Table of Contents

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

Simulated data was generated in part with R code (see file "Behavior of PL metric (Supplementary Materials 2)" ) and in part from simulations taken from Albecker et al. 2022. To create those simulations, we performed the following:

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 affected CovGE, as well as the ability to detect and measure these patterns. We simulated datasets with total sample sizes (number of environments × number of genotypes × sample size) between 32 and 500 individuals. For reciprocal transplant data, we simulated genotypic effects that increased linearly at rate γ along an environmental variable (e) for genotypes equally spaced from environment j = [1, 2,... nenv]. We generated unitless phenotypic data based on the equation: In this equation, the phenotype of individual k from genotype i in environment j is given by the genotypic effect (intercept, (i  −  1)  ×  γ), the reaction norm (where ej is the value of the environment and β is the slope of the reaction norm), an interaction term for genotype i in environment j (ηij) that describes the deviation of the reaction norm from linearity, and error (εijk). When ηij = 0, GxE is absent. When γ  =  0 (i.e. when Vp  = VE  + VGxE, Equation 1), β = 0 (i.e. Vp = VG + VGxE, Equation 1), or ηij is large, CovGE is absent. Interaction terms (ηij) were drawn from a normal distribution with mean of zero and variance equal to the number of genotypes. Random error (εijk) 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 (εijk) were used to assess population parameters (see file "Supplemental Materials 1", figure 3). For common garden designs, we adjusted this approach to model designs in which different numbers of genotypes were reared in two common environments (see file "Supplemental Materials 1", figure 4, panel c). We generated a single phenotypic reaction norm for each group of genotypes (i.e. genotypes native to the same environment) based on the first terms of Equation 4 (e.g. (i − 1)γ + βej). Then we generated reaction norm data for individual genotypes by adding the interaction term (ηij) and error (εijk) to the overall reaction norms.

R (version 4.3.0)

BCO-DMO Data Manager Processing Notes:
First column (an index column) of the output files had no column name so added label "row_id" to:
* Power_output_results.csv (text/csv)
* Variance_output_results.csv
* PL_output_results.csv

Files 20231102StampHadfield_KEL.Rmd and 20231102StampHadfield_KEL.pdf
renamed Behavior_of_PL_metric.Rmd and Behavior_of_PL_metric.pdf and attached as supplemental files to this dataset.

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.