http://lod.bco-dmo.org/id/dataset/699774
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2017-05-08
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Simulations and Scripts to perform Hierarchical Approximate Bayesian Computation analysis (HABC) (Multispecies Connectivity project)
2017-05-08
publication
2017-05-08
revision
BCO-DMO Linked Data URI
2017-05-08
creation
http://lod.bco-dmo.org/id/dataset/699774
Yvonne Ling-Hsiang Chan
University of Hawaiʻi at Mānoa
principalInvestigator
Dr Kimberly Selkoe
University of California-Santa Barbara
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Chan, Y. L., Selkoe, K. (2017) Simulations and Scripts to perform Hierarchical Approximate Bayesian Computation analysis (HABC) (Multispecies Connectivity project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2017-05-08 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/699774 [access date]
HABC simulations and scripts Dataset Description: <p>This dataset includes raw data for model simulations and scripts for analyzing the data and products of a Hierarchical Approximate Bayesian Computation analysis.</p>
<p>* <strong>Download the associated files (12.4 GB)</strong>: <a href="http://dmoserv3.bco-dmo.org/data/toonen/Multispecies_Connectivity/Toonen_et_al_OCE-1260169_Data_Submission_2017-05-04.zip">http://dmoserv3.bco-dmo.org/data/toonen/Multispecies_Connectivity/Toonen_et_al_OCE-1260169_Data_Submission_2017-05-04.zip</a></p> Methods and Sampling: <p>hBayeSSC download available here: https://github.com/UH-Bioinformatics/hBayeSSC</p>
<p>hBayeSSC is a Python script that wraps around Serial SimCoal in order to simulate a multi-taxa community undergoing a coordinated demographic expansion.</p>
<p><strong>Requirements:</strong></p>
<p>The applications required to produce a set of simulations with multitaxa summary statistics for the hABC analysis described in Chan et al. 2014:</p>
<p>- <a href="http://www.stanford.edu/group/hadlylab/ssc/">BayeSSC - Serial Simcoal</a><br />
- <a href="https://www.python.org/">Python 2.x</a> &gt;= 2.4<br />
- hBayeSSC.py<br />
- <a href="https://github.com/UH-Bioinformatics/hBayeSSC#msreject-module" target="_blank">msReject</a></p>
<p><strong>Input files</strong></p>
<p>The input files needed to produce a set of simulations with multitaxa summary statistics for the hABC analysis described in Chan et al. 2014:</p>
<p>- A table of observed summary statistics for each taxon in the community. (<a href="https://github.com/UH-Bioinformatics/hBayeSSC#observation-summary-statistics" target="_blank">details</a>)<br />
- An input par file for serial simcoal. (<a href="https://github.com/UH-Bioinformatics/hBayeSSC#par-file" target="_blank">details</a>)</p>
<p><strong>Observation summary statistics:</strong></p>
<p>Sample observation file:</p>
<p>The table of observed summary statistics consists of columns with the following header names. hBayeSSC replaces the appropriate line in the par file with these values:<br />
<u>Column name &nbsp;= &nbsp;Description</u><br />
species = Name of taxa<br />
nsam = number of samples to be simulated<br />
nsites = Number of base pairs<br />
tstv = % transitions<br />
gamma = Gamma shape parameter<br />
gen = Numbers of years per generation<br />
locuslow = Low estimate of the locus mutation rate per generation<br />
locushigh = High estimate of the locus mutation rate per generation<br />
Nelow = Low estimate for effective population size<br />
Nehigh = High estimate for effective population size<br />
SegSites = Segregating sites<br />
nucdiv = Nucleotide diversity<br />
Haptypes = Number of haplotypes<br />
HapDiver = Haplotypic diversity<br />
TajimasD = Tajima's D<br />
F* = Fu's F<br />
A more complete description of these values can be found on the <a href="http://www.stanford.edu/group/hadlylab/ssc/">BayeSSC website</a>.</p>
<p><strong>par file</strong></p>
<p><strong><a href="https://github.com/UH-Bioinformatics/hBayeSSC/blob/master/example_data/example.par">Sample .par file</a></strong></p>
<p>The par file contains one prior which is not individually replaced, such as expansion magnitude (under historical events) and will apply to all populations.</p>
<p>Then we use the following R-script and abc.R to do the final 1,000 acceptance and parameter estimation using local linear regression.</p>
<p>BayeSSC (Bayesian Serial SimCoal)
<a href="http://web.stanford.edu/group/hadlylab/ssc/">http://web.stanford.edu/group/hadlylab/ssc/</a></p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1260169 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1260169
completed
Yvonne Ling-Hsiang Chan
University of Hawaiʻi at Mānoa
808-349-1810
4639 Waipahee Place
Honolulu
HI
96821
Yvonne.L.Chan@gmail.com
pointOfContact
Dr Kimberly Selkoe
University of California-Santa Barbara
UC Santa Barbara, Marine Science Institute, National Center for Ecological Analysis and Synthesis 735 State Street, Suite 300
Santa Barbara
CA
93101
USA
selkoe@nceas.ucsb.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
Chan_model
service
Deployment Activity
University of Hawaii at Manoa - Hawaii Inst. of Marine Biology (HIMB) lab
place
Locations
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Multispecies connectivity: Comparative analysis of marine connectivity and its drivers for the coral reefs of Hawaii
https://osprey.bco-dmo.org/project/552879
Multispecies connectivity: Comparative analysis of marine connectivity and its drivers for the coral reefs of Hawaii
<p><em>Description from NSF award abstract:</em><br />
The exchange of individuals among populations, termed connectivity, is a central element of population persistence and maintenance of genetic diversity, and influences most ecological and evolutionary processes. To date, field studies of marine connectivity have necessarily focused on one or a few species at a time, providing little understanding of both the extent of variability in connectivity across a whole community and what factors drive that variability. This project will address these questions with population genetic datasets of a diverse marine fauna sampled across the Hawaiian Archipelago. By combining these genetic data with extensive oceanographic, ecological and historical data, this project can potentially transform our understanding of the basis of the genetic structure of populations and the processes influencing genetic patterns. This project will provide unique, and new, knowledge to basic marine ecology and the science of Ecosystem Based Management while incorporating the latest analytical and simulation approaches.</p>
<p>The results will be novel on several fronts: 1) advancing our understanding of community genetics and associated statistical techniques; 2) achieving true integration of genetic, ecological and oceanographic data over large spatial scales for many species simultaneously using a World Heritage Site; the Hawaiian Papahânaumokuâkea Marine National Monument; 3) factoring historical effects into connectivity studies; and 4) providing information on the location of barriers to connectivity, the sources and sinks of individuals and the physical processes influencing ecological patterns at a community level. This project will result in a quantum leap for both the conceptual and empirical understanding of marine connectivity and the utility of population genetic data in basic and applied marine science.</p>
Multispecies Connectivity
largerWorkCitation
project
eng; USA
oceans
University of Hawaii at Manoa - Hawaii Inst. of Marine Biology (HIMB) lab
-157.792
-157.792
21.428
21.428
2017-05-08
Hawaiian Archipelago (approx. 154 deg 40' to 178 deg 25' W longitude and 18 deg 54' to 28 deg 15' N latitude)
0
BCO-DMO catalogue of parameters from Simulations and Scripts to perform Hierarchical Approximate Bayesian Computation analysis (HABC) (Multispecies Connectivity project)
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
12673741426
https://datadocs.bco-dmo.org/file/g7WkAYJT4EYA8Q/Toonen_et_al_OCE-1260169_Data_Submission_2017-05-04.zip
Toonen_et_al_OCE-1260169_Data_Submission_2017-05-04.zip
download
https://osprey.bco-dmo.org/dataset/699774/data/download
download
onLine
dataset
<p>hBayeSSC download available here: https://github.com/UH-Bioinformatics/hBayeSSC</p>
<p>hBayeSSC is a Python script that wraps around Serial SimCoal in order to simulate a multi-taxa community undergoing a coordinated demographic expansion.</p>
<p><strong>Requirements:</strong></p>
<p>The applications required to produce a set of simulations with multitaxa summary statistics for the hABC analysis described in Chan et al. 2014:</p>
<p>- <a href="http://www.stanford.edu/group/hadlylab/ssc/">BayeSSC - Serial Simcoal</a><br />
- <a href="https://www.python.org/">Python 2.x</a> &gt;= 2.4<br />
- hBayeSSC.py<br />
- <a href="https://github.com/UH-Bioinformatics/hBayeSSC#msreject-module" target="_blank">msReject</a></p>
<p><strong>Input files</strong></p>
<p>The input files needed to produce a set of simulations with multitaxa summary statistics for the hABC analysis described in Chan et al. 2014:</p>
<p>- A table of observed summary statistics for each taxon in the community. (<a href="https://github.com/UH-Bioinformatics/hBayeSSC#observation-summary-statistics" target="_blank">details</a>)<br />
- An input par file for serial simcoal. (<a href="https://github.com/UH-Bioinformatics/hBayeSSC#par-file" target="_blank">details</a>)</p>
<p><strong>Observation summary statistics:</strong></p>
<p>Sample observation file:</p>
<p>The table of observed summary statistics consists of columns with the following header names. hBayeSSC replaces the appropriate line in the par file with these values:<br />
<u>Column name &nbsp;= &nbsp;Description</u><br />
species = Name of taxa<br />
nsam = number of samples to be simulated<br />
nsites = Number of base pairs<br />
tstv = % transitions<br />
gamma = Gamma shape parameter<br />
gen = Numbers of years per generation<br />
locuslow = Low estimate of the locus mutation rate per generation<br />
locushigh = High estimate of the locus mutation rate per generation<br />
Nelow = Low estimate for effective population size<br />
Nehigh = High estimate for effective population size<br />
SegSites = Segregating sites<br />
nucdiv = Nucleotide diversity<br />
Haptypes = Number of haplotypes<br />
HapDiver = Haplotypic diversity<br />
TajimasD = Tajima's D<br />
F* = Fu's F<br />
A more complete description of these values can be found on the <a href="http://www.stanford.edu/group/hadlylab/ssc/">BayeSSC website</a>.</p>
<p><strong>par file</strong></p>
<p><strong><a href="https://github.com/UH-Bioinformatics/hBayeSSC/blob/master/example_data/example.par">Sample .par file</a></strong></p>
<p>The par file contains one prior which is not individually replaced, such as expansion magnitude (under historical events) and will apply to all populations.</p>
<p>Then we use the following R-script and abc.R to do the final 1,000 acceptance and parameter estimation using local linear regression.</p>
<p>BayeSSC (Bayesian Serial SimCoal)
<a href="http://web.stanford.edu/group/hadlylab/ssc/">http://web.stanford.edu/group/hadlylab/ssc/</a></p>
Specified by the Principal Investigator(s)
<p>Data was processed using R-3.2.1.</p>
<p dir="ltr"><strong>BCO-DMO Processing Notes:</strong><br />
Compressed submitted files into a .zip file.</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
Deployment: Chan_model
Chan_model
UHawaii_HIMB
laboratory
UHawaii_HIMB
laboratory