TL;DR: Theoretical bases of population dynamics, Dynamics of a host-parasitoid interaction system: Utida's experimental study, and statistical analysis of population fluctuations.
Abstract: Part I: Theoretical bases of population dynamics. Basic properties and structure of population processes. Structures and patterns of population processes. Statistical analysis of population fluctuations. Population process models. Part II: Analysis of classic cases. Analysis of lynx 10-year cycle. Snowshoe hare demography. Density effects on the dynamics of a single-species population: Utida's classic experiments on the azuki bean weevil. Dynamics of a host-parasitoid interaction system: Utida's experimental study. Dynamics of the spruce budworm outbreak processes. Epilogue. Bibliography. Index.
TL;DR: A rigorous construction of the microscopic population process that captures the probabilistic dynamics over continuous time of birth, mutation, and death, as influenced by the trait values of each individual, and interactions between individuals is presented.
TL;DR: In this paper, the authors formulate some simple conditions under which a Markov chain may be approximated by the solution to a differential equation, with quantifiable error probabilities, and the role of a choice of coordinate functions for the Markov Chain is emphasised.
Abstract: We formulate some simple conditions under which a Markov chain may be approximated by the solution to a differential equation, with quantifiable error probabilities. The role of a choice of coordinate functions for the Markov chain is emphasised. The general theory is illustrated in three examples: the classical stochastic epidemic, a population process model with fast and slow variables, and core-finding algorithms for large random hypergraphs.
TL;DR: The authors examines the asymptotic behavior of the stochastic extension of a fundamentally important population process, namely the Lotka-Volterra model. But their results show that a potential deterministic population explosion can be prevented by the presence of even a tiny amount of environmental noise, showing the high level of difference which exists between these two representations.
TL;DR: In this article, the authors used the process of a continuous-time Markov chain to represent situations involving numbers of individuals in different categories or colonies, where the jumps of the chain may be of three types, corresponding to the arrival of a new individual, the departure of an existing one, or the transfer of an individual from one colony to another.
Abstract: The processes of the title have frequently been used to represent situations involving numbers of individuals in different categories or colonies. In such processes the state at any time is represented by the vector n = (n 1, n 2, …, nk ), where nt is the number of individuals in the ith colony, and the random evolution of n is supposed to be that of a continuous-time Markov chain. The jumps of the chain may be of three types, corresponding to the arrival of a new individual, the departure of an existing one, or the transfer of an individual from one colony to another.