http://lod.bco-dmo.org/id/dataset/877425
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
2022-07-22
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Results from a meta-analysis investigating covariance between genetic and environmental (CovGE) effects in phenotypic results in published literature
2025-06-05
publication
2025-06-05
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2022-10-15
publication
https://doi.org/10.26008/1912/bco-dmo.877425.1
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. C., Lotterhos, K. (2025) Results from a meta-analysis investigating covariance between genetic and environmental (CovGE) effects in phenotypic results in published literature. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 2) Version Date 2025-06-05 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.877425.2 [access date]
Results of meta-analysis on CovGE in phenotypic results Dataset Description: Methods and Sampling: <p>We searched the Web of Science database for experimental studies that evaluated differences in phenotypic responses across different genotypes and environments. We conducted the initial search on June 24, 2019. We used the search terms, ("cogradient variation" OR "countergradient variation" OR "cogradient selection" OR "countergradient selection" OR "co-gradient variation" OR "counter-gradient variation" OR "co-gradient selection" OR "counter-gradient selection") OR ("GxE" OR "genotype by environment" or "gene by environment") OR ("nonadaptive plast*" OR "non-adaptive plast*" OR "maladaptive plast*" OR "adaptive plast*") OR ("phenotypic plast*" AND "adapt*") AND ("common garden" OR "reciprocal transplant"). Initial searches returned approximately 5,900 hits. Results were further refined by including only those articles within Web of Science categories that related to ecology, evolution, or any ecological or evolutionary subdiscipline (e.g., papers categorized as engineering or biomedical were excluded). Refining reduced the search results to 4,458 studies. We also added studies that were included in previously published meta-analyses by Murren et al. (2015) and Hereford (2009) for screening.</p>
<p>Results were exported, compiled, and primed using package "metagear" (Lajeunesse 2016) in the R statistical environment (Team 2018). Studies were screened for inclusion by scanning titles and abstracts. We required that studies collect phenotypic data from at least two genotypes or populations across at least two different environments. We assumed that author-specified "populations" or "genotypes" are groups of interbreeding individuals experiencing different selection pressures and therefore are likely to be genetically divergent although we acknowledge that this is not always the case (Merilä and Hendry 2014). We excluded studies that only provided genomic data with no other phenotypic anchors, studies that did not provide information about the native environments of genotypes used in experiments, and studies that used genotypes produced by artificial selection. Additionally, a prerequisite for the estimation of CovGE is that phenotypic data from each genotype is required from the same environment in which the genotype evolved (i.e. its native environment). More simply, we cannot estimate CovGE if any genotype (‘G’) is missing its environment (‘E’). Because we use linear models to generate estimated marginal mean phenotypes (see methods below), if the experimental treatments did not align to the home (native) environments of each genotype, interpolation would be required to predict the mean phenotype for each genotype and environment. In doing so, bias can be introduced. Therefore, we only included studies that match experimental treatments to each genotype’s native environment (i.e., the environment from which genotypes were collected). Furthermore, a challenge in the meta-analysis was the presence of nonlinear reaction norms in experimental designs with continuous environmental treatments that are frequently observed in common garden experimental designs. Thus, we only included studies that used categorical experimental environments.</p>
<p>After compiling studies, we measured CovGE and GxE magnitude on phenotypic data. More methods can be found in the manuscript published in Ecology Letters in 2022.</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: 2
Unknown
Index1
Index
Time
Data_file_name
First_Author
Data_type
Phylum
Genus
Species
Order
Thermy
Total_Sample_Size
Phenotype
Phenotype_unit
Morphological_Trait
Growth_Rate
Developmental_Time
Metabolic_Physiology
Trait_class
Experimental_comparison
Environment_type
Study_Continent
Source_Country
Study_location
Mention_CGV
Sig_GxE
Covariance
Covariance_LCI
Covariance_UCI
Covariance_Pvalue
GxE_Estimate
GxE_LCI
GxE_UCI
GxE_Pvalue
Omega2
Omega2_LCI
Omega2_UCI
Omega2_Pvalue
G_diff
theme
None, User defined
No BCO-DMO term
file_name
author name (publication)
phylum
genus
species scientific name (binomial)
order
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
2025-06-05
United States
0
BCO-DMO catalogue of parameters from Results from a meta-analysis investigating covariance between genetic and environmental (CovGE) effects in phenotypic results in published literature
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/963644.rdf
Name: Index1
Units: unitless
Description: <p>Numbered column which is artifact of creating dataframe in R</p>
http://lod.bco-dmo.org/id/dataset-parameter/963645.rdf
Name: Index
Units: unitless
Description: <p>Number for each phenotype output</p>
http://lod.bco-dmo.org/id/dataset-parameter/963646.rdf
Name: Time
Units: unitless
Description: <p>Amount of seconds to run analysis</p>
http://lod.bco-dmo.org/id/dataset-parameter/963647.rdf
Name: Data_file_name
Units: unitless
Description: <p>Unique identifier for each study/author/phenotype. Used to match across studies to pull in relevant metadata</p>
http://lod.bco-dmo.org/id/dataset-parameter/963648.rdf
Name: First_Author
Units: unitless
Description: <p>Name of first author (mostly used to make sure no oddities in data formatting)</p>
http://lod.bco-dmo.org/id/dataset-parameter/963649.rdf
Name: Data_type
Units: unitless
Description: <p>Were data presented as "raw" or "means"?</p>
http://lod.bco-dmo.org/id/dataset-parameter/963650.rdf
Name: Phylum
Units: unitless
Description: <p>Taxonomic classification</p>
http://lod.bco-dmo.org/id/dataset-parameter/963651.rdf
Name: Genus
Units: unitless
Description: <p>Taxonomic classification</p>
http://lod.bco-dmo.org/id/dataset-parameter/963652.rdf
Name: Species
Units: unitless
Description: <p>Taxonomic classification</p>
http://lod.bco-dmo.org/id/dataset-parameter/963653.rdf
Name: Order
Units: unitless
Description: <p>Taxonomic classification</p>
http://lod.bco-dmo.org/id/dataset-parameter/963654.rdf
Name: Thermy
Units: unitless
Description: <p>Thermal physiology: ecto/endotherm or plant</p>
http://lod.bco-dmo.org/id/dataset-parameter/963655.rdf
Name: Total_Sample_Size
Units: unitless
Description: <p>Total number of samples across genotypes and environments</p>
http://lod.bco-dmo.org/id/dataset-parameter/963656.rdf
Name: Phenotype
Units: unitless
Description: <p>Phenotype measured</p>
http://lod.bco-dmo.org/id/dataset-parameter/963657.rdf
Name: Phenotype_unit
Units: unitless
Description: <p>Units of phenotype measurements</p>
http://lod.bco-dmo.org/id/dataset-parameter/963658.rdf
Name: Morphological_Trait
Units: unitless
Description: <p>Is the phenotype a morphological trait? T = yes, F = no</p>
http://lod.bco-dmo.org/id/dataset-parameter/963659.rdf
Name: Growth_Rate
Units: unitless
Description: <p>Is the phenotype a growth related trait? T = yes, F = no</p>
http://lod.bco-dmo.org/id/dataset-parameter/963660.rdf
Name: Developmental_Time
Units: unitless
Description: <p>Is the phenotype a development trait? T = yes, F = no</p>
http://lod.bco-dmo.org/id/dataset-parameter/963661.rdf
Name: Metabolic_Physiology
Units: unitless
Description: <p>Is the phenotype related to metabolic trait? T = yes, F = no</p>
http://lod.bco-dmo.org/id/dataset-parameter/963662.rdf
Name: Trait_class
Units: unitless
Description: <p>Derelict column, all Nas</p>
http://lod.bco-dmo.org/id/dataset-parameter/963663.rdf
Name: Experimental_comparison
Units: unitless
Description: <p>Brief description of environmental comparison in study</p>
http://lod.bco-dmo.org/id/dataset-parameter/963664.rdf
Name: Environment_type
Units: unitless
Description: <p>Into what of 9 environmental classifications does this comparison fit?</p>
http://lod.bco-dmo.org/id/dataset-parameter/963665.rdf
Name: Study_Continent
Units: unitless
Description: <p>On what continent did this study occur?</p>
http://lod.bco-dmo.org/id/dataset-parameter/963666.rdf
Name: Source_Country
Units: unitless
Description: <p>In what country did this study occur? Multiple countries marked as "multiple"</p>
http://lod.bco-dmo.org/id/dataset-parameter/963667.rdf
Name: Study_location
Units: unitless
Description: <p>As narrow location as possible</p>
http://lod.bco-dmo.org/id/dataset-parameter/963668.rdf
Name: Mention_CGV
Units: unitless
Description: <p>Do authors mention CovGE in original paper?</p>
http://lod.bco-dmo.org/id/dataset-parameter/963669.rdf
Name: Sig_GxE
Units: unitless
Description: <p>Do authors find significant GxE in this phenotype in original paper?</p>
http://lod.bco-dmo.org/id/dataset-parameter/963670.rdf
Name: Covariance
Units: unitless
Description: <p>CovGE estimate</p>
http://lod.bco-dmo.org/id/dataset-parameter/963671.rdf
Name: Covariance_LCI
Units: unitless
Description: <p>CovGE lower confidence interval</p>
http://lod.bco-dmo.org/id/dataset-parameter/963672.rdf
Name: Covariance_UCI
Units: unitless
Description: <p>CovGE upper confidence interval</p>
http://lod.bco-dmo.org/id/dataset-parameter/963673.rdf
Name: Covariance_Pvalue
Units: unitless
Description: <p>Signifiance (pvalue) of CovGE estimate</p>
http://lod.bco-dmo.org/id/dataset-parameter/963674.rdf
Name: GxE_Estimate
Units: unitless
Description: <p>Delta GxE estimate</p>
http://lod.bco-dmo.org/id/dataset-parameter/963675.rdf
Name: GxE_LCI
Units: unitless
Description: <p>Delta GxE lower confidence interval. Not used because unreliable</p>
http://lod.bco-dmo.org/id/dataset-parameter/963676.rdf
Name: GxE_UCI
Units: unitless
Description: <p>Delta GxE upper confidence interval. Not used because unreliable</p>
http://lod.bco-dmo.org/id/dataset-parameter/963677.rdf
Name: GxE_Pvalue
Units: unitless
Description: <p>Pvalue for significance of Delta GxE</p>
http://lod.bco-dmo.org/id/dataset-parameter/963678.rdf
Name: Omega2
Units: unitless
Description: <p>Omega squared estimate (Variance partioning method to generate idea of how much variation is explained by GxE). Not used here</p>
http://lod.bco-dmo.org/id/dataset-parameter/963679.rdf
Name: Omega2_LCI
Units: unitless
Description: <p>Omega Squared lower confidence interval . Not used here</p>
http://lod.bco-dmo.org/id/dataset-parameter/963680.rdf
Name: Omega2_UCI
Units: unitless
Description: <p>Omega Squared upper confidence interval. Not used here</p>
http://lod.bco-dmo.org/id/dataset-parameter/963681.rdf
Name: Omega2_Pvalue
Units: unitless
Description: <p>Omega Squared Pvalue. Not used here</p>
http://lod.bco-dmo.org/id/dataset-parameter/963682.rdf
Name: G_diff
Units: unitless
Description: <p>Average difference in phenotypes across genotypes. Not used here</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
178408
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/b995e3fe-cb93-4a2f-a0b3-86dde604c056/content
download
7352747
https://darchive.mblwhoilibrary.org/bitstream/1912/29428/1/dataset-877425_results-meta-analysis-covge-phenotypic-results__v1.tsv
download
https://doi.org/10.26008/1912/bco-dmo.877425.1
download
onLine
dataset
<p>We searched the Web of Science database for experimental studies that evaluated differences in phenotypic responses across different genotypes and environments. We conducted the initial search on June 24, 2019. We used the search terms, ("cogradient variation" OR "countergradient variation" OR "cogradient selection" OR "countergradient selection" OR "co-gradient variation" OR "counter-gradient variation" OR "co-gradient selection" OR "counter-gradient selection") OR ("GxE" OR "genotype by environment" or "gene by environment") OR ("nonadaptive plast*" OR "non-adaptive plast*" OR "maladaptive plast*" OR "adaptive plast*") OR ("phenotypic plast*" AND "adapt*") AND ("common garden" OR "reciprocal transplant"). Initial searches returned approximately 5,900 hits. Results were further refined by including only those articles within Web of Science categories that related to ecology, evolution, or any ecological or evolutionary subdiscipline (e.g., papers categorized as engineering or biomedical were excluded). Refining reduced the search results to 4,458 studies. We also added studies that were included in previously published meta-analyses by Murren et al. (2015) and Hereford (2009) for screening.</p>
<p>Results were exported, compiled, and primed using package "metagear" (Lajeunesse 2016) in the R statistical environment (Team 2018). Studies were screened for inclusion by scanning titles and abstracts. We required that studies collect phenotypic data from at least two genotypes or populations across at least two different environments. We assumed that author-specified "populations" or "genotypes" are groups of interbreeding individuals experiencing different selection pressures and therefore are likely to be genetically divergent although we acknowledge that this is not always the case (Merilä and Hendry 2014). We excluded studies that only provided genomic data with no other phenotypic anchors, studies that did not provide information about the native environments of genotypes used in experiments, and studies that used genotypes produced by artificial selection. Additionally, a prerequisite for the estimation of CovGE is that phenotypic data from each genotype is required from the same environment in which the genotype evolved (i.e. its native environment). More simply, we cannot estimate CovGE if any genotype (‘G’) is missing its environment (‘E’). Because we use linear models to generate estimated marginal mean phenotypes (see methods below), if the experimental treatments did not align to the home (native) environments of each genotype, interpolation would be required to predict the mean phenotype for each genotype and environment. In doing so, bias can be introduced. Therefore, we only included studies that match experimental treatments to each genotype’s native environment (i.e., the environment from which genotypes were collected). Furthermore, a challenge in the meta-analysis was the presence of nonlinear reaction norms in experimental designs with continuous environmental treatments that are frequently observed in common garden experimental designs. Thus, we only included studies that used categorical experimental environments.</p>
<p>After compiling studies, we measured CovGE and GxE magnitude on phenotypic data. More methods can be found in the manuscript published in Ecology Letters in 2022.</p>
<p>See Related Dataset Albecker et al. (2022) for model code.</p>
Specified by the Principal Investigator(s)
Version 1:
- Adjusted field/parameter names to comply with BCO-DMO naming conventions
- Missing data identifier ‘NA’ replaced with 'nd' (BCO-DMO's default missing data identifier)
- Added a conventional header with dataset name, PI names, version date
- Removed units of temperature from column "exp_env_cont"
- Replaced commas with semi-colons in the “trait_notes” column
- Removed apostrophes from the “trait_notes” column
Specified by BCO-DMO Data Managers
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