Modeling Fish Population Dynamics for Sustainability and Resilience

Introduction

Fish and fisheries provides basic food supplies and make essential contributions to human well-being. This resource needs to be maintained judiciously. If not sufficiently controlled and managed that might cause extinction of fish population and lead to damage to the ecosystem (Cochrane & Garcia, 2009). This speaks for developing a mechanism to maintain a sustainable fish population. Sustainability is an import filed of studies in our time from the standpoint of business, ecological, economic and social, to name a few. In all aspects of human activities sustainability considerations need to be made to make sure living things and human civilization is not in danger. Maintaining an ecological balance is imperative. So, governments, policy makers, scientists and general public need to make adequate measure individually and collectively to ensure sustainability. This paper deals with fish population ecological balance and sustainability. The goal here is to see how we can simulate fish population growth and maintain a balance.

Systems modeling is an extremely powerful technique that easily lends itself as a tool to understand the complexity found in scientific and business world today. It allows us to understand the reason for certain behavior of a system and how it might change over a period of time. Models enable to see how a real-world activity will perform under different conditions and test various hypotheses at a fraction of the cost of performing the actual activity (Laguna & Markland, 2005). Finding the behavior of certain activity through real world scenarios might take longer time and by that time it might be difficult or impossible to make correction or take correct course of action. System dynamics modeling have been applied in different areas. Now a days, the dynamics of a project management risk is evaluated through the approaches of systems dynamics. Simulation is quite often used in performance improvement of a new technology or finding design flaws of a complex technology.

Eliciting and mapping of mental model is always necessary but it is far from sufficient to visualize or address complex real word problems (Sterman, 2000). Mental modeling helps a bit in understanding an activity or user’s world to a certain degree. But it is not practical to think through when we need to deal with a lot of constraints as well as data. And sometimes it is even cumbersome to present. The mental models are runnable in which there is a sense of deriving answers via mental simulation rather than logical reasoning (Forbus & Gentner, 1997). With mental model we cannot take into consideration many aspects of a real-world problems. The mental models are dynamically deficient, omitting feedbacks, time delays, accumulations, and nonlinearities (Sterman, 2000). In fact mental modeling does not provide use with all kinds of if-then-else scenario results. Simulation is a practical way to test any model (Lindenmayer et al., 2000; Rahman, 2018a). It provides us with results or behavior of a system without building it. Thus it provides practical feedback during design of a system. Simulation is a running model in order to estimate or project its behavior, either by solving the equations in the case of systems dynamics, or by generating random numbers representing events and decisions in the case of discrete system simulation (Rahman, 2014).