DOI: 10.5061/dryad.4j0zpc8m6
title: Diversified cropping strengthens herbivore regulation by providing seasonal resource continuity to predators

description: Agricultural practices shape arthropod communities in arable fields,
 consequently influencing their interactions and the resulting ecosystem
 services, in particular pest regulation. Predatory arthropods play a
 pivotal role by preying on herbivores, soil fauna, and on other predators.
 However, the intricate mechanisms through which different agricultural
 practices shapes the dietary preferences of predators and regulates
 herbivore populations remain complex and inadequately understood. We
 assessed how fertilisation with organic fertiliser and extending crop
 rotations with perennial ley affected predation pressure across prey taxa.
 We mapped predator and prey trophic linkages with molecular analysis of
 carabid predator gut contents and measured densities and taxonomic
 richness of predators, herbivores and soil fauna in 19 cereal fields
 during three samplings across the growing season. We derived two food web
 structure metrics: prey vulnerability i.e., the average number of
 predators feeding on a selected prey, and predator trophic redundancy,
 i.e., dietary overlap. Prey vulnerability was compared among soil fauna,
 herbivores and other predator species (i.e., interspecific intraguild
 predation) over the growing season and across treatments. The mechanistic
 underpinnings of observed shifts in vulnerability of herbivorous prey at
 different crop stages were identified by using information criteria to
 select among candidate variables related to the richness, density and
 interaction structure of the different guilds during both the current and
 the previous crop stages. Agricultural diversification via organic
 fertilisation combined with perennial ley in the crop rotation decreased
 the vulnerability of both intraguild prey and soil fauna prey and
 stabilised herbivore vulnerability. Mechanistically, the vulnerability of
 herbivorous prey at crop ripening emerges from the combination of predator
 richness and trophic redundancy during this sampling round, rather than
 from carry over effects from previously in the season. Application and
 Synthesis: Our results suggest that locally provided resource continuity
 through diversified cropping practices bolster biological pest regulation,
 thus underline the importance of lesser disturbance in arable ecosystems
 for the provision of ecosystem services. Enhanced predator species
 richness together with availability of alternative prey through the season
  underpins this enhanced pest regulation.
description: We assessed regulation of herbivores in two steps. First, we
 sampled the realised trophic interactions between predators and their prey
 through metabarcoding of predator gut contents. Second, we characterised
 predator and prey population densities of soil fauna, herbivore and
 intraguild prey, sampled with soil extraction, sweep netting and pitfall
 trapping. With this data, we investigated the vulnerability of soil fauna,
 herbivores and intraguild prey in time and across diversification
 treatments, including as predictors predator species richness, population
 densities and network metrics describing predation redundancy.
 We sampled gut contents of carabid predators to identify trophic
 links between predators and prey below and above ground. To obtain highly
 resolved data on all consumed prey items in the predators’ diet, gut
 content samples were analysed through DNA metabarcoding using the primers
 fwhF2+fwhR2n targeting DNA barcode region in the arthropod mitochondrial
 cytochrome oxidase subunit I (COI) gene region (Vamos et al.,
 2017).
description: # Diversified cropping strengthens herbivore regulation by providing
 seasonal resource continuity to predators
 [https://doi.org/10.5061/dryad.4j0zpc8m6](https://doi.org/10.5061/dryad.4j0zpc8m6) Molecular data from gut-content PCR-based mitochondrial COI gene DNA analysis from carabid beetles in agricultural habitat. ## Description of the data and file structure DNA extraction, followed by COI gene PCR, and library preparation. Sequencing on two Illumina NovaSeq6000 SP Flowcell 2x250bp runs. Data consists of raw FASTQ files with two technical replicates per sample. Retrieved sequence variants (ZOTUs), ZOTU tables, and ZOTU assignations are also included. See the original article for more detailed information. ### File contents and Column names ##### File = 1st\_run\_data.zip, zip-compressed file This file contains data files from the first sequencing run *Compressed file = zotus_1strun.fa, fasta-formatted file* * Each header line begins with character '&gt;', followed by ZOTU ID. * The ZOTU nucleotide sequence begins always after the header line. The sequence is wrapped to 80 character rows. *Compressed file = zotus_to_BOLD_plus_mock_1strun.txt, no header-line* * Column 1 = ZOTU ID. This is the sequence variant ID based on the bioinformatics carried out up to this step. * Column 2 = Taxonomic identification of each ZOTU. This column contains the whole taxonomic path from Phylum to the species level based on probabilistic assignation of the ZOTUs. The file contents are a standard output from USEARCH SINTAX algorithm. Each taxonomic level is separated with a comma and taxa abbreaviation. The probability of each ZOTU for each taxonomic level is given in parentheses. All taxonomic levels are abbreviated as follows: p = Phylum c = Class o = Order f = Family g = Genus s = Species *Compressed file = zotutab_global_1strun.txt, first line is a header-line* * First column = ZOTU ID = "#OTU ID". This is the sequence variant ID based on the bioinformatics carried out up to this step. * Columns 2-7410 = Each column contains a sample, and each samples is represented as two technical PCR replicates. The naming of the samples follows this logic: --. *For example: fwh-1-1004 is the first PCR replicate of the sample number 1,004.* ##### File = 2nd\_run\_data.zip, zip-compressed file This file contains data files from the second sequencing run *Compressed file = zotus_1strun.fa, fasta-formatted file* * Each header line begins with character '&gt;', followed by ZOTU ID. * The ZOTU nucleotide sequence begins always after the header line. The sequence is wrapped to 80 character rows. *Compressed file = zotus_to_BOLD_plus_mock_1strun.txt, no header-line* * Column 1 = ZOTU ID. This is the sequence variant ID based on the bioinformatics carried out up to this step. * Column 2 = Taxonomic identification of each ZOTU. This column contains the whole taxonomic path from Phylum to the species level based on probabilistic assignation of the ZOTUs. The file contents are a standard output from USEARCH SINTAX algorithm. Each taxonomic level is separated with a comma and taxa abbreaviation. The probability of each ZOTU for each taxonomic level is given in parentheses. All taxonomic levels are abbreviated as follows: p = Phylum c = Class o = Order f = Family g = Genus s = Species *Compressed file = zotutab_global_1strun.txt, first line is a header-line* * First column = ZOTU ID = "#OTU ID". This is the sequence variant ID based on the bioinformatics carried out up to this step. * Columns 2-7410 = Each column contains a sample, and each samples is represented as two technical PCR replicates. The naming of the samples follows this logic: --. *For example: fwh-2-1488 is the second PCR replicate of the sample number 1,488.* ##### File = all\_files.tar, tar-ball file containing compressed (guzip) FASTQ-formatted raw sequence files Each file is named as follows, separated by dash '-'. Example file name: fwh-1-1025_S5688_L001_R2_001.fastq.gz, where * fwh = primer pair abbreviation, always fwh in this data * -1- = PCR replicate, 1 or 2 * 1025 = Sample regurgitate ID * \_L001 = Illumina lane information * \_R2\_ = Read direction, 1 or 2 * 001 = Illumina code * .fastq = FASTQ file format extension * .gz = gnzip file format extension, compressed file * Please note that in the raw sequence file name, everything after the underscore is technical Illumina sample code and relevant to the study or data. ##### File = Janina-Heinen\_SLU-Regurgitates\_metadata.xlsx, Excel file Column names: field_ID = Sample field ID treatment = Treatment of the field: OL (organic feriliser + perennial ley in the crop rotation), ONL (organic fertiliser + annual crop rotations), MIN (mineral fertiliser+ annual crop rotation), or NA (not available, only 2 out of 3698 samples) Sampling_session = S1 (early season sampling; May), S2 (mid season sampling; June), S3 (late season sampling; July), NA Regurgitate_ID = Individual beetle number species_morphological_ID = Provisional morphological identification of each specimen INFO = Other information of the samples.