http://lod.bco-dmo.org/id/dataset/877456
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
2022-07-25
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Results using simulated data used to conduct power analyses
2022-10-14
publication
2022-10-14
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2022-10-15
publication
https://doi.org/10.26008/1912/bco-dmo.877456.1
Katie Lotterhos
Northeastern University
principalInvestigator
Geoffrey C. Trussell
Northeastern University
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: Albecker, M., Trussell, G., Lotterhos, K. (2022) Results using simulated data used to conduct power analyses. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-10-14 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.877456.1 [access date]
Power output results Dataset Description: Methods and Sampling: <p>To validate the and G×E&amp; sampling estimates, we created simulations that mimicked experimental data, and provided an array of scenarios to understand how effect size, presence of GxE, total sample size, experimental design, and variability were affected, as well as the ability to detect and measure these patterns. We simulated datasets with total sample sizes (number of environments x number of genotypes x sample size) between 32 and 500 individuals.<br />
<br />
For reciprocal transplant data, we simulated genotypic effects that increased linearly at rate y along an environmental variable (e) for genotypes equally spaced from environment j = [1, 2, ... nenv]. We generated unitless phenotypic data based on an equation in the Supplemental File "Equation for power output results" (<a href="https://datadocs.bco-dmo.org/docs/305/RCN_ECS/data_docs/power_analyses_pasted_graphic.pdf" target="_blank">power_analyses_pasted_graphic.pdf</a>).<br />
&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;<br />
In this equation, the phenotype of individual k from genotype i in environment j is given by the genotypic effect, the reaction norm (where ej is the value of the environment and beta is the slope of the reaction norm), an interaction term for genotype i in environment j that describes the deviation of the reaction norm from linearity, and error.</p>
<p>Interaction terms were drawn from a normal distribution with mean of zero and variance equal to the number of genotypes. Random error was added by sampling from a normal distribution with a mean of zero and standard deviation of either 0.5 (low residual variation) or 1 (high residual variation). Scenarios with no random error (= 0) were used to assess population parameters.</p>
<p>For common garden designs, we adjusted this approach to model designs in which different numbers of genotypes were reared in two common environments. We generated a single phenotypic reaction (see supplemental docs) norm for each group of genotypes (i.e., genotypes native to the same environment) based on the first terms of Eqn. 4. Then we generated reaction norm data for individual genotypes by adding the interaction term and error to the overall reaction norms.</p>
<p>See Related Dataset Albecker et al. (2022) for model code.</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1764316 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1764316
completed
Katie Lotterhos
Northeastern University
781-581-7370 X304
Northeastern University Marine Sciences Center 430 Nahant Rd.
Nahant
MA
01908
USA
k.lotterhos@northeastern.edu
pointOfContact
Geoffrey C. Trussell
Northeastern University
781-581-7370 (ext. 300)
Marine Science Center 430 Nahant Road
Nahant
MA
01908
USA
g.trussell@northeastern.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
row
replicate
env_scenario
delta_env
delta_gen
sample_size
total_samples
n_env
n_pop
std_dev
interaction
Sim_time
true_cov
covariance
covariance_lwrCI
covariance_uprCI
covariance_pvalue
true_cov_means
cov_means
cov_means_lwrCI
cov_means_uprCI
cov_means_pvalue
GxE_Anova
true_GxE_emm
GxE_emm
GxE_emm_lwrCI
GxE_emm_uprCI
GxE_emm_pvalue
true_GxE_omega
GxE_omega
GxE_omega_lwrCI
GxE_omega_uprCI
GxE_omega_pvalue
true_GxE_means
GxE_means
GxE_means_lwrCI
GxE_means_uprCI
GxE_means_pvalue
theme
None, User defined
No BCO-DMO term
replicate
comments
sample identification
number
standard deviation
time_elapsed
featureType
BCO-DMO Standard Parameters
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.
RCN: Evolution in Changing Seas
https://rcn-ecs.github.io/
RCN: Evolution in Changing Seas
<p><em>NSF abstract:</em><br />
How marine species will react to changing environment and climate is not well understood. While the interaction between oceanographic and ecological processes has yielded considerable insight into the ecology of marine species, the evolutionary responses of marine species are not well integrated into this framework. This project research coordinated network on "Evolution in Changing Seas" (ECSRCN), will bring marine scientists together with evolutionary biologists having expertise in population genetics, eco-evolutionary dynamics, and phylogenetics to better understand and predict the evolutionary responses of marine species to climate stressors. ECS-RCN will increase the impact of evolutionary studies in marine systems through increased collaboration among scientists from diverse fields. Furthermore, the empirical robustness of these studies will also be improved through the development of standards for experimental design and statistical analysis, especially for genomics data analysis. ECS-RCN will build a diverse network through a dedicated workshop for early-career participants, by advertising with diversity groups, and by dedicating funds to increase diversity. This project will support one postdoctoral researcher who will play a key role in coordinating scientific activities of the network as well as receive interdisciplinary training through network activities, strongly positioning them to become a leader in the field. ECS-RCN will also build the foundation for a lasting network through establishment of a listserv, open access to publications, development of a website, and development of teaching modules for undergraduate and graduate curriculum.</p>
<p>Specifically, ECS-RCN will consider how coupling between oceanographic and evolutionary processes shape adaptive and plastic responses to climate change, from the fundamental level of genomes scaled up to entire populations. Under this theme, the objectives of ECS-RCN are to synthesize the current state of knowledge, to prioritize lines of inquiry that will advance knowledge in marine and evolutionary biology, to determine the appropriate experimental designs and statistical approaches for robustly testing these lines of inquiry (including genomics approaches), and to build a foundation for a diverse and lasting network. These goals will be realized over the course of 3 years, starting with a Synthesis Workshop in Year 1 where working groups will be established, followed by working group meetings and formation of a Genomics Subcommittee in Year 2, and ending with an Integration and Training Workshop aimed at early career scientists in Year 3. To promote synthesis and self-organization at workshops, the workshops will employ the Open Space format. ECS-RCN will promote evolutionary thinking in biological oceanography and integrate unique aspects of marine life-histories into evolutionary principles. ECS-RCN will also advance knowledge in both marine and evolutionary biology through synthesis and the development of frameworks for merging genomics and ecology. The activities will provide novel insights into pressing questions in both marine and evolutionary ecology, such as: what drives geographic patterns of local (mal)adaptation and plasticity?; what are the mechanisms that generate adaptive vs. nonadaptive plasticity?; what is the role of genotype dependent dispersal in adaptation?; what are the genetic constraints on adaptation of function-valued traits to climate change?; and how do epigenetic modifications act as a mediator between adaptation and plasticity? Ultimately, the RCN aims to develop a quantitative understanding of the relative importance of ecological versus evolutionary responses to climate change.</p>
<p>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.</p>
RCN ECS
largerWorkCitation
project
eng; USA
oceans
2022-10-14
United States
0
BCO-DMO catalogue of parameters from Results using simulated data used to conduct power analyses
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
http://lod.bco-dmo.org/id/dataset-parameter/877555.rdf
Name: row
Units: unitless
Description: <p>identifier</p>
http://lod.bco-dmo.org/id/dataset-parameter/877556.rdf
Name: replicate
Units: unitless
Description: <p>identifier</p>
http://lod.bco-dmo.org/id/dataset-parameter/877557.rdf
Name: env_scenario
Units: unitless
Description: <p>identifies whether reciprocal transplant (1) or common garden design (2) was used</p>
http://lod.bco-dmo.org/id/dataset-parameter/877558.rdf
Name: delta_env
Units: unitless
Description: <p>degree of environmental change</p>
http://lod.bco-dmo.org/id/dataset-parameter/877559.rdf
Name: delta_gen
Units: unitless
Description: <p>degree of genetic change</p>
http://lod.bco-dmo.org/id/dataset-parameter/877560.rdf
Name: sample_size
Units: unitless
Description: <p>sample size</p>
http://lod.bco-dmo.org/id/dataset-parameter/877561.rdf
Name: total_samples
Units: unitless
Description: <p>total sample</p>
http://lod.bco-dmo.org/id/dataset-parameter/877562.rdf
Name: n_env
Units: unitless
Description: <p>total number of environments</p>
http://lod.bco-dmo.org/id/dataset-parameter/877563.rdf
Name: n_pop
Units: unitless
Description: <p>total number of genotypes</p>
http://lod.bco-dmo.org/id/dataset-parameter/877564.rdf
Name: std_dev
Units: unitless
Description: <p>degree of error</p>
http://lod.bco-dmo.org/id/dataset-parameter/877565.rdf
Name: interaction
Units: unitless
Description: <p>deviation from linearity</p>
http://lod.bco-dmo.org/id/dataset-parameter/877566.rdf
Name: Sim_time
Units: unitless
Description: <p>minutes to complete simulation</p>
http://lod.bco-dmo.org/id/dataset-parameter/877567.rdf
Name: true_cov
Units: unitless
Description: <p>population covGE</p>
http://lod.bco-dmo.org/id/dataset-parameter/877568.rdf
Name: covariance
Units: unitless
Description: <p>sample covGE estimate</p>
http://lod.bco-dmo.org/id/dataset-parameter/877569.rdf
Name: covariance_lwrCI
Units: unitless
Description: <p>lower confidence interval for CovGE sample estimate</p>
http://lod.bco-dmo.org/id/dataset-parameter/877570.rdf
Name: covariance_uprCI
Units: unitless
Description: <p>upper confidence interval for CovGE sample estimate</p>
http://lod.bco-dmo.org/id/dataset-parameter/877571.rdf
Name: covariance_pvalue
Units: unitless
Description: <p>significance of sample estimate from 0</p>
http://lod.bco-dmo.org/id/dataset-parameter/877572.rdf
Name: true_cov_means
Units: unitless
Description: <p>population CovGE for data in means format</p>
http://lod.bco-dmo.org/id/dataset-parameter/877573.rdf
Name: cov_means
Units: unitless
Description: <p>sample estimate for CovGE for data in means format</p>
http://lod.bco-dmo.org/id/dataset-parameter/877574.rdf
Name: cov_means_lwrCI
Units: unitless
Description: <p>lower confidence interval for CovGE sample estimate for means data</p>
http://lod.bco-dmo.org/id/dataset-parameter/877575.rdf
Name: cov_means_uprCI
Units: unitless
Description: <p>upper confidence interval for CovGE sample estimate for means data</p>
http://lod.bco-dmo.org/id/dataset-parameter/877576.rdf
Name: cov_means_pvalue
Units: unitless
Description: <p>significance of sample estimate from 0 for means data</p>
http://lod.bco-dmo.org/id/dataset-parameter/877577.rdf
Name: GxE_Anova
Units: unitless
Description: <p>magnitude of interaction</p>
http://lod.bco-dmo.org/id/dataset-parameter/877578.rdf
Name: true_GxE_emm
Units: unitless
Description: <p>population interaction magnitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/877579.rdf
Name: GxE_emm
Units: unitless
Description: <p>sample estimate for interaction magnitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/877580.rdf
Name: GxE_emm_lwrCI
Units: unitless
Description: <p>lower confidence interval for interaction magnitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/877581.rdf
Name: GxE_emm_uprCI
Units: unitless
Description: <p>upper confidence interval for interaction magnitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/877582.rdf
Name: GxE_emm_pvalue
Units: unitless
Description: <p>significance of sample estimate for GxE</p>
http://lod.bco-dmo.org/id/dataset-parameter/877583.rdf
Name: true_GxE_omega
Units: unitless
Description: <p>population GxE using omega squared approach</p>
http://lod.bco-dmo.org/id/dataset-parameter/877584.rdf
Name: GxE_omega
Units: unitless
Description: <p>sample GxE estimate using omega squared approach</p>
http://lod.bco-dmo.org/id/dataset-parameter/877585.rdf
Name: GxE_omega_lwrCI
Units: unitless
Description: <p>lower confidence interval for interaction using omega squared approach</p>
http://lod.bco-dmo.org/id/dataset-parameter/877586.rdf
Name: GxE_omega_uprCI
Units: unitless
Description: <p>upper confidence interval for interaction using omega squared approach</p>
http://lod.bco-dmo.org/id/dataset-parameter/877587.rdf
Name: GxE_omega_pvalue
Units: unitless
Description: <p>significance of sample estimate for interaction using omega squared approach</p>
http://lod.bco-dmo.org/id/dataset-parameter/877588.rdf
Name: true_GxE_means
Units: unitless
Description: <p>populations GxE using means approach</p>
http://lod.bco-dmo.org/id/dataset-parameter/877589.rdf
Name: GxE_means
Units: unitless
Description: <p>sample estimate for GxE using means approach</p>
http://lod.bco-dmo.org/id/dataset-parameter/877590.rdf
Name: GxE_means_lwrCI
Units: unitless
Description: <p>lower confidence interval for interaction for means data</p>
http://lod.bco-dmo.org/id/dataset-parameter/877591.rdf
Name: GxE_means_uprCI
Units: unitless
Description: <p>upper confidence interval for interaction for means data</p>
http://lod.bco-dmo.org/id/dataset-parameter/877592.rdf
Name: GxE_means_pvalue
Units: unitless
Description: <p>significance of interaction term for means data</p>
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
4693909
https://darchive.mblwhoilibrary.org/bitstream/1912/29430/1/dataset-877456_power-output-results__v1.tsv
download
https://doi.org/10.26008/1912/bco-dmo.877456.1
download
onLine
dataset
<p>To validate the and G×E&amp; sampling estimates, we created simulations that mimicked experimental data, and provided an array of scenarios to understand how effect size, presence of GxE, total sample size, experimental design, and variability were affected, as well as the ability to detect and measure these patterns. We simulated datasets with total sample sizes (number of environments x number of genotypes x sample size) between 32 and 500 individuals.<br />
<br />
For reciprocal transplant data, we simulated genotypic effects that increased linearly at rate y along an environmental variable (e) for genotypes equally spaced from environment j = [1, 2, ... nenv]. We generated unitless phenotypic data based on an equation in the Supplemental File "Equation for power output results" (<a href="https://datadocs.bco-dmo.org/docs/305/RCN_ECS/data_docs/power_analyses_pasted_graphic.pdf" target="_blank">power_analyses_pasted_graphic.pdf</a>).<br />
&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;<br />
In this equation, the phenotype of individual k from genotype i in environment j is given by the genotypic effect, the reaction norm (where ej is the value of the environment and beta is the slope of the reaction norm), an interaction term for genotype i in environment j that describes the deviation of the reaction norm from linearity, and error.</p>
<p>Interaction terms were drawn from a normal distribution with mean of zero and variance equal to the number of genotypes. Random error was added by sampling from a normal distribution with a mean of zero and standard deviation of either 0.5 (low residual variation) or 1 (high residual variation). Scenarios with no random error (= 0) were used to assess population parameters.</p>
<p>For common garden designs, we adjusted this approach to model designs in which different numbers of genotypes were reared in two common environments. We generated a single phenotypic reaction (see supplemental docs) norm for each group of genotypes (i.e., genotypes native to the same environment) based on the first terms of Eqn. 4. Then we generated reaction norm data for individual genotypes by adding the interaction term and error to the overall reaction norms.</p>
<p>See Related Dataset Albecker et al. (2022) for model code.</p>
Specified by the Principal Investigator(s)
<p><strong>BCO-DMO Processing:</strong><br />
- Adjusted field/parameter names to comply with BCO-DMO naming conventions<br />
- Added a conventional header with dataset name, PI names, version date</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