Simulating trajectories and phylogenies from population dynamics models with TiPS

AbstractWe introduce TiPS, an R-based simulation software to generate time series and genealogies associated with a population dynamics model. The approach is flexible since it can capture any model defined with a set of ordinary differential equations (ODE), and allow parameter values to vary over time periods. Computational time is minimal thanks to the use of the Rcpp package to compile the ODEs into a program corresponding to an implementation of the Gillespie algorithm. This software is particularly suited for epidemiology and phylodynamics, where there is a need to generate numerous phylogenies for a variety of infections life cycles, and in population genetics as well.

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Inequality in resource allocation and population dynamics models

Royal Society Open Science ◽

10.1098/rsos.182178 ◽

2019 ◽

Vol 6 (7) ◽

pp. 182178 ◽

Cited By ~ 3

Author(s):

Masahiro Anazawa

Keyword(s):

Resource Allocation ◽

Population Dynamics ◽

First Principles ◽

Dynamics Model ◽

Fixed Amount ◽

Resource Unit ◽

Population Dynamics Model ◽

Population Dynamics Models ◽

Dynamics Models ◽

Competition For Resources

The Hassell model has been widely used as a general discrete-time population dynamics model that describes both contest and scramble intraspecific competition through a tunable exponent. Since the two types of competition generally lead to different degrees of inequality in the resource distribution among individuals, the exponent is expected to be related to this inequality. However, among various first-principles derivations of this model, none is consistent with this expectation. This paper explores whether a Hassell model with an exponent related to inequality in resource allocation can be derived from first principles. Indeed, such a Hassell model can be derived by assuming random competition for resources among the individuals wherein each individual can obtain only a fixed amount of resources at a time. Changing the size of the resource unit alters the degree of inequality, and the exponent changes accordingly. As expected, the Beverton–Holt and Ricker models can be regarded as the highest and lowest inequality cases of the derived Hassell model, respectively. Two additional Hassell models are derived under some modified assumptions.

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A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f2012-103 ◽

2012 ◽

Vol 69 (11) ◽

pp. 1881-1893 ◽

Cited By ~ 6

Author(s):

Verena M. Trenkel ◽

Mark V. Bravington ◽

Pascal Lorance

Keyword(s):

Population Dynamics ◽

Random Effects ◽

Fixed Effects ◽

Random Effect ◽

Total Mortality ◽

Estimation Bias ◽

Effective Sample Size ◽

Dynamics Model ◽

Population Dynamics Model ◽

Study Parameter

Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

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A microbial treatment population dynamics optimization approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210127 ◽

2021 ◽

pp. 1-15

Author(s):

Jinding Gao

Keyword(s):

Population Dynamics ◽

Information Exchange ◽

Information Diffusion ◽

Optimization Problems ◽

Microbial Control ◽

Function Optimization ◽

Optimization Approach ◽

Dynamics Model ◽

Population Dynamics Model ◽

Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

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