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Dataset Title:  Performance traits (e.g., survival, growth, size) for hatchery-produced oyster
cohorts
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_770157)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Exp {
    String bcodmo_name "exp_id";
    String description "Unique identifier for the 2 experiments included in this dataset";
    String long_name "Exp";
    String units "unitless";
  }
  Site {
    String bcodmo_name "site";
    String description "Unique identifier for the site location within each experiment";
    String long_name "Site";
    String units "unitless";
  }
  Eff_allelic_diversity {
    Float32 _FillValue NaN;
    Float32 actual_range 3.781, 6.025;
    String bcodmo_name "sample_descrip";
    String description "Effective allelic diversity for that oyster cohort";
    String long_name "Eff Allelic Diversity";
    String units "unitless";
  }
  Allelic_richness {
    Float32 _FillValue NaN;
    Float32 actual_range 8.333, 12.917;
    String bcodmo_name "sample_descrip";
    String description "Number of alleles for that oyster cohort";
    String long_name "Allelic Richness";
    String units "Number of alleles";
  }
  Genetic_relatedness {
    Float32 _FillValue NaN;
    Float32 actual_range 0.02, 0.21;
    String bcodmo_name "sample_descrip";
    String description "Metric of genetic relatedness within that oyster cohort calculated using STORM (Frasier 2008)";
    String long_name "Genetic Relatedness";
    String units "unitless";
  }
  Cohort {
    String bcodmo_name "sample";
    String description "Unique identifier for one of 6 oyster cohorts used in the experiments";
    String long_name "Cohort";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  Final_avg_size {
    Float64 _FillValue NaN;
    Float64 actual_range 9.5, 30.45363636;
    String bcodmo_name "height";
    String description "Average shell height of the oysters remaining on that experimental replicate at the end of the experiment";
    String long_name "Final Avg Size";
    String units "millimeters (mm)";
  }
  Initial_avg_size {
    Float64 _FillValue NaN;
    Float64 actual_range 4.833333333, 13.83333333;
    String bcodmo_name "height";
    String description "Average shell height of the oysters on that experimental replicate at the start of the experiment";
    String long_name "Initial Avg Size";
    String units "millimeters (mm)";
  }
  Cage_alive {
    Float64 _FillValue NaN;
    Float64 actual_range 2.0, 12.0;
    String bcodmo_name "number";
    String description "Number of oysters alive at the end of the experiment in the cage (no predator) treatment";
    String long_name "Cage Alive";
    String units "Number of oysters";
  }
  Cage_dead {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 10.0;
    String bcodmo_name "number";
    String description "Number of oysters that were dead at the end of the experiment in the cage (no predator) treatment";
    String long_name "Cage Dead";
    String units "Number of oysters";
  }
  Open_tile_alive {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 11.0;
    String bcodmo_name "number";
    String description "Number of oysters alive at the end of the experiment in the open tile (control) treatment";
    String long_name "Open Tile Alive";
    String units "Number of oysters";
  }
  Open_tile_dead {
    Float64 _FillValue NaN;
    Float64 actual_range 1.0, 12.0;
    String bcodmo_name "number";
    String description "Number of oysters that were dead at the end of the experiment in the open tile (control) treatment";
    String long_name "Open Tile Dead";
    String units "Number of oysters";
  }
  Growth {
    Float64 _FillValue NaN;
    Float64 actual_range 1.24, 20.80545455;
    String bcodmo_name "growth";
    String description "Average difference in final shell height and initial shell height standardized by initial shell height for each oyster per experimental replicate";
    String long_name "Growth";
    String units "millimeters (mm)";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"In April 2012, we collected 100 adult oysters (80-100 mm shell length) from
3-5 separate reefs at each of 6 sites: St. Augustine, FL (FL-1; 30.0224,
-81.3287), Jacksonville, FL (FL-2; 30.4446, -81.4199), Sapelo Island, GA
(GA/SC-1; 31.4777, -81.2726), Ace Basin, SC (GA/SC-2; 32.4846, -80.6001),
Masonboro, NC (NC-1; 34.1510, -77.8551), and Middle Marsh, NC (NC-2; 34.6951,
-76.6183). They were held in flowing seawater tanks or suspended in cages from
docks in their home region for 2-3 weeks until 30 oysters from each site could
be tested and certified as disease free. The remaining 70 oysters were then
shipped on ice to a single hatchery facility in Florida (Research Aquaculture
Inc., Tequesta, FL; 26.9607, -80.0931) at the end of April.
 
The adult oysters from each site were used as the broodstock to produce 6
separate site-specific \\\"cohorts\\\" (one cohort per site). From their arrival
at the hatchery, the adult oysters were held for 2 weeks until they were ready
to spawn under the same conditions in separate flow-through seawater systems
to prevent cross-contamination. All families were manually spawned (i.e.,
strip spawned) on May 7 (see details below). Because the original FL-1 family
did not produce many offspring, the remaining broodstock oysters from this
site were spawned on June 1 using the same process. Due to variation in
ripeness and sex, the number of oysters spawned and the ratio of males to
females varied across broodstock (Table 1 of Hughes et al., 2019), though our
broodstock numbers for each cohort are comparable to those commonly used in
hatchery settings (30-60 individuals; Morvezen et al. 2016).
 
The broodstock oysters from each source site were strip spawned, sexed, and
fertilized on the same day by a team of 7 people, who each had a specific job
to perform: shucking the animals, sampling and preparing tissue for
microscopic analysis of sex, identifying the sex, stripping the male sperm,
stripping the female eggs, mixing the sperm and eggs after all of the animals
from a particular source were stripped, overseeing the process and keeping
track of broodstock source. We sanitized equipment between individuals and
again between broodstock sources. Stripping was done by broodstock source
independently and quickly so that the sperm and eggs would remain viable, and
all viable sperm and eggs were used. During the gamete mixing process, the
eggs from all females and the sperm from all males were first pre-mixed and
then combined to ensure equal access of gametes to one another. We allowed
30-60 minutes for fertilization; once 75-90% of the eggs were fertilized, they
were moved to larval tanks. All larvae were retained except for minimal
numbers of individuals in each cohort that did not grow or had improper
development. Larval culture occurred in 60-gallon conical tanks utilizing a
flow-through seawater system with Banjo screens that is commercially used in
multiple bivalve hatcheries (e.g., Taylor Shellfish in WA; Cherrystones in
VA).
 
Over a period of 3 days the week of May 28, oysters were sieved on a
250-micron sieve and settled on crushed oyster cultch in a recirculating flow-
through system. The week of June 11, once they reached 800 microns in size,
they were moved into a nursery facility compliant with state regulations,
again under flow-through seawater conditions (salinity = 32 ppt, temperature =
30\\u00baC). In the hatchery and nursery stages, the oysters were fed a mixed
diet of T. isochrysis, Chaetocerous gracilis, and Tetraselmis via a constantly
running peristaltic pump. Although growth was similar during the larval
culture phase, some cohorts produced more juvenile oysters (\\\"spat\\\") than
others during settlement, despite following the same procedures for all. To
maintain consistency in their growing conditions, we selected a random sample
of each cohort to yield similar total abundances across cohorts on June 18. At
the end of June (June 27) at approximately 4mm in size, the 6 cohorts were
transferred to a common flow-through facility at the Whitney Marine Biological
Laboratory in St. Augustine, FL. To assess genetic diversity within and
between oyster cohorts produced in the hatchery, 50 individuals were
haphazardly collected from each juvenile cohort prior to the start of the
field experiments and preserved at -80\\u1d52C for genetic analysis. This
sample size is sufficient to estimate allele frequencies accurately (Hale et
al. 2012).
 
To extract DNA, we ground each tissue sample with a pestle, and used the
tissue centrifugation protocol from the Omega Bio-Tek E-Z 96 Tissue DNA Kit.
We determined genetic diversity and population structure using 12 highly
variable microsatellite loci developed for C. virginica: Cvi9, Cvi11, and
Cvi13 from Brown et al. (2000); Cvi1i24b, Cvi2i23, Cvi2j24, and Cvi2k14 from
Reece et al. (2004); Cvi4313E-VIMS from Carlsson and Reece (2007); and RUCV1,
RUCV66, RUCV73, and RUCV74 from Wang and Guo (2007). We amplified four loci in
each multiplexed polymerase chain reaction (PCR) using the Qiagen Type-It
Microsatellite PCR Kit. Each 10 l reaction consisted of 1 l DNA template, 5 l
2X type-it multiplex master mix (Qiagen), 2.4 l water, and 0.2 l each 10 M
primer. Using a T100 thermal cycler (Bio-Rad), PCR cycling conditions included
initial activation/denaturation at 95\\u1d52C for 5 min, followed by 28 cycles
of 95\\u1d52C for 30 sec, 60\\u1d52C for 90 sec, and 72\\u1d52C for 30 sec, and
final extension at 60\\u1d52C for 30 min. PCR products were separated on a
3730xl Genetic Analyzer (Applied Biosystems) with the internal size standard
GeneScan 500 LIZ (Applied Biosystems), and fragment analysis was performed
using GeneMarker version 2.6 (SoftGenetics).
 
We created panels for each multiplexed reaction in GeneMarker, which included
bins that were assigned manually for all alleles; the same panels were used to
score all samples, and the alignment of the panels was checked prior to each
analysis to account for any run-to-run variation and to identify any new
alleles. We used these panels to do a preliminary first assignment of alleles
based on peak position and bin position, but every sample was then scored
manually for all loci to examine signal intensity, to confirm the
presence/absence of alleles, and to identify any reruns. A subset of samples
was then rerun (at least 15% per multiplex PCR reaction) and manually scored
again to confirm any uncertain allele calls and account for any genotyping
error.
 
We experimentally evaluated the performance (size, growth, survivorship) of
each 2012 juvenile oyster cohort in the field as a function of within-cohort
effective allelic diversity. These same oysters were analyzed for different
response variables as part of two other studies (Hanley et al. 2016, Hughes et
al 2017; see Appendix S1 of Hughes et al., 2019 for additional information).
These studies used the same experimental design. Namely, in each experiment,
12 spat from a single cohort were affixed to 10*10 cm experimental tiles using
the marine adhesive Z-spar. Tiles were held in flow-through seawater tables
for less than 48 hours until being deployed to the field. Prior to deployment,
we measured shell height of each spat and photographed all tiles. At the end
of each experiment, live oysters were counted and measured.
 
Oysters at three of the five sites included here have previously been analyzed
in a test of genetic by environmental variation across oyster cohorts (Hughes
et al. 2017): spat from each cohort were deployed on July 12-14, 2012 across 3
field sites in the South Atlantic Bight that spanned the geographic range of
the source populations: FL-EXP (29.6714, -81.2162); GA-EXP (31.9213,
-80.9880), or NC-EXP (34.7069, -76.7631). At each field site, we deployed 18
tiles (6 cohorts * 3 tiles per cohort) to each of 9 natural intertidal oyster
reefs. Low spat abundance in the FL-1 cohort limited replication of this
cohort to 4 reefs per experimental site (N=147 tiles total). The 3 tiles from
each cohort were haphazardly assigned to one of three predation treatments
(full cage, with mesh with 6mm*6mm openings; partial cage to control for
caging artifacts; no cage) and deployed on the reef in a completely randomized
design; only the full cage and no cage treatments are addressed further here.
This experiment lasted 6 weeks.
 
Data collected on oysters deployed at the other two field sites used in the
present study come from a concurrent longer-term experiment focused on the
effects of oyster cohort diversity that included additional treatments not
analyzed here (Hanley et al. 2016). In this study, 36 tiles were deployed (6
cohorts * 6 tiles per cohort) at each of two sites in the Matanzas River
estuary, FL (FL-North: 29.75177, -81.25578; FL-South: 29.65838, -81.22193) on
July 24-25, 2012. The 6 tiles from each cohort were split across the same
three predation treatments as above and deployed in a completely randomized
design. This experiment lasted 6 months.";
    String awards_0_award_nid "709941";
    String awards_0_award_number "OCE-1652320";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1652320";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Oyster Cohort Traits 
  PI: Randall Hughes 
  Version date: 06-June-2019";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2019-06-07T15:52:30Z";
    String date_modified "2019-06-11T18:39:16Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.770157.1";
    String history 
"2020-08-13T16:54:45Z (local files)
2020-08-13T16:54:45Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_770157.das";
    String infoUrl "https://www.bco-dmo.org/dataset/770157";
    String institution "BCO-DMO";
    String instruments_0_acronym "Automated Sequencer";
    String instruments_0_dataset_instrument_nid "770186";
    String instruments_0_description "General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.";
    String instruments_0_instrument_name "Automated DNA Sequencer";
    String instruments_0_instrument_nid "649";
    String instruments_0_supplied_name "3730xl Genetic Analyzer (Applied Biosystems)";
    String instruments_1_acronym "Thermal Cycler";
    String instruments_1_dataset_instrument_nid "770185";
    String instruments_1_description 
"General term for a laboratory apparatus commonly used for performing polymerase chain reaction (PCR). The device has a thermal block with holes where tubes with the PCR reaction mixtures can be inserted. The cycler then raises and lowers the temperature of the block in discrete, pre-programmed steps.

(adapted from http://serc.carleton.edu/microbelife/research_methods/genomics/pcr.html)";
    String instruments_1_instrument_name "PCR Thermal Cycler";
    String instruments_1_instrument_nid "471582";
    String instruments_1_supplied_name "T100 thermal cycler (Bio-Rad)";
    String keywords "alive, allelic, Allelic_richness, average, bco, bco-dmo, biological, cage, Cage_alive, Cage_dead, chemical, cohort, data, dataset, dead, diversity, dmo, eff, Eff_allelic_diversity, erddap, exp, final, Final_avg_size, genetic, Genetic_relatedness, growth, initial, Initial_avg_size, management, oceanography, office, open, Open_tile_alive, Open_tile_dead, preliminary, relatedness, richness, site, size, tile";
    String license "https://www.bco-dmo.org/dataset/770157/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/770157";
    String param_mapping "{'770157': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/770157/parameters";
    String people_0_affiliation "Northeastern University";
    String people_0_affiliation_acronym "NEU";
    String people_0_person_name "A. Randall Hughes";
    String people_0_person_nid "522929";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Shannon Rauch";
    String people_1_person_nid "51498";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "Seagrass and Oyster Ecosystems";
    String projects_0_acronym "Seagrass and Oyster Ecosystems";
    String projects_0_description 
"NSF Award Abstract:
Disease outbreaks in the ocean are increasing, causing losses of ecologically important marine species, but the factors contributing to these outbreaks are not well understood. This 5-year CAREER project will study disease prevalence and intensity in two marine foundation species - the seagrass Zostera marina and the Eastern oyster Crassostrea virginica. More specifically, host-disease relationships will be explored to understand how genetic diversity and population density of the host species impacts disease transmission and risk. This work will pair large-scale experimental restorations and smaller-scale field experiments to examine disease-host relationships across multiple spatial scales. Comparisons of patterns and mechanisms across the two coastal systems will provide an important first step towards identifying generalities in the diversity-density-disease relationship. To enhance the broader impacts and utility of this work, the experiments will be conducted in collaboration with restoration practitioners and guided by knowledge ascertained from key stakeholder groups. The project will support the development of an early career female researcher and multiple graduate and undergraduate students. Students will be trained in state-of-the-art molecular techniques to quantify oyster and seagrass parasites. Key findings from the surveys and experimental work will be incorporated into undergraduate courses focused on Conservation Biology, Marine Biology, and Disease Ecology. Finally, students in these courses will help develop social-ecological surveys and mutual learning games to stimulate knowledge transfer with stakeholders through a series of workshops.
The relationship between host genetic diversity and disease dynamics is complex. In some cases, known as a dilution effect, diversity reduces disease transmission and risk. However, the opposite relationship, known as the amplification effect, can also occur when diversity increases the risk of infection. Even if diversity directly reduces disease risk, simultaneous positive effects of diversity on host density could lead to amplification by increasing disease transmission between infected and uninfected individuals. Large-scale field restorations of seagrasses (Zostera marina) and oysters (Crassostrea virginica) will be utilized to test the effects of host genetic diversity on host population density and disease prevalence/intensity. Additional field experiments independently manipulating host genetic diversity and density will examine the mechanisms leading to dilution or amplification. Conducting similar manipulations in two marine foundation species - one a clonal plant and the other a non-clonal animal - will help identify commonalities in the diversity-density-disease relationship. Further, collaborations among project scientists, students, and stakeholders will enhance interdisciplinary training and help facilitate the exchange of information to improve management and restoration efforts. As part of these efforts, targeted surveys will be used to document the perceptions and attitudes of managers and restoration practitioners regarding genetic diversity and its role in ecological resilience and restoration.";
    String projects_0_end_date "2022-01";
    String projects_0_geolocation "Coastal New England";
    String projects_0_name "CAREER: Linking genetic diversity, population density, and disease prevalence in seagrass and oyster ecosystems";
    String projects_0_project_nid "709942";
    String projects_0_start_date "2017-02";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Performance traits (e.g., survival, growth, size) for hatchery-produced oyster cohorts.";
    String title "Performance traits (e.g., survival, growth, size) for hatchery-produced oyster cohorts";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.


 
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