Predictive Biology for Aquaculture

Predictive & Real-time Interactive Shellfish Modeling

Explore how AI, data science, and predictive biology are transforming oyster aquaculture. Interact with real research data, build machine-learning models, and discover the future of sustainable food production.

3
U.S. Coastlines
2
Oyster Species
6+
Field Deployment Sites
100k+
Data Points
5
Partner Institutions

Discover Predictive Aquaculture

Dive into real research data from oyster breeding programs across the Atlantic, Gulf, and Pacific coasts. See how environmental priming and AI-driven phenotyping are rewriting the rules of shellfish production.

🧬

Genotype x Environment Explorer

Visualize how oyster families from different breeding programs respond to temperature, salinity, and disease pressure across six field sites. Filter by genetic line, priming treatment, and coastline.

Interactive Data
🤖

AI Broodstock Selector

Use the project's machine-learning models to predict offspring performance. Choose broodstock, apply priming treatments, select deployment environments, and see how your choices shape survival and growth.

Machine Learning
🔬

Resazurin Assay Simulator

Run a virtual metabolic phenotyping experiment. Watch fluorescence change in real time, calculate metabolic rates, and compare families to see how early-life measurements predict field outcomes.

Virtual Lab
🌊

Environmental Risk Mapper

Explore ocean conditions across U.S. oyster-growing regions. Layer temperature, salinity, pH, and disease data to understand the multi-stressor environments that shape aquaculture decisions.

Geospatial
🎮

Priming Challenge

Design your own priming experiment: choose stressor type, intensity, and timing, then deploy virtual cohorts and compete with other users to maximize survival using data-driven strategies.

Gamified
🎓

Career Pathways

Meet the scientists, data engineers, hatchery managers, and marine economists behind the project. Explore diverse careers at the intersection of biology, technology, and food production.

Profiles

Write Code.
Query Real Data.
See Predictions.

A browser-based coding sandbox where you write R or Python to explore project datasets, build visualizations, and train your own predictive models. No installation required.

Python R / tidyverse scikit-learn Guided Tutorials
code-the-ocean.py
# Load oyster survival data from PRISM API
import prism

families = prism.load_families(
  species="C. virginica",
  coast="Atlantic"
)

# Compare primed vs control survival
prism.plot_survival(
  families,
  group_by="priming_treatment",
  metric="field_survival_18mo"
)

>> Loaded 30 families, 3 treatments
>> Rendering survival curves...

Built for Aquaculture Professionals

A dedicated workspace for hatchery managers, growers, and breeders. Access predictive tools, benchmark your operations, and connect with the research driving next-generation shellfish production.

prism-portal.org/industry/dashboard
Predicted Survival (Primed)
87.3%
Predicted Survival (Control)
61.8%
Family Performance by Priming Treatment
Immune-Primed
Control

Predictive Dashboard

Input your resazurin assay results and environmental conditions to receive AI-generated performance predictions for your seed cohorts. Compare families across priming treatments and optimize planting decisions using portfolio theory.

Integrated with the Objective 3 risk-assessment and portfolio-optimization engine, the industry dashboard helps growers construct seed portfolios along the efficient frontier, balancing expected yield against environmental risk for their specific lease sites.

📊 Performance Predictions

Upload phenotyping data from resazurin assays and receive survival and growth forecasts for specific deployment environments, powered by the project's validated ML models.

🌐 Regional Benchmarking

Compare your operation's metrics against anonymized, aggregated performance data from project field sites across the Atlantic, Gulf, and Pacific coasts.

📖 Protocol Library

Access step-by-step priming protocol manuals, resazurin assay guides, and video tutorials developed and field-tested with commercial hatchery partners.

💬 Community Forum

Connect with researchers and fellow industry professionals. Share field observations, discuss results, and contribute feedback that shapes ongoing research priorities.

The ADAPT Certificate

Aquaculture Data Analytics & Predictive Technologies. A cross-university micro-credential equipping the next generation with computational skills for data-driven shellfish production.

01

Foundations of Aquaculture Data Science

R and Python programming, statistical modeling, and data visualization using real aquaculture datasets. No prior coding experience required.

Walton (VIMS) · Plough (OSU)
02

Genomics & Bioinformatics for Breeding

Sequence analysis, variant calling, parentage assignment, and genomic selection methods applied to shellfish breeding programs.

Small (VIMS) · Hollenbeck (TAMU)
03

Machine Learning & Predictive Modeling

Ensemble methods, neural networks, feature selection, and model validation for predicting field performance from early-life phenotypic data.

Roberts (UW) · Gurr (OSU)
04

Environmental Risk Analysis & Decision Science

Modern portfolio theory, cost-benefit analysis, and environmental data integration for risk-based aquaculture management decisions.

Anderson (UW)

Jointly administered by UW, VIMS/William & Mary, Texas A&M, and OSU. Delivered as hybrid courses with synchronous Zoom sessions, asynchronous content, and rotating in-person intensives. Open to students and working professionals.

Apply for ADAPT Certificate

A National Research Network

Spanning three coastlines, two species, and the full Research-Education-Extension continuum.