Table of Contents

• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Project Information
• Funding

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.

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.

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.

See Related Dataset Albecker et al. (2022) for model code.

BCO-DMO Processing:
- 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

NSF abstract:
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.

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.

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.