Harvesting in a Random Varying Environment: Optimal, Stepwise and Sustainable Policies for the Gompertz Model

Abstract

In a random environment, the growth of a population subjected to harvesting can be described by a stochastic differential equation. We consider a population with natural growth following a Gompertz model with harvesting proportional to population size and to the exerted fishing effort. The main goal of this work is to compare the performance of three fishing policies: one with variable effort, named optimal policy, the second based on variable effort but with small periods with constant effort, named stepwise policy, and another with constant effort, denoted by optimal sustainable policy. We will show that the first policy is inapplicable from the practical point of view, whereas the second one, although being suboptimal, is applicable but has some shortcomings common to the optimal policy. On the contrary, the constant effort policy is easily implemented and predicts the sustainability of the population as well as the existence of a stationary density for its size. The performance of the policies will be assessed by the profit obtained over a finite time horizon. We also show how changes in the parameter values affect the profit differences between the variable effort policy and the constant effort policy.