple Matrix Regression with Randomization) model.</p> <p><br> Script_4_Species_linkage_Fig2_FigS4.R<br> Uses 7 distribution matrices of Parasitoid, Plant, and Hamiltonella relationships with Aphids (excluding Aphid genetic distance matrices) to generate the species linkage diagrams for Fig. 2 and Fig. S4, For aesthetic purposes, unconnected sample points have been moved.</p> <p><br> Script_5_MMRR_Fig3_FigS5.R<br> Plots the MMRR correlations using the 10 distance/distribution matrices for Parasitoid, Plant, Hamiltonella, and Aphid relationships (Fig. 3 & Fig. S5). </p> <p> Script_6_parasitoid_specialization_Fig4.R<br> Computes specialization levels using the H2 Index for:<br> Parasitoid-Aphid<br> Aphid-Hamiltonella<br> Parasitoid-Hamiltonella relationships <br> Generates Figure 4.</p> <p> Script_7_Ecologicial_indices&plots_Table1_FigS3.R<br> Uses 7 distribution matrices of Parasitoid, Plant, and Hamiltonella relationships with Aphids (excluding Aphid genetic distance matrices) to:<br> Calculate ecological indices (Richness, Shannon Index, Simpson Index).<br> Use linear models to analyze the relationships between Parasitoid/Plant-Aphid and Aphid-Hamiltonella communities. <br> Outputs results for Table 1 and Figure S3.</p> <p><br> Script_8_Bubble_plot_FigS2.R<br> This script using <br> Hamiltonella_Aphid_matrix_31species.csv <br> and two phylogeny trees <br> Data_8_Aphid_species_phylogeny.txt & <br> Data_9_Hamiltonella_phylogeny.txt<br> To create a Cophylogeny tree of Hamiltonella strains and aphid species that we identified in this study and previously known strains. (Fig. S2)</p> <p> Script_9_DADA2_pipeline.R<br> This script is the DADA2 pipeline used to processing the raw COI sequencing data into ASV (Amplicon Sequence Variant) count files. After processing, the ASV files required manual curation to link each ASV with a distinct parasitoid and aphid species name based on BLAST results. In our updated version, we have provided both the raw data (BioProject PRJNA1139364) as well as the manually curated files (Data_1 and Data_2) which includes the ASV species names. All subsequent analyses can be reproduced using the curated datasets (Data_1 and Data_2), ensuring clarity and replicability for users.<br> We have incorporated the SRA Toolkit to streamline the process. This allows users to download raw SRR sequencing files directly and convert them into FASTQ files, which can then be input into the DADA2 pipeline. The pipeline produces two ASV count files, one for each sequencing run.</p> <p> </p> <p>Raw data and inofrmation:</p> <p> Table_S2.csv<br> The Dataset from Supplementary Table S2: After manual pooling the Parasitoid/Aphid species based on the Data_1,2 and 6.<br> No: the number of this sample<br> City: The city that collected this sample<br> Location: the location of sample collection<br> Sample category: Aphid mummies or alive aphids<br> Sample code: the name of sample (self defined)<br> Collection year: when this sample was collected<br> Aphid taxa / Wasp taxa / Plant species /Hamiltonella strain: The Aphid/Parasitoid/Plant/Hamiltonella species <br> Sequencing method: Illumina or Sanger sequencing <br> Sequencing date: the date of sending this sample to sequencing<br> Data source: From this study or from Wu et al. 2022: Local adaptation to hosts and parasitoids shape Hamiltonella defensa genotypes across aphid species</p> <p> Data_1_Deep_sequencing_data_block_1.xlsx & Data_2_Deep_sequencing_data_block_2.xlsx <br> Sample: The sample name from Illumina sequencing, which can later be found on GenBank (BioProject PRJNA1139364).<br> Aphid: The detected aphid species for each sample.<br> Parasitoid: The detected parasitoid species.<br> The remaining columns contain ASV (Amplicon Sequence Variant) data derived from high-throughput barcoding sequencing.<br> Data1 and Data_2 are the manually curated versions of the BioProject PRJNA1139364 with added species names for each ASV. </p> <p> Data_3_Parasitoid sequences.fas & <br> Data_4_Aphid sequences.fas & <br> Data_5_Hamiltonella sequences.fas,<br> FASTA files for:<br> Parasitoid species<br> Aphid species<br> Hamiltonella strains<br> These sequences were used for phylogenetic reconstruction and subsequent analyses.</p> <p> Data_6_Parasitoid_Aphid_pooled_table.xlsx <br> Contains the OTU (Operational Taxonomic Unit) manual pooling results for Parasitoid and Aphid species based on 99% (4 base pair) sequence similarity:<br> Parasitoid pooling together group & Aphid pooling together group: Original names from Illumina sequencing.<br> Original name: Representative sequences for each group.<br> Pooled species name: Final species names used in all analyses.<br> Pooled sequences: Final representative sequences used in all analyses.<br> The result of manual pooling see the Table_S2.csv</p> <p> Data_7_Parasitoid_species_phylogeny.txt & Data_8_Aphid_species_phylogeny.txt & Data_9_Hamiltonella_phylogeny.txt <br> Phylogenetic trees for:<br> Parasitoid species<br> Aphid species<br> Hamiltonella strains<br> These trees were generated using the PhyML tool on the ATGC Montpellier platform, original fasta file was Data_3, 4 & 5.</p> <p> Data_10_aphid_host_info.csv <br> Provides aphid host information for generating Figure 2 and Figure S4:<br> Aphid: Names of aphid species included in this study.<br> Host: Host categories:<br> 1: Herb aphids<br> 2: Grass aphids<br> 3: Tree aphids<br> Host_category: Detailed descriptions of host categories.</p> <p> Data_11_Parasitoid_genus_aphid_22_species_Fig.1.csv & Data_12_Plant_genus_aphid_31_species_Fig.1.csv <br> Reduced matrices used for Figure 1, created by merging data from:<br> OTUs associated with the same parasitoid species.<br> Plant species belonging to the same genus.</p> <p> 7 Distribution Matrices (from Script_1_Extract_from_Table_S2.R)<br> Hamiltonella-Aphid Matrices:<br> Hamiltonella_Aphid_matrix_16species.csv<br> Hamiltonella_Aphid_matrix_22species.csv<br> Hamiltonella_Aphid_matrix_31species.csv<br> Parasitoid-Aphid Matrices:<br> Para_Aphid_matrix_16species.csv<br> Para_Aphid_matrix_22species.csv<br> Plant-Aphid Matrices:<br> Plant_Aphid_matrix_16species.csv<br> Plant_Aphid_matrix_31species.csv<br> Structure:<br> Rows represent aphid species.<br> Columns represent Hamiltonella strains, parasitoid species, or host plant species linked to each aphid species.<br> Usage: These matrices were used to generate Figures 2, 3, 4, Figures S2, S3, S4, S5, and for MMRR tests, ecological indices, and H2 index calculations.</p> <p> 3 Aphid phylogenetic relatedness matrices derived from Script_1_Extract_from_Table_S2.R<br> Aphid_phylogenetic_relatedness_16species.csv<br> Aphid_phylogenetic_relatedness_22species.csv<br> Aphid_phylogenetic_relatedness_31species.csv<br> These matrices provide phylogenetic distance information, showing pairwise genetic distances between the 31 aphid species included in this study.</p> <p> SraRunTable.csv<br> The BioProject (PRJNA1139364) information downloaded directly from Genbank. </p>
Data from: Molecular analysis reveals high compartmentalisation in aphid-primary parasitoid networks and low parasitoid sharing between crop and non-crop habitats.
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Data from: Differential effects of fertilisers on pollination and parasitoid interaction networks
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Data from: The geographical variation of network structure is scale dependent: understanding the biotic specialization of host-parasitoid networks
Research on the structure of ecological networks suggests that a number of universal patterns exist. Historically, biotic specialization has been thought to increase towards the Equator. Yet, recent studies have challenged this view showing non-conclusive results. Most studies analysing the geographical variation in biotic specialization focus, however, only on the local scale. Little is known about how the geographical variation of network structure depends on the spatial scale of observation (i.e., from local to regional spatial scales). This should be remedied, as network structure changes as the spatial scale of observation changes, and the magnitude and shape of these changes can elucidate the mechanisms behind the geographical variation in biotic specialization. Here we analyse four facets of biotic specialization in host-parasitoid networks along gradients of climatic constancy, classifying the networks according to their spatial extension (local or regional). Namely, we analyse network connectance, consumer diet overlap, consumer diet breadth, and resource vulnerability at both local and regional scales along the gradients of both current climatic constancy and historical climatic change. While at the regional scale none of the climatic variables are associated to biotic specialization, at the local scale, network connectance, consumer diet overlap, and resource vulnerability decrease with current climatic constancy, whereas consumer generalism increases (i.e., broader diet breadths in tropical areas). Similar patterns are observed along the gradient of historical climatic change. We provide an explanation based on different beta-diversity for consumers and resources across the geographical gradients. Our results show that the geographical gradient of biotic specialization is not universal. It depends on both the facet of biotic specialization and the spatial scale of observation.
Data from: The geographical variation of network structure is scale dependent: understanding the biotic specialization of host-parasitoid networks
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