Data Paper
Data Paper
COI amplicon sequence data of environmental DNA collected from the Bronx River Estuary, New York City
expand article infoEugenia Naro-Maciel, Melissa R. Ingala§, Irena E. Werner|, Brendan N. Reid, Allison M. Fitzgerald#
‡ New York University, New York, United States of America
§ Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, United States of America
| City University of New York, New York, United States of America
¶ Rutgers University, New Brunswick, United States of America
# New Jersey City University, Jersey City, United States of America
Open Access


In this data paper, we describe environmental DNA (eDNA) cytochrome c oxidase (COI) amplicon sequence data from New York City’s Bronx River Estuary. As urban systems continue to expand, describing and monitoring their biodiversity is increasingly important for sustainability. Once polluted and overexploited, New York City’s Bronx River Estuary is undergoing revitalization and restoration. To investigate and characterize the area’s diversity, we collected and sequenced river sediment and surface water samples from Hunts Point Riverside and Soundview Parks (ntotal = 48; nsediment = 25; nwater = 23). COI analysis using universal primers mlCOIintF and jgHCO2198 detected 27,328 Amplicon Sequence Variants (ASVs) from 7,653,541 sequences, and rarefaction curves reached asymptotes indicating sufficient sampling depth. Of these, eukaryotes represented 9,841ASVs from 3,562,254 sequences. At the study sites over the sampling period, community composition varied by substrate (river sediment versus surface water) and with water temperature, but not pH. The three most common phyla were Bacillariophyta (diatoms), Annelida (segmented worms), and Ochrophyta (e.g. brown and golden algae). Of the eukaryotic ASVs, we identified 614 (6.2%) to species level, including several dinoflagellates linked to Harmful Algal Blooms such as Heterocapsa spp., as well as the invasive amphipod Grandidierella japonica. The analysis detected common bivalves including blue (Mytilus edulis) and ribbed (Geukensia demissa) mussels, as well as soft-shell clams (Mya arenaria), in addition to Eastern oysters (Crassostrea virginica) that are being reintroduced to the area. Fish species undergoing restoration such as river herring (Alosa pseudoharengus, A. aestivalis) failed to be identified, although relatively common fish including Atlantic silversides (Menidia menidia), menhaden (Brevoortia tyrannus), striped bass (Morone saxatilis), and mummichogs (Fundulus heteroclitus) were found. The data highlight the utility of eDNA metabarcoding for analyzing urban estuarine biodiversity and provide a baseline for future work in the area.

Key Words

eDNA, MEGAN, metabarcoding, next-generation sequencing, QIIME2, river sediment, river water, urban ecology


Urbanization is increasingly disrupting ecological layouts of cities and their surroundings (Alberti 2008; Douglas and James 2015). Research on urban wildlife can inform strategies to combat related threats such as habitat loss, pollution, and climate change. Further, invasive species and pathogen identification can lead to early action, and conservation planning depends on accurate taxonomic classification.

Despite having one of the world’s largest human populations and containing several key habitats such as coastal ecosystems and forests, New York City’s wildlife areas remain insufficiently characterized (Gandy 2003; Sanderson 2009). The Bronx River, which flows through Westchester County and the Bronx, is currently considered ‘impaired’. This riparian system is recovering from decades of abuse and still suffering from fecal coliform growth, floating debris, and legacy pollutants such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and metals in the sediments. Combined Sewer Overflow (CSO) drains pump surface run-off and household waste into the river, increasing microplastics and fecal coliforms (NYSDEC 2020). Several local citizen groups host regular cleanups, run reclamation and restoration projects towards targeted species and areas of the river, and educate the public about its resources (e.g., American eels (Anguilla rostrata), river herring (Alosa pseudoharengus, A. aestivalis), and eastern oysters (Crassostrea virginica)).

The Estuary Section of the Bronx River Watershed (Fig. 1) contains diverse habitats such as wetlands and streams that face a mix of conservation threats from CSOs and other pollution (NYCParks 2021). Toxins, pathogens, and invasive species occur in urban estuaries, and in the Bronx River several marine and estuarine organisms have established populations. Green crabs (Carcinus maenas), Asian shore crabs (Hemigrapsus sanguineus), as well as harmful algae that can cause blooms, have all been observed in the river (Fuss and O’Neill 2015). In addition, due to the proximity to roads, housing, and businesses, pathogens that affect humans and marine life (e.g. oyster pathogens Perkinsus marinus and Haplosporidium nelsoni) can be problematic. To address these issues, habitat assessment, plankton and fish sampling, and water quality and benthic monitoring are in progress (NYCParks 2021). Two key areas of the lower estuary are Hunts Point Riverside Park, a previous garbage dump, and Soundview Park, which borders the estuary and the East River, and is the site of ongoing restoration projects of oysters and salt marshes (Grizzle et al. 2012; Kimmelman 2012; Fitzgerald 2013).

Figure 1.

Location of the Hunts Point Riverside and Soundview Park study sites in the Bronx River Estuary (New York City, USA). Samples from each park were collected within 2/10th kilometer. The inset shows the location of the study site (boxed) within the greater New York City metropolitan area. Map data 2019 Google.

To appropriately characterize and manage such a complex and impacted system, biodiversity inventories and monitoring are key first steps, starting with the correct identification of organisms. Locally in the Bronx and around the world, this has traditionally been achieved through manual surveys requiring organismal capture and/or collection. While providing important information, these methods are potentially labor-intensive and costly, require specific taxonomic expertise, may fail to detect cryptic, microscopic, or elusive taxa, and could provide incorrect or incomplete information. Environmental DNA (eDNA), or DNA sequenced directly from a substrate such as water, sediment, or air, is a flourishing new, non-invasive, rapid, and standardized technology that addresses some of these shortcomings and provides extensive genetic information useful for identifying species through next-generation sequencing (Bik et al. 2012; Bohmann et al. 2014; Taberlet et al. 2018; Deiner et al. 2021).

Biodiversity characterization and monitoring have substantially benefitted from the high quality next-generation bioinformatics pipelines now available to accurately analyze genetic markers with rapidly growing reference databases (Taberlet et al. 2018). For instance, our pilot study titled “16S rRNA Amplicon Sequencing of Urban Prokaryotic Communities in the South Bronx River Estuary” revealed the dominant phyla Proteobacteria, Epsilonbacteraeota, Cyanobacteria, Bacteroidetes, Actinobacteria, and Acidobacteria, and found that sediments had higher mean diversity than surface waters (Naro-Maciel et al. 2020). The sequences also contributed to the growing database for the 16S rRNA V4 region, the gold standard employed by the Earth Microbiome Project for prokaryotic metabarcoding (Gilbert et al. 2014). Further, our 18S rRNA gene amplicon (V1–V3 region) Data Paper provided information on an important but less studied 18S region, and successfully amplified a broad range of animals, fungi, and protists (Ingala et al. 2021). We found that community composition varied over time and by substrate (river sediment versus surface water). The sediments were dominated by the phyla Diatomea (diatoms), Annelida, and Nematoda, while the most common phyla in surface waters were Cryptophyceae (algae), Ciliophora (ciliates), Diatomea, and Dinoflagellata. The 18S analysis also detected organisms of management interest such as Eastern oysters (Crassostrea virginica) and their pathogens, as well as taxa linked to Harmful Algal Blooms. Although commonly observed species such as soft-shell clams (Mya arenaria) and blue mussels (Mytilus edulis) were identified, other key common or management-critical taxa such as the fish and invasive species described above were not recovered.

Here we expand our analysis with new COI sequences amplified from the previously analyzed environmental samples (n = 48). In traditional single-species barcoding, COI has been the standard marker for animals due to its conserved priming regions and informatively variable target segment (Hebert et al. 2003). We continued to focus on Amplicon Sequence Variants (ASVs) because the data are reproducible, consistent, and standardized (Callahan et al. 2017), and included distance-based classifications primarily due to incomplete taxonomic databases. Our objectives were to identify organisms, explore biodiversity patterns, and establish a baseline for future work in the Bronx River Estuary.


Study sites and samples

We sampled benthic sediments and surface waters at Hunts Point (HP, 40.82°N, 73.88°W; nsediment = 9; nwater = 8) and Soundview (SVP, 40.81°N, 73.87°W) Parks (Fig. 1), located in Reach 1 of the Bronx River Estuary (NYCParks 2021). At SVP we collected both from a restored oyster reef (SVP-BRO: nsediment = 8; nwater = 7) and an area containing wild oysters about one to two tenths of a kilometer distant (SVP-BRC: nsediment = 8; nwater = 8). We worked from August 2015 to September 2016, monthly from May–October during low tide as previously described (Fitzgerald 2013; Naro-Maciel et al. 2020; Ingala et al. 2021). We took water pH and temperature measurements using a YSI Pro Plus Probe (YSI, USA) when samples were collected, first at Soundview and later, usually in the same day, at Hunts Point.

DNA metabarcoding

We processed and extracted DNA from these environmental samples within 24 hours as previously described (Naro-Maciel et al. 2020; Ingala et al. 2021). The water samples were filtered with 0.45 μm Whatman Cellulose Nitrate Sterile filters (Cytiva, USA). At the time we did not include extraction blanks or positive controls and worked in a turtle-focused university molecular lab that was not PCR-free (no turtles were detected in this Data Paper). We stringently followed standard decontamination and sterilization procedures in the lab, and later conducted state-of-the-art bioinformatic quality control that removed contaminants and low-quality sequences as discussed below.

A commercial laboratory performed the polymerase chain reaction, clean-up, and sequencing procedures (MRDNA, Molecular Research LP, Shallowater, TX, USA) using previously described industry-standard procedures and controls (Dowd et al. 2008; Naro-Maciel et al. 2020; Ingala et al. 2021). We obtained COI sequences from 48 total samples representing the same river sediment and surface waters samples formerly analyzed for other markers, but in this study amplified with primers mlCOIintF (GGWACWGGWTGAACWGTWTAYCCYCC) (Leray et al. 2013) and jgHCO2198 (TANACYTCNGGRTGNCCRAARAAYCA) (Yu et al. 2012). Polymerase chain reactions were carried out using the Qiagen HotStarTaq Plus Master Mix Kit (Qiagen, USA) with an index on the forward primer, 3 PCR replicates per sample, and standard conditions and controls as reported before (Dowd et al. 2008; Naro-Maciel et al. 2020; Ingala et al. 2021). Following successful 2% agarose gel checks, the samples were pooled in equal proportions and purified with calibrated Ampure XP beads (Agencourt Bioscience, USA). After creating an Illumina amplicon library, an Illumina MiSeq was used to conduct 2 × 300 bp v.3 paired-end sequencing following manufacturer instructions. Samples were sequenced over 3 runs in one initial batch of 33 containing Hunts Point collections and Soundview Park restored oyster reef samples. Later, to add the second Soundview Park site, additional batches of 10 and then the remaining 5 samples from there were processed. All runs produced COI sequences, but due to run-to-run variation the reads produced were shorter in the small last batch. This length variation was dealt with in the bioinformatic processing pipeline as discussed below.

Bioinformatic quality control and analyses

We used the FASTQ Processor to extract indexes and sort forward and reverse reads (MRDNA 2021), and then analyzed raw reads with the QIIME2 v. 2021.4 pipeline of tools (Bolyen et al. 2019). First, using the DADA2 algorithm (Callahan et al. 2016), reads were joined, dereplicated, chimera-filtered, and then processed as paired-end (Suppl. material 1: Document 1). We ran each sequencing run through DADA2 independently using default parameters (QIIME2 2021), and only after this step were all runs merged into a final, cumulative ASV feature table. We trimmed primers and low-quality base calls from all reads prior to merging with DADA2, and truncated reads to account for declines in quality scores at the sequence ends. DADA2 uses a quality-aware algorithm to identify and correct, if possible, sequencing errors. The software further filters out chimeric sequences and artifacts, leaving behind only joined and dereplicated target sequences (Callahan et al. 2016). For two larger batches a truncation length of 260 bp was used, while for the smallest set of 5 samples a truncation length of 220 bp was used due to shorter overall lengths in this batch. We then aligned the dereplicated sequences using MAFFT (Katoh et al. 2002) and constructed approximate maximum likelihood trees using the FastTree q2-plugin (Price et al. 2010). The average percentage of sequences retained and median of sequences kept per sample are shown in Tables 1, 2.

Table 1.

Summary of COI sample data. Sample ID and statistics on the recovery of reads per sample after filtering, denoising, merging, and chimeric sequence removal are displayed, along with the index sequence and sequencing batch. The linker primer sequence for all samples was GGWACWGGWTGAACWGTWTAYCCYCC.

Sample Index Sequence input Filtered % input passed filter Denoised Merged % of input merged Non-chimeric % of input non-chimeric Batch
S.B.BRC AATGCAGG 404307 378045 93.5 363757 322683 79.81 305828 75.64 3
S.B.BRO AATGCTAT 224552 171248 76.26 169030 164525 73.27 160883 71.65 1
S.B.HP AATGCGAC 264262 197507 74.74 194996 187122 70.81 180612 68.35 1
S.C.BRC AATGCCGT 446701 419281 93.86 403346 359484 80.48 339110 75.91 3
S.C.BRO AATTAAGC 230633 172908 74.97 169692 163307 70.81 157334 68.22 1
S.C.HP AATGTTCG 232082 180166 77.63 176501 168019 72.4 165625 71.36 1
S.D.BRC AATGCGAC 357594 335024 93.69 320807 284262 79.49 267806 74.89 3
S.D.BRO AATTATGT 202121 154403 76.39 150533 144088 71.29 142652 70.58 1
S.D.HP AATTATAA 200934 150589 74.94 147721 141728 70.53 138902 69.13 1
S.E.BRC16 AATCTATT 292305 179623 61.45 161324 140460 48.05 119493 40.88 2
S.E.BRO16 AATTTAGG 261540 203142 77.67 199995 191698 73.3 174901 66.87 1
S.E.HP16 AATTCTCA 225186 170786 75.84 166593 158551 70.41 150573 66.87 1
S.F.BRC16 AATGAGCA 156813 101302 64.6 89909 71922 45.86 64045 40.84 2
S.F.BRO16 AATTTCTA 212778 160538 75.45 158567 153911 72.33 145613 68.43 1
S.F.HP16 ACAAGGCC 239942 181575 75.67 179036 169157 70.5 161451 67.29 1
S.G.BRC16 AATGCAGG 147928 95165 64.33 85196 68675 46.42 60917 41.18 2
S.G.BRO16 ACAATAGA 212958 167753 78.77 165091 159365 74.83 154835 72.71 1
S.G.HP16 ACAATCTG 261294 197228 75.48 193922 185907 71.15 180603 69.12 1
S.H.BRC16 AATGCCGT 176614 111803 63.3 100290 81044 45.89 71905 40.71 2
S.H.BRO16 ACAATTCG 190999 147290 77.12 143306 135326 70.85 133829 70.07 1
S.H.HP16 ACACAAAT 199858 153767 76.94 150265 142697 71.4 139658 69.88 1
S.I.BRC16 AATGCGAC 157544 100475 63.78 90631 73741 46.81 62025 39.37 2
S.I.BRO16 ACACAGCG 180039 138230 76.78 134493 127002 70.54 126083 70.03 1
S.I.HP16 ACACAGGT 251257 191589 76.25 188477 179857 71.58 176210 70.13 1
S.J.HP16 ACACCCAG 296667 220466 74.31 217602 210582 70.98 202212 68.16 1
W.B.BRC AATCTATT 539622 501610 92.96 493535 470360 87.16 448905 83.19 3
W.B.BRO AATGAGCA 246358 188743 76.61 185575 175281 71.15 168676 68.47 1
W.B.HP AATCTATT 219499 173898 79.22 171749 163737 74.6 157321 71.67 1
W.D.BRC AATGAGCA 497087 462883 93.12 454574 427476 86 403036 81.08 3
W.D.BRO AATGCAGG 233173 178523 76.56 174353 164570 70.58 160054 68.64 1
W.D.HP AATGCCGT 163774 123746 75.56 115874 105270 64.28 101538 62 1
W.E.BRC16 AATGCTAT 215382 153878 71.44 134734 124968 58.02 98006 45.5 2
W.E.BRO16 ACACCGGT 234991 184536 78.53 181026 171483 72.97 165766 70.54 1
W.E.HP16 ACACCGAG 180305 137446 76.23 133047 128092 71.04 124416 69 1
W.F.BRC16 AATGTTCG 264274 186824 70.69 164570 153668 58.15 116958 44.26 2
W.F.BRO16 ACAGCGTC 186549 140451 75.29 137606 130972 70.21 127594 68.4 1
W.F.HP16 ACAGCACC 173939 129442 74.42 126420 120337 69.18 116456 66.95 1
W.G.BRC16 AATTAAGC 213600 150028 70.24 131103 121232 56.76 89620 41.96 2
W.G.BRO16 ACAGGGAT 167412 126516 75.57 122513 114505 68.4 109860 65.62 1
W.G.HP16 ACAGTCGT 233719 172803 73.94 168503 159402 68.2 145463 62.24 1
W.H.BRC16 AATTATAA 245212 174443 71.14 152455 141996 57.91 109579 44.69 2
W.H.BRO16 ACAGTTAG 215300 163339 75.87 159885 148340 68.9 142294 66.09 1
W.H.HP16 ACAGTTGC 236955 181379 76.55 178385 168769 71.22 163511 69.01 1
W.I.BRC16 AATTATGT 246099 171387 69.64 149653 137933 56.05 105361 42.81 2
W.I.BRO16 ACATGGCC 229960 178352 77.56 175925 166024 72.2 159860 69.52 1
W.I.HP16 ACATTCTC 228584 177506 77.65 175016 166120 72.67 162118 70.92 1
W.J.HP16 ACATTGAT 210529 162533 77.2 159328 150060 71.28 146483 69.58 1
W.J.SVP16 ACATTGTG 214000 167062 78.07 162840 152186 71.11 147561 68.9 1
TOTALS 11623231 7653541

To assign taxonomic identity to ASVs, a sequence search was conducted against the NCBI database (downloaded 1/27/22) using the blastn algorithm with default parameters in BLAST+ v.2.11.0. (Camacho et al. 2009). BLAST hits were then employed to assign sequences to taxa using the weighted lowest common ancestor, or LCA-assignment algorithm (which identifies the lowest common ancestor in the set of BLAST hits for a given sequence) using MEGAN Community Edition v.6.2.17 (Huson et al. 2016). We used a minimum bitscore of 200 to increase specificity for the LCA analysis. For identifications at the species level we required a minimum 97% match, and we relaxed this to 80% for higher taxonomic levels. Otherwise, default parameters were retained for the LCA analysis. We added phylum-level identifications for any ASVs identified to the order level or lower that did not have these in the NCBI database.

Statistical analyses

We used R v.4.0.0 (R Core Team 2021) as implemented in RStudio v. 1.4.1103 (R Studio Team 2020) for statistical analyses (Suppl. material 1: Document 2). We exported the ASV feature table, taxonomy, rooted phylogeny, and sample metadata to BIOM format and imported these files into R for analysis using the PHYLOSEQ v. 1.32.0 suite of tools (McMurdie and Holmes 2013). First, we identified potential contaminants using the DECONTAM program and filtered them from the ASV feature table (Davis et al. 2018). DECONTAM considers any ASV whose frequency is significantly inversely correlated with sample DNA concentration across all samples as a potential contaminant. We used a conservative threshold of 0.1 to identify significance of contaminants and discarded them from the data set. To assess whether we had sequenced communities deeply enough to detect robust differences in beta diversity, we performed rarefaction analysis using the rarecurve function in VEGAN v. 2.5 – 7, and determined that species accumulation curves for all samples had reached asymptotes (Oksanen et al. 2017).

Next, we removed ASVs identified as Archaea (n = 1,185) and Bacteria (n = 12,774) or Domain Unclassified (n = 3,526) from further analysis. We computed sequence abundance-based basic alpha diversity metrics (Observed ASVs, Shannon richness, Faith’s phylogenetic diversity, and Pielou’s evenness) using a combination of custom functions and commands from the BTOOLS v. 0.0.1 package (Battaglia 2018). We tested for differences in alpha diversity metrics among sites and substrates using the GGPUBR v. 0.4.0 package (Kassambara and Kassambara 2020). We then normalized the data to account for differences in library size among samples by applying the Hellinger transform, which takes the square root of the relative abundance for each taxon and bounds the response between 0 and 1 (Legendre and Gallagher 2001).

We then performed Principal Coordinates (PCoA) ordinations on the abundance-based Bray-Curtis distance matrix and visualized the results by plotting the ordination. 95% confidence ellipses for each site + sample type combination were produced using the stat_ellipse function in ggplot2. To test for turnover in beta diversity among sites and substrates, we performed a PERMANOVA (nperm = 1000) on the Bray-Curtis distance matrix. Because a key assumption of this test is homogeneity of dispersion, we assessed whether our samples met this condition by using the betadisper and permutest functions in VEGAN. We also tested for the effects of pH, surface water temperature, and year on community composition using a Canonical Correspondence Analysis (CCA) as implemented in VEGAN. Significance was assessed through ANOVA performed on the CCA matrix.

Results and discussion

A total of 48 environmental samples were successfully collected, sequenced, and analyzed for COI (nwater = 23; nsediment = 25). Following quality control and contaminant removal, 27,328 ASVs representing Archaea, Bacteria, and Eukarya were recovered from 7,653,541 sequences (Tables 1, 2; Suppl. material 2: Tables S1, S2). Average read depth across samples varied, but in general, high estimates were returned (global minimum: 33,000), and fewer than 1% of ASVs were flagged as contaminants and removed by DECONTAM. Species accumulation curves of each sample reached an asymptote indicative of sufficient sampling depth to detect robust differences in community structure and composition (Suppl. material 2: Fig. S1). Following prokaryote removal, 9,841 ASVs representing algae, animals, fungi, plants, and protists were recovered from 3,562,254 sequences (Suppl. material 2: Table S1). However, data should be interpreted with caution given limitations such as a lack of extraction blanks and positive controls, as well as potential lab-related errors in estimating relative abundance (Fonseca 2018; Taberlet et al. 2018). We also note that batch effects in sequencing and analysis can affect interpretation of ASV data (Callahan et al. 2017). Although we did not exhaustively test for sequencing batch effects as this was beyond the scope of this exploratory, data-focused paper, differences among runs could have affected the results and should be accounted for in any future usage of these data. As ASVs retain all variable characteristics of the sequences recovered, including variation in length, counts of ASVs may represent an overestimate of the true number of underlying COI haplotypes, especially since sequences were truncated to different lengths for one batch of sequences.

Table 2.

COI sequence and ASV statistics of the Bronx River Estuary. Total or mean values across samples are reported and standard error is shown in parentheses.

Total samples 48
Sample Sites HP sediment (n = 9)
HP water (n = 8)
SVP sediment (n = 16)
SVP water (n = 15)
Total raw reads 11,623,231
Total reads, passed filter 7,653,541
Raw reads per sample (mean) 242,151 (± 11,768)
Reads per sample, passed filter (mean) 159,449 (± 11,768)
Percent reads passed filter 64.1%
Unique ASVs, pre-filter 27,567
Unique ASVs, contaminants removed 27,328
Total ASVs removed by DECONTAM 239

Variation by substrate, time, and environmental variable

We tested whether there were differences in eukaryotic community composition. There was no significant overall distinction among sites and substrates in phylogenetic diversity (Kruskal-Wallis p = 0.71; Fig. 2A) or observed diversity (Kruskal-Wallis p = 0.7; Suppl. material 2: Fig. S2). There were differences among sites in Shannon diversity (Kruskal-Wallis p = 0.007) and evenness (Kruskal-Wallis p = 0.045), with Hunts Point water showing higher Shannon diversity and evenness than sediments from the same site (Suppl. material 2: Fig. S2). The community turnover (i.e., beta diversity) of eDNA from water samples was significantly different from that of sediment (r2 = 0.07, P < 0.001, Fig. 2B). There was no significant differentiation in community composition among sampling years (r2 = 0.03, P = 0.057). As regards environmental measurements, at Soundview the average water temperature and pH were 21.1 °C (range 14.5 – 24.2 °C) and 6.9 (range 6.6 – 7.4), respectively. At Hunts Point the average water temperature and pH were 22.6 °C (range 17 – 25.8 °C) and 7.0 (range 6.7 – 7.7). Water temperature had a significant impact on COI community composition (F1,36 = 1.838, P = 0.001; Suppl. material 2: Fig. S3), but there was no significant impact of water pH on river sediment or water profiles (F1,36 = 0.959, P = 0.621).

Figure 2.

COI diversity comparison between sediment and water samples from Hunts Point (HP) Riverside and Soundview (SVP) Parks. A) Faith’s Phylogenetic Diversity. Result of a global Kruskal-Wallace significance test is shown at the top of the plot. Letters indicate no groupings were significantly different from one another based on pairwise significance tests (p <0.05). B) Principal Coordinates Analysis (PCoA) of Bray-Curtis distances. 95% confidence ellipses for each site + sample type combination were produced using the stat_ellipse function in ggplot2.

Comparing eukaryotic eDNA metabarcodes to known Bronx River biodiversity

The analysis detected a variety of common organisms (Fitzgerald 2013; Werner 2016; BRA 2022; iNaturalist 2022), as well as those of management concern, including invasive species (Smithsonian 2022) and potential Harmful Algal Blooms (USNOHAB 2022) (Table 3; Suppl. material 2: Table S1). Of the eukaryotic ASVs, 36.9% were classified to the phylum level or below and 6.2% were classified to species. The gaps in taxonomic resolution are likely linked to a dearth of database information on less studied organisms.

Table 3.

Eukaryotic species of special interest detected by COI from the Bronx River Estuary. C = Commonly observed; M = of Management Concern.

Class and Genus species Common name TYPE COI
Alosa pseudoharengus River Herring/Alewife M
Alosa estivalis River Herring M
Ameiurus nebulosus Brown bullhead C
Ameiurus spp. Bullhead catfish C
Anguilla rostrata American eel M
Brevoortia tyrannus Menhaden C
Fundulus heteroclitus Mummichog C
Fundulus majalis Striped Mummichog (or killifish) C
Gobiesox strumosus Skillet fish C
Lepomis spp. Sunfish C
Menidia menidia Atlantic silverside C
Morone americana White perch C
Morone saxatilis Striped bass C, M
Perca flavescens Yellow perch C
Botryllus schlosseri Golden star tunicate C
Molgula spp. Sea grape C
Perophora sagamiensis Sea squirt C
Branta canadensis Canada goose C
Egretta spp. Egrets, Herons C
Larus spp. Gulls C
Crassostrea virginica Eastern oyster M
Euglesa casertana Pea Clam
Geukensia demissa Ribbed mussel C
Macoma petalum Atlantic Macoma
Mercenaria mercenaria Hard or chowder clam C
Mulinia lateralis Dwarf surf clam C
Mya arenaria Soft-shell clam C
Mytilus edulis Blue mussel C
Nucula proxima Atlantic nut clam
Petricolaria pholadiformis False angelwing C
Cliona spp. Boring sponge C
Halichondria panicea Breadcrumb sponge C
Alexandrium spp. HAB (potential) M
Amphidinium carterae HAB (potential) M
Dinophysis sacculus HAB (potential) M
Gymnodinium spp. HAB (potential) M
Gyrodinium spp. HAB (potential) M
Heterocapsa rotundata HAB (potential) M
Heterocapsa triquetra HAB (potential) M
Heterocapsa spp. HAB (potential) M
Karlodinium sp. RS-24 HAB (potential) M
Margalefidinium polykrikoides HAB (potential) M
Corambe obscura Obscure Corambe
Crepidula fornicata Common slipper snail C
Ercolania fuscata Sea Slug
Tritia obsoleta (syn Ilyanassa obsoleta) Eastern mudsnail C
Urosalpinx cinerea Oyster drill C
Callinectes sapidus Blue crab C, M
Carcinus maenas Green crab C, M
Dyspanopeus sayi Mud crab C
Gammarus oceanicus Scud amphipod C
Grandidierella japonica Invasive amphipod M
Hemigrapsus sanguineus Asian shore crab C, M
Microdeutopus gryllotalpa Slender tube maker C
Pagurus longicarpus Long-clawed hermit crab C
Palaemonetes pugio Common shore shrimp C
Panopeus herbstii Black fingered mud crab C
Rhithropanopeus harrisii White fingered mud crab C
Homo sapiens Human C
Ondatra zibethicus Muskrat C
Rattus norvegicus Brown rat C
Limulus polyphemus Horsheshoe crab C, M
Alitta succinea (syn Nereis succinea) Clam worm C
Amphitrite ornata Ornate worm
Capitella teleta Thread worm C
Glycera americana Blood worm
Lycastopsis pontica Spring worm C
Platynereis dumerilii Dumeril’s clam worm C
Streblospio benedicti Ram's horn worm C

Sequences from the diatom phylum Bacillariophyta were the most commonly detected at most sites and in the dataset overall (Fig. 3; Suppl. material 2: Table S1). Several diatoms were identified including multiple species in the genera Chaetoceros, Lithodesmium, Melosira, Paralia, and Thalassiosira. For the second most abundant phylum (Annelida) the majority of ASVs mapped to classes Clitellata and Polychaeta. Within Clitellata, the following species were identified: Amphichaeta sannio, Baltidrilus costatus, Bothrioneurum vejdovskyanum, Limnodrilus hoffmeisteri, Monopylephorus rubroniveus, Nais elinguis, Octolasion cyaneum, Paranais litoralis, Tubificoides benedii, T. brownie, T. fraseri, and T. parapectinatus. Worms of Class Polychaeta identified to species were: Amphitrite ornate, Capitella teleta (common in the area), Glycera americana (American bloodworm), Glycinde multidens, Hypereteone heteropoda, Parasabella microphthalma, Polydora cornuta, and Streblospio benedicti (the common Ram’s horn worm).

Figure 3.

Community profiles of eukaryotic COI Amplicon Sequence Variants (ASVs) in sediment and water samples from Hunts Point Riverside and Soundview Parks. Shown in temporal order of collection at the level of phylum; bar heights indicate relative abundance of sequences from each taxon.

Further, several key organisms being restored and monitored in the Bronx River, as well as commonly observed species, were detected (Table 3; Suppl. material 2: Table S1). For example, sequences exhibiting a perfect match to American eels (Anguilla rostrata) were found, although these data also matched sequences assigned to the family level in NCBI. Other fish species whose presence is associated with healthy tidal areas, including mummichogs (Fundulus heteroclitus), Atlantic silversides (Menidia menidia), and menhaden (Brevoortia tyrannus), were detected. River herring (Alosa pseudoharengus and A. aestivalis), however, were not identified. Arthropod ASVs included a non-native belostomatid water bug (Appasus major), the invasive malacostracan Grandidierella japonica, and Limulus polyphemus, the Atlantic horseshoe crab. Various bivalves were present, including the eastern oyster, Crassostrea virginica, which has been the focus of targeted restoration efforts in New York City waterways, in addition to commonly observed blue (Mytilus edulis) and ribbed (Geukensia demissa) mussels, and soft-shell clams (Mya arenaria). Dinoflagellate taxa potentially linked to harmful algal blooms were also recovered including in the genus Heterocapsa. In conclusion, this COI Data Paper complements our prior 16S and 18S pilot work (Naro-Maciel et al. 2020; Ingala et al. 2021), and provides a baseline for future metabarcoding efforts to characterize urban estuarine biodiversity in the Bronx River, with applications for other areas.

Data availability

All amplicon gene sequences from this study are posted on the NCBI Sequence Read Archive (SRA>) under BioProject PRJNA606795. DNA extracts are stored at the American Museum of Natural History.

Conflicts of interest

The authors declare no competing interests.


We are grateful to the New York University (NYU) Research Challenge Fund and NYU Liberal Studies New Faculty Scholarship and Creative Production Awards (to ENM), and to private donors through (to IW) for funding the research. Site access was provided by NY/NJ Baykeeper and the New York City Department of Parks and Recreation (Natural Resources Group). Our special thanks to Michael Tessler for initial guidance, as well as student assistants Christian Bojorquez, NaVonna Turner, Sean Thomas, Jennifer Servis, Patrick Shea, Vanessa Van Deusen, and Seth Wollney. We are very thankful for two anonymous reviewers, Reviewer Lise Klunder, and our Subject Editor Florian Leese, whose helpful comments improved the manuscript.


  • Battaglia T (2018) btools: A suite of R function for all types of microbial diversity analyses. R package version 0.0.1.
  • Bik HM, Porazinska DL, Creer S, Caporaso JG, Knight R, Thomas WK (2012) Sequencing our way towards understanding global eukaryotic biodiversity. Trends in Ecology & Evolution 27(4): 233–243.
  • Bohmann K, Evans A, Gilbert MTP, Carvalho GR, Creer S, Knapp M, Yu DW, de Bruyn M (2014) Environmental DNA for wildlife biology and biodiversity monitoring. Trends in Ecology & Evolution 29(6): 358–367.
  • Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu Y-X, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson II MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37(8): 852–857.
  • Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Amy Jo A (2016) DADA2: High resolution sample inference from Illumina amplicon data. Nature Methods 13(7): 48–56.
  • Callahan BJ, McMurdie PJ, Holmes SP (2017) Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. The ISME Journal 11(12): 2639–2643.
  • Davis NM, Proctor D, Holmes SP, Relman DA, Callahan BJ (2018) Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6(1): 221499–221499.
  • Deiner K, Yamanaka H, Bernatchez L (2021) The future of biodiversity monitoring and conservation utilizing environmental DNA. Environmental DNA 3(1): 3–7.
  • Douglas I, James P (2015) Urban Ecology: An Introduction. Routledge, Taylor and Francis Group, New York, New York, 500 pp.
  • Dowd SE, Sun Y, Wolcott RD, Domingo A, Carroll JA (2008) Bacterial Tag-Encoded FLX Amplicon Pyrosequencing (bTEFAP) for Microbiome Studies: Bacterial Diversity in the Ileum of Newly Weaned Salmonella -Infected Pigs. Foodborne Pathogens and Disease 5(4): 459–472.
  • Fitzgerald AM (2013) The effects of chronic habitat degradation on the physiology and metal accumulation of eastern oysters (Crassostrea virginica) in the Hudson Raritan Estuary. PhD Thesis. Graduate Center, The City University of New York.
  • Fuss , O’Neill (2015) Citizen Science on the Bronx River: An Analysis of Water Quality Data. Bronx, New York, 57 pp.
  • Grizzle R, Ward K, Lodge J, Suszkowski D, Mosher-Smith K, Kalchmayr K, Malinowski P (2012) Oyster Restoration Research Project (ORRP) Technical Report. New York, New York.
  • Hebert PDN, Cywinska A, Ball SL, deWaard JR (2003) Biological identifications through DNA barcodes. Proceedings of the Royal Society of London. Series B, Biological Sciences 270(1512): 313–321.
  • Huson DH, Beier S, Flade I, Górska A, El-hadidi M (2016) MEGAN Community Edition – Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data. Computational Biology 12: e1004957.
  • Ingala MR, Werner IE, Fitzgerald AM, Naro-Maciel E (2021) 18S rRNA amplicon sequence data (V1–V3) of the Bronx river estuary, New York. Metabarcoding and Metagenomics 5: e69691.
  • Kassambara A, Kassambara MA (2020) Package ‘ggpubr.’ R package version 0.4.0.
  • Katoh K, Misawa K, Kuma K, Miyata T (2002) MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research 30(14): 3059–3066.
  • Kimmelman M (2012) Bronx River Now Flows by Parks. The New York Times.
  • Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, Boehm JT, Machida RJ (2013) A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Frontiers in Zoology 10(1): e34.
  • Naro-Maciel E, Ingala MR, Werner IE, Fitzgerald AM (2020) 16S rRNA Amplicon Sequencing of Urban Prokaryotic Communities in the South Bronx River Estuary. Microbiology Resource Announcements 9(22): e00182-20.
  • Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Peter Solymos M, Stevens HH, Szoecs E, Wagner H (2017) vegan. Community Ecology Package 2: 4–5.
  • Sanderson EW (2009) Mannahatta: A Natural History of New York City. Harry N. Abrams, New York, New York, 352 pp.
  • Werner I (2016) Assessing urban oyster restoration through classical and next-generation approaches. Master’s Thesis. College of Staten Island, The City University of New York.
  • Yu DW, Ji Y, Emerson BC, Wang X, Ye C, Yang C, Ding Z (2012) Biodiversity soup: Metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods in Ecology and Evolution 3(4): 613–623.

Supplementary materials

Supplementary material 1 

Supplementary Data Files 1, 2

Author: Brendan Reid, Melissa Ingala

Data type: QIIME AND R Scripts

Explanation note: Scripts used for metabarcoding analysis. Document 1: QIIME2 workflow; Document 2: R script.

This dataset is made available under the Open Database License ( The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (1.16 MB)
Supplementary material 2 

Tables S1,S2, Figures S1–S3

Author: Brendan Reid, Melissa Ingala

Data type: Figures and tables

Explanation note: Fig. S1. Sample-based species accumulation curves of COI Amplicon Sequence Variant (ASV) diversity by substrate type (sediment, water). Calculated using the VEGAN 2.4-3 package. Fig. S2. Eukaryotic alpha diversity comparison between sites and substrate types. Measured by COI for Observed ASVs, Shannon richness, and Pielou’s evenness. Results of a global Kruskal-Wallace significance test are shown at the top of each plot. Letters indicate groupings that were significantly different from one another based on pairwise significance tests (p <0.05). Fig. S3. Canonical Correspondence Analysis indicating the influence of water temperature and pH on eukaryotic community composition inferred by COI. Study sites (Hunts Point (HP) and Soundview (SVP) Parks) and substrates (sediment, water) are shown as different shapes, and arrow lengths indicate the strength and direction of the influence. Table S1. Taxonomic Assignment including COI ASV identification to Domain, Kingdom, Phylum, Class, Order, Family, Genus, and/or Species. ASVs identified as putative contaminants are included at the end of the table. Table S2. Taxonomic Assignment Totals of COI ASVs (number and percentage) identified to Domain, Kingdom, Phylum, Class, Order, Family, Genus, and/or Species.

This dataset is made available under the Open Database License ( The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (6.76 kb)
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