DOI: 10.5061/dryad.pnvx0k6tf
title: Environmental DNA metabarcoding differentiates between micro-habitats within the rocky intertidal

description: While the utility of environmental DNA (eDNA) metabarcoding surveys for
 biodiversity monitoring continues to be demonstrated, the spatial and
 temporal variability of eDNA, and thus the limits of the differentiability
 of an eDNA signal, remains under-characterized. In this study, we
 collected eDNA samples from distinct micro-habitats (~40 m apart) in a
 rocky intertidal ecosystem over their exposure period in a tidal cycle.
 During this period, the micro-habitats transitioned from being
 interconnected, to physically isolated, to interconnected again. Using a
 well-established eukaryotic (cytochrome oxidase subunit I) metabarcoding
 assay, we detected 415 species across 28 phyla. Across a variety of
 univariate and multivariate analyses, using exclusively taxonomically
 assigned data as well as all detected amplicon sequence variants (ASVs),
 we identified unique eDNA signals from the different micro-habitats
 sampled. This difference paralleled expected ecological gradients and
 increased as the sites became more physically disconnected. Our results
 demonstrate that eDNA biomonitoring can differentiate micro-habitats in
 the rocky intertidal only 40 m apart, that these differences reflect known
 ecology in the area, and that physical connectivity informs the degree of
 differentiation possible. These findings showcase the potential power of
 eDNA biomonitoring to increase the spatial and temporal resolution of
 marine biodiversity data, aiding research, conservation, and management
 efforts.
description: <em>1.1. 
 </em><em>Reproducibility</em> To
 improve reproducibility (e.g. Dickie et al., 2018; Shea et al., 2023),
 enable open data science (e.g. Fredston & Lowndes, 2024), and aid
 in the initiation of new eDNA biomonitoring projects, we have published
 detailed, step-by-step protocols for many of the methods described,
 including specific materials used, photographs, and additional
 methodological notes not possible to include here. See Shea and Boehm for
 sample collection and filtering (2023b), for DNA extractions (2023c), for
 PCR amplification (2023d), and shipping samples (2023e). Additionally, we
 have published all data and code for replicating our analyses via Dryad,
 including FASTQ files and eDNA datasets (pre-processing &
 post-processing). Through Dryad and Zenodo, our modified Anacapa Container
 and scripts for bioinformatics (Shea & Boehm, 2023a), as well as a
 GitHub repository including an R Markdown file that reproduces all methods
 & results (Shea & Boehm, 2023f) can also be
 accessed. <em>1.2.  </em><em>Sampling
 </em><em>Area</em> To better
 understand spatial and temporal differences in eDNA signals in a complex
 coastal environment, we sought a rocky intertidal field location that had
 consistent, large, accessible tide pools that were fully isolated from one
 another at some low tides but interconnected during other parts of their
 exposure period. We selected the intertidal at Pillar Point, a headlands
 promontory to the west of Pillar Point Harbor in San Mateo County,
 California, USA. Pillar Point is a popular recreational intertidal area
 that is directly adjacent to the Pillar Point State Marine Conservation
 Area (no specific use scientific collection permit required).
 Within Pillar Point, we sampled three discrete locations: two
 individual tide pools with a range of physical connectivity (Tide Pool 1,
 S1: 37.495306°, -122.498744°; Tide Pool 2, S2: 37.494992°, -122.498955°)
 and an equidistant location (Nearshore, N: 37.495288°, -122.499198°) where
 there was well-mixed offshore water for the duration of the tidal cycle.
 Each site was approximately 40 meters from all other sites. Tide Pool 1
 and Tide Pool 2 are fully isolated at tidal heights of around 0 m (mean
 low low water, MLLW) or lower, and substantially connected at around 0.25
 m or higher. On the day we sampled, this meant water actively flowed
 between the locations at the start (11:30 PST) and end (17:00 PST) of the
 sampling period, but the sites were disconnected at low tide in the middle
 of the sampling period. Ecologically, Tide Pool 1 was located closer to
 shore and interior to a channel that divides the Pillar Point intertidal,
 characteristic of the high to middle intertidal. Tide Pool 2 was located
 across the channel and further from shore, characteristic of the low
 intertidal. <em>1.3.  </em><em>Sample
 Collection & Filtration</em> We collected
 1 L surface samples from each site every 30 minutes for the duration of
 time the rocky intertidal was exposed on 28 January 2022, using single-use
 enteral feeding pouches (Covidien, Dublin, Ireland). The sampling volume
 used, 1 L, is sufficient for detecting a representative range of marine
 organisms in nearshore locations (Gold et al., 2022) and is commonly used
 in aquatic eDNA studies (Takahashi et al., 2023). Sampling commenced at
 11:30 PST. At each site, samples were collected from a consistent position
 across time points. Following the approach used by Gold, Sprague, et al.
 (2021), we attached a sterile 0.22 μm pore size Sterivex cartridge
 (MilliporeSigma, Burlington, MA, USA) to the tubing of each feeding pouch,
 allowing samples to be immediately gravity filtered in the field. While
 gravity filtering (1-2 hours per sample), samples were shaded with an
 awning to prevent any degradation by sunlight(Andruszkiewicz et al.,
 2017). One sample fell during gravity filtration, resulting in a missing
 sample from S1 at 16:00 PST. At three time points at
 the beginning and end of the sampling period as well as at low tide (at
 14:00 PST), we collected triplicate 1 L samples from each location as
 biological replicates. At the beginning and end of the sampling period, we
 also filtered 1 L MilliQ water via the procedure described above to serve
 as negative field controls. Additionally, using an Orion Model 1230 meter
 (Orion Research Inc., Beverly, MA, USA), we recorded temperature and
 salinity in each location directly after samples were collected.
 Once finished filtering, Sterivex cartridges were dried by
 pushing air through them using a sterile 3 mL syringe, capped, and placed
 in sterile Whirl-Pak bags (Whirl-Pak, Madison, WI, USA). Then, samples
 were stored in a cooler on ice until transported back to the laboratory at
 the end of the sampling period. Samples were transferred to a -20°C
 freezer for up to 18 days, at which time they were processed to extract
 nucleic acids from the captured materials. This sampling scheme resulted
 in 53 field samples. <em>1.4. 
 </em><em>DNA Extraction & Library
 Preparation</em> Within 18 days of collection, we
 extracted and purified DNA from the Sterivex cartridge using the DNeasy
 Blood and Tissue Kit (Qiagen, Germantown, MD, USA) and the modifications
 described in Spens et al. (2017). In short, we incubated the filter
 cartridge overnight with proteinase K and ATL. Then, we extracted the
 liquid from the cartridge with a syringe and mixed it with equal volumes
 of AL buffer and 0°C ethanol before proceeding with the manufacturer’s
 extraction protocol. One negative extraction control (DNA-grade water in
 place of a sample) was included in each of the four batches of
 extractions. Nucleic acids were stored at -20°C for up to 6 months. DNA
 extractions were conducted at a lab bench distant from all subsequent
 handling of PCR products.  We PCR amplified the
 extracted DNA in triplicate within 6 months of storage, using the
 mlCOIintF/ jgHCO2198 primer set targeting a 313 bp fragment of the
 mitochondrial COI region optimized by Leray et al. (2013) (Forward:
 GGWACWGGWTGAACWGTWTAYCCYCC; Reverse: TAIACYTCIGGRTGICCRAARAAYCA) with
 Nextera modifications. Following Curd et al. (2019), we used a 25 μl PCR
 reaction mixture consisting of 12.5 μl of Qiagen Multiplex Mix (Qiagen,
 Germantown, MD, USA), 2.5 μl each of the forward and reverse primers at 2
 μM (Integrated DNA Technologies, Inc., Coralville, IA, USA), 6.5 μl of
 PCR-grade water, and 1 μl of undiluted DNA template. The PCR thermocycling
 touchdown profile began with an initial denaturation at 95° for 15 minutes
 to activate the DNA polymerase, followed by 13 cycles of denaturation (94°
 for 30 seconds), annealing (starting at 69.5° for 30 seconds, with the
 temperature decreased by 1.5° each cycle), and extension (72° for 1
 minute). Then, an additional 35 cycles were run with the same denaturation
 and extension steps as above with an annealing temperature of 50°,
 followed by a final extension at 72° for 10 minutes. PCR reactions were
 prepared in a designated DNA-free hood until the template was
 added. PCR amplification was conducted in two batches;
 in each batch, we included one no-template negative PCR control (DNA-grade
 water used as a template). Additionally, we extracted and purified DNA
 from tissue from five organisms across a range of phyla we expected to
 amplify with the mlCOIintF/ jgHCO2198 primers, but not expected to be
 present at Pillar Point in particular (<em>Mytilus edulis,
 Mizuhopecten yessoensis, Xiphias gladius, Mercenaria mercenaria, Lutjanus
 campechanus</em>) using the standard tissue extraction protocol
 detailed in the DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD, USA).
 These tissues were obtained from a local grocery store and it was assumed
 that they were labeled correctly, although previous work has indicated
 mislabeling in seafood stores can occur (e.g. Willette et al., 2017).
 Extracts from the 5 tissue samples were combined in equimolar amounts to
 form a mock community used as a positive PCR control in each batch.
 Triplicate PCR amplicons from both samples and controls were not
 subsequently pooled but were carried through the remaining library
 preparation and sequencing steps as technical replicates. We
 electrophoresed and visualized a subset of PCR products on a 1.5% agarose
 gel stained with GelRed® (Biotium, Fremont, CA, USA) to ensure successful
 amplification and correct product sizes as well as lack of
 contamination. Post-PCR library preparation and
 sequencing were conducted at the Georgia Genomics and Bioinformatics Core
 (GGBC, UG Athens, GA, RRID: SCR_010994). In short, provided PCR amplicons
 were cleaned using AMPure XP magnetic beads (Beckman Coulter,
 Indianapolis, IN, USA), barcoded with Nextera adapters (Illumina, San
 Diego, CA, USA) during a second PCR (3 min at 95 °C; 15 cycles of 30 sec
 at 95 °C, 30 sec at 67 °C, and 30 sec at 72 °C; and 4 min at 72 °C),
 cleaned again using AMPureXP magnetic beads, and pooled in equimolar
 ratio. The resulting library was sequenced on a MiSeq PE 2x250bp (500
 cycles) using Reagent Kit V2 with 25% PhiX spike-in (Illumina, San Diego,
 CA, USA). Given our technical replication of samples and controls, our
 final library included 6 negative field controls (1 at beginning and end
 of field sampling, amplified in triplicate), 12 negative extraction
 controls (1 in each of 4 extraction sets, amplified in triplicate), 2
 positive PCR controls (1 in each of 2 amplification batches), 2
 no-template negative PCR controls (1 in each of 2 amplification batches),
 and 159 samples (53 field samples, amplified in
 triplicate). <strong>FASTQ files are in this Dryad repository as
 FASTQ_Files.zip and are also available through the NCBI SRA (Accession:
 PRJNA1083727).</strong> <em>1.5. 
 </em><em>Bioinformatics</em> We
 processed sequencing data using the <em>Anacapa Toolkit</em>,
 which contains two core modules: one for quality control and ASV parsing,
 and one for classifying taxonomy (Curd et al., 2019). Briefly, we ran the
 first module using default parameters, which uses <em>cutadapt
 </em>(version 1.16) (Martin, 2011)<em> </em>for adapter
 and primer trimming, <em>FastX-Toolkit </em>(version: 0.0.13)
 (Gordon & Hannon, 2010) for quality trimming, and <em>dada2
 </em>(version 1.6) (Callahan et al., 2016) for assigning ASVs. For
 the second module, we utilized the MIDORI2 reference database, a
 quality-controlled and updated database built from GenBank release 253 (20
 December 2022) that has been technically validated (Leray et al., 2022).
 Following Gold et al. (2022), we adjusted the identity and query coverage
 to 95% (default: 80%) to account for the relative incompleteness of the
 broad COI reference database compared to more taxonomically-specific
 databases (Curd et al., 2019). The second module relies on
 <em>Bowtie 2</em> (version 2.3.5) (Langmead &
 Salzberg, 2012) and a modified instance of<em> BLCA</em> (Gao
 et al., 2017) as dependencies. <strong>The Anacapa Toolkit output is
 in this Dryad repository as Anacapa_Toolkit_Output.zip.</strong>
 Following Gold, Curd, et al. (2021) we only kept taxonomic assignments
 that had a bootstrap confidence cutoff score of 60 or higher in BLCA, to
 avoid spurious assignments from the incomplete reference database. We
 modified the Anacapa Container (Ogden, 2018), a Singularity container with
 all the needed dependencies for executing the Anacapa Toolkit, to enable
 the pipeline to be run in a high-performance computing environment
 requiring two-step authentication; the updated container, scripts, and
 reference database with the required <em>Bowtie 2</em> index
 library needed to reproduce our bioinformatics process are archived on
 Zenodo (Shea & Boehm, 2023a). Raw ASVs,
 taxonomy assignments, and sample information were converted into
 interchangeable <em>ampvis2</em> (version 2.8.6) (Andersen et
 al., 2018) and <em>phyloseq</em> (version 1.46.0) (McMurdie
 & Holmes, 2013) objects in R (version 4.3.1) to facilitate
 decontamination and further analyses. Singletons were removed using
 <em>ampvis2</em>, and samples were further decontaminated
 using <em>phyloseq</em> by removing all ASVs that appeared in
 any negative field control, negative extraction control, or no-template
 negative PCR control, a choice made due to the very low number of
 overlapping ASVs between samples and negative controls (Table S1). Samples
 were rarified to the minimum number of reads of any sample using
 <em>ampvis2</em>. <strong>This processed eDNA dataset is
 in this Dryad repository as Processed_eDNA_Data.zip. </strong>None
 of these decontamination steps changed the interpretation of subsequent
 analyses, as verified by replicating all analyses with datasets
 representing all 8 combinations of the presence/absence of the three
 decontamination and processing steps. To ensure the
 accuracy of taxonomic assignments, we analyzed occurrence data from the
 Global Biodiversity Information Facility to investigate whether identified
 species had occurrence records in the California Current System and known
 ranges that encompassed Pillar Point, using the <em>spocc</em>
 (version 1.2.2) (S. Chamberlain, 2021) package. A phylogenetic tree based
 on taxonomic assignments was created using the <em>taxize
 </em>(version 0.9.100) (S. A. Chamberlain & Szöcs, 2013) and
 <em>ggtreeExtra </em>(version 1.12.0) (Xu et al., 2021)
 packages.<em> </em> <em>1.6. 
 </em><em>Data Analysis</em> To
 investigate whether eDNA signals could be distinguished by location, we
 first analyzed individual-level differences between locations; that is,
 whether ASVs or individual taxa (agglomerated species-level taxonomic
 assignments) were unique to, or associated with particular locations. We
 calculated and visualized unique ASVs and taxa using the
 <em>eulerr</em> (version 7.0.0) (Larsson, 2022) package.
 However, with metabarcoding data in particular, taxa or ASVs that are
 unique to a given location are not necessarily ecologically meaningful;
 they could include rare taxa present elsewhere but not amplified and
 exclude taxa that are well correlated with particular locations but
 sometimes detected at others. Thus, we also analyzed ASVs and taxa using
 an indicator species framework (Dufrêne & Legendre, 1997). In this
 framework, the null hypothesis is that the frequency of taxon or ASV
 presence in samples from a particular location is not higher than the
 frequency of that taxon or ASV presence in samples from other locations.
 For each location, we identified all statistically-significant indicator
 taxa and ASVs with indicator value indices above 0.7—that is, indicators
 that are well-associated with a site group even if they are detected in
 samples from other sites—based on presence-absence data per sample using
 the<em> indicspecies</em> package (version 1.7.14) (Cáceres
 & Legendre, 2009). We followed our
 individual-level analyses with approaches to test whether community
 composition varied by location. We calculated a Jaccard dissimilarity
 matrix across all samples using <em>vegan</em> (version 2.6.4)
 (Oksanen et al., 2022). Then, we tested for differences in community
 composition among locations using a permutational multivariate analysis of
 variance (PERMANOVA) with the model eDNA Presence ~ Location + Time +
 Biological Replicates using the <em>adonis</em> function in
 <em>vegan</em>. We confirmed that locational differences found
 via PERMANOVA were not a result of differences in dispersions by testing
 for homogeneity of dispersions using the <em>betadisper</em>
 function in <em>vegan</em>. We also visualized Jaccard
 dissimilarity using non-metric multidimensional scaling (NMDS) using the
 <em>metaMDS</em> function in <em>vegan</em>. We
 coupled these analyses with a partitioning among medoids algorithm
 (Kaufman & Rousseeuw, 1990) implemented with the
 <em>pam</em> function in the <em>cluster</em>
 package (version 2.1.6) (Maechler et al., 2022). Rather than assuming
 location clusters a priori, we validated the optimal number of clusters
 for a given dataset by finding the number of clusters (k) that maximized
 the average silhouette width, a measure of how well-structured the
 clusters are. Finally, to better understand how the difference between
 locations varied over the time sampled, for each time point, we calculated
 the Jaccard dissimilarity between each unique combination of replicates
 within each site, and then the pairwise Jaccard dissimilarity between each
 unique combination of samples across the three pairs of sites: S1-N, S2-N,
 and S1-S2. To further investigate whether differences
 in eDNA detections corresponded with underlying ecological gradients, we
 compared the unique and indicator taxa identified at each site to their
 ecological zonation in a highly regarded field guide to the Pacific
 intertidal, <em>Between Pacific Tides</em> (Ricketts et al.,
 1985). To account for potential variations in taxonomic names between
 <em>Between Pacific Tides</em> and the MIDORI2 reference
 database, we used the World Register of Marine Species (WoRMS) to identify
 all synonymized names for each unique and indicator taxon identified using
 the <em>taxize </em>(version 0.9.100) (S. A. Chamberlain
 & Szöcs, 2013) package. Then, we searched <em>Between
 Pacific Tides</em> for all synonyms, both manually using the index
 and in R by extracting the text from a PDF of <em>Between Pacific
 Tides</em> using the <em>pdftools</em> package (version
 3.4.0) (Ooms, 2023). We compared the proportion of Zone
 1 (uppermost horizon), Zone 2 (high intertidal), and Zone 3 (middle
 intertidal) species were identified across each location using a
 chi-squared test with p-values computed via Monte Carlo
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description: # Environmental DNA metabarcoding differentiates between micro-habitats
 within the rocky intertidal This repository includes the necessary data to
 process marine eDNA metabarcoding samples collected from Pillar Point,
 Half Moon Bay, CA, USA on 28 January 2022. ## Description of Data, Code,
 & File Structure ### FASTQ\_Files.zip This archive contains compressed
 FASTQ sequencing files generated from an Illumina MiSeq PE 2x250bp (500
 cycles) sequencing run. More details on sequencing methods can be found in
 the "Methods" section on Dryad and the manuscript. The zip file
 also contains FASTA files of the forward and reverse COI primers used.
 These FASTA and FASTQ files are formatted for use with the modified
 Anacapa Container described below. ### Anacapa\_Toolkit\_Output.zip This
 archive contains the unprocessed output from a run of the modified Anacapa
 Container on 31 January 2023 on the FASTQ files contained in
 FASTQ_Files.zip. In the archive are three folders: #### CO1 This folder
 contains the primary Anacapa Container output. The top level contains two
 summary .txt files. * CO1_ASV_taxonomy_brief.txt: The rows are each ASV
 generated through the Anacapa Container, and columns are each eDNA sample.
 The numbers reflect the number of sequence reads of a given ASV in a given
 sample. The final columns contain the taxonomic classification of the
 given ASV, if any, the confidence level for each taxonomic assignment, and
 accession numbers. * CO1_ASV_taxonomy_detailed.txt: This contains the same
 information as CO1_ASV_taxonomy_brief.txt, but has some additional
 columns. "sequence", "sequencesF", and
 "sequencesR" include the sequence information for the given ASV.
 Several additional columns are added by the Anacapa Container
 automatically, but are not used for any subsequent analysis or
 interpretation, including: "forward_CO1_seq_number",
 "merged_CO1_seq_number", "reverse_CO1_seq_number",
 "single_or_multiple_hit", "end_to_end_or_local",
 "max_percent_id", and "input_sequence_length" Then,
 there is a "Summary_by_percent_confidence" folder generated that
 has inner folders (40, 50, 60, 70, 80, 90, 95, 100) for identifying only
 those taxonomic assignments from the Anacapa Container output that had a
 bootstrap confidence cutoff score of FOLDER-NAME or higher in BLCA. Each
 folder contains two .txt files, in identifical formats across folders. *
 CO1_ASV_raw_taxonomy_FOLDER-NAME.txt: The rows are each ASV generated
 through the Anacapa Container, and columns are each eDNA sample. The
 numbers reflect the number of sequence reads of a given ASV in a given
 sample. The final columns contain the taxonomic classification of the
 given ASV, if any, the confidence level for each taxonomic assignment, and
 accession numbers. This is an identical format to
 CO1_ASV_taxonomy_brief.txt above. *
 CO1_ASV_sum_by_taxonomy_FOLDER-NAME.txt: The rows are each unique
 taxonomic assignment, summed across all ASVs from
 CO1_ASV_raw_taxonomy_FOLDER-NAME.txt with the same assignment, and columns
 are each eDNA sample. The numbers reflect the number of sequence reads of
 a given taxonomic assignment in a given sample. #### Metadata-Added This
 folder contains four .txt files that include the metadata that we compiled
 to integrate with the Anacapa Container output. Most of the information
 contained in these files gets synthesized into the SampleData.csv file in
 the Processed-eDNA_Data.zip archive below. *
 PillarPoint_Sample_Info_EnviroData.txt: contains the "T"
 (temperature; Degrees Celsius) and "S" (salinity; ppt) measured
 at each time (PST) and location combination with an Orion Model 1230 meter
 (Orion Research Inc., Beverly, MA, USA) *
 PillarPoint_SampleInfo_FieldSampling.txt: contains the sample number for
 each sample, its corresponding field time (PST) and location, and the
 extraction group in which it was processed in the laboratory *
 PillarPoint_SampleInfo_Location.txt: contains the GPS coordinates for the
 three sampling locations * PillarPoint_SampleInfo_Sequencing.txt: contains
 the sample name, several descriptive portions of the sample name, whether
 it is a "sample" or "control", and the PCR plate in
 which it was processed in the laboratory #### Run\_info This folder
 contains a variety of scripts, outputs, and logs generated automatically
 during a run of the modified Anacapa Container. These will likely only be
 relevant to users who are trying to run the modified Anacapa Container
 (see below) on their own, and need to troubleshoot why their output is not
 identical to our output. More details on these files, and the Anacapa
 Toolkit pipeline, can be found on its original GitHub page. ###
 Processed\_eDNA\_Data.zip This archive contains processed versions of the
 ASVs, taxonomy assignments, and sample information from the Anacapa
 Toolkit Output. More details on processing can be found in the
 "Methods" section on Dryad and in the manuscript, but in short,
 we used only taxonomic assignments from the Anacapa Toolkit output (above)
 that had a bootstrap confidence cutoff score of 60 or higher in BLCA.
 Then, we removed singletons; we removed all ASVs that appeared in any
 negative field control, negative extraction control, or no-template
 negative PCR control; and we rarefied to the minimum number of reads of
 any sample. These processing steps can also all be found and reproduced in
 PillarPoint.Rmd in the Intertidal-eDNA GitHub Repo. The processed eDNA
 dataset represents the core dataset analyzed in the manuscript. In the
 archive are three .csv files: ASVTable.csv, SampleData.csv, and
 TaxTable.csv. #### ASVTable.csv Rows are each ASV generated through the
 Anacapa Toolkit pipeline, and columns are each eDNA sample. The numbers
 reflect the number of sequence reads of a given ASV in a given sample.
 #### SampleData.csv This file contains 12 columns of additional context
 for the eDNA samples listed in ASVTable.csv: * "Sample" &
 "X": contain the same eDNA sample names from ASVTable.csv *
 "Sample_Name" & "Dereplicated_Sample_Name":
 extract descriptive portions of the full sample name that are useful for
 differentiating samples * "Type": describes whether a given row
 represents a sample or a control * "PCR": denotes whether a
 sample was processed in the first or second PCR plate during laboratory
 work * "Time": sample collection time (PST) *
 "Location": sample collection location (S1, S2 or N; context for
 these locations in section 1.2 of Dryad Methods or in manuscript) *
 "Extraction": denotes the group in which this sample was
 extracted during laboratory work * "T" & "S": the
 temperature (Degrees Celsius) and salinity (ppt) measured at the time the
 sample was collected with an Orion Model 1230 meter (Orion Research Inc.,
 Beverly, MA, USA) * "SiteByTime": a composite of
 "Location" and "Time" columns used to differentiate
 samples in some analyses #### TaxTable.csv Rows are each ASV generated
 through the Anacapa Toolkit pipeline (same as ASVTable.csv), and columns
 are the taxonomic information assigned to that ASV, if any, through the
 Anacapa Toolkit pipeline. ## Related Works: Software, Code, and Data ###
 Anacapa Container (modified) The modified Anacapa Container, published to
 Zenodo and found in the "Related Works" on Dryad, is the
 workflow used to process FASTQ_Files.zip. The output from this workflow is
 Anacapa_Toolkit_Output.zip. More details on the Anacapa Container and how
 it works can be found on its Zenodo page. ### Intertidal-eDNA GitHub
 Repository The Intertidal-eDNA GitHub repository, published to Zenodo and
 found in the "Related Works" on Dryad, contains all of the data
 and code needed to reproduce the analyses in the manuscript, starting from
 Anacapa_Toolkit_Output.zip (which is already included in the repo). Using
 PillarPoint.Rmd, you can generate the processed eDNA dataset saved here as
 Processed_eDNA_Data.zip and use that dataset to reproduce all of the
 analyses, as well as most of the figures, in the manuscript. More details
 on the Intertidal-eDNA GitHub repository can be found on its Zenodo page.
 ### NCBI SRA Sequence Data Submission The FASTQ files in FASTQ_Files.zip
 have also been submitted to the NCBI Sequence Read Archive; the BioProject
 containing all of these files can be found with accession: PRJNA1083727.
 #### GBIF and OBIS Biodiversity Data Submission Biodiversity data
 generated from this project has also been submitted to the Global
 Biodiversity Information Facility (GBIF) and the Ocean Biodiversity
 Information System (OBIS). It is important to note that the biodiversity
 information submitted to GBIF/OBIS differs slightly from the
 Processed_eDNA_Data.zip files above, since best practices for submitting
 DNA-derived data to GBIF recommend not rarefying to the minimum number of
 reads of any sample and not including an controls. The exact data
 submitted to GBIF/OBIS can be produced through the Intertidal-eDNA GithUb
 repository published to Zenodo.