The purpose of this study was to apply a quantitative measure to phenotypic data that were collected by other studies to test hypotheses about natural prevalence and patterns of Covariance between genotypic and environmental effects on phenotypes (CovGE).

Table of Contents

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

There were three major phases to this study which are pertinent for data storage and collection. 

1. The first phase was a quantitative review in which the literature was systematically surveyed, assessed for inclusion, and relevant data extracted. This process resulted in the dataset "Extraction_Initialize.csv", which contains metadata from all studies initially considered for data collection. In some cases, studies were later deemed inappropriate and excluded from subsequent phases, but their metadata remains in this file. Once data collection was completed for the 102 studies that met the inclusion criteria, all phenotypic data—used in the second phase—were compiled into a single .csv file, "Meta_data_checked.csv". 

2. The second phase involved data analysis, where the phenotypic data and metadata were imported into R, formatted, and processed using a for-loop to estimate CovGE and ΔG×E for each phenotype independently. This loop fully implements CovGE estimation following the methods outlined in Albecker et al. (2022) and also generates confidence intervals for CovGE and p-values for ΔG×E. This analysis was executed in lines 1–220 of the "CovGE_Meta_Analysis.R" script, with essential functions housed in the companion script "CovarianceDataFunctions.R". Results of this analysis were saved as, "Meta_output_Nov27_2024.csv" 

3. The third phase involved testing the questions/hypotheses outlined in the manuscript (e.g., Is CnGV or CoGV more prevalent in nature?). These analyses were conducted in the "CovGE_Meta_Analysis.R" script, following the conclusion of the primary analysis on Line 220. This phase also included all plotting and formatting code. Any modifications made to plots in PowerPoint were documented with notes. All code is fully annotated for clarity and reproducibility.

 

Files in this dataset:

1. Code file - "CovGE_Meta_Analysis.R" - R code including all analyses, data formatting, and plotting. See phase 2 and 3 
2. Code file - "CovarianceDataFunctions.R" - R companion code storing all essential functions for analyses. See phase 2 and 3.  Compiled dataset with the extracted phenotypic, genotypic, and environmental data for each study included in quantitative review. See Phase 1 in the description of Contents for additional details. 

Website: https://rcn-ecs.github.io/

Coverage: United States

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.