| Contributors | Affiliation | Role |
|---|---|---|
| Lotterhos, Katie | Northeastern University | Principal Investigator |
| Small, Jessica | Virginia Institute of Marine Science (VIMS) | Scientist |
| Leung, Elisabeth | Northeastern University | Student, Contact |
| Soenen, Karen | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Juvenile oysters ranging from 30mm to 60mm were sourced from the Aquaculture Genetics and Breeding Technology Center (ABC) at the Virginia Institute of Marine Science (VIMS). Each oyster was cleaned using a 1% bleach solution, air-dried for an hour, and labeled with a shellfish tag (Hallprint FPN 8x4 mm) using coral epoxy (Instant Ocean HoldFast® Epoxy Stick) (Figure 1A). Shell width and length were measured for each oyster before the experiment (Kraeuter et al., 2007). To store the oysters before the experiment, the oysters were stored at 5°C for up to 48 hours before experimentation. After each trial, the oysters were transferred to sea tables (flow-through tanks filled with minimally filtered seawater from the adjacent York River), and mortality was monitored daily for 11 days. Daily mortality checks were conducted where each oyster was removed from the sea table briefly to check for shell gapes, which indicated loss of adductor muscle function and death.
Treatment groups: We planned nine treatments to determine which protocol would have the best DNA yield with the lowest mortality (Figure 1 of Leung et al. 2026 (in press)). Each treatment had at least 20 individual oysters. Groups of 10 oysters per each treatment were placed into separate trays in the sea tables. The treatments and short names are described in Table 1 of Leung et al. 2026 (in press).
Accessing the oyster tissue within the shell: Two methods were used to enter the oyster shell: creating a notch in the shell or relaxing the oyster. Creating a notch allowed for the extraction of fluid that contains genetic material (e.g., hemolymph or EPF), while relaxing the oyster allowed for tissue swabbing or direct sampling of the tissue.
Collection of genetic material
Cell collection for notched oysters: Cells were collected from notched oysters by sampling hemolymph or EPF fluids. For the preservation of fluids, we split the samples and placed half in liquid nitrogen and the other half in FTA cards (see below):
Cell collection for relaxed oysters: Cells were collected from relaxed oysters by taking a mantle biopsy or swabbing the mantle tissue.
Preservation of genetic material: We used three preservation protocols for the genetic samples: ethanol, flash-freezing, and FTA cards:
Statistical analysis of the data is described in Leung et al. 2026
This section documents curation actions performed prior to publication review with the submitter, and additional information relevant to understanding and reusing this dataset. It distinguishes changes made to the submitted (meta)data from unresolved issues and/or enhancements that improve future reuse and interoperability.
CURATION ACTIONS PERFORMED ON DATA
- Data were provided as a CSV file (Mortality_data.csv). Empty cells were treated as missing and appear as blank cells in the final file.
- Dates and times were standardized to ISO 8601: Date is YYYY-MM-DD, Time_extracted and Time_in_table were converted from 12hr AM/PM to 24hr HH:MM:SS, and Date_Time_Dead and Date_time_extracted are YYYY-MM-DDTHH:MM:SS (local time, no time zone).
- Final table was renamed 1000264_v1_oysters
CURATION ACTIONS PERFORMED ON METADATA
- Metadata were made specific to this mortality dataset: data analysis methods (see related publication) and DNA extraction, quantification and quality methods (see related dataset, once available) were removed from the abstract and methods.
- The title was updated to reflect the dataset’s timespan (2022) and a typo was corrected (“Crossosrea” to “Crassostrea”).
ISSUES POTENTIALLY IMPACTING REUSE
- Time zone of the date-time values was not provided.
- Units of Fluid_mL provided as mililiters (ml), but likely microliters (ul)?
| Parameter | Description | Units |
| Tag_ID | The number on the shellfish tag that was glued to the oyster at the beginning of the experiment | unitless |
| Length | The length of the oyster from the top to the bottom | mm (millimeter) |
| Width | The width of the oyster at the widest part | mm (millimeter) |
| Exterior_Trt | The treatment to access the oyster tissues within the shell. It is either Notch or Relaxation | unitless |
| Tissue_Trt | The treatment for collection of genetic material. It is either Hemolymph, EPF, Mantle, or Swab. | unitless |
| Preserv_Trt | The treatment for the preservation of genetic material. It will be ethanol, flash-freeze, or FTA. | unitless |
| Overall_Trt | All three treatments combined into one term to easily identify all at once. | unitless |
| Fluid_mL | The amount of fluid collected from oysters in the Hemolymph or EPF treatment groups. | mL |
| Vial_Number | The number of the vial where tissue or fluid samples were placed. | unitless |
| Tray | The tray id that an oyster was placed into in the sea table. | unitless |
| Date | The date that the oyster was treated | unitless |
| Time_extracted | The time that the oyster was treated, in ISO format. | unitless |
| Time_in_table | The time that the oyster was placed in the sea table, in ISO format. | unitless |
| Date_Time_Dead | The date and time that an oyster was found dead, in ISO format. | unitless |
| Notes | Any notes about treatment or death | unitless |
| Date_time_extracted | The date and time an oyster was treated. This column was used to calculate the Lifespan_days column, in ISO format. | unitless |
| Lifespan_days | The number of days an oyster was alive if it died during the experiment | unitless |
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.
| Funding Source | Award |
|---|---|
| NSF Division of Ocean Sciences (NSF OCE) |