DOI: 10.5061/dryad.dv41ns27x
title: Dataset containing ITS2 and rbcL trimmed sequences from managed grassland plant communities

description: Global resource depletion and climate change pressures are intensifying,
 endangering species and leading to broad changes in plant community
 composition, diversity, and distribution. Monitoring plant communities via
 botanical surveys is often reliable but can be time-consuming and requires
 taxonomic expertise. Here we explore DNA metabarcoding as a faster,
 scalable alternative, comparing metabarcoding with quadrat-based botanical
 surveys to assess species composition in experimental grassland plots
 under various defoliation management regimes (continuous grazing,
 rotational grazing, frequent cutting, conservation cutting). Botanical
 surveys identified 16 taxa, while DNA metabarcoding using ITS2 and rbcL
 markers identified 25 taxa, with both methods detecting dominant species
 such as Holcus lanatus and Lolium perenne. There were significant
 differences in plant composition between defoliation treatments, with
 higher species diversity in cut versus grazed plots across both
 methods.  Despite some discrepancies, such as DNA metabarcoding
 identifying more taxa at the genus level (e.g., Agrostis spp. instead of
 Agrostis capillaris) and potential misidentifications (e.g., Cardaminopsis
 helleri vs. Cardamine flexuosa), both methods provided comparable results
 with DNA metabarcoding offering a broader, albeit semi-quantitative,
 assessment of species presence. The findings support integration of DNA
 metabarcoding into biodiversity monitoring alongside traditional methods.
 Further refinement of bioinformatics tools and reference databases will
 enhance the utility of metabarcoding in ecological studies, contributing
 to more efficient monitoring of grassland biodiversity and management
 practices. This study demonstrates the potential of DNA metabarcoding as a
 complementary tool for traditional botanical surveys, offering insights
 into plant community composition under varying management regimes.
description: Plant DNA extracted with DNeasy 96 Plant Kit (Qiagen), followed
 by a two-step PCR to prepare indexed Illumina MiSeq libraries for ITS2 and
 rbcL barcode regions. Amplifications were conducted separately for each
 marker using specific primers and Phusion Hot Start II Master Mix.
 Products were verified on agarose gel, purified with AMPure XP beads,
 indexed, and pooled equimolarly. Libraries were sequenced on an Illumina
 MiSeq (2x300 bp, v3 kit) at the Centre for Genomic Research, University of
 Liverpool, with negative controls included throughout to check for
 contamination.
description: # Dataset containing ITS2 and rbcL trimmed sequences from managed
 grassland plant communities
 [https://doi.org/10.5061/dryad.dv41ns27x](https://doi.org/10.5061/dryad.dv41ns27x) ## Description of the data and file structure This dataset was collected in 2022 from experimental grassland plots at Pwllpeiran Upland Research Centre (230 m a.s.l.), Aberystwyth University. Mixed-species swards, sown in 2019, were subject to four defoliation treatments (continuous grazing, rotational grazing, frequent cutting, conservation cutting) across 10 plots. Botanical composition was assessed via quadrat-based surveys, and DNA metabarcoding of biomass samples provided detailed taxonomic data. The resulting species matrix includes sequence read counts and relative abundances per taxon. ### Files and variables #### File: Trimmed\_-\_Copy.zip **Description:** The data consists of paired FASTQ files for each sample, containing sequence data for ITS2 and rbcL plant DNA markers. * Each sample has two associated files: * `filename`: Forward read (R1) file in compressed `.fastq.gz` format. * `filename2`: Reverse read (R2) file in compressed `.fastq.gz` format. * File names follow the format `{sample_name}__L001_R1/R2_001.fastq.gz`. * `sample_name`: Unique identifier for each sample, combining sample number and additional identifier (e.g., `100-PW156`).