Asymptotic harvesting of populations in random environments

We consider the harvesting of a population in a stochastic environment whose dynamics in the absence of harvesting are described by the logistic Verhulst-Pearl model. Using ergodic optimal control, we find the optimal harvesting strategy which maximizes the asymptotic yield of harvested individuals. To our knowledge, ergodic optimal control has not been used before to study harvesting strategies. However, it is a natural framework because the optimal harvesting strategy will never be such that the population is harvested to extinction – instead the harvested population converges to a unique invariant probability measure. When the yield function is the identity, we show that the optimal strategy has a bangbang property: there exists a threshold x∗ > 0 such that whenever the population is under the threshold the harvesting rate must be zero, whereas when the population is above the threshold the harvesting rate must be at the upper limit. We provide upper and lower bounds on the maximal asymptotic yield, and explore via numerical simulations how the harvesting threshold and the maximal asymptotic yield change with the growth rate, maximal harvesting rate or the competition rate. In the case when the yield function is strictly concave, we prove that the optimal harvesting strategy is continuous.