Contributors | Affiliation | Role |
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Choy, C. Anela | University of California-San Diego Scripps (UCSD-SIO) | Principal Investigator |
Hetherington, Elizabeth D. | University of California-San Diego Scripps (UCSD-SIO) | Scientist |
Rauch, Shannon | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Samples were collected in the central and southern California Current. Most samples were collected in the Monterey Bay region, but a subset of samples were collected in southern California in 2020 and 2021. Samples were collected from between 0 to 3,000 meters depth. Samples were collected on four cruises across three years. All cruises were on the R/V Western Flyer. Dr. Steven Haddock (haddock@mbari.org) was the Chief Scientist on all cruises. Cruises occurred in March 2019 (Cruise ID: WF0319), January 2020 (Cruise ID: WF0120), July 2020 (Cruise ID: WF0720), and July 2021 (Cruise ID: WF0721). Sample locations and dates are provided as columns in the data file.
Siphonophores were collected using three methods: (1) a remotely operated vehicle, (2) blue water diving, and (3) a midwater trawl.
(1) We used the Remotely Operated Vehicle (ROV) Doc Ricketts (https://www.mbari.org/technology/rov-doc-ricketts/) to collect siphonophores, which is an electro-hydraulic vehicle that operates between 200 and 4000 meters. The vehicle was fitted with high-definition video cameras, environmental data instrumentation (e.g. depth, temperature, salinity, and oxygen sensors), and suction and detritus samples to collect in-tact siphonophore specimens. ROV collections occurred during daylight hours.
(2) Siphonophores were collected by blue water diving between 0 and 20 meters during daylight hours. Blue water diving techniques followed the guidelines in the following publication: Haddock, Steven HD, and John N. Heine. "Scientific blue-water diving." (2005). From Haddock and Heine (2005): "In a typical blue-water dive, working divers are connected to a surface platform (and indirectly to each other) by tethers attached to a central hub, which is tended by a safety diver. This hub is connected to a down-line, providing a vertical point of reference. A surface float allows the divers to drift freely through the upper water-column, focusing on their work while the safety diver acts as a buddy for everyone."
(3) A Tucker Trawl with a frame area: 2 square meters (m²), mesh size: 500 micrometers (μm) was towed obliquely for ~2 hours between 900 meters and the surface at night.
Upon collection, siphonophores were identified to the finest taxonomic level, which was either genus or species. For some genera, there are likely undescribed and/or cryptic species (e.g., Apolemia) and for these taxa, genera-level identifications were used. All siphonophores were rinsed with DI water and frozen at -80°C until further processing. Siphonophore tissues were weighed, lyophilized, packaged into tin capsules for bulk isotope analysis, and analyzed at the University of Hawaii's Isotope Geochemistry Facility.
For bulk stable isotope analysis, siphonophore samples were analyzed using a Costech (Valencia, CA, USA) elemental combustion system coupled to a Thermo-Finnigan Delta XP isotope ratio mass spectrometer with N2 standard for nitrogen and Vienna Pee Dee Belemnite for carbon. The bulk stable isotope data are available in a separate BCO-DMO dataset (see 'Related Datasets').
A subset of samples was selected for compound-specific isotope analysis of amino acids (CSIA-AA). CSIA-AA was also conducted at the University of Hawaii's Isotope Geochemistry Facility using acid hydrolysis followed by derivatization (see Popp et al. (2007) and Hannides et al. (2013) for details). Derivatives were analyzed using a Thermo-Finnigan Delta V Plus isotope ratio mass spectrometer (IRMS), interfaced with a Thermo Trace GC gas chromatograph via GC-C III combustion furnace (980°C), reduction furnace (680°C), and a liquid nitrogen cold trap. Samples were injected (split/splitless injector, splitless mode) with a 180°C injector temperature and a constant helium flow rate of 1.4 milliliters per minute (mL min-1). For quality control, we analyzed an amino acid suite, with known δ15N values of 14 amino acids, every 3-4 sample injections. Internal reference compounds, L-2-Aminoadipic acid and L-(+)-Norleucine of known nitrogen isotopic composition, were co-injected with samples and suites and used as a measure of accuracy and instrument precision. Samples for CSIA-AA are typically analyzed in triplicate runs. Our samples, however, required six runs to obtain peaks for all amino acids (AAs) due to the inordinate relative abundance of glycine compared to all other amino acids. It is unclear why glycine peaks were large, although we note that the relative abundances of the different AAs can vary depending on the taxa and tissue type observed. It is unknown whether this is common for siphonophores since there are no other published siphonophore CSIA studies. Glycine peaks were so large that the chromatography surrounding glycine was deleteriously affected when injecting volumes large enough to detect all AAs of interest. To overcome this, we analyzed samples in triplicate at injection volumes that allowed for good chromatography around glycine, and then again in triplicate at a larger injection volume to allow smaller AAs to be detected while back flushing the large glycine peak out of the chromatogram. We obtained well-defined peaks for 14 amino acids, which were grouped into standard 'trophic' and 'source' categories based on previous studies.
Related Resources:
Some of the siphonophores collected in this dataset were also used for metabarcoding. Those data are published Damian-Serrano, et al. (2022) (doi: 10.1371/journal.pone.0267761)
Illumina sequencing data files can be found in the NCBI BioProject PRJNA733192 (https://www.ncbi.nlm.nih.gov/bioproject/733192)
Prey 18S reference database enhancement sequences are available in NCBI (accession numbers between MZ333540 - MZ333629)
Other data, intermediary files, and all code can be found in the GitHub repository: https://github.com/dunnlab/siphweb_metabarcoding (see DOI: doi.org/10.1371/journal.pone.0267761)
- Imported original file "Siphonophore CSIA-AA data (NSF Project Number 1829812).csv" into the BCO-DMO system.
- Renamed fields to comply with BCO-DMO naming conventions.
- Added separate columns for Year and Month based on the Collection_Date column.
- Saved the final file as "917239_v1_siphonophore_csia-aa.csv".
File |
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917239_v1_siphonophore_csia-aa.csv (Comma Separated Values (.csv), 4.57 KB) MD5:5b40410153edd6c885b23ff2bdd9a1d4 Primary data file for dataset ID 917239, version 1 |
Parameter | Description | Units |
Collection_Date | Month and year of the specimen collection | unitless |
Year | Year of sample collection | unitless |
Month | Month of sample collection | unitless |
Genus | Taxonomic genus of specimen | unitless |
Best_Taxonomic_ID | Best taxonomic identification of specimen, usually at the genus or species level | unitless |
Alanine | mean d15N value (in parts per thousand) from multiple sample injections for alanine | parts per thousand |
Glycine | mean d15N value (in parts per thousand) from multiple sample injections for glycine | parts per thousand |
Threonine | mean d15N value (in parts per thousand) from multiple sample injections for threonine | parts per thousand |
Serine | mean d15N value (in parts per thousand) from multiple sample injections for serine | parts per thousand |
Valine | mean d15N value (in parts per thousand) from multiple sample injections for valine | parts per thousand |
Leucine | mean d15N value (in parts per thousand) from multiple sample injections for leucine | parts per thousand |
Isoleucine | mean d15N value (in parts per thousand) from multiple sample injections for isoleucine | parts per thousand |
Proline | mean d15N value (in parts per thousand) from multiple sample injections for proline | parts per thousand |
Aspartic_acid | mean d15N value (in parts per thousand) from multiple sample injections for aspartic acid | parts per thousand |
Methionine | mean d15N value (in parts per thousand) from multiple sample injections for methionine | parts per thousand |
Glutamic_acid | mean d15N value (in parts per thousand) from multiple sample injections for glutamic acid | parts per thousand |
Phenylalanine | mean d15N value (in parts per thousand) from multiple sample injections for phenylalanine | parts per thousand |
Tyrosine | mean d15N value (in parts per thousand) from multiple sample injections for tyrosine | parts per thousand |
Lysine | mean d15N value (in parts per thousand) from multiple sample injections for lysine | parts per thousand |
TP | Trophic position estimate, using the equation from Chikaraishi et al. 2009: (Glutamic acid-Phenylalanine-3.4)/7.6 + 1 | unitless |
Alanine_SD | standard deviation of d15N value (in parts per thousand) for alanine | parts per thousand |
Glycine_SD | standard deviation of d15N value (in parts per thousand) for glycine | parts per thousand |
Threonine_SD | standard deviation of d15N value (in parts per thousand) for threonine | parts per thousand |
Serine_SD | standard deviation of d15N value (in parts per thousand) for serine | parts per thousand |
Valine_SD | standard deviation of d15N value (in parts per thousand) for valine | parts per thousand |
Leucine_SD | standard deviation of d15N value (in parts per thousand) for leucine | parts per thousand |
Isoleucine_SD | standard deviation of d15N value (in parts per thousand) for isoleucine | parts per thousand |
Proline_SD | standard deviation of d15N value (in parts per thousand) for proline | parts per thousand |
Asparticacid_SD | standard deviation of d15N value (in parts per thousand) for aspartic acid | parts per thousand |
Methionine_SD | standard deviation of d15N value (in parts per thousand) for methionine | parts per thousand |
Glutamic_acid_SD | standard deviation of d15N value (in parts per thousand) for glutamic acid | parts per thousand |
Phenylalanine_SD | standard deviation of d15N value (in parts per thousand) for phenylalanine | parts per thousand |
Tyrosine_SD | standard deviation of d15N value (in parts per thousand) for tyrosine | parts per thousand |
Lysine_SD | standard deviation of d15N value (in parts per thousand) for lysine | parts per thousand |
Dataset-specific Instrument Name | combustion furnace |
Generic Instrument Name | furnace |
Dataset-specific Description | Derivatives were analyzed using a Thermo-Finnigan Delta V Plus isotope ratio mass spectrometer (IRMS), interfaced with a Thermo Trace GC gas chromatograph via GC-C III combustion furnace (980°C), reduction furnace (680°C), and a liquid nitrogen cold trap. |
Generic Instrument Description | An enclosed chamber designed to produce heat. |
Dataset-specific Instrument Name | reduction furnace |
Generic Instrument Name | furnace |
Dataset-specific Description | Derivatives were analyzed using a Thermo-Finnigan Delta V Plus isotope ratio mass spectrometer (IRMS), interfaced with a Thermo Trace GC gas chromatograph via GC-C III combustion furnace (980°C), reduction furnace (680°C), and a liquid nitrogen cold trap. |
Generic Instrument Description | An enclosed chamber designed to produce heat. |
Dataset-specific Instrument Name | Thermo Trace GC gas chromatograph |
Generic Instrument Name | Gas Chromatograph |
Dataset-specific Description | Derivatives were analyzed using a Thermo-Finnigan Delta V Plus isotope ratio mass spectrometer (IRMS), interfaced with a Thermo Trace GC gas chromatograph via GC-C III combustion furnace (980°C), reduction furnace (680°C), and a liquid nitrogen cold trap. |
Generic Instrument Description | Instrument separating gases, volatile substances, or substances dissolved in a volatile solvent by transporting an inert gas through a column packed with a sorbent to a detector for assay. (from SeaDataNet, BODC) |
Dataset-specific Instrument Name | Thermo-Finnigan Delta V Plus isotope ratio mass spectrometer (IRMS) |
Generic Instrument Name | Isotope-ratio Mass Spectrometer |
Dataset-specific Description | CSIA-AA was also conducted at the University of Hawaii’s Isotope Geochemistry Facility using acid hydrolysis followed by derivatization (see Popp et al. [2007] and Hannides et al. [2013] for details). Derivatives were analyzed using a Thermo-Finnigan Delta V Plus isotope ratio mass spectrometer (IRMS), interfaced with a Thermo Trace GC gas chromatograph via GC-C III combustion furnace (980°C), reduction furnace (680°C), and a liquid nitrogen cold trap. |
Generic Instrument Description | The Isotope-ratio Mass Spectrometer is a particular type of mass spectrometer used to measure the relative abundance of isotopes in a given sample (e.g. VG Prism II Isotope Ratio Mass-Spectrometer). |
Dataset-specific Instrument Name | blue water diving |
Generic Instrument Name | Manual Biota Sampler |
Dataset-specific Description | Siphonophores were collected by blue water diving between 0-20 meters during daylight hours. Blue water diving techniques following the guidelines in the following publication: Haddock, Steven HD, and John N. Heine. "Scientific blue-water diving." (2005). |
Generic Instrument Description | "Manual Biota Sampler" indicates that a sample was collected in situ by a person, possibly using a hand-held collection device such as a jar, a net, or their hands. This term could also refer to a simple tool like a hammer, saw, or other hand-held tool. |
Dataset-specific Instrument Name | Remotely Operated Vehicle Doc Ricketts |
Generic Instrument Name | ROV Doc Ricketts |
Dataset-specific Description | We used the Remotely Operated Vehicle Doc Ricketts (https://www.mbari.org/technology/rov-doc-ricketts/) to collect siphonophores, which is an electro-hydraulic vehicle that operates between 200 and 4000 meters. The vehicle was fitted with high-definition video cameras, environmental data instrumentation (e.g. depth, temperature, salinity, and oxygen sensors), and suction and detritus samples to collect in-tact siphonophore specimens. ROV collections occurred during daylight hours. |
Generic Instrument Description | The remotely operated vehicle (ROV) Doc Ricketts is operated by the Monterey Bay Aquarium Research Institute (MBARI). ROV Doc Ricketts is capable of diving to 4000 meters (about 2.5 miles). The R/V Western Flyer is the support vessel for Doc Ricketts and was designed with a center well whose floor can be opened to allow Doc Ricketts to be launched from within the ship into the water below. For a complete description, see: https://www.mbari.org/at-sea/vehicles/remotely-operated-vehicles/rov-doc... |
Dataset-specific Instrument Name | Tucker Trawl |
Generic Instrument Name | Tucker Trawl |
Dataset-specific Description | A Tucker Trawl with a frame area of 2 square meters and a mesh size of 500 micrometers was towed obliquely for ~2h hours between 900 meters and the surface at night. |
Generic Instrument Description | The original Tucker Trawl, a net with a rectangular mouth opening first built in 1951 by G.H. Tucker, was not an opening/closing system, but shortly thereafter it was modified so that it could be opened and closed. The original had a 183 cm by 183 cm flexible rectangular mouth opening 914 cm long net with 1.8 cm stretched mesh for the first 457 cm and 1.3 cm mesh for last 457 cm. 152 cm of coarse plankton or muslin netting lined the end of the net. Tucker designed the net to collect animals associated with the deep scattering layers, principally euphausiids, siphonophores, and midwater fish. (from Wiebe and Benfield, 2003). Currently used Tucker Trawls usually have 1-m2 openings and can have a single net or multiple nets on the frame. |
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Platform | R/V Western Flyer |
Description | Cruise occurred in March 2019 |
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Platform | R/V Western Flyer |
Description | Cruise occurred in January 2020 |
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Platform | R/V Western Flyer |
Description | Cruise occurred in July 2020 |
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Platform | R/V Western Flyer |
Description | Cruise occurred in July 2021 |
Food webs describe who eats whom, tracing the flow of energy from plants up to large animals. While many connections in food webs on land are quite familiar (lions eat antelope and antelope eat grass, for example), there are large gaps in our understanding of ocean food webs. Closing these gaps is critical to understanding how nutrients and energy move through ocean ecosystems, how organisms interact in the ocean, and how best to manage ocean resources. This project will study ocean food web structure with a focus on siphonophores, an abundant group of predators in the open ocean that range in length from less than an inch to more than one hundred feet. Siphonophores are closely related to corals and many jellyfish. They are known to be important predators within ocean food webs, but they are difficult to study because they live across great ocean depths and are gelatinous and fragile. The details of what they eat, as well as many other features of their biology, remain poorly known. This project will combine direct observations of feeding, genetic analysis of siphonophore gut contents, and stable isotope analyses to identify what different species of siphonophores eat. The team will also examine why they eat what they do. This will provide a new understanding of how the structure of food webs arise, aiding in our ability to predict future changes to food webs as the global climate shifts. Siphonophores feed in a very unique manner--they have highly specialized tentacles that are used solely for capturing prey--thus, the prey captured is determined largely by the anatomy and function of these tentacles. The project will describe these tentacles, reconstruct their evolutionary history, and investigate how evolutionary shifts in tentacle structure have led to changes in diet. This project will train one PhD student, one Master's student, a postdoc, and undergraduate students, including individuals of underrepresented groups. This project will support the production of scientifically rigorous yet engaging videos, foster the expansion of a citizen-science program, and create K-12 teaching modules.
This project will advance three scientific aims: First, it will identify the diet of a diverse range of siphonophores using DNA metabarcoding of gut contents and prey field, remotely operated vehicle (ROV) video of prey encounters, and stable isotope analysis. These approaches are highly complementary and allow for extensive cross validation. Second, the project will characterize the selectivity of siphonophore diets by comparing them to the relative prey abundances in the habitats of each of these species. Third, the project will characterize the structure of the siphonophore prey capture apparatus across species through detailed morphological analysis of their tentacles and nematocysts. These data will be integrated in an ecological and evolutionary framework to identify predator features associated with prey specialization. In a larger context, addressing these questions will advance our understanding of oceanic predation by revealing how evolutionary changes in predator selectivity correspond to evolutionary changes in habitat and feeding apparatus and how these changes shape current food web structure in the open ocean. We will test and refine an integrated approach to describing the structure and origin of food web topology, and evaluate the potential for phylogenetic relationships to explain prey selectivity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) |