DOI: 10.5061/dryad.rn8pk0pg3
title: Ichthyoplankton metabarcoding: an efficient tool for early detection of invasive species establishment

description: Detection of invasive species is critical for management but is often
 limited by challenges associated with capture, processing, and
 identification of early life stages. DNA metabarcoding facilitates
 large-scale monitoring projects to detect establishment early. Here, we
 test the use of DNA metabarcoding to monitor invasive species by
 sequencing over 5000 fishes in bulk ichthyoplankton samples (larvae and
 eggs) from four rivers of ecological and cultural importance in southern
 Canada. We were successful in detecting species known from each river, and
 three invasive species in two of the four rivers. This includes the first
 detection of early life-stage rudd in the Credit River. We evaluated
 whether sampling gear affected detection of invasive species and estimates
 of species richness and found that light traps outperform bongo nets in
 both cases. We also found that the primers used for amplification of
 target sequences and the number of sequencing reads generated per sample,
 affect the consistency of species detections. However, these factors have
 less impact on detections and species richness estimates than the number
 of samples collected and analyzed.  Our analyses also
 show that incomplete reference databases can result in incorrectly
 attributing DNA sequences to invasive species. Overall, we conclude that
 DNA metabarcoding is an efficient tool for monitoring the early
 establishment of invasive species by detecting evidence of reproduction,
 but requires careful consideration of sampling design and the primers used
 to amplify, sequence, and classify the diversity of native and potential
 invasive species. 
description: DNA extracted from homogenized bulk ichthyoplankton samples using
 salt extraction protocol, 1st PCR amplification using MiFish 12S or
 modified PS1 Teleost Primers with heterogeneity spacers and Nextera
 adaptor sequences, 2nd PCR using combinatorial Nextera i5/i7 indices,
 purified using Ampure XP magnetic beads and quantification via QuBit broad
 range kit, Illumina MiSeq using the V2 (150bp x 2). 
 Sequencing data were processed using the Qiime2 software package,
 utilizing the following algorithms (cudadapt / DADA2 / Vsearch). Processed
 sequencing data were analyzed using custom scripts in R and software
 packages VEGAN, MUSCLE, APE, and DECIPHER. Plots were generated using
 ggplot2. 
description: Qiime2 R Mesquite
description: # Ichthyoplankton metabarcoding: an efficient tool for early detection of
 invasive species establishment --- In this study, we used DNA
 metabarcoding of bulk samples of early life-stage fishes to analyze
 species composition and monitor invasive fishes in four major rivers in
 southern Ontario, Canada. Samples were collected using two different gear
 types, and we analyzed each sample by amplifying and sequencing two
 different barcode sequence markers in the COI and 12S mitochondrial genes,
 allowing comparison of how differences in methodology affect our results.
 DNA was extracted from homogenized bulk ichthyoplankton samples using salt
 extraction protocol. 1st PCR amplification using MiFish 12S or modified
 PS1 Teleost Primers with heterogeneity spacers and Nextera adaptor
 sequences. 2nd PCR using combinatorial Nextera i5/i7 indices, purified
 using Ampure XP magnetic beads and quantification via QuBit broad range
 kit. Sequenced using an Illumina MiSeq using the V2 (150bp x 2). We
 compared invasive species detections, species richness, and community
 composition among the four rivers sampled. We also evaluate invasive
 species detections and species richness in relation to the number of
 individuals sequenced, gear type, primers, sequence markers, depth of
 sequencing, and reference database completeness. Based on the results of
 this study, we evaluated the most effective methods for detecting invasive
 species early in establishment using DNA metabarcoding. --- ## Description
 of the data and file structure ABBREVIATIONS AND ACRONYMS: ASV : amplicon
 sequence variant NTC : no template control ├── 00_initial_database.zip #
 Files from an initial analysis using a reference database that did not
 include Notropis hudsonius, Notropis buchanani, and Notropis volucellus
 12S sequences │   ├── Database_build # Files used to generate the
 reference database for QIIME2/VSEARCH classification of sequencing reads
 │   │   ├── GL_mito_seq.fas # Fasta file containing mitochondrial DNA
 sequences for fishes from the Great Lakes region │   │   ├──
 GL_mito_tax.txt # Taxonomy file for the reference mitochondrial DNA
 sequences NOT including Notropis hudsonius, Notropis buchanani, and
 Notropis volucellus │   │   ├── GL_mito_seq_plus.fas # Fasta file
 containing mitochondrial DNA sequences for fishes from the Great Lakes
 region and Notropis hudsonius, Notropis buchanani, and Notropis volucellus
 12S sequences generated in this study │   │   ├── GL_mito_tax_plus.txt #
 Taxonomy file for the reference mitochondrial DNA sequences including
 Notropis hudsonius, Notropis buchanani, and Notropis volucellus │   │  
 ├── sequences_plus.qza # QIIME2 formatted sequence feature file of
 GL_mito_seq_plus.fas │   │   ├── sequences.qza # QIIME2 formatted sequence
 feature file of GL_mito_seq.fas │   │   ├── taxonomy_plus.qza # QIIME2
 formatted taxonomy feature file of GL_mito_tax_plus.txt │   │   └──
 taxonomy.qza # QIIME2 formatted taxonomy feature file of GL_mito_tax.txt
 │   └── Read_correction # Files from the classification of sequencing
 reads using both reference databases │   ├── qiime2_*_out.csv # Raw output
 tables of reads per ASV by sample from QIIME2 sequence classification
 pipeline │   ├── \_Read_correction.R` # R code used to correct reads for
 contamination from NTCs and single fish samples and generate
 supp_fig1_corrected_data.pdf │   ├── sample_data_DFO.csv # Table
 containing location data and collection information for each sampling
 effort │   ├── supp_fig1_corrected_data.pdf # Output figure visualizing
 the information from "metabarcoding_results_corrected.csv" │  
 └── metabarcoding_results_corrected.csv # Output table containing read
 counts by classified species for each sample after correction for
 contamination ├── 01_sample_map.zip # Files used to generate the map in
 figure 1 showing sampling locations and the drainages sampled │   ├──
 sample_data_DFO.csv # Table containing location data and collection
 information for each sampling effort │   ├── \_sample_location_map.R # R
 code used to generate map figures │   ├── thames_river.pdf # Output plot
 for the Thames River samples │   ├── ausable_river.pdf # Output plot for
 the Ausable River samples │   ├── credit_river.pdf # Output plot for the
 Credit River samples │   ├── grand_river.pdf # Output plot for the Grand
 River samples │   └── ont_map.pdf # Output plot for the map of Ontario
 with the sampled rivers and watersheds displayed ├── 02_database_build.zip
 # Files used to generate the reference database of mitochondrial sequences
 for ASV classification │   │   ├── GL_mito_seq.fas # Fasta file containing
 mitochondrial DNA sequences for fishes from the Great Lakes region │   │  
 ├── GL_mito_tax.txt # Taxonomy file for the reference mitochondrial DNA
 sequences NOT including Notropis hudsonius and Notropis volucellus │   │  
 ├── GL_mito_seq_plus.fas # Fasta file containing mitochondrial DNA
 sequences for fishes from the Great Lakes region and Notropis hudsonius,
 and Notropis volucellus 12S sequences generated in this study │   │   ├──
 GL_mito_tax_plus.txt # Taxonomy file for the reference mitochondrial DNA
 sequences including Notropis hudsonis and Notropis volucellus │   ├──
 Ontario_freshwater_fishes_db.csv # Table of fish species that inhabit or
 are likely invaders of rivers in Ontario │   ├──
 \_Qiime2_database_formatter.sh # Bash script used to generate the QIIME2
 formatted files for ASV classification │   ├──
 \_Reference_database_build.R # R code used to format the fasta files and
 taxonomy files for QIIME2 │   ├── sequences_plus.qza # QIIME2 formatted
 sequence feature file of GL_mito_seq_plus.fas │   ├── sequences.qza #
 QIIME2 formatted sequence feature file of GL_mito_seq.fas │   ├──
 taxonomy_plus.qza # QIIME2 formatted sequence feature file of
 GL_mito_tax_plus.txt │   └── taxonomy.qza # QIIME2 formatted sequence
 feature file of GL_mito_seq.txt ├── 03_qiime2_analysis.zip # Files used to
 run the QIIME2 pipeline that trims/denoises/classifies metabarcoding reads
 (**raw sequencing reads can be found at SRA sequence archive BioProject
 PRJNA948129**) │   ├── 01_sample_manifest.sh # Bash script used to
 generate the tab-delimited file required for QIIME2 sample processing │  
 ├── 02_qiime2_denoising.sh # Bash script used to trim and denoise samples
 using cutadapt and dada2 in QIIME2 │   ├── 03_qiime2_classification.sh #
 Bash script used to classify ASVs using VSEARCH in QIIME2 │   ├──
 glcoi_results # Results from the analysis of the sequencing reads
 generated using the GLcoi primers │   │   ├── qiime2_output # Output files
 from QIIME2 pipeline │   │   │   ├── classified_rep_seqs_98.qza # Qiime2
 output data file of the representative sequences classified using 98%
 cutoff for classification and the reference database WITHOUT addition of
 Notropis species │   │   │   ├── classified_rep_seqs_98.qzv # Qiime2
 output visualization file of the representative sequences using 98% cutoff
 for classification and the reference database WITHOUT addition of Notropis
 species │   │   │   ├── classified_rep_seqs_98_supp.qza # Qiime2 output
 data file of the representative sequences classified using 98% cutoff for
 classification and the reference database WITH additional Notropis species
 │   │   │   ├── classified_rep_seqs_98_supp.qzv # Qiime2 output
 visualization file of the representative sequences using 98% cutoff for
 classification and the reference database WITH additional Notropis species
 │   │   │   ├── dada2_rep_set.qza # Qiime2 output data file of the
 representative sequences from denoised raw sequencing reads │   │   │  
 ├── dada2_stats.qza # Qiime2 output data file containing the dada2
 denoising statistics\ │   │   │   ├── dada2_table.qza # Qiime2 output data
 file containing the dada2 denoised reads per sample by ASV │   │   │   ├──
 dada2_table.qzv # Qiime2 output visualization file of the denoised reads
 per sample by ASV │   │   │   ├── MiSeq_raw_reads.qza # Qiime2 output data
 file of the raw reads │   │   │   ├── MiSeq_raw_reads.qzv # Qiime2 output
 visualization file of the raw reads │   │   │   ├──
 MiSeq_trimmed_reads.qza # Qiime2 output data file of the trimmed reads
 (trimmed using cutadapt) │   │   │   ├── MiSeq_trimmed_reads.qzv # Qiime2
 output visualization file of the reads remaining after trimming │   │  
 │   ├── rep-seqs.qzv # Qiime2 output visualization file of the
 representative sequences after denoising │   │   │   ├──
 taxa_barplot_98.qzv # Qiime2 output visualization file summarizing the
 number of reads by classified species per sample for the reference
 database WITHOUT addition of Notropis species │   │   │   └──
 taxa_barplot_98_supp.qzv # Qiime2 output visualization file summarizing
 the number of reads by classified species per sample for the reference
 database WITH additional Notropis species │   │   ├── sample_manifest.txt
 # tab-delimited file containing sample IDs and path to the forward and
 reverse reads │   │   └── sample_metadata.txt # tab-delimited file
 containing sample IDs and other pertinent sample metadata │   ├──
 mifish_results # Results from the analysis of the sequencing reads
 generated using the MiFish-U primers (**all output files are in the same
 format as for the GLcoi primer analysis above**) │   │   ├── qiime2_output
 │   │   │   ├── classified_rep_seqs_98.qza │   │   │   ├──
 classified_rep_seqs_98.qzv │   │   │   ├── classified_rep_seqs_98_supp.qza
 │   │   │   ├── classified_rep_seqs_98_supp.qzv │   │   │   ├──
 dada2_rep_set.qza │   │   │   ├── dada2_stats.qza │   │   │   ├──
 dada2_table.qza │   │   │   ├── dada2_table.qzv │   │   │   ├──
 MiSeq_raw_reads.qza │   │   │   ├── MiSeq_raw_reads.qzv │   │   │   ├──
 MiSeq_trimmed_reads.qza │   │   │   ├── MiSeq_trimmed_reads.qzv │   │  
 │   ├── rep-seqs.qzv │   │   │   ├── taxa_barplot_98.qzv │   │   │   └──
 taxa_barplot_98_supp.qzv │   │   ├── sample_manifest.txt # tab-delimited
 file containing sample IDs and path to the forward and reverse reads │  
 │   └── sample_metadata.txt # tab-delimited file containing sample IDs and
 other pertinent sample metadata │   └── sequence_db │   ├──
 sequences_plus.qza # QIIME2 formatted sequence feature file of
 GL_mito_seq_plus.fas │   ├── sequences.qza # QIIME2 formatted sequence
 feature file of GL_mito_seq.fas │   ├── taxonomy_plus.qza # QIIME2
 formatted taxonomy feature file of GL_mito_tax_plus.txt │   └──
 taxonomy.qza # QIIME2 formatted taxonomy feature file of GL_mito_seq.txt
 ├── 04_read_correction.zip # Files used to remove erroneous sequencing
 reads through comparison to no template controls and single-individual
 samples │   ├── \_Read_correction.R # R code used to correct reads for
 contamination from NTCs and single fish samples and generate
 Supp_table_X.csv and supp_fig1_corrected_data.pdf │   ├──
 metabarcoding_results_corrected.csv # Output table containing read counts
 by classified species for each sample after correction for contamination\
 │   ├── qiime2_glcoi_out.csv # Tabular output of the QIIME2 visualization
 file taxa_barplot_98.qzv from the glcoi analysis │   ├──
 qiime2_glcoi-supp_out.csv # Tabular output of the QIIME2 visualization
 file taxa_barplot_98_supp.qzv from the glcoi analysis │   ├──
 qiime2_mifish_out.csv # Tabular output of the QIIME2 visualization file
 taxa_barplot_98.qzv from the MiFish-U analysis │   ├──
 qiime2_mifish-supp_out.csv # Tabular output of the QIIME2 visualization
 file taxa_barplot_98_supp.qzv from the MiFish-U analysis │   ├──
 sample_data_DFO.csv # Table containing location data and collection
 information for each sampling effort │   ├── supp_fig1_corrected_data.pdf
 # Output figure visualizing the information from
 "metabarcoding_results_corrected.csv" (Supplementary Figure S1
 in manuscript) │   └── Supp_table_X.csv # Simplified table of results in
 "metabarcoding_results_corrected.csv" for manuscript
 (Supplementary table S3 in manuscript) ├── 05_sampling_stats.zip # Files
 used to summarize and generate simple statistics relating to the number of
 fish collected from each river and the number of reads generated per
 sample │   ├── metabarcoding_results_corrected.csv # Output table
 containing read counts by classified species for each sample after
 correction for contamination\ │   ├── \_Sample_stats.R # R code used to
 summarize the number of samples and fishes collected in each river and the
 number of reads generated per sample │   └── Table2.numbers # Table
 containing the summary of the number of samples and fishes collected in
 each river (Table 1 in manuscript) ├── 06_river_richness.zip # Files used
 for the comparison of species detections (total and per sample) across
 rivers (Figure 2 in manuscript) │   ├──
 metabarcoding_results_corrected.csv # Table containing read counts by
 classified species for each sample after correction for contamination\ │  
 ├── Fig2A_sp_richness_per_river.pdf # Output barplot showing the incidence
 of each species by river │   ├── Fig2B_sp_richness_per_sample.pdf # Output
 scatterplot showing the number of species detected in each sample by the
 total number of individuals in the sample │   ├── Fig2C_jaccard_dist.pdf #
 Output violin plots visualizing the pairwise Jaccard distances of species
 incidence between samples in each river │   ├── regression line.png #
 Screenshot of the output from the regression analysis in Figure 2B │   └──
 \_Species_richness_by_river.R # R code used to generate figure 2 showing
 the comparisons of species richness in the four rivers sampled ├──
 07_gear_richness.zip # Files used for the analysis of differences in
 species detections between gear types (Figure 3 in manuscript) │   ├──
 metabarcoding_results_corrected.csv # Table containing read counts by
 classified species for each sample after correction for contamination\ │  
 ├── Fig3A_frequency_by_gear.pdf # Output dumbbell plot comparing the
 detection frequency of species with each gear type │   ├──
 Fig3B_sp_accumulation_plot.pdf # Output scatter plot showing the
 accumulation of species and individuals after X number of random samples
 │   └── \_Species_richness_by_gear.R # R code used to generate figure 3
 showing the differences in frequencies of species detection by gear type
 ├── 08_primer_consistency.zip # Files used for the comparison of the coi
 (GLcoi) and 12S (Mi-Fish-U) primer efficiency and consistency in species
 detection across samples (Figure 4 in manuscript) │   ├──
 metabarcoding_results_corrected.csv # Table containing read counts by
 classified species for each sample after correction for contamination\ │  
 ├── Fig4A_primer_consistency_plot.pdf # Output barplot showing the
 frequency each species was detected with each primer type │   ├──
 Fig4B_primers_missed_species.pdf # Output scatterplot showing the sample
 properties associated with the failure to detect specific species │   ├──
 GL_mito_seq_supp.fas # Fasta file containing mitochondrial DNA sequences
 for fishes from the Great Lakes region │   ├── GL_mito_tax_supp.txt #
 Taxonomy file for the reference mitochondrial DNA sequences including
 Notropis hudsonis, Notropis buchanani, and Notropis volucellus │   ├──
 in_silico_PCR_df.csv # Output table of the results of the in silico
 analysis of the species detected using both primer sets │   └──
 \_Primer_efficiency_and_consistency.R # R code used to run the in silico
 PCR and generate figure 4 in the manuscript ├── 09_sampling_depth.zip #
 Files used to compare if species incidence data are better estimated from
 fewer samples with more reads or more samples with fewer reads (Figure 5
 in manuscript) │   ├── metabarcoding_results_corrected.csv # Table
 containing read counts by classified species for each sample after
 correction for contamination\ │   ├── Fig5_reads_vs_samples.pdf # Scatter
 plot showing the reduction in the number of species detected after a
 reduction in the number of reads or samples │   └──
 \_Sample_frequency_vs_depth.R # R code used to run simulations,
 subsampling reads and samples to compare the importance of each in the
 total number of species detected across rivers (figure 5 in manuscript)
 ├── 10_database_comparison.zip # Files used to identify the number of
 individuals of each species represented in the reference sequence database
 for each gene fragment │   ├── \_Amplicon_matches_in_database.sh # Shell
 script used to call QIIME2 in silico processes used to identify which
 sequences in the reference database overlap with each sequence fragment
 targeted by each pair of metabarcoding primers (Figure 6 in manuscript)
 │   ├── glcoi_is_output # Output file from the QIIME2 analysis of sequence
 overlap with the target region of the GLcoi primers │   │   └──
 dna-sequences.fas # FASTA file of glcoi sequences in the reference
 database │   ├── Glcoi_is_pcr_seq.qza # QIIME2 output data file from the
 QIIME2 analysis of sequence overlap with the GLcoi primers │   ├──
 glcoi_species_detected.txt # List of individuals of each species detected
 in database with sequences overlapping the target region of the GLcoi
 primers │   ├── GL_mito_seq.fas # Fasta file containing mitochondrial DNA
 sequences for fishes from the Great Lakes region │   ├──
 mitofish_is_output # Output file from the QIIME2 analysis of sequence
 overlap with the target region of the MiFish-U primers │   │   └──
 dna-sequences.fasta # FASTA file of glcoi sequences in the reference
 database │   ├── Mitofish_is_pcr_seq.qza # QIIME2 output data file from
 the QIIME2 analysis of sequence overlap with the MiFish-U primers │   ├──
 mitofish_species_detected.txt # List of individuals of each species
 detected in database with sequences overlapping the target region of the
 MiFish-U primers │   ├── Primer_list.tsv # List of primers used in this
 study (GLcoi and MiFish-U) │   ├── sequence_frequency_plot.pdf # Output
 barplot showing the number of representative individuals for each species
 included in the reference database │   └── sequences.qza # QIIME2
 formatted sequence feature file of GL_mito_seq.fas ├──
 11_amplicon_distance.zip # Files used to compare the genetic distance
 between species for each sequence fragment targeted by the two primer sets
 used and generate a gene tree for comparing distances among Notropis
 species (Figure 6 in manuscript) │ ├── Database_distance_differences.R # R
 code used to align the metabarcoding sequence fragment libraries and
 calculate and compare pairwise distances between species │ ├── dist_df.csv
 # Output data table of all pairwise distances between species │   ├──
 glcoi_is_output # Output file from the QIIME2 analysis of sequence overlap
 with the target region of the GLcoi primers │   │   └── dna-sequences.fas
 # FASTA file of glcoi sequences in the reference database │ ├──
 glcoi_seq_aln.fas # Sequence alignment of the glcoi sequence fragments │
 ├── glcoi_seq.fas # Unaligned glcoi sequence fragments │   ├──
 mitofish_is_output # Output file from the QIIME2 analysis of sequence
 overlap with the target region of the MiFish-U primers │   │   └──
 dna-sequences.fasta # FASTA file of glcoi sequences in the reference
 database │ ├── mitofish_seq_aln.fas # Sequence alignment of the MiFish-U
 sequence fragments │ ├── sequence_distance_plot.pdf # Output scatter plot
 of the proportion of species with pairwise distances less than a series of
 frequently used cutoffs for species classification │ ├── mitofish_seq.fas
 # Unaligned MiFish-U sequence fragments │ ├── Notropis_12S.fas # Sequence
 alignment of the MiFish-U 12S sequence fragments for the Notropis species
 that were difficult to classify in our analyses │ ├──
 Notropis_12S_tree.pdf # Phylogenetic tree of Notorpis species │ └──
 notropis_gene_tree # Files used to generate the Notropis gene tree │   
 ├── 12S_distance_tree.R # R code used to reconstruct and plot the Notropis
 gene tree │    ├── Notripis_distance_tree.pdf # Output plot of the
 Notropis 12S gene tree │    ├── Notropis_12S.fas # Notropis MiFish-U 12S
 gene fragment alignment (FASTA file) │    └── Notropis_12S.fas.nex #
 Notropis MiFish-U 12S gene fragment alignment nexus file for Mesquite ---
 File/Folder Details Details for: sample_data_DFO.csv * Description: a
 comma-delimited file containing location data and collection information
 for each sampling effort * Format(s): .csv * Size(s): 7 KB * Dimensions:
 73 rows by 12 columns * Variables: * Extraction_code: Code relating sample
 information to the DNA extraction * Project_code: River that the sample(s)
 was collected from * Date: Date of collection; dd-mm-yyyy * Latitude:
 Initial latidude for sample collection (DD) * Longitude: Initial longitude
 for sample collection (DD) * Gear: Gear type used to collect sample *
 Total_fish: Total number of larval fishes collected * Total_eggs: Total
 number of eggs collected; blank cells = 0 eggs * Total_weight: Total
 weight of the ichthyoplankton sample (g) * Start_time: Start time of the
 sampling effort; blank cells = not recorded; hh:mm * Stop_time: Stop time
 of the sampling effort; blank cells = not recorded; hh:mm * Notes:
 Additional habitat information pertaining to each sample; "voucher
 combined" refers to the pooling of larval fish from each of the three
 light traps set at a location; blank cells = nothing reported Details for:
 metabarcoding_results_corrected.csv * Description: a comma-delimited file
 containing read counts by classified species for each sample after
 correction for contamination * Format(s): .csv * Size(s): 73 KB *
 Dimensions: 479 rows by 17 columns * Variables: * index: Sequencing code
 referring to the demulitplexed forward and reverse reads from the MiSeq
 sequencer * Extraction_code: Code relating sample information to the DNA
 extraction * Species: Species classification in scientific format * Reads:
 Number of denoised sequencing reads generated for each classified species
 * Primers: Refers to primer set and reference database; glcoi/mitofish
 refer to the primer set used to amplify the extracted DNA; (-supp)
 identifies which reference database was used for the species
 classification; no suffix = original reference database was used; -supp =
 supplemented database was used. * Corrected_reads: Number of denoised
 sequencing reads remaining after correcting for contamination (by
 subtracting maximum number of reads detected for each species in NTCs) *
 Project_code: River that the sample(s) was collected from * Date: Date of
 collection; dd-mm-yyyy * Latitude: Initial latidude for sample collection
 (DD) * Longitude: Initial longitude for sample collection (DD) * Gear:
 Gear type used to collect sample * Total_fish: Total number of larval
 fishes collected * Total_eggs: Total number of eggs collected; blank cells
 = 0 eggs * Total_weight: Total weight of the ichthyoplankton sample (g) *
 Start_time: Start time of the sampling effort; blank cells = not recorded;
 hh:mm * Stop_time: Stop time of the sampling effort; blank cells = not
 recorded; hh:mm * Read_sums: Total number of denoised sequencing reads in
 each sample after correcting for contamination Usage notes for QIIME
 formatted files * Files with ".qza" file extensions are zipped
 QIIME2 data artifacts containing data and metadata for qiime2 analyses
 used as input for further processing and/or exporting data to other tools
 using the suite of software included in the QIIME2 bioinformatics platform
 * Files with ".qzv" file extensions are zipped QIIME2 archives
 containing interactive graphical output from QIIME2 analyes that can be
 opened using the online QIIME2 viewer
 ([https://view.qiime2.org](https://view.qiime2.org)). --- ##
 Sharing/Access information Raw metabarcoding sequencing data available on
 NCBI SRA (BioProject PRJNA948129) Genbank accession numbers for the
 Notropis 12S sequences OQ679414:OQ679415