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| Dataset Title: | [Palau lakes: invertebrate barcodes] - Specimen log with OTU identifiers collected from Palau marine lakes (Do Parallel Patterns Arise from Parallel Processes?) 
|
| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_768138) |
| Information: | Summary
| License
| ISO 19115
| Metadata
| Background
| Files
|
Attributes {
s {
OTU_id {
String bcodmo_name "sample";
String description
"Operational Taxonomic Unit identifier. The first four-letters describe the taxon:
ASCI: Ascidiacea
BIVA: MolluscaBivalvia
BRYO: Bryozoa
CNID: Cnidaria
CRUS: Crustacea
ECHI: Echinodermata
GAST: MolluscaGastropoda
POLY: Polychaeta
PORI: Porifera";
String long_name "OTU Id";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
String units "unitless";
}
ID {
String bcodmo_name "taxon";
String description "Identification of specimens in OTU";
String long_name "ID";
String units "unitless";
}
lake_code {
String bcodmo_name "site";
String description "3-letter code for sampled lake name";
String long_name "Lake Code";
String units "unitless";
}
SequenceName {
String bcodmo_name "sample";
String description "This is the name of the DNA sequences in the alignment (a prefix of \"PG_\" has been added for individuals that were taken from the popgen dataset; a prefix of \"V_\" has been added for individuals in the voucher dataset identified by taxonomic experts)";
String long_name "Sequence Name";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
String units "unitless";
}
Phylum {
String bcodmo_name "phylum";
String description "Phylum assigned by taxonomic expert";
String long_name "Phylum";
String units "unitless";
}
Class {
String bcodmo_name "class";
String description "Class assigned by taxonomic expert";
String long_name "Class";
String units "unitless";
}
Order {
String bcodmo_name "order";
String description "Order assigned by taxonomic expert";
String long_name "Order";
String units "unitless";
}
Family {
String bcodmo_name "family";
String description "Family assigned by taxonomic expert";
String long_name "Family";
String units "unitless";
}
Genus {
String bcodmo_name "genus";
String description "Genus assigned by taxonomic expert";
String long_name "Genus";
String units "unitless";
}
Species {
String bcodmo_name "species_epithet";
String description "Species assigned by taxonomic expert";
String long_name "Species";
String units "unitless";
}
CRRF_ID {
String bcodmo_name "sample";
String description "internal ID number for voucher sample";
String long_name "CRRF ID";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
String units "unitless";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
String acquisition_description
"After completion of fieldwork, a subset of specimens from the transect surveys
were chosen for DNA barcoding to confirm or amend field identifications. These
specimens included (i) at least one specimen from each field-ID (except
obvious species such as Mastigias papua) and (ii) several specimens
representing the range of phenotypic variation of field-IDs that showed
considerable variation or were challenging to distinguish (e.g. small sponge
specimens of similar color and texture). Additionally, specimens from a
previously collected voucher collection (indicated with \\u201cV_\\u201d in
prefix of sequence ID) were barcoded and identified by taxonomic experts.
Specimens from population genetic collections (indicated with \\u201cPG_\\u201d
in prefix of sequence ID) were also barcoded. DNA was purified using a
modified phenol-chloroform CTAB extraction protocol (1) or AcroPrep PALL 5053
glass fiber plates procedure (2, 3). We amplified the Cytochrome c Oxidase
subunit I (COI) barcode locus using 0.5 \\u00b5L of purified DNA in a
25-\\u00b5L polymerase chain reaction (PCR) with 0.05 \\u00b5L AMPLITAQ (Applied
Biosystems, Foster City, California, USA), 2.5 \\u00b5L 10x buffer (Applied
Biosystems), 0.63 \\u00b5L of 20 \\u00b5M primers (Operon Biotechnologies Inc.,
Huntsville, Alabama, USA), 2.5 \\u00b5L of 25 mM MgCl2 (Applied Biosystems),
0.5 \\u00b5L of 10 mg/mL bovine serum albumin (BSA) and 0.5 \\u00b5L of 10 mM
dNTPs. Several primer sets were used (Table 1). Amplicons were sequenced at
the University of California Berkeley DNA Sequencing Facility (Berkeley,
California, USA). Base calls in electropherograms were visually checked and
manually corrected for errors and forward and reverse reads were assembled in
Sequencher 4.8 (GeneCodes, Ann Arbor, Michigan, USA). We used Basic Local
Alignment Search Tool (BLASTn) to determine the higher level taxonomic
assignment for each sequence (which we used to process batches of similar
sequences) \\u2014 ascidians, bivalves, bryozoans, cnidarians, crustaceans,
echinoderms, gastropods, polychaetes, and poriferans. Sequences organized by
these broad groups were then aligned using Muscle v3.8.425 (4). For each
group, alignments were manually adjusted and trimmed to the same length in
Mesquite v3.5 (5) to balance total individuals retained and sequence length.
The resulting alignment lengths were: ascidians 395bp, bivalves 567bp,
bryozoans 622bp, cnidarians 612bp, crustaceans 299bp, echinoderms 357bp,
gastropods 562bp, polychaetes 509bp, and poriferans 688bp. Sequences were
translated to amino acid sequence to confirm an open reading frame. Short
sequences were excluded from further analysis, but percent pairwise identity
with the closest match was recorded for each based on the shortest sequence.
Pairwise sequence distance was calculated using dist.dna with Kimura\\u2019s
2-parameter distance model of evolution (6) in the ape package v4.1 (7) in R
(8). OTUs, or clusters of sequences, similar at 97% were identified using
tclust in the spider package v1.5.0 (9) in R (8) for each taxonomic group,
except for poriferans, which were clustered at 99% sequence similarity given
their slow sequence evolution (10).
1\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Dawson MN, Raskoff KA, Jacobs
DK (1998) Field preservation of marine invertebrate tissue for DNA analyses.
Mol Mar Biol Biotechnol 7(2):145\\u201352.
2\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Ivanova N V., Dewaard JR,
Hebert PDN (2006) An inexpensive, automation-friendly protocol for recovering
high-quality DNA. Mol Ecol Notes 6(4):998\\u20131002.
3\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Schiebelhut LM, Abboud SS,
G\\u00f3mez Daglio LE, Swift HF, Dawson MN (2017) A comparison of DNA
extraction methods for high-throughput DNA analyses. Mol Ecol Resour
17(4):721\\u2013729.
4\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Edgar RC (2004) MUSCLE:
Multiple sequence alignment with high accuracy and high throughput. Nucleic
Acids Res 32(5):1792\\u20131797.
5\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Maddison WP, Maddison DR (2018)
Mesquite: a modular system for evolutionary analysis.
6\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Kimura M (1980) A simple method
for estimating evolutionary rates of base substitutions through comparative
studies of nucleotide sequences. J Mol Evol 16(2):111\\u2013120.
7\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Paradis E, Claude J, Strimmer K
(2004) APE: Analyses of phylogenetics and evolution in R language.
Bioinformatics 20(2):289\\u2013290.
8\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 R Core Team (2018) R: A
language and environment for statistical computing (R Foundation for
Statistical Computing, Vienna, Austria).
9\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 BROWN SDJ, et al. (2012)
Spider: An R package for the analysis of species identity and evolution, with
particular reference to DNA barcoding. Mol Ecol Resour 12(3):562\\u2013565.
10\\. \\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 Huang D, Meier R, Todd PA, Chou LM
(2008) Slow mitochondrial COI sequence evolution at the base of the metazoan
tree and its implications for DNA barcoding. J Mol Evol 66(2):167\\u2013174.
See Table 1.\\u00a0Primers and thermocycle conditions used for PCR of
macroinvertebrates by taxonomic group in Supplemental Documents, below.
For the sequence alignment files (.fas) mentioned in the methods above, see
the Supplemental Files section below.";
String awards_0_award_nid "55103";
String awards_0_award_number "OCE-1241255";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1241255";
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 "David L. Garrison";
String awards_0_program_manager_nid "50534";
String cdm_data_type "Other";
String comment
"PaPaPro barcoding: surveyed specimens and vouchers
M. Dawson (UC-Merced)
version date: 2019-05-13";
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 "2019-05-16T13:22:49Z";
String date_modified "2020-03-05T13:50:02Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.768138.1";
String history
"2024-12-03T17:10:42Z (local files)
2024-12-03T17:10:42Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_768138.html";
String infoUrl "https://www.bco-dmo.org/dataset/768138";
String institution "BCO-DMO";
String instruments_0_acronym "Automated Sequencer";
String instruments_0_dataset_instrument_nid "768177";
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_1_acronym "Thermal Cycler";
String instruments_1_dataset_instrument_nid "768178";
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 keywords "array, array-data, bco, bco-dmo, biological, chemical, class, code, comprehensive, crrf, CRRF_ID, data, dataset, dmo, erddap, family, genus, lake, lake_code, large, management, name, oceanography, office, order, otu, OTU_id, phylum, preliminary, sequence, SequenceName, species, stewardship, system";
String license "https://www.bco-dmo.org/dataset/768138/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/768138";
String param_mapping "{'768138': {}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/768138/parameters";
String people_0_affiliation "University of California-Merced";
String people_0_affiliation_acronym "UC Merced";
String people_0_person_name "Michael N Dawson";
String people_0_person_nid "51577";
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 "Nancy Copley";
String people_1_person_nid "50396";
String people_1_role "BCO-DMO Data Manager";
String people_1_role_type "related";
String project "PaPaPro";
String projects_0_acronym "PaPaPro";
String projects_0_description
"This project will survey the taxonomic, genetic, and functional diversity of the organisms found in marine lakes, and investigate the processes that cause gains and losses in this biodiversity. Marine lakes formed as melting ice sheets raised sea level after the last glacial maximum and flooded hundreds of inland valleys around the world. Inoculated with marine life from the surrounding sea and then isolated to varying degrees for the next 6,000 to 15,000 years, these marine lakes provide multiple, independent examples of how environments and interactions between species can drive extinction and speciation. Researchers will survey the microbes, algae, invertebrates, and fishes present in 40 marine lakes in Palau and Papua, and study how diversity has changed over time by retrieving the remains of organisms preserved in sediments on the lake bottoms. The project will test whether the number of species, the diversity of functional roles played by organisms, and the genetic diversity within species increase and decrease in parallel; whether certain species can greatly curtail diversity by changing the environment; whether the size of a lake determines its biodiversity; and whether the processes that control diversity in marine organisms are similar to those that operate on land.
Because biodiversity underlies the ecosystem services on which society depends, society has a great interest in understanding the processes that generate and retain biodiversity in nature. This project will also help conserve areas of economic importance. Marine lakes in the study region are important for tourism, and researchers will work closely with governmental and non-governmental conservation and education groups and with diving and tourism businesses to raise awareness of the value and threats to marine lakes in Indonesia and Palau.";
String projects_0_end_date "2017-12";
String projects_0_geolocation "Western Pacific; Palau; Indonesia (West Papua)";
String projects_0_name "Do Parallel Patterns Arise from Parallel Processes?";
String projects_0_project_nid "2238";
String projects_0_project_website "http://marinelakes.ucmerced.edu/";
String projects_0_start_date "2013-01";
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 "List of all barcoded specimens of collected invertebrates with sequence name and OTU identifier collected from Palau marine lakes. FASTA files for major invertebrate groups are included in supplemental files.";
String title "[Palau lakes: invertebrate barcodes] - Specimen log with OTU identifiers collected from Palau marine lakes (Do Parallel Patterns Arise from Parallel Processes?)";
String version "1";
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
Data Access Protocol (DAP) and its
selection constraints.
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,
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constraints (e.g., variable<value),
each preceded by '&' (which is interpreted as "AND").
For details, see the tabledap Documentation.