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Population Dynamics Based on Individual Stochasticity 1st ed. 2022 [Mīkstie vāki]

  • Formāts: Paperback / softback, 101 pages, height x width: 235x155 mm, weight: 185 g, 1 Illustrations, color; 8 Illustrations, black and white; X, 101 p. 9 illus., 1 illus. in color., 1 Paperback / softback
  • Sērija : Population Studies of Japan
  • Izdošanas datums: 18-Sep-2022
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811935475
  • ISBN-13: 9789811935473
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  • Mīkstie vāki
  • Cena: 46,91 €*
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  • Formāts: Paperback / softback, 101 pages, height x width: 235x155 mm, weight: 185 g, 1 Illustrations, color; 8 Illustrations, black and white; X, 101 p. 9 illus., 1 illus. in color., 1 Paperback / softback
  • Sērija : Population Studies of Japan
  • Izdošanas datums: 18-Sep-2022
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9811935475
  • ISBN-13: 9789811935473
Citas grāmatas par šo tēmu:

This book demonstrates that population structure and dynamics can be reconstructed by stochastic analysis. Population projection is usually based on age-structured population models. These models consist of age-dependent fertility and mortality, whereas birth and death processes generally arise from states of individuals. For example, a number of seeds are proportional to tree size, and amount of income and savings are the basis of decision making for birth behavior in human beings. Thus, even though individuals belong to an identical cohort, they have different fertility and mortality. To treat this kind of individual heterogeneity, stochastic state transitions are reasonable rather than the deterministic states. This book extends deterministic systems to stochastic systems specifically, constructing a state transition model represented by stochastic differential equations. The diffusion process generated by stochastic differential equations provides statistics determining population dynamics, i.e., heterogeneity is incorporated in population dynamics as its statistics. Applying this perspective to demography and evolutionary biology, we can consider the role of heterogeneity in life history or evolution. These concepts are provided to readers with explanations of stochastic analysis.

1 Deterministic and Stochastic Population Models
1(12)
1.1 Malthus Equation and Stochastic Malthus Equations
1(3)
1.2 Basic Properties for SDE Models
4(3)
1.3 SDEs and Partial Differential Equations
7(6)
References
12(1)
2 Linear Structured Population Based on SDE
13(36)
2.1 Age-Structured Population Model with Individual Stochasticity
14(3)
2.2 Path Integral Representation and Classical Life Schedule
17(7)
2.2.1 Path Integral Representation and Analytical Mechanics in Demography
17(4)
2.2.2 Lagrangian and Hamiltonian Expressions in Path Integral
21(3)
2.3 Characteristic Function and Eigenfunctions
24(8)
2.3.1 Characteristic Function and Asymptotic Population Behavior
25(2)
2.3.2 Eigenfunctions and Feynman-Kac Formula
27(5)
2.4 Demographic Indicators
32(2)
2.5 Application to a Semelparity Model
34(15)
References
46(3)
3 Non-linear Structured Population Models
49(16)
3.1 R/K Selection Theory
49(2)
3.2 Non-linear Multi-state Population Model
51(1)
3.3 Stability of Stationary Population
52(5)
3.3.1 Stationary Population
52(2)
3.3.2 Characteristic Equation for Local Stability
54(3)
3.4 Concrete Example
57(8)
3.4.1 Resource Acquisition Competition Model and Stationary Population
57(2)
3.4.2 Local Stability of Stationary Population in Semelparous Species
59(4)
References
63(2)
4 Life History Evolution and Adaptive Stochastic Controls
65(22)
4.1 Basic Theorem for Adaptive Life History
65(3)
4.2 Optimal Stopping Problem in Life Histories
68(3)
4.3 Adaptive Reproductive Timing for Semelparous Species
71(1)
4.4 Optimal Life Schedule Problem and Hamilton--Jacobi--Bellman Equation
72(5)
4.4.1 Stationary Control
75(2)
4.5 Reconsideration of r/K Selection Theory
77(10)
4.5.1 Adaptive Resource Utilization Model in Optimal Foraging Problem
77(3)
4.5.2 Complicated Relationship among Demographic Indices under the Density Effect
80(4)
4.5.3 Summary
84(1)
References
84(3)
5 Application to External Stochasticity
87(12)
5.1 External Stochasticity and Perturbation Method
87(12)
References
97(2)
Epilogue: Population Dynamics from the Perspective of Individual Stochasticity 99
Ryo Oizumi, National Institute of Population and Social Security Research