sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 1; referees: 2 approved]

This article describes the sPop packages implementing the deterministic and stochastic versions of an age-structured discrete-time population dynamics model. The packages enable mechanistic modelling of a population by monitoring the age and development stage of each individual. Survival and development are included as the main effectors and they progress at a user-defined pace: follow a fixed-rate, delay for a given time, or progress at an age-dependent manner. The model is implemented in C, Python, and R with a uniform design to ease usage and facilitate adoption. Early versions of the model were previously employed for investigating climate-driven population dynamics of the tiger mosquito and the chikungunya disease spread by this vector. The sPop packages presented in this article enable the use of the model in a range of applications extending from vector-borne diseases towards any age-structured population including plant and animal populations, microbial dynamics, host-pathogen interactions, infectious diseases, and other time-delayed epidemiological processes

Population Growth in Space and Time

How great an effect does self-generated spatial structure have on logistic population growth? Results are described from an individual based model (IBM) with spatially localized dispersal and competition, and from a deterministic approximation to the IBM describing the dynamics of the first and spatial moments. The dynamical system incorporates a novel closure that gives a close approximation to the IBM in the presence of strong spatial structure. Population growth given by the spatial logistic equation can differ greatly from that of the non-spatial logistic model. Numerical simulations show that populations may grow more slowly or more rapidly than would be expected from the non-spatial model, and may reach their maximum rate of increase at densities other than half of the carrying capacity. Populations can achieve asymptotic densities substantially greater than or less than the carrying capacity of the non-spatial logistic model, and can even tend toward extinction. These properties of the spatial logistic equation are caused by a local dispersal and competition which effect spatial structure, which in turn affects population growth. Accounting for these spatial processes brings the theory of single-species population growth a step closer to the growth of real spatially-structured populations

Population dynamics of Ichthyophthirius Multifiliis

Imperial Users onl

Data-based Mechanistic Modelling (DBM) of Nonlinear Environmental Systems.

The main focus of the research studies presented in this thesis centre on the application and development of data-based mechanistic (DBM) transfer function models for nonlinear environmental systems. The data-based mechanistic modelling approach exploits the available time series data, in statistical terms, to expose the model structure, generating dynamic stochastic models that are parsimonious in nature and can be interpreted in a physical manner. For nonlinear systems, the DBM approach centres on the use of transfer function models whose parameters are free to vary over time. The presence of such time variable parameters may reflect either nonstationarity or nonlinearity; the latter arising if the variations are also shown to be state dependent. Statistical time series methods for estimating time varying parameters (TVP) and the use of these methods in state dependent parameter modelling (SDPM) are discussed and applied to the modelling of nonlinear ecological population dynamics and hydrological processes. Further, DBM modelling techniques are applied to an oceanic ecosystem simulation model in order to investigate model uncertainty and over-parameterisation, set within the context of data assimilation. In each application, the DBM methodology is shown to successfully identify the system nonlinearities, so providing additional physical insight and ensuring a good explanation of the data with the minimum of model parameters (parsimony). Further, the DBM approach provides an approach to both the evaluation and reduction in the complexity of highly parameterised simulation models