Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

366 larvae of Gigantidas childressi (urn:lsid:marinespecies.org:taxname:1346725) and Bathymodiolus heckerae (urn:lsid:marinespecies.org:taxname:420693) were collected from eight methane seep sites during the three research cruises using a suction sampler mounted to the Deep Submersible Vehicle (DSV) Alvin (AT- cruises) and the ROV Jason (TN391). Shells were sorted and stored at -20 celsius for 1 year prior to sample preparation. Larval shells were separated into two valves using fine paintbrushes under a dissecting microscope, soaked for 5 minutes in a solution of 15% H₂O₂ (Suprapur^®, Supelco) and 0.05 M NaOH (Suprapur^®, Supelco) diluted with Milli-Q^® water (EMD Millipore). Valves were then rinsed thoroughly with Milli-Q^® water, placed in a drop of 1% HNO₃ (Optima^® grade, Fisher Scientific) for 5 seconds, and again rinsed with Milli-Q^® to remove debris and organic material from valves. Valves were then affixed to microscope slides in random order using double-sided tape. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) was used to determine larval shell trace elemental composition ("fingerprints") by means of a New Wave 193 nm FX laser ablation system attached to an Agilent 7500ce ICP-MS at the Jackson School of Geosciences at the University of Texas at Austin. Pre-ablation was conducted to remove any surface contaminants (line scan mode; spot size = 35 µm, scan rate = 50 µm/s, repetition rate = 10 Hz; laser fluence = ~1.35 J cm^-2). After pre-ablation, samples were ablated within the pre-ablation track (line scan mode; line scan mode; spot size 25 µm; scan rate = 5 µm s^-1; repetition rate = 20 Hz; laser fluence = ~1.47 J cm^-2) with a sampling depth of 6-7 µm. The concentrations of magnesium (²⁴Mg and ²⁵Mg), calcium (⁴⁴Ca), chromium (⁵³Cr), manganese (⁵⁵Mn), iron (⁵⁷Fe), cobalt (⁵⁹Co), nickel (⁶⁰Ni), copper (⁶³Cu), strontium (⁸⁸Sr), cadmium (¹¹¹Cd), tin (¹¹⁸Sn), barium (¹³⁸Ba), and lead (²⁰⁸Pb) were measured. Calcium was used as the internal standard, for which Ca⁴³ was measured and a concentration of 400432 ppm Ca in the shell was assumed (i.e., stoichiometrically pure CaCO₃). IOLITE (v.4) trace elemental reduction software was used to convert analyte counts-per-second (CPS) to elemental ratios of calcium (X:CaCO₃) and to compute the limits of detection. Samples were bracketed using standard reference materials ECRM-752-NP, NIST 612, and USGS GSE-1G to evaluate instrument precision. 

The processing workflow described below to produce this dataset is available at [citation pending] and contains all the files described below associated with this geochemical project; raw data, analytical order and parameters, quality assurance and control, sample and standard-specific data, and subsample-specific means.

The instrument measured samples following the analytical order and analytical parameters described by files in the raw_data > analytical_order_and_parameters folder, with specific information for the first cruise and, in aggregate, the second and third cruises:

Raw data output from the instrument are included in the "runs" folder:

raw_data > runs

Unprocessed intensities versus time (counts per second; CPS) for measured trace elements (analytes) output from spectrometer for each analytical session (run), organized by cruise (e.g., at4224_runs; tn391_at5004_runs).

These data were processed using IOLITE (v.4) trace elemental reduction software (Paton et al., 2011) following Longerich et al., (1996) with MACS-3 as the calibration material. When used in IOLITE, these files require a "laser log" to be read; a laser log has been included for each run.  See the README file for a full organizational scheme that associates each log with each run. This software output sample-specific continuous trace elemental data in units of CPS, X:Ca CPS (elemental ratio to calcium), and ppm (calibrated values), included in the "sample_and_standard_specific_data" folder:

raw_data > sample_and_standard_specific_data

Sample-specific continuous data for samples from each cruise. Each sheet is a sample (whole valve). Data are in units of: i) CPS, ii) X:Ca CPS, and iii) ppm (calibrated). 

The 'timeseries' datasets contain multiple sheets; each sheet is data from the transect of one valve, from the start of the valve (at or near the umbo) to the edge of the shell across the maximum growth axis.

The 'whole_valve_means_macs3' datasets contain mean values for whole valve samples and standards, averaged across each transect. Also included are standard errors (2x) and levels of detection.

The 'whole_valve_means_all_calib' dataset contains the same values as the 'whole_valve_means_macs3' datasets, but for all calibration materials (e.g., NIST612, GSE-1G, etc.). This dataset was used to determine that MACS-3 was the best calibration material for these samples.

Whole-valve data were used primarily for quality assurance and quality control procedures, which included i) comparison of magnitude of signal versus level of detection and ii) assessment of sample % residual standard deviation (% RSD; precision). 

Additionally, measurement of standard reference materials NIST 612, GSE-1G, ECRM 752, and MACS3 facilitated assessment of standard % RSD and recoveries (accepted versus measured values). Approximately every 15 samples were bracketed by standard reference materials ECRM-752-NP, NIST 612, and USGS GSE-1G to evaluate instrument precision (residual standard deviation; % RSD; ideally below 15%) and accuracy (ratio of average analyte concentration to NIST-certified values or GeoReM Preferred Values; ideally close to 1). Datasets containing values for measurements of standards are above in the "sample_and_standard_specific_data" folder and in the "qa_qc" folder:

raw_data > qa_qc

The file 'magnitudes_rsd_recoveries' includes a comparison of the magnitude of signal versus level of detection, sample % RSD, and the standard % RSD for MACS-3, as well as recoveries for standards calibrated using MACS-3.

The file 'reference_material_values' contains the GeoReM reference material values for MACS-3, ECRM 752, and NIST 612 in units of ppm, with uncertainty.

Average % RSD for reference materials was below 15%, and recovery across analytes was close to 1 for MACS-3, which was therefore used as the primary calibration standard. Precision and other quality assurance metrics were best for ²⁵Mg, ⁵⁵Mn, ⁶⁰Ni, ⁸⁸Sr, and ¹³⁸Ba.

The MATLAB program 'WindowProgram' (processing_data_and_tools > WindowProgram) was used to smooth the original data ('origppm') for each whole-valve sample using 7-point moving median and mean windows, and output the 'reduced' dataset.

Because larval shells have up to three shell growth regions (i.e., larval, prodissoconch growth regions I and II; settled, juvenile dissoconch), it was necessary to isolate geochemical measurements within each shell growth region. To this end, MATLAB was used to set the start point (along the laser transect) for prodissoconch I for small to medium-length valves following SEM-based growth region length measurements for the species (Arellano et al., 2014), although we applied more conservative intervals. That is, for most valves, prodissoconch I was automatically terminated at 75 µm for G. childressi and 100 µm for B. heckerae, and prodissoconch II was set to start at 150 µm for G. childressi and 175 µm for B. heckerae. For particularly long valves, prodissoconch I was sometimes not selected at all or instead was terminated at 50 µm for G. childressi and 75 µm for B. heckerae; likewise, prodissoconch II was set to start around 125 µm for G. childressi and 150 µm for B. heckerae. Growth region intervals were manually set for valves presenting issues such as laser ablation burn-through or unusual elemental time series using RegionPicker, a custom-built MATLAB application for comparing valve images alongside geochemical time series. Additionally, an elemental transition zone approximately 30-50 µm long was apparent in time series between each growth region for most valves, possibly reflecting distinct but gradual shifts in growth rate, physiology, metabolism, or change in other vital effects. Thus, an automatic offset to each growth region was applied to avoid overlapping regions. The concentration of tin (Sn¹¹⁸) was used as a proxy for tape ablation (shoot-through) despite poor precision and accuracy for tin. Intervals with very high tin concentrations (e.g., > 4 log X:Ca) were typically excluded regardless of visible holes in the valve, as were intervals with low tin concentrations that did overlap visible holes. Intervals absent of tin were typically not excluded despite visible holes, as this could indicate post-ablation valve collapse in lieu of shoot-through during ablation.

processing_data_and_tools > RegionPicker

The 'valve_images' folder contains z-stacked mosaic images of each valve after ablation. These images are seen alongside geochemical time series within the RegionPicker program.

The 'RegionPicker_screenshots' folder contains screenshots of each valve sample in its final processed state in the RegionPicker application.

The 'RegionPicker_output' spreadsheet contains metadata for each sample processed in RegionPicker, including the intervals along the transects designated by the user for later extraction (of the geochemical time series).

MATLAB was used to extract shell geochemical data associated with defined intervals (i.e., rather than using data from entire transects or entire growth regions) using IntervalExtractor.mlx:

processing_data_and_tools > IntervalExtractor

The 'combinedoutput' spreadsheet (and .mat file) is simply a renamed version of the RegionPicker_output spreadsheet. The IntervalExtractor program output 'subsamples_extracted', a dataset of mean elemental values in ppm for each subsample (growth region-specific dataset). These data were then organized as follows:

raw_data > subsample_specific_means

The dataset 'means_by_run' contains multiple sheets; each sheet contains those subsamples that were measured during a given analytical session (run).

The dataset ' means_agg' aggregates all subsamples in one sheet (columns B-P). These values were used in the conversion of elemental ratios to mmol:mol X:Ca ratios.

Before conversion of elemental ratios to mmol:mol X:Ca ratios, values below level of detection (LOD) and outliers were identified:

processing_data_and_tools > data_cleaning

The file 'below_lod_and_outlier_removal' contains six spreadsheets associated with a step of outlier identification and removal of values below LOD. First, subsample values for indivdual elements below LOD were identified; second, values below LOD were removed; third, prodissoconch I shell growth region values above 3 standard deviations + mean were identified as outliers; fourth, this same criteria was applied to prodissoconch II values; fifth, this same criteria was applied to dissoconch values. Finally, all values below LOD and those identified as outliers (> 3 standard deviations + mean) were removed (see the 'outliers_removed' sheet).

The file 'subsamples_conversion_to_ratios' contains four spreadsheets associated with the process of converting subsample elemental values in ppm to ratios to calcium in units of mmol:mol. First, the ratio of trace elemental values in ppm to bulk calcium in ppm was calculated. Second, the relative atomic mass for each trace element was used to convert the units of that ratio to mmol:ppm (i.e., Mg24  in mmol:bulk calcium in ppm). Third, the natural abundance of each trace element was used to calculate concentrations of bulk elements (e.g., Mg instead of Mg24) in mmol. Fourth, the conversion factor of bulk Ca in ppm to Ca in mol was used to convert these ratios to bulk elemental concentrations in mmol relative to bulk calcium in mol (i.e., Mg:Ca mmol:mol).

This produced the following dataset:

processed_data_preanalysis

The dataset 'subsamples_xca_mmolmol' is the highest-level, reduced and cleaned dataset input to the statistical analysis software PRIMER, but is not itself prepared for statistical analysis (i.e., it has not been normalised, transformed, grouped by shell growth region, etc.).

NSF Award Abstract:
Ever since hydrothermal vents and methane seeps were first discovered in the deep ocean more than 40 years ago, scientists have wondered how these isolated communities, fully dependent on underwater "islands" of toxic chemicals, are first colonized by organisms, and how the populations of these specialized animals are exchanged and maintained. These fundamental processes depend on the transport of babies (larvae) by the ocean currents, yet because the larvae are microscopic and diluted in the vastness of the ocean, it is very difficult to determine where and how they drift. This project uses an autonomous underwater vehicle to collect larvae from precise regions of the water column. Larval traps on the bottom and chemical analyses of larval shells will also be used to determine the depths where larvae swim. These findings will provide realistic estimates for mathematical models that show how biology interacts with ocean currents to predict which methane seeps will be colonized by larvae originating at different depths. A detailed knowledge of larval dispersal is needed for conservation and management of the deep sea. Without such information, we cannot know the best placement of marine protected areas, nor can we facilitate the reestablishment of communities impacted by deep-sea mining, drilling, or other human activities. This project will provide hands-on at-sea training for college students to learn the rapidly vanishing skills needed for studies of larvae and embryos in their natural habitats. Learning opportunities will also be available to individuals of all ages through new, interactive exhibits on deep-sea biology and larval ecology produced for small museums and aquaria on the coasts of Oregon, Washington and North Carolina.

Reliable estimates of connectivity among metapopulations are increasingly important in marine conservation biology, ecology and phylogeography, yet biological parameters for biophysical models in the deep sea remain largely unavailable. The movements of deep-sea vent and seep larvae among islands of habitat suitable for chemosynthesis have been inferred from current patterns using numerical modeling, but virtually all such models have used untested assumptions about biological parameters that should have large impacts on the predictions. This project seeks to fill in the missing biological parameters while developing better models for predicting the dispersal patterns of methane seep animals living in the Gulf of Mexico and on the Western Atlantic Margin. Despite the existence of similar seeps at similar depths on two sides of the Florida peninsula, the Western Atlantic seeps support only a subset of the species found in the Gulf of Mexico. It is hypothesized that the ability of larvae to disperse through the relatively shallow waters of the Florida Straits depends on an interaction between the adult spawning depth and the dispersal depth of the larvae. Dispersal depth, in turn, will be influenced by larval flotation rates, swimming behaviors, feeding requirements, and ontogenetic migration patterns during the planktonic period. The recently developed SyPRID sampler deployed on AUV Sentry will be used to collect larvae from precise depth strata in the water column, including layers very near the ocean floor. Larval traps deployed on the bottom at three depths in each region will be used in conjunction with the plankton collections to determine what proportion of larvae are demersal. Comparisons of stable oxygen isotopes between larval and juvenile mollusk shells will provide information on the temperatures (and therefore depths) that larvae develop, and geochemical analyses of larval and juvenile shells will determine whether larval cohorts mix among depth strata. Ocean circulation and particle transport modeling incorporating realistic biological parameters will be used to predict the movements of larvae around the Florida Peninsula for various spawning depths and seasons.

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.