The effects of random and seasonal environmental fluctuations on optimal harvesting and stocking

TL;DR

This paper develops a comprehensive framework to analyze optimal harvesting and stocking strategies for populations affected by multiple layers of environmental fluctuations, including random regime switches, seasonal changes, and stochastic noise, incorporating economic variability.

Contribution

It introduces a novel multi-layered environmental fluctuation model and provides rigorous numerical methods to determine optimal strategies in complex, realistic ecological-economic settings.

Findings

Optimal strategies often deviate from threshold policies.

Environmental fluctuations significantly influence harvesting decisions.

Numerical methods converge to the true optimal strategies.

Abstract

We analyze the harvesting and stocking of a population that is affected by random and seasonal environmental fluctuations. The main novelty comes from having three layers of environmental fluctuations. The first layer is due to the environment switching at random times between different environmental states. This is similar to having sudden environmental changes or catastrophes. The second layer is due to seasonal variation, where there is a significant change in the dynamics between seasons. Finally, the third layer is due to the constant presence of environmental stochasticity -- between the seasonal or random regime switches, the species is affected by fluctuations which can be modelled by white noise. This framework is more realistic because it can capture both significant random and deterministic environmental shifts as well as small and frequent fluctuations in abiotic factors. Our framework also allows for the price or cost of harvesting to change deterministically and stochastically, something that is more realistic from an economic point of view. The combined effects of seasonal and random fluctuations make it impossible to find the optimal harvesting-stocking strategy analytically. We get around this roadblock by developing rigorous numerical approximations and proving that they converge to the optimal harvesting-stocking strategy. We apply our methods to multiple population models and explore how prices, or costs, and environmental fluctuations influence the optimal harvesting-stocking strategy. We show that in many situations the optimal way of harvesting and stocking is not of threshold type.