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Fire in the forest

by Roland, B., Kampis, G., Karsai, I. (Submitted: 10/08/2016)

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This model explores the dynamics of an animal/tree/fire ecosystem with random fires.
The result of this model has been published in Ecological Complexity 28 (2016): 12-23.


Our model considers a new element in forest fire modeling, namely the dynamics of a forest animal, intimately linked to the trees. We show that animals and trees react differently to different types of fire. A high probability of fire initiation results in several small fires, which do not allow for a large fuel accumulation and thus the destruction of many trees by fire, but is found to be generally devastating to the animal population at the same time. On the other hand, a low fire initiation probability allows for the accumulation of higher quantities of fuel, which in turn results in larger fires, more devastating to the trees than to the animals. Thus, we suggest that optimal fire management should take into account the relation between fire initiation and its different effects on animals and trees. Further, wildfires are often considered as prime examples for power-law-like frequency distributions, yet there is no agreement on the mechanisms responsible for the observed patterns. Our model suggests that instead of a single unified distribution, a superposition of at least two different distributions can be detected and this suggests multiform mechanisms acting on different scales. None of the discovered distributions are compatible with a power-law hypothesis.

Please cite this Netlogo model as:
Roland, B., Kampis, G. and Karsai, I (2016): Fire in the forest. Netlogo v. 5.3.1 simulation.


The model has three hierarchical levels: entities, interactions, and environment, with the first two being modeled explicitly and the environment being modeled implicitly.
Populations are characterized by the census of each organism type at the end of a given year. The number of burned trees and animals are counted every year.
The model is spatially explicit. Trees are immobile, while animals and fires can move, following explicit rules. The state of the agents is tracked through time and defined by the location of each individual and each interaction between individuals and the environment. The population's dynamics and individual behaviors emerge from the interactions at the individual (agent) level. All sensing and interaction are strictly local to the agents. Individuals "know" (access) their own current activity status (i.e., this status is explicitly represented in a state variable) and they can check (i.e., sense) the existence and the status of other agents in the neighborhood.

Things to Know
The unit of time is 1 tick = 1 month (12 months is a year).

Animals can breed, whereby one individual becomes 2 individuals:
- There must be enough trees around (AnimalTreeVariableBreeding).
- There must NOT be more than a certain number of other animals around (AnimalAnimalVariableBreeding).

Animals die by:
- Old age (AnimalDeathPercent).
- Tree Density dependent death rate (AnimalTreeDDDeath).
- Fire burns.

Trees produce seeds:
- Some percent of the total current population (TreeBreedingPercent).
- A tree only develops if the seend falls into a tree-less spot.

Trees die by:
- Fire burn.

Fires are initiated:
- Random location.
- FireStrength describes the maximum number of fire initiation point/year.
- If there is a tree in the adjacent neighborhood, the fire can spread to that neighborhood.

Fires kill:

Fires die if:
-There are no more live trees to burn in the adjacent neighborhood.


1.) Initialize the number of animals, trees, and fires.
2.) Adjust the slider parameters (see below), or use the default settings.
3.) Press the SETUP button.
4.) Press the GO button to begin the simulation.
5.) Look at the main monitor to watch the ecosystem develop.
6.) Look at the POPULATIONS plot to watch the populations fluctuate over time.
7.) Look at the DEAD POPULATIONS plot to watch the number of animals and trees that die.

NumberofAnimals: The initial number of animals.
NumberofTrees: The initial number of trees.
YearsPerSetup: The number of years per setup run.
FireStrength: The maximum number of fires created each year.
AnimalTreeDDDeath: Density dependent death of animals based on the trees.
TreeBreedingPercent: The percentage of trees that breed every year.
AnimalMovementSpeed: The number of times a animal moves per month (1 tick).
AnimalDeathPercent: The percent of animals that will die each year due to old age.
AnimalAnimalVariableBreeding: Number of animals around an animal that limits its breeding (birth rate depend on animal density).
AnimalTreeVariableBreeding: The number of trees needed for an animal to breed.


Small fire strength will produce a small number of devastating large fires. This is very detrimental to the trees.

Large fire strength produces many smaller and medium sized fires. This is more devastatiung to the animal populations than to the trees.

The distribution of fires DOES NOT follow a power law (see paper for detailed analysis).


Try changing the TreeBreedingPercent and FireStrength parameters. Notice the significant changes in the population graph.

Try finding specific parameters to create a stable ecosystem between the three "breeds" such that none of them become extinct.

The fire strength parameter corresponds to the number of lightning that can initiate fires in the forest.


Add extra parameters such as estimating wind speed and direction as well as temperature and movement patterns of the animals, or add the possibility of the animals moving towards the trees rather than moving randomly looking for an appropriate breeding habitat.


Note the use of breeds to model three different kinds of "turtles": animals, trees, and fires.

Note the use of "if random 100 < AnimalDeathPercent" to determine the percent of animals that die each year.

Also note the random fire strength to signify a random number of fires in a range between the user defined maximal amount and 0. This is much more realistic than a rigidly deterministic number of fires each year.


Look at "Wolf Sheep Predation" for another model of ecosystem dynamics involving three elements. Also see different forest fire models in the community page, but those mainly focus on 2 components only.


Roland, Byron ETSU BISC Johnson City TN USA
Kampis, George German Research Center for Artificial Intelligence (DFKI GmbH)
Karsai, Istvan ETSU BISC Johnson City TN USA

Paper based on this program:

Karsai, I., Roland, B. and Kampis, G. 2016: The effect of fire on an abstract forest ecosystem: An agent based study. Ecological Complexity Volume 28, Pages 12–23.

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