Age‐Structured Harvesting Models: A Structural Comparison of Rate‐Control and Effort‐Control Optimality Systems

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Authors: Jiguang Yu, Louis Shuo Wang, Ye Liang
Cited by: 1
References: 47

Abstract

We study optimal harvesting in continuous‐time, age‐structured population models of McKendrick–von Foerster type, and we compare two harvesting mechanisms. In the rate‐control formulation, harvesting enters the state equation as an additive removal term; in the effort‐control formulation, harvesting acts multiplicatively as an additional mortality intensity, and the mortality coefficient depends on the aggregate stock. For the rate‐control problem, we first establish the existence of an optimal control for the infinite‐horizon discounted problem, and we then derive, under explicitly stated regularity and constraint‐qualification assumptions, a conditional Pontryagin‐type necessary optimality system consisting of the adjoint equation, the terminal‐age and transversality conditions, the switching relations for the distributed and boundary controls and the complementary‐slackness relation for the state constraint. For the effort‐control problem, we formally derive the associated adjoint equation and identify the nonlocal coupling term generated by aggregate (density) dependence, with the sign of that term verified by a step‐by‐step variational computation; a rigorous infinite‐horizon maximum principle for this nonlinear, nonlocally coupled problem is beyond the present scope and is stated as such. We complement the analysis with autonomous stationary reductions, with explicit representations of the state and adjoint and with a reproducible numerical study. The central message is structural: The harvesting mechanism is not a cosmetic modelling choice. Rate‐control produces an additive/affine/local optimality structure, whereas effort‐control produces a multiplicative/nonlinear/nonlocal one, with direct consequences for persistence, stationary profiles and bioeconomic interpretation.

Fields of Study & Subjects

Mathematical and Theoretical Epidemiology and Ecology Models Mathematical Biology Tumor Growth stochastic dynamics and bifurcation

Publication details

Journal / source
International Journal of Mathematics and Mathematical Sciences
Publisher
Wiley
Volume
2026
Issue
1
Publication date
1 January 2026
Language
EN