10
1. Introduction
for analysis, span a wide range of marine conservation issues, and are intend ed to stimulate in-depth analysis and modelin g of these and other marine systems. Examples range from simple single-species popul ation models to models involving multiple species and large spatial extents. As you
work through these models you will not only become fluent in the use of
the modeling langu age STELLA but you will develop a new way of thinking
about dynamic systems. Practice is the foundation of the modelin g art.
STELLA is designed to ease model development , facilitate model specification , automate the computation processes, and easily generate output in
numeric or graphical form. Its main computational strengths are most apparent when STELLA is used to solve for the time-varying beh avior of a system that consists of severa l interacting compone nts.
An understand ing of the dynamics and changing interrelationships of systems, such as SOcial, biological and physical systems, is of particular importance in a wo rld in which we face increasing complexity. In a variety of disciplines, scientists ask questions that involve complex and changing
interrelationships among systems. In marine conservation, we may be interested to find answers for questions such as: What are the impacts of the introdu ction of a disease on the popul ation in an ecosystem? How does a
catch of a species by humans influence the predator-pre y relationships of
which this species is a part? What are the implications of alternative management regimes in a fishery for fish popul ation sizes and profits for fishermen? All goo d modeling pro cesses begin (and end) with a goo d set of
que stions. These questions keep the modeler focused and away from the
miasma of random exploration.
Models help us understand the dynamics of real-world processes by
mimicking with the computer the actual but simplified forces that lie behind a system's behavior. For example, it may be assumed that the number
of manatees contracting a disease is proportional to the number of manatees carrying a virus. In a simple version of this epidemic model, we may
abstract away from a variety of factors that impede or stimulate the spread
of a disease, such as changes in manatee behavior with the progression of
the disease or mutation of the virus. Such an abstraction may leave us with
a sufficiently good predictor of the known infection rates, or it may not. If
it doe s not, we re-examine the abstractions , redu ce the assumptions, and
re-test the model for its new behavior.
Computer models are causal in the sense that they are built by using general rules that describe how each element in a system will respond to the
changes of other elemen ts. In the example of an epidemic in a manatee population , the number of newly infected individuals can be assumed to be proportional to the number of individuals already carrying the virus. With infection , the sizes of the population carrying the virus changes over time, thereby
leading to an increased potential to spread the disease. However, since infection reduces the number of new individuals to whom the disease can be
spread, a limit is placed on the severity of the outbreak at each point in time.
1. Introduction
for analysis, span a wide range of marine conservation issues, and are intend ed to stimulate in-depth analysis and modelin g of these and other marine systems. Examples range from simple single-species popul ation models to models involving multiple species and large spatial extents. As you
work through these models you will not only become fluent in the use of
the modeling langu age STELLA but you will develop a new way of thinking
about dynamic systems. Practice is the foundation of the modelin g art.
STELLA is designed to ease model development , facilitate model specification , automate the computation processes, and easily generate output in
numeric or graphical form. Its main computational strengths are most apparent when STELLA is used to solve for the time-varying beh avior of a system that consists of severa l interacting compone nts.
An understand ing of the dynamics and changing interrelationships of systems, such as SOcial, biological and physical systems, is of particular importance in a wo rld in which we face increasing complexity. In a variety of disciplines, scientists ask questions that involve complex and changing
interrelationships among systems. In marine conservation, we may be interested to find answers for questions such as: What are the impacts of the introdu ction of a disease on the popul ation in an ecosystem? How does a
catch of a species by humans influence the predator-pre y relationships of
which this species is a part? What are the implications of alternative management regimes in a fishery for fish popul ation sizes and profits for fishermen? All goo d modeling pro cesses begin (and end) with a goo d set of
que stions. These questions keep the modeler focused and away from the
miasma of random exploration.
Models help us understand the dynamics of real-world processes by
mimicking with the computer the actual but simplified forces that lie behind a system's behavior. For example, it may be assumed that the number
of manatees contracting a disease is proportional to the number of manatees carrying a virus. In a simple version of this epidemic model, we may
abstract away from a variety of factors that impede or stimulate the spread
of a disease, such as changes in manatee behavior with the progression of
the disease or mutation of the virus. Such an abstraction may leave us with
a sufficiently good predictor of the known infection rates, or it may not. If
it doe s not, we re-examine the abstractions , redu ce the assumptions, and
re-test the model for its new behavior.
Computer models are causal in the sense that they are built by using general rules that describe how each element in a system will respond to the
changes of other elemen ts. In the example of an epidemic in a manatee population , the number of newly infected individuals can be assumed to be proportional to the number of individuals already carrying the virus. With infection , the sizes of the population carrying the virus changes over time, thereby
leading to an increased potential to spread the disease. However, since infection reduces the number of new individuals to whom the disease can be
spread, a limit is placed on the severity of the outbreak at each point in time.