About Tiny Sea Simulation
A Scientific Research Platform for Marine Ecosystem Dynamics
Research Purpose
Tiny Sea Simulation is a scientific research platform developed in collaboration with marine biologist Brian Helmuth to study climate change impacts on marine food webs. The simulation models a 2-tier ecosystem with realistic predator-prey dynamics, thermal adaptation, and sophisticated population mechanics using accumulator systems for accurate tracking of births and deaths.
Ecosystem Structure
6 Marine Species organized in a two-tier food web with three thermal variants optimized for different temperature ranges (Arctic: 5°C, Common: 20°C, Tropical: 35.5°C).
Temperature Components
Starting point (default 20°C)
Summer/winter cycles
Long-term trend (1°C/year)
Year-to-year fluctuations
Short-term weather patterns
Seven-Step Biology Cycle
Each simulation day follows this precise sequence:
Calculate using Arrhenius equation
Predators hunt with variable efficiency
Thermal × Feeding Rate
If performance < 30% threshold
When pop ≥ 2 and perf ≥ 25%
Scaled by performance
Convert to integers and preserve fractional values
Accumulator Systems
Early versions failed when populations became small. A predator with 2 individuals would calculate 0.14 births per day, which rounded to 0 - the species would never reproduce. Similarly, the last creature would calculate 0.02 deaths per day, which rounded to 0 - making it immortal. Accumulator systems solve this by tracking fractional values across days until they accumulate to whole numbers.
Birth Accumulator
Why: Prevents reproductive failure in small populations. Predators can reproduce even with only 2 individuals.
Predation Accumulator
Why: Ensures rare variants are preyed upon proportionally to their abundance, not ignored because of rounding.
Natural Death Accumulator
Why: Eliminates the "immortal last creature" bug. Even struggling individuals eventually die through accumulated mortality.
Negative Feedback Mechanisms
Without negative feedback, populations explode exponentially and crash catastrophically. Real ecosystems maintain stability through self-regulating mechanisms. We implemented four feedback loops that prevent runaway population growth and create realistic predator-prey oscillations.
Why: Prevents infinite prey growth by introducing resource limitations
Why: Models competition - hunting becomes harder when too many predators compete for prey
Why: Strong penalty prevents predators from depleting all prey - ensures ecosystem recovery
Why: Weak individuals (poor temperature or starving) die faster - models natural selection
Thermal Performance
Species achieve maximum performance at their optimal temperature and experience reduced efficiency in warmer or cooler conditions. Performance is calculated using Arrhenius equations which accurately model how biological rates change with temperature.
- At 5°C (optimal temperature): 100% performance ✓ - thriving conditions
- At 20°C (stressed): 35% performance ⚠ - reduced feeding, slower reproduction
- At 30°C (extreme stress): 15% performance ✗ - high mortality, population decline
Simple linear models don't reflect biological reality. Arrhenius equations capture the non-linear nature of biological processes: species tolerate moderate temperature deviations well, but extreme temperatures cause rapid performance collapse. This matches real-world observations of thermal tolerance in marine organisms.