1. Dynamic Complex Ecosystems Simulation / Modeling Bill Yu

2. Purpose
The purpose of my research project is to create a simulation of a many-species, non-static, many-variable ecosystem. According to user preferences, many desired ecosystem simulations will be able to be run.
The scope of my project will include hypothetical situations, which will be applicable to real-life, but not necessarily a real-world model.

3. Abstract
Predator / Prey ecosystem with (unlike other simulations) not 100% rates for chance factors
Reproduction algorithm for evolution
Trait accumulation
Reproduction algorithm for mutations and creations of new species
Natural disasters

4. Expected Results
I will validate success or failure when I have an accurate program for modeling – while this might not follow the standard equations, at least I will have some type of imaginary “ideal” world simulation where the rules are the same as the ones I have programmed.
I will try to make the modeling program as realistic as possible, but with so many factors, it will be difficult.
By the end of the 2nd quarter, I will probably have implemented my evolutionary reproduction system. There will be mutations, adaptations, developments, new species, etc.
By the end of the 3rd quarter I expect my goals to be finished. Hopefully, this will be in python.

5. Other Research Other ecosystem simulations
Have not found an example that is close my simulation – they have all been focused on a specific aspect of an ecosystem
Growth ecosystem
Population trait accumulation ecosystem

6. Procedures / Methods Using NetLogo Accomplishing by building top-down
One new algorithm at a time
Few new variables at a time
Eventually, there will be a complicated system with many variables for the user to control
BehaviorSpace

7. Timeline
Weeks 1 – 2
-Project exploration, decided on a final project, and narrowed it down
Weeks 3 – 4
-Basic NetLogo coding, getting used to it, and beginning some basic ecosystem model like the grass, wolves, and sheep
Weeks 5 – 6
-Algorithm research
-Programming (Predator / Herbivore / Grass)
Weeks 7 – 9
-Programming
-Fixing simulation, fixing predator/prey by streamlining predation simulation algorithm
-Bug fixing (variable assignment, modeling function)

8. Problems
Predators have to be overlapping prey to be able to eat them to have to be able to eat prey
Prey have to be overlapping grass to be able to eat them (true in real life)
Implementing strike radius method for predators
Modeling function is broken for the while (variable errors, but I can bypass by using the BehaviorSpace)

9. Results 1 Predator / High Energy Gain / High Reproduction
Invasive Species Effect

10. Results
Unless equilibrium can be reached, (grass won't grow back fast enough, etc)