DOI: 10.5061/dryad.dncjsxm9t
title: Sources of uncertainty in DNA metabarcoding of whole communities: implications for its use in biomonitoring

description: These data were derived from a structured experiment to assess factors
 influencing the precision and accuracy of molecular methods for freshwater
 macroinvertebrate community assessment. Benthic macroinvertebrates were
 sorted, identified, and counted from nine individual kick-net samples
 using standard morphometric protocols, then reconstituted. These bulk
 specimen samples were then homogenised. From each bulk sample, aliquots of
 the homogenate were distributed among seven laboratories across Europe for
 DNA extraction, PCR amplification, library preparation, and sequencing.
 Additionally, each laboratory was provided with a DNA extract from the
 bulk homogenate to allow for amplification through to sequencing, in order
 to assess the influence of DNA extraction. The data consist of abundances
 of individual taxa identified using morphometric protocols and read counts
 of operational taxonomic units identified by the molecular methods. In
 addition, we provide a taxonomically harmonised matrix of the community
 composition derived from morphometric and molecular approaches to
 facilitate comparison between the two methods.   
description: Sample collection and processing Field
 sampling Nine samples of the benthic macroinvertebrate
 community were collected from seven river sites in southern England, UK
 (see Jones <em>et al</em>., 2025). Each had been the subject
 of at least three years study prior to the current sample collection,
 providing a comprehensive record of taxa likely to be present. Samples
 were collected using the UK standard macroinvertebrate biomonitoring
 protocol, comprising a 3-minute kick sample and 1-minute search with a 1
 mm mesh size net sampling all habitats in proportion to their areal
 occurrence (BS EN 16150:2012). All material retained in the net was
 preserved on-site in 96% ethanol and returned to the laboratory.
 Morphological identification
 Macroinvertebrates were manually sorted from other material,
 identified to the lowest practicable taxonomic level (most to species or
 genus level, see Jones <em>et al</em>., 2025) and counted.
 Samples were processed by qualified, experienced freshwater biologists.
 All material and any picked animals were then reconstituted in the
 original 96% ethanol and stored at 4 <sup>o</sup>C. To ensure
 data obtained through morphological analysis did not influence molecular
 workflows or outputs, the nine samples were anonymised (A to I at
 random). Molecular Analysis
 Molecular analysis was carried out independently at seven
 laboratories across Europe, which were assigned a code (2–8) at random to
 preserve anonymity and eliminate bias. At the Natural
 History Museum (NHM) in London, the nine samples were homogenised
 following Pereira-da-Conceicoa et al. (2021), with minor modifications
 (see Jones <em>et al</em>., 2025). Sub-samples of the
 homogenate were sent to the seven participating laboratories, where each
 performed their own DNA extraction. The NHM also extracted DNA from each
 of the homogenised samples. Library preparation and
 sequencing Each laboratory processed and prepared
 their library within their own institution following the two-step PCR
 approach as outlined by Buchner et al. (2021) using the BF3/BR2 freshwater
 invertebrate primer set developed for the cytochrome c oxidase subunit I
 (COI) gene (Elbrecht et al. 2019). Minor modifications to the protocol
 were made by individual laboratories due to facility constraints (see
 Jones <em>et al</em>., 2025), however, key steps were kept
 consistent: DNA extraction kit and primers used, PCR profiles and clean up
 steps. Two-step PCRs were performed on each extraction replicate using the
 Qiagen Multiplex PCR Plus Kit (Qaigen, Germany) with 0.2 μM of each primer
 in a final volume of 25 µl. PCRs were run with the following conditions:
 95 °C for five minutes followed by 30 cycles of 95 °C for 30 seconds; 50
 °C  for 30 seconds; 72 °C for 30 seconds with a final extension at 68 °C
 for 10 minutes. Three PCR replicates were carried out from each sub-sample
 of NHM/Lab extract. In PCR 2, 1 μl of amplicon from PCR 1 was used in the
 following PCR conditions: 95 °C for five minutes, followed by 20 cycles of
 95 °C for 30 seconds; 61 °C for 30 seconds; 72 °C for 42 seconds and a
 final extension at 68 °C for 10 minutes. All positive and negative
 controls were processed alongside samples. PCR products were then cleaned
 using the recommended Agencourt AMPure beads at a 0.7x ratio. The PCR
 product size was then checked on an agarose gel. The concentration of each
 of these samples was quantified using Qubit, Tapestation or qPCR
 (dependant on laboratory facilities) and samples were then pooled in
 equimolar concentrations with the negative controls added at the maximum
 volume for any single sample. Libraries were loaded at 12 pM
 concentration, with 5% PhiX control. Samples were run on an Illumina MiSeq
 (MiSeq Reagent Kit v3, 600 cycles) following the manufacturer's run
 protocols for 300 bp PE sequencing (Illumina, Inc. San Diego, CA, USA).
 Raw read data were compiled with participating laboratories anonymised,
 and then processed with the APSCALE pipeline (Buchner et al., 2022: v
 1.6.3, https://github.com/DominikBuchner/apscale) using default settings. Taxonomic assignment was performed using BOLDigger (Buchner & Leese, 2020) (v 2.1.1). The best hit was determined with the BOLDigger method and then further corrected using the API correction function (see Jones <em>et al</em>., 2025). Data harmonisation and index calculation Data harmonisation Output from the bioinformatics pipeline comprised 7680 operational taxonomic units (OTUs) identified from the 504 PCR replicates (six replicate PCRs for nine samples, as well as nine negative controls and nine positive controls processed by each of the seven laboratories). Records of non-target taxa were removed (see Jones <em>et al</em>., 2025), where target taxa were defined as the freshwater macroinvertebrates considered in the mixed taxonomic level (MTL) system of the Environment Agency (2014). The resultant matrix consisted of 519 OTUs detected across the 504 PCR replicates. To facilitate comparison between morphologically derived and metabarcoding data, both datasets were harmonised to the same operational MTL (see Jones <em>et al</em>., 2025), ensuring that it only contained discrete taxa (see Jones <em>et al</em>., 2025). The final harmonised list comprised 162 discrete taxa.
description: # Sources of uncertainty in DNA metabarcoding of whole communities:
 implications for its use in biomonitoring Dataset DOI:
 [10.5061/dryad.dncjsxm9t](10.5061/dryad.dncjsxm9t) ## Description of the
 data and file structure Advancements have been made in the use of
 DNA-based methods for detection of single species. However, the routine
 application of DNA-based methods to monitor whole communities using a
 metabarcoding approach and derive ecosystem status continues to be
 limited. We undertook a structured experiment to assess factors
 influencing the precision and accuracy of molecular methods for freshwater
 macroinvertebrate community assessment. To quantify the contributions of
 sequencing, laboratory, DNA extraction and sample composition to
 uncertainty and biases in macroinvertebrate taxonomic detection and
 identification through metabarcoding, we compared data generated by a
 well-established biomonitoring protocol used in the UK from a set of nine
 kick-net samples with data simultaneously produced by seven molecular
 laboratories. The experimental design allowed us to: (1) compare the
 accuracy and precision of taxa identification within and between molecular
 laboratories and benchmark results to morphological outputs; (2)
 investigate factors influencing variability in the detection of taxa
 derived through DNA metabarcoding; and (3) utilise information gathered
 from our analyses to provide advice on best practise to support
 development of reproducible protocols for metabarcoding and identify
 future research requirements. ### Files and variables #### File:
 Morphological\_analysis\_raw\.csv **Description:** These are the
 abundances of the different taxa morphologically identified in each of the
 nine samples processed by qualified taxonomists at Queen Mary University
 of London. Here we provide an explanation for each of the column headers
 in the Morphological_analysis_raw.csv file. ##### Variables * Major Group:
 Higher taxonomic level of each morphologically identified taxon * Taxon
 Name: Taxon name of each morphologically identified taxon * A - I: Count
 of each morphologically identified taxon in stream kick samples A - I ####
 File: Morphological\_Bioinformatics\_Harmonised\_Assemblage\_Data.csv
 **Description:** Here we provide an explanation for each of the column
 headers in the Morphological_Bioinformatics_Harmonised_Assemblage_Data.csv
 file. These are the morphological abundance data and DNA metabarcoding
 read count data following harmonisation. DNA metabarcoding data from each
 laboratory have also been corrected for the associated negative control
 results as detailed in the related work. ##### Variables * Major Group:
 Higher taxonomic level of each morphologically identified taxon * Taxon
 Name: Taxon name of each morphologically identified taxon * A - I: Count
 of each morphologically identified taxon in samples A - I - L2_a_Lab2_rep1
 - L8_i_NHM_rep3: Read count for each PCR replicate designated by a sample
 code made up of four elements separated by underscores. The four elements
 indicate (i) which laboratory processed the PCR replicate, (ii) which
 sample was processed, (iii) whether the DNA was extracted by the
 laboratory itself or by the Natural History Musueum (NHM), (iv) which
 replicate was processed. | Molecular analysis laboratory | Stream kick
 sample | DNA Extraction | PCR Replicate | | :----------------------------
 | :----------------- | :------------- | :------------ | | L2 | a | NHM |
 rep1 | | L3 | b | Lab2 | rep2 | | L4 | c | Lab3 | rep3 | | L5 | d | Lab4 |
 | | L6 | e | Lab5 | | | L7 | f | Lab6 | | | L8 | g | Lab7 | | | | h | Lab8
 | | |   | i |   |   | #### File: Bioinformatics\_output\_raw\.csv
 **Description:** Here we provide an explanation for each of the column
 headers in the Bioinformatics_output_raw.csv file. These are the data
 outputted by the bioinformatics pipeline as detailed in the related work.
 Raw read data were compiled with participating laboratories anonymised,
 and then processed with the APSCALE pipeline (v 1.6.3,
 [https://github.com/DominikBuchner/apscale](https://github.com/DominikBuchner/apscale)) using default settings. Taxonomic assignment was performed using BOLDigger (v 2.1.1, [https://github.com/DominikBuchner/BOLDigger](https://github.com/DominikBuchner/BOLDigger)). The best hit was determined with the BOLDigger method and then further corrected using the API correction function (see related work for more details). ##### Variables * OTU_STATUS: The OTU returned by the bioinformatics was assigned to one of five categories for exclusion from subsequent data analyses or it was retained for data analyses. The five exclusion categories were: un-matched, non-target taxa, improbable taxa, poorly resolved taxa, and OTUs with &lt;80% similarity library match * ID: Unique identifier for each operational taxonomic unit (OTU) detected * Phylum: Assigned phlyum for OTU * Class: Assigned class for OTU. The cell is left blank if the OTU could not be assigned to a Class. * Order: Assigned order for OTU. The cell is left blank if the OTU could not be assigned to an Order. * Family: Assigned family for OTU. The cell is left blank if the OTU could not be assigned to a Family. * Genus: Assigned genus for OTU. The cell is left blank if the OTU could not be assigned to a Genus. * Species: Assigned species for OTU. The cell is left blank if the OTU could not be assigned to a Species. * Similarity: Percent similarity of OTU base pair sequence to that of assigned library match * Status: Source of assigned library match * Flags: Flags are added when assigning the library match via BOLDigger and can be looked up on the Github readme: [https://github.com/DominikBuchner/BOLDigger](https://github.com/DominikBuchner/BOLDigger). A closer look at all flagged hits is advised since they represent a certain degree of uncertainty for the selected hit. If no flags were added when assigning the library match then the cell is left empty. * L2_a_Lab2_rep1 - L8_i_NHM_rep3: Read count for each PCR replicate designated by a sample code made up of four elements separated by underscores. The four elements indicate (i) which laboratory processed the PCR replicate, (ii) which sample was processed, (iii) whether the DNA was extracted by the laboratory itself or by the Natural History Musueum (NHM), (iv) which replicate was processed. * | Molecular analysis laboratory | Stream kick sample | DNA Extraction | PCR Replicate | | :---------------------------- | :----------------- | :------------- | :------------ | | L2 | a | NHM | rep1 | | L3 | b | Lab2 | rep2 | | L4 | c | Lab3 | rep3 | | L5 | d | Lab4 | | | L6 | e | Lab5 | | | L7 | f | Lab6 | | | L8 | g | Lab7 | | | | h | Lab8 | | |   | i |   |   | #### File: Sample\_location\_details.csv **Description:** Location and environmental description of the river sampling locations from which the nine samples have been taken. ##### Variables * Location and environmental description of the river sampling locations from which the nine samples have been taken.: Location and environmental description of the river sampling locations from which the nine samples have been taken * River Name: Name of sampled river * Latitude: Latitude * Longitude: Longitude * Distance from source (km): distance from sampled site along river channel to river source * Elevation (m above sea level): elevation of sampled location * Channel slope (m km-1): slope of river channel at sampled location * Channel width (m): mean width of water surface at sampled location at time of sampling * Channel depth (m): mean depth of water at sampled location at time of sampling * Conductivity (microScm-1): measured conductivity of stream water at time of sampling * Stream bed substrate composition1 (%): visually assesed percent cover of boulders and cobbles in stream bed at sampled location at time of sampling * Stream bed substrate composition2 (%): visually assesed percent cover of pebbles and gravel in stream bed at sampled location at time of sampling * Stream bed substrate composition3 (%): visually assesed percent cover of sand in stream bed at sampled location at time of sampling * Stream bed substrate composition4 (%): visually assesed percent cover of silt and clay in stream bed at sampled location at time of sampling #### File: Bioinformatics\_output\_raw\_READ\_ME.txt **Description:** Associated information to aid understanding of Bioinformatics_output_raw.csv file ##### #### File: Morphological\_analysis\_raw\_READ\_ME.txt **Description:** Associated information to aid understanding of Morphological_analysis_raw.csv file #### #### File: Morphological\_Bioinformatics\_Harmonised\_Assemblage\_Data\_READ\_ME.txt **Description:** Associated information to aid understanding of Morphological_Bioinformatics_Harmonised_Assemblage_Data.csv file ##### #### File: SAS\_code.txt **Description:** Hierarchical analysis of variance (ANOVA) was used to estimate the variance in estimates of the various calculated metrics. Read count data were log10 transformed before analysis to avoid heteroscedasticity. The hierarchical analysis of variance was repeated for each metric derived from the taxa detected, and the variance and significance (i.e., a consistent directional difference), and relative contributions to the total and within-sample variance calculated to test the following hypotheses. All analyses were undertaken using General Linear Models in SAS/STAT® (SAS Institute Inc., Cary, NC, USA). Here we provide the SAS code used to run the hierarchical analysis of variance. ## Code/software All files are either in comma-separated values (.csv) or text (.txt) format and as such should be readable by any spreadsheet or text based software. All analyses in the related work were undertaken in SAS/STAT® (SAS Institute Inc., Cary, NC, USA). The SAS code used to run the hierarchical analysis of variance is provided in SAS_code.txt