Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Curation Notes
  • Problem Description
• Related Publications
• Related Datasets
• Parameters
• Project Information
• Funding

Eastern oyster, Crassostrea virginica, urn:lsid:marinespecies.org:taxname:140657

The experimental source population dataset consisted of historic data for our six wild populations and two selection lines, which was input as the starting environment in the offset model (i.e., the environment being predicted from). We developed abiotic and biotic (e.g., disease caused by parasites) environmental datasets for genomic offset modeling, and explored how the inclusion of biotic variables affected the predictive performance of offset models.

For abiotic variables, we included extreme quantiles (10th and 90th quantiles) for temperature (ºC) and salinity (ppt). Extreme quantiles were chosen to represent environmental conditions as they presumably capture the extreme conditions likely to impose selective pressures, without suffering from the statistical issues associated with averaging maximums or minimums. Environmental data for the seascape training dataset and experimental source population site-of-origin data included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC) across as many years as data were available (Eppley et al. 2026, Bajaj & Mongillo et al. 2026). Environmental data for the common garden environment at Lewisetta and York River included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC)  for only the experimental period. 

For biotic (disease) variables, we included population-level prevalence of the two parasites (disease prevalence). In the experimental source population dataset, which was collected during months with historic precedent of peak disease, the prevalence of MSX and Dermo for each population was calculated according to the proportion of individual oysters with each disease as determined by multiplex qPCR assay (supplemental methods) (Piesz et al. 2022; Ewart and Ford 1993). For the common garden sites, the prevalence of MSX and Dermo in the surrounding environment was determined by using Ray's Fluid Thioglycollate Medium (RFTM) assay on a set of 20 naive “sentinel” oysters deployed on both locations and then assessed monthly during peak disease season (July through November) (Ray 1952; Dungan and Bushek 2015).

For abiotic variables, we included extreme quantiles (10th and 90th quantiles) for temperature (ºC) and salinity (ppt). Extreme quantiles were chosen to represent environmental conditions as they presumably capture the extreme conditions likely to impose selective pressures, without suffering from the statistical issues associated with averaging maximums or minimums. Environmental data for the seascape training dataset and experimental source population site-of-origin data included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC) across as many years as data were available (Eppley et al. 2026, Bajaj & Mongillo et al. 2026). Environmental data for the common garden environment at Lewisetta and York River included the 10th and 90th quantiles for salinity (ppt) and temperature (ºC)  for only the experimental period. 

For biotic (disease) variables, we included population-level prevalence of the two parasites (disease prevalence). In the experimental source population dataset, which was collected during months with historic precedent of peak disease, the prevalence of MSX and Dermo for each population was calculated according to the proportion of individual oysters with each disease as determined by multiplex qPCR assay (supplemental methods) (Piesz et al. 2022; Ewart and Ford 1993). For the common garden sites, the prevalence of MSX and Dermo in the surrounding environment was determined by using Ray's Fluid Thioglycollate Medium (RFTM) assay on a set of 20 naive “sentinel” oysters deployed on both locations and then assessed monthly during peak disease season (July through November) (Ray 1952; Dungan and Bushek 2015).

Curation Actions Performed on Data

- Loaded CviMVP_expdat_env.csv, naming resource cvimvp_expdat_env, with missing values "", "NA" set
- Set column types on cvimvp_expdat_env: Dermo_Prevalence, Latitude_manual, Longitude_manual, MSX_Prevalence, salinity_quantile_10, salinity_quantile_90, temp_quantile_10, temp_quantile_90 as number; site_name as string
- Updated column metadata on cvimvp_expdat_env: added descriptions, standard name IDs, and supplied units for Dermo_Prevalence, Latitude_manual, Longitude_manual, MSX_Prevalence, salinity_quantile_10, salinity_quantile_90, site_name, temp_quantile_10, temp_quantile_90; marked Latitude_manual and Longitude_manual as primary parameters
- Renamed resource cvimvp_expdat_env to 1007572_cvimvp-env-data
- Loaded CviMVP_disease_averages_common_gardens.csv, naming resource disease-averages-common-gardens, with missing values "", "nd", "NA" set; Pea_crab_se values of 0 retained as genuine numeric zero rather than treated as missing
- Set column types on disease-averages-common-gardens: Dermo_Prevalence, Dermo_Prevalence_se, MSX_Prevalence, MSX_Prevalence_se, Pea_crab, Pea_crab_se as number; site_name as string
- Updated column metadata on disease-averages-common-gardens: added descriptions, standard name IDs, and supplied units for Dermo_Prevalence, Dermo_Prevalence_se, MSX_Prevalence, MSX_Prevalence_se, Pea_crab, Pea_crab_se, site_name
- Saved as final tables: 1007572_cvimvp-env-data.csv, disease-averages-common-gardens.csv

Curation Actions Performed on Metadata
* References for in text citations were not provided. Most were found in the metadata of the Related Datsets about the same experiment, or the results publication. However these two were not found "Piesz et al. 2022, Dungan and Bushek 2015"
* Time range of the dataset was not supplied. The data manager entered it as the time of:
From: 2022-09 (start of broodstock collection) since this dataset reports the source population environmental data from those broodstock sources.
To: 2025-05 (final tissue sampling date 2025-05-27, marking the end of the ~2-year monitoring period)
* Pending - the data submitter is revising the submission with metadata changes.

Known Issues [Potentially Impacting Reuse]
* TBD (submitter is revising submission)

NSF Award Abstract:
Environmental change can be rapid and involve multiple aspects of the environment changing at the same time, such as warming and increased disease pressure. Rapid environmental change threatens the productivity of aquaculture and crops on which humans depend. Predicting organisms' vulnerabilities to rapid and multifactor environmental change, however, is a major scientific challenge. A hurdle to addressing this challenge arises from the complex and non-intuitive ways that organisms adapt, through changes at the level of the DNA sequence, to many environmental stresses at the same time. Thus, there is a need for new approaches to understand and predict adaptation in multivariate environments. To address this need, this project integrates research and education with a Model Validation Program (MVP). The research is developing and evaluating Machine Learning Algorithms (MLAs) for understanding and predicting adaptation of organisms to multivariate environments from their DNA sequences. To evaluate MLAs, this research combines both data simulation and an empirical test in the field with the Eastern Oyster, which provide important ecosystem services and support a multi-million dollar industry. For oysters, this research is studying how temperature, disease pressure, and salinity interact with evolutionary history to determine fitness in the field. This research advances efforts toward addressing the major scientific challenge of predicting adaptation in complex environments by integrating concepts across the frontiers of marine, evolutionary, and statistical sciences in a new way. Machine learning and model validation are not traditionally taught in the marine and environmental sciences, but are becoming increasingly relevant to these fields. As part of a broader education program, this research is developing MVP Learning Modules for high school students and undergraduates, which help students build the foundational knowledge they need to critically evaluate and apply models. Modules are being disseminated to hundreds of students in the greater Boston area and are being made available online for widespread use. The MVP mentoring program is training graduate students, undergraduates, and high school students in marine evolutionary ecology, statistical genomics, and machine learning. This research addresses a pressing societal need to more informatively match genotypes to environments for restoration, farming, and assisted gene flow efforts. Results are being disseminated to stakeholders in the oyster industry.

The goal of this research is to evaluate if MLAs, which can model non-linearities, can be used to understand and predict adaptation to multivariate environments under a wide range of scenarios. In Objective 1, the Principal Investigator (PI) is creating simulated datasets with different aspects of realism, and using them to evaluate and refine the MLAs. This novel set of simulations is studying genome evolution under high gene flow in complex, multivariate environments. In Objective 2, the PI is building on their expertise with the Eastern oyster to evaluate the MLAs in a field setting. The PI is first developing a comprehensive seascape genomic dataset and using it to train MLAs to predict an individual's multivariate environment based on a single nucleotide polymorphism genotype. Then, the PI is testing if the MLA prediction can predict the fitness of different genotypes from across the species range when raised in common garden field conditions. In Objective 3, the PI is integrating research and education by using the data obtained from Objs. 1 and 2 to develop a series of original "MVP Learning Modules" with interactive web apps for persons at different levels of understanding, using the relatable example of an oyster restoration project. This research lays the foundation for future studies by producing datasets that could become classical examples for developing and benchmarking innovative modeling approaches.

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.