Simplified Natural Selection Game

Simplified Natural Selection Game preview image

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Default-person Michael Zito (Author)

Tags

biology 

Tagged by Michael Zito 14 days ago

coevolution 

Tagged by Michael Zito 14 days ago

education 

Tagged by Michael Zito 14 days ago

evolution 

Tagged by Michael Zito 14 days ago

natural selection 

Tagged by Michael Zito 14 days ago

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## WHAT IS IT?

This simulation models coevolution of a predator-prey sytem. It is based on the board game **SIMULATING NATURAL SELECTION** by Robert P. Gendron from Indiana University of Pennsylvania. The model incorporates the following elements of a natural system: Variability, heritability, competition, predation, carrying capacity and differential reproduction. Users can set the intial values for adaptation scores, variability of adaptation scores and population size. The simulation produces behaviors that demonstrate directional selection, the coevolution of traits, genetic drift and predator-prey population cycles.

## HOW IT WORKS

The two populations in the model are hawks and mice. Hawks have a vision score and mice have a camouflage score. Populations are intialized with user specified number, mean score and score variation. Each population is then randomly distributed in the world.

Each tick in the model represents a discrete set of events that occur in the following order:

1. **PREDATION:**

If a hawk and mouse are on the same patch and the hawk's vision score is greater than the mouse's camouflage score then the hawk eats the mouse, reproduces and dies (See 2 below). If there is a tie in the scores a coin flip determines if the mouse is eaten or survives. If there are many mice on a patch, the mouse with the minimum camouflage score is eaten.

2. **HAWK REPRODUCTION:**

Each hawk that successfully eats produces two offspring as follows:

**> If hawks-coevolve? is ON:** One with a vision score one less than the parent hawk and one with a vision score one greater than the parent.

**> If hawks-coevolve? is OFF:** Both with vision scores drawn randomly from the normal distribution defined by the init-mean-vision and init-SD-vision sliders.

Each new hawk is assigned an age equal to the tick count when it is hatched.

The parent hawk dies immediately after reproducing.

If there are still mice on the patch where the new hawks are hatched, the newly hatched hawks get a chance to hunt and reproduce until all mice on a given patch with camouflage scores lower than hawk vision scores are eaten.

3. **HAWK STARVATION:**

If a hawk does not eat within 2 ticks of being hatched it dies.

4. **MOUSE REPRODUCTION:**

Mice that survive and are alone on a patch (i.e., no competition for resources from other mice) reproduce two offpring as follows:

**> If mice-coevolve? is ON:** One with a camouflage score one less than the parent mouse and one with a camouflage score one greater than the parent.

**> If mice-coevolve? is OFF:** Both with camouflage scores drawn randomly from the normal distribution defined by the init-mean-camo and init-SD-camo sliders.

The parent mouse dies immediately after reproducing.

5. **RANDOM DISTRIBUTION:**

All surviving hawks and mice are randomly redistributed in the world and the the above steps are repeated.

## HOW TO USE IT

1. The **mice-coevolve?** and **hawks-coevolve?** switches determine if the mouse and hawk populations evlove in response to each other (On) or randomly (Off)

2. The **init-?**, **init-mean-?** and **init-SD-?** sliders set the initial population size (mice or hawks), the intial mean value of the adpatations (camouflage or vision) and the variation within the population for each adaptation (camouflage or vision), respectively.

3. The **Reset** button returns all settings to their default values.

4. Use the **Setup** button to initalize the simulation to the values you set using the sliders and switches described above. Press the **Run** button to run the simulation continuously or use the **5 Gens** button to advance the simulation 5 generations at a time.

## THINGS TO TRY

Use the model to experiment. Start by making changes to only one setting at a time. Run each experiment several times as the results may differ. Make note of these different outcomes and the frequency with which they occur. For each experiment be sure to explain what you think might be causing the results you obseve.

Some suggested experiments are listed below

(**NOTE:** All settings are assumed to be default EXCEPT those indicated):

1. Run the model using the default settings.

2. Set the init-hawks slider to zero.

3. Set both hawk and mice evolve? switches to off.

4. Turn off mice evolution only.

5. Turn off hawk evolution only.

6. Change the mean value of the mice camouflage value.

7. Change the variation (SD) value of the mice camouflage value.

8. Change the mean value of the hawk vision value.

9. Change the variation (SD) of the hawk vision value.

## THINGS TO NOTICE

What happens to the vision and camouflage scores under different settings?

How do these scores change relative to one another?

What happens to the vision and camouflage variation under different settings?

How do the populations change relative to one another?

Does either population go extinct? reach a carrying capacity?

## EXTENSIONS

Play the board game (see link below) and explore the code to see how it works.

## RELATED MODELS

In the NetLogo Library:

1. Bug Hunt Camouflage

2. Bug Hunt Coevolution

3. Bug Hunt Drift

4. Red Queen

5. Wolf Sheep Predation

## CREDITS AND REFERENCES

Dr Robert Gendron’s Faculty Page at IUP:

http://www.iup.edu/page.aspx?id=85437

A copy of the original board game directions:

http://evolution.berkeley.edu/evolibrary/search/lessonsummary.php?source=Gendron,%20Robert&thisaudience=13-16&resource_id=47

## COPYRIGHT AND LICENSE

Copyright 2013 Michael Zito.

![CC BY-NC-SA 3.0](http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

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Click to Run Model

breed [hawks hawk]
breed [mice mouse]

hawks-own [vision age]
mice-own [camo]

to setup
  ca

  ask patches [
    set pcolor 63 + random 4                  ; its a patchwork world!
    ]

  create-hawks init-hawks [
    set shape "hawk"
    set color 36
    set size .75
    set age 0
    set vision round random-normal init-mean-vision init-SD-vision
    if vision < 0 [set vision 0]              ; keep scores non-negative
    ]

  create-mice init-mice [
    set shape "mouse top"
    set size .2
    set color 32
    set camo round random-normal init-mean-camo init-SD-camo
    if camo < 0 [set camo 0]                  ; keep scores non-negative
    ]

  distribute

  reset-ticks
end 

to go
  if ticks = 30 [stop]
  ask hawks [hunt]
  ask hawks [starve]
  ask mice [evolve-mice?]
  distribute
  tick
end 

to hunt
  if any? mice-here [                         ; if there are mice on this patch
    if vision >= min [camo] of mice-here [    ; and they can be seen
      if-else vision > min [camo] of mice-here
        [evolve-hawks?]                       ; if vision > camo eat and reproduce
        [if random 2 = 0 [evolve-hawks?]]     ; else vision = camo so flip a coin
    ]
  ]
  ; recurse to continue eating until all possible meals eaten
  if any? mice-here and vision >= min [camo] of mice-here [hunt]
end 

to evolve-hawks?
  if-else hawks-coevolve?
    [eat-and-evolve]
    [eat-and-reproduce]
end 

to eat-and-evolve
  ask min-one-of mice-here [camo] [die]  ; eat the mouse
    hatch 1 [                            ; produce an offspring with poorer vision
      set age ticks
      set vision  vision - 1
      if vision < 0 [set vision 0]       ; keep scores non-negative
      ]

    hatch 1 [                            ; produce an offspring with better vision
      set age ticks
      set vision  vision + 1
      ]
  die                                    ; die
end 

to eat-and-reproduce
  ask min-one-of mice-here [camo] [die]  ; eat the mouse
  hatch 2 [                              ; produce 2 offspring with random vision
    set age ticks
    set vision round random-normal init-mean-vision init-SD-vision
    if vision < 0 [set vision 0]         ; keep scores non-negative
    ]
  die                                    ; die
end 

to starve
  if (ticks - age >= 2) [die]            ; if a hawk hasn't eaten in 2 tickss it dies
end 

to evolve-mice?
  if-else mice-coevolve?
    [reproduce-and-evolve]
    [reproduce]
end 

to reproduce-and-evolve
  if not any? other mice-here [          ; if there is no competition
    hatch 1 [                            ; produce an offspring with worse camouflage
        set camo  camo - 1
        if camo < 0 [set camo 0]         ; keep scores non-negative
      ]
    hatch 1 [                            ; produce an offspring with better camouflage
      set camo  camo + 1
      ]

    die                                  ; die
  ]
end 

to reproduce
  if not any? other mice-here [          ; if there is no competition
  hatch 2 [                              ; produce 2 offspring with random camo
    set camo round random-normal init-mean-camo init-SD-camo
    if camo < 0 [set camo 0]             ; keep scores non-negative
    ]
    die                                  ; die
  ]
end 

to distribute
  ask mice [setxy random-xcor random-ycor set heading random 359]
  ask hawks [setxy random-xcor random-ycor set heading random 359]
end 

to reset
  set mice-coevolve? true
  set init-mice 16
  set init-mean-camo 5
  set init-SD-camo 1.5
  set hawks-coevolve? true
  set init-hawks 16
  set init-mean-vision 5
  set init-SD-vision 1.5
end 

There is only one version of this model, created 18 days ago by Michael Zito.

Attached files

File Type Description Last updated
NatSelGameLab_original.doc word Student Lab 18 days ago, by Michael Zito Download
Simplified Natural Selection Game.png preview Simplified Natural Selection Game That Runs Better in NetLogo Web 18 days ago, by Michael Zito Download

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