DOI: 10.5061/dryad.h70rxwdrk
title: Data from: Ecological forensic testing: Using multiple primers for eDNA detection of marine vertebrates in an estuarine lagoon subject to anthropogenic influences

description: Many critical aquatic habitats are in close proximity to human activity
 (i.e., adjacent to residences, docks, marinas, etc.), and it is vital to
 monitor biodiversity in these and similar areas that are subject to
 ongoing urbanization, pollution, and other environmental disruptions.
 Environmental DNA (eDNA) metabarcoding is an accessible, non-invasive
 genetic technique used to detect and monitor species diversity and is a
 particularly useful approach in areas where traditional biodiversity
 monitoring methods (e.g., visual surveys or video surveillance) are
 challenging to conduct. In this study, we implemented an eDNA approach
 that used a combination of three distinct PCR primer sets to detect marine
 vertebrates within a canal system of Biscayne Bay, Florida, an ecosystem
 representative of challenging sampling conditions and a myriad of impacts
 from urbanization. We detected fish species from aquarium, commercial, and
 recreational fisheries, as well as invasive, cryptobenthic, and endangered
 vertebrate species, including charismatic marine mammals such as the
 protected West Indian manatee, Trichechus manatus. Our results support the
 potential for eDNA analyses to supplement traditional biodiversity
 monitoring methods and ultimately serve as an important tool for ecosystem
 management. This approach minimizes stress or disturbance to organisms and
 removes the intrinsic risk and logical limitations of SCUBA diving,
 snorkeling, or deploying sensitive equipment in areas that are subject to
 high vessel traffic and/or low visibility. Overall, this work sets the
 framework to understand how biodiversity may change over different spatial
 and temporal scales in an aquatic ecosystem heavily influenced by
 urbanization and validates the use of eDNA as a complementary approach to
 traditional ecological monitoring methods.
description: <strong>Field Sites</strong> For
 this study, vertebrate animals were targeted for eDNA analyses because
 they encompass a wide variety of categories commonly prioritized for
 ecology and conservation studies, such as endangered species, commercially
 valuable species, cryptobenthic species, and nonnative species; many of
 these species fulfill critical roles in aquatic ecosystems (Kelley et al.
 2016) and provide a source of protein for humans (Tucker and Rogers 2014).
 Sampling sites were selected based on their diversity of benthic habitat
 structure (e.g., seagrass habitat, mud/sand, or coral reef) as well as
 proximity to human activity and sources of environmental stressors (i.e.,
 recreational boating, fishing, and both public and private land use),
 representing the diversity of habitats found in Biscayne Bay.
 Sampling sites were selected within two areas of Biscayne Bay
 that are approximately 30 miles apart from each other (Fig. 1A,
 Supplementary Table 1). Seven field sites were selected in total: one site
 at Florida International University’s Biscayne Bay Campus (FIU BBC) (Fig.
 1B) and six sites at Paradise Point (Fig. 1C). Paradise Point is located
 on the western shoreline of South Florida within the village of Palmetto
 Bay in Miami-Dade County, FL. Directly to the east of Paradise Point is a
 nature preserve, the Deering Estate North Addition Preserve. Paradise
 Point itself, however, is primarily a private residential area, with many
 homes having direct water access in the form of beaches or boat docks.
 Sites 1 and 2 have a soft, bare sediment benthic structure (i.e., mud and
 sand), which are located within a canal that separates the nature preserve
 from the residential community. Site 2 is located at the opening of this
 canal, adjacent to a beach allotted to community residents. Site 3 is
 located at the southeastern end of Paradise Point and has a benthic
 seagrass community composed of a mixture of the seagrass species
 <em>Thalassia testudinum</em>, <em>Syringodium
 filiforme,</em> and <em>Halodule wrightii</em>. Site 3
 is also commonly subjected to heavy boat traffic because this area
 connects the Paradise Point canal system to the larger Cutler
 Channel. Sites 4-6 are within a canal on the northern
 coast of Paradise Point. The benthic structure at these sites is
 predominately soft sediment, with sparse strands of <em>S.
 filiforme</em> and <em>H. wrightii</em> present. Site 4,
 located at the most inland end of this canal, is lined by mangrove
 communities, and is largely inaccessible to motorized boats. In contrast,
 Sites 5 and 6 are adjacent to residences and privately-owned boat docks
 and are accessible to both motorized and non-motorized vehicles. Sites 5
 and 6 were also selected due to their being southward of the Deering Bay
 Marina, another perceived anthropogenic influence (Alleman 1995) (Fig.
 1C). Site 7 was selected in an area further north
 within Biscayne Bay, adjacent to FIU BBC (Fig. 1B). This site is
 frequented year-round by recreational tourists visiting Oleta River State
 Park in North Miami Beach, FL. Historically, the shoreline within North
 Bay has been bulkheaded with limited mangrove shoreline, most of the
 bottom has been dredged, and benthic vegetation has been in decline
 (Cantillo et al. 2000). The benthic structure of Site 7 is primarily a
 hardbottom, rocky substrate with sparse patches of stony corals. Site 7
 was used as a comparative site to Sites 1-6 at Paradise Point to further
 test the eDNA approach.
 <em> </em><strong>Sample collection and water
 filtration</strong> Water sampling was conducted
 across two consecutive days on April 28 (Site 7) and April 29 (Sites 1— 6)
 in 2022. This minimized the potential effects of seasonal variation in
 biological, chemical, and environmental conditions across sites. Water
 collections were performed (n = 3 replicate deployments) at each site
 using a 5 L Niskin bottle at a depth of 0-1 meters. The water from each
 Niskin deployment was stored within three 1.5 L water collection bottles
 that had been previously cleaned via immersion in a 5% bleach solution,
 followed by a DI water rinse, a 70% ethanol rinse, and a final
 sterilization under ultraviolet light for 15 minutes. Prior to sampling
 each site, all collection equipment (5 L Niskin bottle, plastic tubing)
 was sterilized using 5% bleach and rinsed with deionized (DI) water. This
 was followed by the collection of a negative field control sample, in
 which the Niskin bottle was used to collect 1.5 L of DI water. Negative
 field controls were used to detect and remove any incidental DNA
 contamination introduced to the samples during collection and processing.
 Sampling produced nine 1.5 L field samples and one 1.5 L negative control
 sample for each site, for a total of n = 70 water samples across all
 sites. All water samples were immediately transported to facilities at FIU
 BBC where they were frozen at -20 °C until processing. A vacuum pump
 (model: Rocker300, 23 L/min) was used to filter each water sample through
 a 0.22 µM Sterivex filter. All filtration processes were conducted within
 a clean fume hood to minimize potential contamination. All equipment was
 sterilized between samples using 5% bleach, DI water, and 70% ethanol. All
 0.22 µM filters from each of the 70 samples were subsequently stored at
 -70 °C in FIU’s Environmental Epigenetics Lab until DNA extractions were
 performed. <strong>Environmental DNA isolation
 and PCR amplification</strong>s DNA extractions
 and PCR amplifications were performed using stringent molecular laboratory
 practices to limit potential contamination. Lab benches were cleaned
 regularly with 10% bleach, followed by DI water, and 70% ethanol. All
 pipets, tube racks, and other equipment were cleaned with ELIMINase (Decon
 Labs, Inc.), followed by DI water, 70% ethanol, and UV sterilization. Only
 sterile, RNase free, DNase free, and pyrogen free tubes and filtered pipet
 tips were used. Lab personnel wore gloves and face masks at all
 times. DNA extractions were performed using a Qiagen
 DNeasy Blood and Tissue kit following the manufacturer’s instructions,
 with minor modifications optimized for isolating eDNA from the Sterivex
 filters. Specifically, lysis was performed in a greater reagent volume per
 sample (720 µL of Buffer ATL and 80 µL of proteinase K solution) to ensure
 the filters were completely immersed in lysis buffer, and samples were
 incubated overnight at 56 °C with end-over-end rotation. DNA purity,
 quantity, and quality were verified using a NanoVue Plus, Qubit 2.0, and
 gel electrophoresis. A total of 70 eDNA samples were isolated, including
 those extracted from negative field control water samples (i.e., deionized
 water that was collected and preserved concurrently with field water
 sampling). Three primer sets (Table 1) were used to
 target and amplify gene regions corresponding to teleost fish and broader
 marine vertebrate taxa: 1) MiFish, developed by Miya et al. 2015 to
 amplify a section of the 12S rRNA gene region; 2) MarVer1, developed by
 Valsecchi et al. 2020 to amplify a section of the 12S rRNA gene region;
 and 3) MarVer3, developed by Valsecchi et al. 2020 to amplify a section of
 the 16S rRNA gene region. These primer sets were selected to compare their
 efficiency in detecting marine vertebrate taxa and to maximize the capture
 of taxa that a single primer set alone may otherwise fail to
 detect. PCRs were performed on each water sample in
 triplicate for each of the three primer sets, resulting in a total of 630
 PCR products (70 eDNA samples x 3 primer sets x 3 reactions; Fig. 2). Each
 PCR reaction for the MiFish primer set was performed in a 25 μL reaction
 volume containing 0.5 μL of KAPA HiFi HotStart DNA Polymerase (1U/μL), 5
 μL of KAPA HiFi Fidelity Buffer (5X), 0.75 μL of KAPA dNTP Mix (10 mM
 each), 0.5 μL of BSA (20mg/μL), 1 μL of the forward primer (10 μM), 1 μL
 of the reverse primer (10 μM), 15.25 μL of sterile water, and 1 μL of the
 eDNA extraction template. The MiFish PCR thermocycler protocol employed a
 touchdown profile following Pitz et al. 2020 with an initial 15-min
 denaturation step at 95 °C, followed by 13 cycles of a 30-s step at 94 °C,
 a 30-s annealing step that started at 69.5 °C and then decreased by 1.5°C
 for each subsequent cycle (the last cycle was at 50 °C), and a 90-s step
 at 72 °C. This initial touchdown profile was followed by 25 additional
 cycles of a 30-s step at 94°C, a 30-s annealing step at 50 °C, and a 45-s
 step at 72°C. The final extension step was for 10-min at 72°C.
 Each PCR reaction for both the MarVer1 and MarVer3 primer sets
 was performed in a 20 μL reaction volume containing 0.5 μL of KAPA HiFi
 HotStart DNA Polymerase (1U/μL), 4 μL of KAPA HiFi Fidelity Buffer (5X),
 0.6 μL of KAPA dNTP Mix (10 mM each), 0.4 μL of BSA (20mg/μL), 1 μL of the
 forward primer (10 μM), 1 μL of the reverse primer (10 μM), 10.5 μL of
 sterile water, and 2 μL of the eDNA extraction template. The MarVer1 and
 MarVer3 PCR thermocycler protocol employed touch-down profiles following
 Valsecchi et al. 2020 with an initial 4-min denaturation step at 94 °C.
 For MarVer1, the denaturation step was followed by 10 cycles using an
 annealing temperature at 54 °C, 10 cycles using an annealing temperature
 at 55 °C, and 18 cycles using an annealing temperature at 56 °C. For
 MarVer3, the denaturation step was followed 8 cycles using an annealing
 temperature at 54 °C, 10 cycles using an annealing temperature at 55 °C,
 10 cycles using an annealing temperature at 56 °C, and 10 cycles using an
 annealing temperature at 57 °C. For both MarVer primer sets, each of the
 38 cycles included a 30-s step at 95 °C, a 30-s step at the given
 annealing temperature, and a 40-s step at 72°C. The final extension step
 was for 5 min at 72°C. For every PCR reaction mixture
 created, a negative control sample was included in which molecular grade
 water replaced the addition of the eDNA template. All PCR products were
 visualized via electrophoresis on 2% agarose gels to ensure amplification
 success and correct product size. PCR products were quantified using a
 Qubit dsDNA HS (High Sensitivity) Assay Kit and Qubit 2.0 fluorometer. PCR
 products were pooled so that equal amounts of amplified DNA were added to
 ensure equal representation for every collection site, and so that equal
 amounts of amplified DNA were used for the library preparation for each
 site for each primer set. Overall, a total of 24 PCR product pooled
 samples were produced (one sample from each of the seven sites, for each
 of the three primer pairs). A field control sample for each of the three
 primer pairs was also included (Fig. 2).  
 <strong>Sequencing and Bioinformatic
 analyses</strong>  Pooled PCR products were
 submitted to the NextGen DNA Sequencing core facility at the University of
 Florida’s Interdisciplinary Center for Biotechnology Research (UF ICBR).
 Samples that contained a short 40-50 bp peak were removed and amplicons
 were size selected for library peaks below 1000 kb. After quantification
 of pooled PCR products, libraries were constructed with a limited PCR
 cycle to ligate overhang adaptor sequences that are compatible with
 Illumina sequencing adapters and dual indexes. Library preparation was
 performed for paired end 2x300 bp sequencing on an Illumina MiSeq, and
 libraries were normalized prior to sequencing. The
 bioinformatics workflow for post sequencing data was processed and adapted
 from the DADA2 pipeline (https://benjjneb.github.io/dada2/tutorial.html).
 Accordingly, primer sequences were first removed from the beginning and
 ends of the sequence FastQ files using “cutadapt” (Martin 2011) in R.
 FastQ files were then fed into the DADA2 package to model DNA sequencing
 error on an Illumina run, controlling for read quality and picking
 Amplicon Sequence Variable (ASV) sequences that represent biological
 variability. Reads were subsequently trimmed to remove low-quality regions
 and filtered by quality score, which was visualized using
 “plotQualityProfile” to identify sequences that did not meet the quality
 score requirements and remove them from the dataset using the
 “filterAndTrim” function, with a low level mismatch between exact
 sequences of primers and observed reads. After error profiles were
 characterized, forward and reverse reads were merged using “mergePairs”,
 and chimeras were removed using “removeBimeraDenovo”.
 Once sequences were trimmed and assessed for quality control,
 each sequence was given a taxonomic identity using the “assignTaxonomy”
 algorithm, which matched sequences to a reference database (minimum
 threshold of 50%, which were the default settings). Here, the MIDORI2
 database of eukaryotic mitochondrial sequences (Leray, Knowlton, and
 Machida 2022) which includes reference files for both 12S and 16S rRNA,
 was used for all three primer sets (i.e., the 12S rRNA reference for
 MiFish and MarVer1 datasets, and the 16S rRNA reference for the MarVer3
 dataset). Sequences were combined with field site metadata using Phyloseq
 (Mcmurdie & Holmes, 2013) For each primer set, ASVs identified in
 the negative control were removed from all of the samples (Supplementary
 Table 2). Any taxa that were unidentified (i.e., did not match to the
 reference database) at the order level or above were also excluded; taxa
 identified to the family, genus, or species level remained in the dataset.
 Data were subset further to exclude all taxa except for Chordates and then
 conglomerated by genus using the “tax_glom” function in phyloseq to
 simplify downstream presence-absence tables for each site.
 Taxonomic identities were assigned to all three primer sets.
 Primer sets were analyzed to determine the identity and quantity of taxa
 that were detected by more than one primer set as well as the identity and
 quantity of taxa that were only identified by one of the three primer
 sets. In a subsequent analysis, the taxa that were detected by all three
 primer sets, and therefore have high confidence that their presence was
 not a false positive, were explored further. After examining how taxa
 identification varied by primer set, the results from all primers were
 combined into one dataset. A taxon was included in the dataset as long as
 it fulfilled the requirement that at least one primer set had detected it
 in at least one of the seven study sites. Although read counts were
 collected as part of the dataset, for the present study they were not used
 to estimate organism abundance (see Discussion). Therefore, only taxa
 presence or absence per site was assessed.
description: # Ecological forensic testing: Using multiple primers for eDNA detection
 of marine vertebrates in an estuarine lagoon subject to anthropogenic
 influences
 [https://doi.org/10.5061/dryad.h70rxwdrk](https://doi.org/10.5061/dryad.h70rxwdrk) ## Overview This repository contains comprehensive data and scripts associated with the study titled "Ecological forensic testing: Using multiple primers for eDNA detection of marine vertebrates in an estuarine lagoon subject to anthropogenic influences." The study is focused on utilizing environmental DNA (eDNA) metabarcoding to detect and monitor marine vertebrate species diversity within the canal system of Biscayne Bay, Florida. The dataset comprises essential components including supplemental tables, taxonomic assignments, raw sequencing data, and bioinformatic analyses. Additionally, the repository provides R scripts utilized for graph generation, enabling detailed insights into the sequencing process, bioinformatic analyses, and taxonomic assignments integral to the study's findings. ## Brief Summary of Methodology Sequencing and bioinformatic analyses were conducted to characterize vertebrate species diversity in Biscayne Bay, Florida. Libraries were prepared for paired-end 2x300 bp sequencing on an Illumina MiSeq platform, with pre-sequencing normalization, and samples containing short peaks below 1000 kb were removed. The bioinformatics workflow used DADA2, encompassing a primer sequence removal, quality control, and taxonomic assignment utilizing the MIDORI2 database of eukaryotic mitochondrial sequences. Negative control ASVs were excluded, and taxa unidentified at or above the order level were removed. Data were subsetted to include only Chordates by genus using phyloseq. Taxonomic identities were assigned for each of three primer sets (MarVer1, MarVer3, MiFish), and overlap between sets were compared using a Venn Diagram. Feeding types and species diversity focused on taxa detected by all primer sets. ## Contents ### **Supplemental files** * **S1_SiteCoordinatesHabitatType.csv**: Sampling site locations with GPS coordinates and key characteristics of the sampling sites * **S2_NegativeControls.csv**: List of ASVs from Negative Control samples that were removed from the dataset. * **S3_ReadStatistics.csv**: Number of raw sequence reads, filtered and cleaned reads for forward and reverse reads, merged reads, and merged reads with chimeras removed for each sample and primer set. * **S4_AllTaxa_PresAbs_PrimersComb.csv**: List of all 145 taxa identified (all primer sets combined) and their presence/absence at each site. A taxon was included under the condition that at least one of the three primer sets identified that it was present at at least one of the seven sites. * **S5_MarVer1Taxa.csv**: List of all taxa identified using the MarVer1 primer set, including read counts per site. * **S6_MiFishTaxa.csv**: List of all taxa identified using the MiFish primer set, including read counts per site. * **S7_MarVer3Taxa.csv**: List of all taxa identified using the MarVer3 primer set, including read counts per site. * **S8_BySite_PrimersCombined.csv**: List of all taxa (all primer sets combined) by site, including trophic description (i.e., herbivore, carnivore, or omnivore). A taxon was included under the condition that at least one of the three primer sets identified that it was present in at least one of the seven sites. * **SupplementalTables_final_clean_Updated7Dec23_copy.xlsx**: All the supplemental tables in one excel sheet ### **FASTQ Files for each primer (zip files), plus MIDORI2 reference fasta file** * FastQ files were then fed into the DADA2 package to model DNA sequencing error on an Illumina run, controlling for read quality and picking Amplicon Sequence Variable (ASV) sequences that represent biological variability. * **MiFish_fastq.zip**: Compressed file containing FASTQ files for the MiFish primer. * **MarVer3_fastq.zip**: Compressed file containing FASTQ files for the MarVer3 primer. * **MarVer1_fastq.zip**: Compressed file containing FASTQ files for the MarVer1 primer. * **MIDORI2_UNIQ_NUC_SP_GB253_srRNA_DADA2.fasta**: FASTA file of the MIDORI2 database of eukaryotic mitochondrial sequences (Leray, Knowlton, and Machida 2022) which includes reference files for both 12S and 16S rRNA, was used for all three primer sets (i.e., the 12S rRNA reference for MiFish and MarVer1 datasets, and the 16S rRNA reference for the MarVer3 dataset) ### Scripts * **Figures_Rscript (1).R**: R script for generating figures. This includes loading packages and code to run a Venn diagram, Trophic bar plot, and Upset plot. **MarVer1** * **MarVer1_DADA2_Script.R**: R script for DADA2 processing of MarVer1 data. * **AssignTax_DADA2_MarVer1.R**: R script for taxonomic assignment from DADA2 data for MarVer1 primer. * **AssignTaxa_MarVer1.sh**: Shell script for taxonomic assignment of MarVer1 data. **MarVer3** * **DADA2_MarVer3_Script.R**: R script for DADA2 processing of MarVer3 data. * **AssignTax_DADA2_MarVer3.R**: R script for taxonomic assignment from DADA2 data for MarVer3 primer. * **AssignTaxa_MarVer3.sh**: Shell script for taxonomic assignment of MarVer3 data. **MiFish** * **MiFish_DADA2_Script.R**: R script for DADA2 processing of MiFish data. * **AssignTax_DADA2_MiFish.R**: R script for taxonomic assignment from DADA2 data for MiFish primer. * **AssignTaxa_MiFish.sh**: Shell script for taxonomic assignment of MiFish data. ### Taxonomic results & metadata files for each primer set * Metadata provides additional information about the samples, such as sample identifiers, experimental conditions, sample collection details, and other relevant information. * **metadata_MarVer1.csv**: Metadata providing additional information about samples processed with the MarVer1 primer. * **metadata_MarVer3.csv**: Metadata providing additional information about samples processed with the MarVer3 primer. * **metadata_MiFish.csv**: Metadata providing additional information about samples processed with the MiFish primer. * **MarVer1_Taxa.csv**: Taxonomic results derived from the MarVer1 primer. * **MarVer3_Taxa.csv**: Taxonomic results derived from the MarVer3 primer. * **MiFish_Taxa.csv**: Taxonomic results derived from the MiFish primer. * **SitePresenceAbsence.csv**: Presence and absence data across different sampling sites. * **TrophicBySite_PrimersCombined.csv**: Trophic levels of organisms identified across sites. * **MiFish_Site[X].csv:** Taxonomic results derived from the MiFish (12S) primer across individual study sites within Biscayne Bay. * **MarVer1_Site[X].csv:** Taxonomic results derived from the MarVer 1 (12S) primer across individual study sites within Biscayne Bay. * **MarVer3_Site[X].csv:** Taxonomic results derived from the MarVer 3 (16S) primer across individual study sites within Biscayne Bay. * ***Taxonomic Assignments**: The "taxa.rds" files are not included, however our R scripts show how we generated a taxa.rds file for each primer. These assignments typically include information such as the taxonomic lineage (from domain to species level) for each sequence or operational taxonomic unit (OTU).* Missing data code- NA