  This paper examines the objective of optimally harvesting a single species in
a stochastic environment. This problem has previously been analyzed in Alvarez
(2000) using dynamic programming techniques and, due to the natural payoff
structure of the price rate function (the price decreases as the population
increases), no optimal harvesting policy exists. This paper establishes a
relaxed formulation of the harvesting model in such a manner that existence of
an optimal relaxed harvesting policy can not only be proven but also
identified. The analysis embeds the harvesting problem in an
infinite-dimensional linear program over a space of occupation measures in
which the initial position enters as a parameter and then analyzes an auxiliary
problem having fewer constraints. In this manner upper bounds are determined
for the optimal value (with the given initial position); these bounds depend on
the relation of the initial population size to a specific target size. The more
interesting case occurs when the initial population exceeds this target size; a
new argument is required to obtain a sharp upper bound. Though the initial
population size only enters as a parameter, the value is determined in a
closed-form functional expression of this parameter.
