NetLogo User Community Models
WHAT IS IT?
A fleet of boats looks for fish and when it finds some, catches them. Fish stocks are depleted by fishing but recover by growth and reproduction.
The fish are in schools of varying size, and the amount of fish that a boat can catch in one day is about 20% of the maximum size of a school. The schools, represented by red icons of varying size, change due to both natural growth at a constant relative rate and fishing pressure - if fish schools become too large they split into two smaller schools.
The boats move around looking for schools of fish. If they catch fish they stay where they are, otherwise they keep moving.
There are four outputs - the number of boats (which is currently constant but could be modelled), the number of fish schools, the biomass of fish (which depends on the mean size of the schools) and the daily yield to the fishery. The yield is very variable and reflects the frequency with which boats encounter schools of fish.
The dynamics of how the fishing fleet and fish schools move is currently random. The major remaining development is to use weighting factors to reflect migration of fish and the use of fishing strategies based on past experience.
Currently the search range of each fishing boat is the eight nearest neighbours, but this will probably be an adjustable parameter in later versions.
The purpose of creating this model was to explore the use of cellular automata in modelling the spatial dynamics of fisheries. It is being distributed to the NetLogo community in hopes of inspiring further collaboration to this end.
HOW TO USE IT
2. Adjust the slider parameters (see below), or use the default settings.
THINGS TO TRY
Try adjusting the parameters under various settings. How sensitive is the stability of the model to the particular parameters?
This model was developed by Bill Silvert (firstname.lastname@example.org) and is based on the Wolf Sheep Predation model distributed with NetLogo. It is intended to imitate a fishery model developed by Aristidis Moustakas using cellular automata. The Moustakas model was programmed in C++ and is far more sophisticated than this. It has been accepted for publication in Ecological Modelling: A. Moustakas, W. Silvert and A. Dimitromanolakis. 2005. A spatially explicit learning model of migratory fish and fishers for evaluating closed areas. Ecological Modelling (in press).
Note the use of breeds to model two different kinds of "turtles": boats and fish.
Note use of the RANDOM-ONE-OF agentset command to select a fish school to be caught by a boat.
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