Optimal life schedule with stochastic growth in age-size structured models: theory and an application.

Reproduction timing is one of the most important factors for the life history because it is closely related to subsistence of species. On the other hand, ecological demographers recently noted the effects of environmental stochasticity on the population dynamics by using linear demographic models because stochasticity reduces the population growth rate. Linear demographic models are generally composed of reproduction timing, several life history traits and stochasticity. The stochasticity is generated by twofold stochasticity, that is, internal and external stochasticities. In transition matrix models, the internal stochasticity gives a species a set of transition probabilities to other states, whereas the external stochasticity annually variegates the value of these transition probabilities. If the population vector has only the internal stochasticity, it satisfies a partial differential equation, in which it is described by a stochasticity in body-size growth rate. In this paper, we focus on the stochasticity which affects the body-size growth rate under r-selection. We construct a mathematical model of stochastic life history of each individual by using a stochastic differential equation, and analyze the relationship between optimal life schedule and the population dynamics by finding Euler-Lotka equation. Then, we use the formalism of path-integral expression to the population dynamics and show that the expression is consistent with other expressions in linear demographic models. Finally, we apply our method to a simple example, and obtain a characteristic of the stochasticity which has not only negative effect on the fitness but also positive effect from our model.

[1]  N. Asmar,et al.  Partial Differential Equations with Fourier Series and Boundary Value Problems , 2004 .

[2]  H. M. Taylor,et al.  Natural selection of life history attributes: an analytical approach. , 1974, Theoretical population biology.

[3]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[4]  R. Feynman Space-Time Approach to Non-Relativistic Quantum Mechanics , 1948 .

[5]  W. D. Evans,et al.  PARTIAL DIFFERENTIAL EQUATIONS , 1941 .

[6]  Roberto Salguero-Gómez,et al.  Matrix projection models meet variation in the real world , 2010 .

[7]  C. Pfister,et al.  Patterns of variance in stage-structured populations: evolutionary predictions and ecological implications. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[8]  J. A. León Life histories as adaptive strategies. , 1976, Journal of theoretical biology.

[9]  Y. Iwasa,et al.  Shoot/root balance of plants: Optimal growth of a system with many vegetative organs , 1984 .

[10]  María B. García,et al.  Life span correlates with population dynamics in perennial herbaceous plants. , 2008, American journal of botany.

[11]  E. Allen Derivation of Stochastic Partial Differential Equations , 2008 .

[12]  B. Baaquie Quantum Finance: Path Integrals and Hamiltonians for Options and Interest Rates , 2004 .

[13]  T. Takada,et al.  The relationship between the transition matrix model and the diffusion model , 1994 .

[14]  H. Pham On some recent aspects of stochastic control and their applications , 2005, math/0509711.

[15]  Pieter A. Zuidema,et al.  Integral Projection Models for trees: a new parameterization method and a validation of model output , 2010 .

[16]  S. Peng A general stochastic maximum principle for optimal control problems , 1990 .

[17]  N. Goel,et al.  Stochastic models in biology , 1975 .

[18]  E. Allen Derivation of stochastic partial differential equations for size- and age-structured populations , 2009, Journal of biological dynamics.

[19]  O. Diekmann,et al.  The Dynamics of Physiologically Structured Populations , 1986 .

[20]  S. Shreve Stochastic calculus for finance , 2004 .

[21]  Y. Iwasa,et al.  Optimal Growth Schedule of a Perennial Plant , 1989, The American Naturalist.

[22]  P. Adler,et al.  Demography of perennial grassland plants: survival, life expectancy and life span , 2008 .

[23]  Shripad Tuljapurkar,et al.  Population dynamics in variable environments I. Long-run growth rates and extinction , 1980 .

[24]  Stephen P. Ellner,et al.  Stochastic stable population growth in integral projection models: theory and application , 2007, Journal of mathematical biology.

[25]  S. Ellner,et al.  Integral Projection Models for Species with Complex Demography , 2006, The American Naturalist.

[26]  Michael A. Buice,et al.  Path Integral Methods for Stochastic Differential Equations , 2015, Journal of mathematical neuroscience.

[27]  B. Øksendal Stochastic differential equations : an introduction with applications , 1987 .

[28]  P. Holgate,et al.  Matrix Population Models. , 1990 .

[29]  S. Tuljapurkar Population dynamics in variable environments. II. Correlated environments, sensitivity analysis and dynamics , 1982 .

[30]  B. Kendall,et al.  Longevity can buffer plant and animal populations against changing climatic variability. , 2008, Ecology.

[31]  M. Tweedie Statistical Properties of Inverse Gaussian Distributions. II , 1957 .

[32]  G. Letac,et al.  A characterization of the generalized inverse Gaussian distribution by continued fractions , 1983 .

[33]  Horst R. Thieme,et al.  Mathematics in Population Biology , 2003 .

[34]  Nathan Keyfitz,et al.  Applied Mathematical Demography , 1978 .

[35]  R. Macarthur,et al.  The Theory of Island Biogeography , 1969 .

[36]  M. Kac On distributions of certain Wiener functionals , 1949 .

[37]  Shripad Tuljapurkar,et al.  Population dynamics in variable environments. III. Evolutionary dynamics of r-selection , 1982 .

[38]  H. Hara Path integrals for Fokker-Planck equation described by generalized random walks , 1981 .

[39]  X. Zhou,et al.  Stochastic Controls: Hamiltonian Systems and HJB Equations , 1999 .

[40]  James W. Sinko,et al.  A New Model For Age‐Size Structure of a Population , 1967 .

[41]  Fox,et al.  Functional-calculus approach to stochastic differential equations. , 1986, Physical review. A, General physics.

[42]  David P. Smith,et al.  On the Integral Equation of Renewal Theory , 2013 .

[43]  M. Suzuki,et al.  Passage from an Initial Unstable State to A Final Stable State , 2007 .

[44]  Ioannis Karatzas,et al.  Brownian Motion and Stochastic Calculus , 1987 .