Harvesting of interacting stochastic populations

TL;DR

This paper develops a mathematical framework for optimal harvesting and seeding of interacting stochastic populations, accounting for non-linear interactions, state-dependent prices, and species injections, with applications to fisheries and endangered species management.

Contribution

It introduces a novel model allowing simultaneous harvesting and seeding, characterizes the value function via viscosity solutions, and provides numerical methods for complex multi-species systems.

Findings

Optimal strategies rarely involve harvesting or seeding more than one species at a time.

The value function is characterized as a viscosity solution of the HJB equations.

Numerical approximations converge to the true optimal strategies for multi-species systems.

Abstract

We analyze the optimal harvesting problem for an ecosystem of species that experience environmental stochasticity. Our work generalizes the current literature significantly by taking into account non-linear interactions between species, state-dependent prices, and species injections. The key generalization is making it possible to not only harvest, but also `seed' individuals into the ecosystem. This is motivated by how fisheries and certain endangered species are controlled. The harvesting problem becomes finding the optimal harvesting-seeding strategy that maximizes the expected total income from the harvest minus the lost income from the species injections. Our analysis shows that new phenomena emerge due to the possibility of species injections. It is well-known that multidimensional harvesting problems are very hard to tackle. We are able to make progress, by characterizing the value function as a viscosity solution of the associated Hamilton-Jacobi-Bellman (HJB) equations. Moreover, we provide a verification theorem, which tells us that if a function has certain properties, then it will be the value function. This allows us to show heuristically, as was shown in Lungu and $\O$ksendal (Bernoulli '01), that it is almost surely never optimal to harvest or seed from more than one population at a time. We approximate the continuous-time systems by Markov chains and show that the optimal harvesting-seeding strategies of the Markov chain approximations converge to the correct optimal harvesting strategy. This is used to provide numerical approximations to the optimal harvesting-seeding strategies and is a first step towards a full understanding of the intricacies of how one should harvest and seed interacting species. In particular, we look at three examples: one species modeled by a Verhulst-Pearl diffusion, two competing species and a two-species predator-prey system.