Impulsive fishery resource transporting strategies based on an open-ended stochastic growth model having a latent variable

In inland fisheries, transporting fishery resource individuals from a habitat to spatially apart habitat(s) has recently been considered for fisheries stock management in the natural environment. However, its mathematical optimization, especially finding when and how much of the population should be transported, is still a fundamental unresolved issue. We propose a new impulse control framework to tackle this issue based on a simple but new stochastic growth model of individual fishes. The novel growth model governing individuals’ body weights uses a Wright-Fisher model as a latent driver to reproduce plausible growth dynamics. The optimization problem is formulated as an impulse control problem of a cost-benefit functional constrained by a degenerate parabolic Fokker-Planck equation of the stochastic growth dynamics. Because the growth dynamics have an observable variable and an unobservable variable (a variable difficult or impossible to observe), we consider both full-information and partial-information cases. The latter is more involved but more realistic because of not explicitly using the unobservable variable in designing the controls. In both cases, resolving an optimization problem reduces to solving the associated Fokker-Planck and its adjoint equations, the latter being non-trivial. We present a derivation procedure of the adjoint equation and its internal boundary conditions in time to efficiently derive the optimal transporting strategy. We finally provide a demonstrative computational example of a transporting problem of Ayu sweetfish (Plecoglossus altivelis altivelis) based on the latest real data set.

[1]  Hyunbin Jo,et al.  Responses of fish assemblage structure to large-scale weir construction in riverine ecosystems. , 2019, The Science of the total environment.

[2]  Carlos Vázquez,et al.  Equilibrium models with heterogeneous agents under rational expectations and its numerical solution , 2021, Commun. Nonlinear Sci. Numer. Simul..

[3]  A. Namadchian,et al.  Pseudo-spectral optimal control of stochastic processes using Fokker Planck equation , 2019, Cogent Engineering.

[4]  T. Hoffnagle,et al.  The Influence of Size at Release on Performance of Imnaha River Chinook Salmon Hatchery Smolts , 2016 .

[5]  C. Bertucci Fokker-Planck equations of jumping particles and mean field games of impulse control , 2018, Annales de l'Institut Henri Poincaré C, Analyse non linéaire.

[6]  Y. Onoda,et al.  Turbid water induces refuge behaviour of a commercially important ayu: A field experiment for interstream movement using multiple artificial streams , 2018, Ecology of Freshwater Fish.

[7]  Jianxian Qiu,et al.  A Hybrid Finite Difference WENO-ZQ Fast Sweeping Method for Static Hamilton–Jacobi Equations , 2020, J. Sci. Comput..

[8]  W. Feller TWO SINGULAR DIFFUSION PROBLEMS , 1951 .

[9]  Olivier Pironneau,et al.  Numerical analysis of degenerate Kolmogorov equations of constrained stochastic Hamiltonian systems , 2019, Comput. Math. Appl..

[10]  I. Okyere,et al.  Physical distancing and risk of COVID-19 in small-scale fisheries: a remote sensing assessment in coastal Ghana , 2020, Scientific reports.

[11]  Jürgen Jost,et al.  The free energy method and the Wright–Fisher model with 2 alleles , 2015, Theory in Biosciences.

[12]  Andrzej Wałęga,et al.  Sensitivity of methods for calculating environmental flows based on hydrological characteristics of watercourses regarding the hydropower potential of rivers , 2020 .

[13]  Paolo Baldi,et al.  Lévy processes and stochastic von Bertalanffy models of growth, with application to fish population analysis. , 2009, Journal of theoretical biology.

[14]  Paul J. Askey,et al.  Linking Fish and Angler Dynamics to Assess Stocking Strategies for Hatchery-Dependent, Open-Access Recreational Fisheries , 2013 .

[15]  Raimund M. Kovacevic,et al.  Optimal control and the value of information for a stochastic epidemiological SIS-model , 2019, Journal of Mathematical Analysis and Applications.

[16]  J. Thornley,et al.  An open-ended logistic-based growth function , 2005 .

[17]  A. Borzì,et al.  Pedestrian motion modelled by Fokker–Planck Nash games , 2017, Royal Society Open Science.

[18]  Jingfang Huang,et al.  Finite element approximations of impulsive optimal control problems for heat equations , 2019, Journal of Mathematical Analysis and Applications.

[19]  Paolo Luchini,et al.  A probabilistic framework for the control of systems with discrete states and stochastic excitation , 2017, Autom..

[20]  R. Arlinghaus,et al.  How ecological processes shape the outcomes of stock enhancement and harvest regulations in recreational fisheries. , 2018, Ecological applications : a publication of the Ecological Society of America.

[21]  Juan Carlos De Los Reyes,et al.  Numerical PDE-Constrained Optimization , 2015 .

[22]  Kai Moriguchi Estimating polymorphic growth curve sets with nonchronological data , 2020, Ecology and evolution.

[23]  Aurélien Alfonsi,et al.  Affine Diffusions and Related Processes: Simulation, Theory and Applications , 2015 .

[24]  R. Jacobson,et al.  Evaluating flow management as a strategy to recover an endangered sturgeon species in the Upper Missouri River, USA , 2018, River Research and Applications.

[25]  Laurent Mertz,et al.  Approximate Solutions of a Stochastic Variational Inequality Modeling an Elasto-Plastic Problem with Noise , 2013 .

[26]  J. O’Hanley,et al.  The importance of spatiotemporal fish population dynamics in barrier mitigation planning , 2019, Biological Conservation.

[27]  Jun Xu,et al.  Contemporary changes in structural dynamics and socioeconomic drivers of inland fishery in China. , 2019, The Science of the total environment.

[28]  Phil Cowan,et al.  Large scale faecal (spraint) counts indicate the population status of endangered Eurasian otters (Lutra lutra) , 2020 .

[29]  Songhe Song,et al.  High-order maximum-principle-preserving and positivity-preserving weighted compact nonlinear schemes for hyperbolic conservation laws , 2020 .

[30]  Lars Grüne,et al.  Estimates on the Minimal Stabilizing Horizon Length in Model Predictive Control for the Fokker-Planck Equation , 2016 .

[31]  Hidekazu Yoshioka,et al.  Analysis and computation of a discrete costly observation model for growth estimation and management of biological resources , 2020, Comput. Math. Appl..

[32]  Jacques Henry,et al.  A theoretical connection between the Noisy Leaky integrate-and-fire and the escape rate models: The non-autonomous case , 2017, Mathematical Modelling of Natural Phenomena.

[33]  Sebastian Aniţa,et al.  An Introduction to Optimal Control Problems in Life Sciences and Economics: From Mathematical Models to Numerical Simulation with MATLAB® , 2010 .

[34]  M. M. Butt,et al.  Two-level method for a time-independent Fokker–Planck control problem , 2020, Int. J. Comput. Math..

[35]  S. Shreve,et al.  Stochastic differential equations , 1955, Mathematical Proceedings of the Cambridge Philosophical Society.

[36]  Paolo Baldi,et al.  Progress in modelling herring populations: an individual-based model of growth , 2009 .

[38]  Zhengfu Xu,et al.  An Explicit High-Order Single-Stage Single-Step Positivity-Preserving Finite Difference WENO Method for the Compressible Euler Equations , 2014, J. Sci. Comput..

[39]  Liying Cao,et al.  A New Flexible Sigmoidal Growth Model , 2019, Symmetry.

[40]  Renato Spigler,et al.  Numerical treatment of degenerate diffusion equations via Feller's boundary classification, and applications , 2012 .

[41]  T. Klanjšček,et al.  Dynamic energy budget of endemic and critically endangered bivalve Pinna nobilis: A mechanistic model for informed conservation , 2020 .

[42]  Vladimir I. Bogachev,et al.  Fokker-planck-kolmogorov Equations , 2015 .

[43]  M. Iida,et al.  Downstream migration of newly-hatched ayu (Plecoglossus altivelis) in the Tien Yen River of northern Vietnam , 2017, Environmental Biology of Fishes.

[44]  A. Filipe,et al.  An accessible optimisation method for barrier removal planning in stream networks. , 2021, The Science of the total environment.

[45]  H. Risken Fokker-Planck Equation , 1996 .

[46]  Zhengfu Xu,et al.  A parametrized maximum principle preserving flux limiter for finite difference RK-WENO schemes with applications in incompressible flows , 2013, J. Comput. Phys..

[47]  Chi-Wang Shu,et al.  A brief review on the convergence to steady state solutions of Euler equations with high-order WENO schemes , 2019, Advances in Aerodynamics.

[48]  Daniele Venturi,et al.  Data-driven closures for stochastic dynamical systems , 2018, J. Comput. Phys..

[49]  N. Fangue,et al.  Applying a simplified energy-budget model to explore the effects of temperature and food availability on the life history of green sturgeon (Acipenser medirostris) , 2019, Ecological Modelling.

[50]  Alfio Borzì,et al.  The Pontryagin maximum principle for solving Fokker–Planck optimal control problems , 2020, Comput. Optim. Appl..

[51]  Jun Zhu,et al.  A new fifth order finite difference WENO scheme for solving hyperbolic conservation laws , 2016, J. Comput. Phys..

[52]  Ioannis S. Stamatiou,et al.  A boundary preserving numerical scheme for the Wright-Fisher model , 2017, J. Comput. Appl. Math..

[53]  A. Lynch,et al.  COVID-19 pandemic impacts on global inland fisheries , 2020, Proceedings of the National Academy of Sciences.

[54]  Hidekazu Yoshioka,et al.  Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations , 2020 .

[55]  C. Mackenzie,et al.  Using Decision Analysis to Balance Angler Utility and Conservation in a Recreational Fishery , 2020 .

[56]  H. Yoshioka,et al.  Optimal harvesting policy of an inland fishery resource under incomplete information , 2019, Applied Stochastic Models in Business and Industry.

[57]  H. Yoshioka,et al.  A short note on analysis and application of a stochastic open-ended logistic growth model , 2019 .

[58]  António N. Pinheiro,et al.  Water-energy-ecosystem nexus: Balancing competing interests at a run-of-river hydropower plant coupling a hydrologic–ecohydraulic approach , 2020 .

[59]  Ahmed Nafidi,et al.  A stochastic diffusion process based on the Lundqvist-Korf growth: Computational aspects and simulation , 2021, Math. Comput. Simul..

[60]  B. Beckman,et al.  Winter-Rearing Temperature Affects Growth Profiles, Age of Maturation, and Smolt-to-Adult Returns for Yearling Summer Chinook Salmon in the Upper Columbia River Basin , 2018, North American Journal of Fisheries Management.

[61]  Darren J. Wilkinson,et al.  Fast Bayesian parameter estimation for stochastic logistic growth models , 2013, Biosyst..

[62]  Roberto Guglielmi,et al.  Bilinear Optimal Control of the Fokker-Planck Equation* , 2016 .

[63]  Hui Guo,et al.  High-order bound-preserving finite difference methods for miscible displacements in porous media , 2020, J. Comput. Phys..

[64]  Martin Larsson,et al.  Polynomial jump-diffusions on the unit simplex , 2018, The Annals of Applied Probability.

[65]  Abigail E. Bennett,et al.  A fresh look at inland fisheries and their role in food security and livelihoods , 2019, Fish and Fisheries.

[66]  Anastasios Xepapadeas,et al.  Robust control of parabolic stochastic partial differential equations under model uncertainty , 2019, Eur. J. Control.

[67]  S. Sharma,et al.  The Fokker-Planck Equation , 2010 .

[68]  J. Yong,et al.  Controlled Stochastic Partial Differential Equations for Rabbits on a Grassland , 2020, Acta Mathematicae Applicatae Sinica, English Series.

[69]  Kaveh Fardipour,et al.  A modified seventh-order WENO scheme with new nonlinear weights for hyperbolic conservation laws , 2019, Comput. Math. Appl..

[70]  Hidekazu Yoshioka,et al.  Stochastic optimization model of aquacultured fish for sale and ecological education , 2017 .