You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: Sample Models/Biology/Wolf Sheep Predation.nlogox
+5-4Lines changed: 5 additions & 4 deletions
Original file line number
Diff line number
Diff line change
@@ -198,9 +198,9 @@ This model explores the stability of predator-prey ecosystems. Such a system is
198
198
199
199
There are two main variations to this model.
200
200
201
-
In the first variation, the "sheep-wolves" version, wolves and sheep wander randomly around the landscape, while the wolves look for sheep to prey on. Each step costs the wolves energy, and they must eat sheep in order to replenish their energy - when they run out of energy they die. To allow the population to continue, each wolf or sheep has a fixed probability of reproducing at each time step. In this variation, we model the grass as "infinite" so that sheep always have enough to eat, and we don't explicitly model the eating or growing of grass. As such, sheep don't either gain or lose energy by eating or moving. This variation produces interesting population dynamics, but is ultimately unstable. This variation of the model is particularly well-suited to interacting species in a rich nutrient environment, such as two strains of bacteria in a petri dish (Gause, 1934).
201
+
In the first variation, the "sheep-wolves" version, wolves and sheep wander randomly around the landscape. When a wolf finds a sheep on its patch, it consumes it. Each step costs the wolves energy, and they must eat sheep in order to replenish their energy - when they run out of energy they die. To allow the population to continue, each wolf or sheep has a fixed probability of reproducing at each time step. In this variation, we model the grass as "infinite" so that sheep always have enough to eat, and we don't explicitly model the eating or growing of grass. As such, sheep don't either gain or lose energy by eating or moving. This variation produces interesting population dynamics, but is ultimately unstable. This variation of the model is particularly well-suited to modeling interacting species in a rich nutrient environment, such as two strains of bacteria in a petri dish (Gause, 1934).
202
202
203
-
The second variation, the "sheep-wolves-grass" version explicitly models grass (green) in addition to wolves and sheep. The behavior of the wolves is identical to the first variation, however this time the sheep must eat grass in order to maintain their energy - when they run out of energy they die. Once grass is eaten it will only regrow after a fixed amount of time. This variation is more complex than the first, but it is generally stable. It is a closer match to the classic Lotka Volterra population oscillation models. The classic LV models though assume the populations can take on real values, but in small populations these models underestimate extinctions and agent-based models such as the ones here, provide more realistic results. (See Wilensky & Rand, 2015; chapter 4).
203
+
The second variation, the "sheep-wolves-grass" version explicitly models grass (green) in addition to wolves and sheep. The behavior of the wolves is identical to the first variation. However, this time, when a sheep moves it loses a little energy and when it lands on a patch with grass on it, it eats the grass and gains energy. When a sheep runs out of energy it dies. Once grass is eaten it will only regrow after a fixed amount of time. This variation is more complex than the first, but it is generally stable. It is a closer match to the classic Lotka Volterra population oscillation models. The classic LV models though assume the populations can take on real values, but in small populations these models underestimate extinctions and agent-based models such as the ones here, provide more realistic results. (See Wilensky & Rand, 2015; chapter 4).
204
204
205
205
The construction of this model is described in two papers by Wilensky & Reisman (1998; 2006) referenced below.
206
206
@@ -222,10 +222,9 @@ WOLF-GAIN-FROM-FOOD: The amount of energy wolves get for every sheep eaten
222
222
SHEEP-REPRODUCE: The probability of a sheep reproducing at each time step
223
223
WOLF-REPRODUCE: The probability of a wolf reproducing at each time step
224
224
GRASS-REGROWTH-TIME: How long it takes for grass to regrow once it is eaten (Note this is not used in the sheep-wolves model version)
225
-
SHOW-ENERGY?: Whether or not to show the energy of each animal as a number
225
+
SHOW-ENERGY?: Whether or not to show the energy of each animal as a label on the animal
226
226
227
227
Notes:
228
-
229
228
- one unit of energy is deducted for every step a wolf takes
230
229
- when running the sheep-wolves-grass model version, one unit of energy is deducted for every step a sheep takes
231
230
@@ -269,6 +268,8 @@ Note the use of the ONE-OF agentset reporter to select a random sheep to be eate
269
268
270
269
### BEHAVIORSPACE FEATURES
271
270
271
+
This model includes a set of experiments through the NetLogo tool BehaviorSpace.
272
+
272
273
For more information about BehaviorSpace and the features introduced in NetLogo 6.4.0 see the [documentation](https://ccl.northwestern.edu/netlogo/docs/behaviorspace.html).
273
274
274
275
The “New BehaviorSpace Features” experiment illustrates some of the BehaviorSpace features introduced in NetLogo 6.4.0. You can open BehaviorSpace using the Tools -> BehaviorSpace menu item. Click the EDIT button to see the details of the experiment.
0 commit comments