2022 Vertebrate eDNA

CalCOFI October 2022 Vertebrate eDNA

calcofi_2022-ednaCalCOFI CC BY 4.0 doi:10.15468/n52j6r v2026.10.05validated

years: 2022
observations: 148
sampling events: 47
stations: 13
species: 12
depth: 5–380 m

observations per year, 2022–2022

13 CalCOFI stations sampled, sized by observations, over the survey grid.

eDNA metabarcoding survey of vertebrates from water samples collected during the October 2022 CalCOFI cruise. Water filters from CalCOFI/GEMCAP stations off Southern California were run through one or two molecular assays: a mitochondrial D-loop assay (cetacean detections, incl. Delphinus and Megaptera novaeangliae) and a 12S rRNA “MiFish” assay (fish detections). Published as an intercalibration exercise (source datasetID “CalCOFI_Intercal”) on GBIF/OBIS, linked to Patin et al. 2026 (Methods in Ecology and Evolution, https://doi.org/10.1111/2041-210x.70370). Sequence read counts are a semi-quantitative signal of PCR-amplified DNA, not abundance, and are not comparable between assays. Published as detections only: edna_presence = 1 and the read counts. The source archive holds positive detections only, so the absence of a row for a filter and taxon is not a non-detection for this dataset (a filter or assay run with no detection is simply missing, and absence cannot be inferred); this differs from sio_cetacean-edna, where 0 means not detected in a sequenced sample.

Principal investigators: Nastassia Patin
Creators: Nastassia Patin (Scripps Institution of Oceanography, UC San Diego) · Garret O'Donnell (Scripps - California Cooperative Oceanic Fisheries Investigations)
Contact: nvpatin@ucsd.edu
Funding: ONR MURI N00014-22-1-2719; NSF OCE-2224726
Keywords: eDNAmetabarcodingdolphinsrare targets

Open with the provider

Q10 citation_main — open

source ↗ingest notebook ↗record JSON JSON-LD

Coverage

15 variables

obs_bio profile · one value per taxon at a sampling event · 148 values · 2022 · 3 variables

edna_presence dimensionless 74 edna_reads_12s reads 23 edna_reads_dloop reads 51

sample_measurement per cast · one value for the whole cast or tow, at no depth · 482 values on 47 sampling events · 2022 · 12 variables

ammonia umol/L 47 nitrate umol/L 47 nitrite umol/L 47oxygen_mg_l mg/L 47phosphate umol/L 47 silicate umol/L 47 chl_fluor ug/L 46dna_concentration ng/uL 46edna_reads_filtered_dloop reads 37edna_reads_raw_dloop reads 37edna_reads_filtered_12s reads 17edna_reads_raw_12s reads 17

Species and taxa

Delphinus delphis Short-Beaked Common Dolphin 56
Delphinus common dolphins 34
Engraulis mordax Northern anchovy 10
Leuroglossus 6
Cololabis saira Pacific saury 6
Coryphaena hippurus Common dolphinfish 4
Megaptera novaeangliae Humpback Whale 4
Tursiops truncatus Bottlenose Dolphin 4
Merluccius productus Pacific hake or whiting 4
Scomber 2
Thunnus 2
Orcinus orca Killer Whale 2
Physeter macrocephalus Sperm Whale 2
Haemulon 2
Sardinops 2
Paranthias 2
Symbolophorus californiensis California lanternfish 2
Balistes polylepis Finescale triggerfish 2
Citharichthys sordidus Pacific sanddab 2

Access

Every endpoint this dataset can be reached through, grouped by how you would use it. Each name is the link; the copy button beside it copies the address. Everything is in the release record and was answered when the release was cut.

Apps that read this dataset from the release. The icons after a name are the app’s lenses — the spatial grain it shows the data at.

CalCOFI ExplorerAcross datasetsopens on this dataset

Station ExplorerAcross datasetsopens on this dataset

Hexagon ExplorerAcross datasetsopens on this dataset

Cruise ExplorerAcross datasetsopens on this dataset

Every way to have the bytes, by source. Nothing here asks you to register first.

Tables from the release (Parquet)

The release’s own tables, as the parquet objects it is frozen from — the same bytes every app and package below reads. A table this dataset shares with others holds every dataset’s rows, so filter on dataset_key; a partition holds only this dataset’s. since is the release whose rows these are: an unchanged table keeps its object.

table holds rows size since objectobs The occurrence-headline long table: one scalar measurement per row, environmental (`realm = 'env'`) and biological (`realm = 'bio'`) together. this dataset’s rows 5.7 KB v2026.10.05 d6336393… parquet ↗ sample_measurement event-level effort and conditions — volume filtered, tow depth, wind whole table, every dataset 2.4 MB v2026.10.05 4ec85c94… parquet ↗

From the provider

The dataset as its provider publishes it, before CalCOFI ingested it.

sourceexternalOther

The same release, from a script or a browser SQL shell.

Packages

The whole release, pinned to a version, with the citation one call away.

R · calcofi4r

con <- calcofi4r::cc_get_db()
calcofi4r::cc_cite("calcofi_2022-edna")

Python · calcofi4py

con = calcofi4py.cc_get_db()
calcofi4py.cc_cite("calcofi_2022-edna")

DuckDB, anywhere

No CalCOFI package needed: each table above is a plain parquet object, readable by any DuckDB (or Arrow, pandas, Spark) from its URL. The path carries a content hash — a table whose rows did not change between releases keeps the same object, so nothing unchanged is stored or downloaded twice. Swap in any table above; one shared with other datasets needs WHERE dataset_key = 'calcofi_2022-edna'. Every object of every release is listed in db-schema.

obs · this dataset’s rows

SELECT *
FROM read_parquet('https://storage.googleapis.com/calcofi-db/ducklake/tables/obs/dataset_key=calcofi_2022-edna/f4bd530899502d593fd4a585/data_0.parquet')
LIMIT 100;

sample_measurement · whole table, every dataset

SELECT *
FROM read_parquet('https://storage.googleapis.com/calcofi-db/ducklake/tables/sample_measurement/6f9bbadd02916a95528c69d4/sample_measurement.parquet')
WHERE dataset_key = 'calcofi_2022-edna'
LIMIT 100;

db-query, in the browser

__TBL:obs__ is db-query’s name for the pinned release’s obs object — the hashed path above, resolved for you — so the same SQL keeps working when a release changes the object.

db-query — the SQL shell, prefilledDuckDB-WASM in your browser; nothing downloaded until a query asks for it

-- calcofi_2022-edna in the CalCOFI release v2026.10.05
SELECT *
FROM __TBL:obs__
WHERE dataset_key = 'calcofi_2022-edna'
LIMIT 100;

Everything here is open — nothing on calcofi.io asks you to register first. If this dataset ends up in something you build or publish, register your use so it can be credited and linked back; to hear when a release changes it, stay informed. Questions: data@calcofi.io.

Cite

This dataset's own citation. What to cite, and how, is a chapter of the docs book.

Patin N, ODonnell G (2026). CalCOFI October 2022 Vertebrate eDNA. Version 1.2. Occurrence dataset. https://doi.org/10.15468/n52j6r

License: CC-BY-4.0
DOI: https://doi.org/10.15468/n52j6r
Acknowledgement: This work was made possible by the Office of Naval Research Multidisciplinary University Research Initiative (MURI) funding for MMARINeDNA (award no.: N00014-22-1-2719) with additional support from the National Science Foundation (award no.: OCE-2224726). It includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health SIG grant (no.: S10 OD026929). We thank the captain and crew of the NOAA Ship Reuben Lasker, Rachel Pound from CalCOFI and Harrison Huang and Dylan Inskeep from the California Department of Fish and Wildlife for their assistance with sample collection.

BibTeX

@misc{calcofi_2022-edna,
  title        = {CalCOFI October 2022 Vertebrate eDNA},
  howpublished = {Patin N, ODonnell G (2026). CalCOFI October 2022 Vertebrate eDNA. Version 1.2. Occurrence dataset. https://doi.org/10.15468/n52j6r
},
  year         = {2026},
  doi          = {10.15468/n52j6r},
  url          = {https://doi.org/10.15468/n52j6r},
  note         = {License: CC-BY-4.0; Acknowledgement: This work was made possible by the Office of Naval Research Multidisciplinary University Research Initiative (MURI) funding for MMARINeDNA (award no.: N00014-22-1-2719) with additional support from the National Science Foundation (award no.: OCE-2224726). It includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health SIG grant (no.: S10 OD026929). We thank the captain and crew of the NOAA Ship Reuben Lasker, Rachel Pound from CalCOFI and Harrison Huang and Dylan Inskeep from the California Department of Fish and Wildlife for their assistance with sample collection.
}
}

Acknowledgement. This work was made possible by the Office of Naval Research Multidisciplinary University Research Initiative (MURI) funding for MMARINeDNA (award no.: N00014-22-1-2719) with additional support from the National Science Foundation (award no.: OCE-2224726). It includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health SIG grant (no.: S10 OD026929). We thank the captain and crew of the NOAA Ship Reuben Lasker, Rachel Pound from CalCOFI and Harrison Huang and Dylan Inskeep from the California Department of Fish and Wildlife for their assistance with sample collection.

…and the release it came from

CalCOFI (2026). CalCOFI Integrated Database, release v2026.10.05 [Data set]. Scripps Institution of Oceanography, NOAA Fisheries, and California Department of Fish and Wildlife. https://calcofi.io/db-schema/?v=v2026.10.05