Asymptotic rates of growth of the extinction probability of a mutant gene

We prove that a result of Haldane (1927) that relates the asymptotic behaviour of the extinction probability of a slightly supercritical Poisson branching process to the mean number of offspring is true for a general Bienaymé-Galton-Watson branching process, provided that the second derivatives of the probability-generating functions converge uniformly to a non-zero limit. We show also by examples that such a result is true more widely than our proof suggests and exhibit some counter-examples.Research supported by NSERC     

A branching process, its application in biology: influence of demographic parameters on the social structure in mammal groups

Branching processes are widely used in biology. This theoretical tool is used in cell dynamics, epidemics and population dynamics. In population dynamics, branching processes are mainly used to access extinction probabilities of populations, groups or families, with the Galton-Watson branching process. Many mammal species live in socially-structured groups, and the smallest units of these groups are lineages (or families) of kin-related individuals. In many primate species, these lineages are matrilines, as females remain in their natal groups most of the time, whereas males generally disperse. Lineage parameters, such as numbers of matrilines, size of each matriline and average degree of relatedness, could strongly influence the genetic composition of groups. Evidence indicates that division along matrilines could induce substantial differentiation among fission groups. Here, we develop a novel mathematical model based on the branching process theory describing demographic dynamics of groups. The main result of this model is an explicit analytical expression of the joint distribution of numbers of lineages and sizes of socially-structured groups. We investigated the influence of parameters such as natality and mortality on the outcome of the process, including extinction probability. Finally, we discuss this theoretical result with respect to biological significance.     

Rates of decay for the survival probability of a mutant gene

If qk is the extinction probability of a slightly supercritical branching process with offspring distribution P _kr : r = 0, 1, 2,..., then it is shown that if sup _r r ³ p _kr , < , inf ² _k > 0, and m _k 1, then 1 – q _k 2(m _k –1) _k ^–2, where m _k = _r rp _kr , ² _k = ^k _r r ² p _kr – m _k ². This provides a simple set of sufficient conditions for the validity of a conjecture of Ewens (1969) for the survival probability of a slightly advantageous mutant gene.Research supported in part by NSF grants DMS-8803639 and DMS-9007182     

On the probability of loss of new mutations in the presence of linkage disequilibrium

A new selectively neutral mutation occurs in a multilocus genetic background that has achieved a stable equilibrium at which there is a linkage disequilibrium. Perturbation techniques are applied to an extension of the branching process formulation of Fisher in order to address the question of extinction probabilities. We show that under appropriate conditions the probability of extinction of the new mutant is increased by the existence of linkage disequilibrium in the genetic background.Research supported in part by NIH grant GM 28016     

On the survival probability of a slightly advantageous mutant gene with a general distribution of progeny size—A branching process model

A branching process method is employed to study the survival probability of a slightly advantageous mutant gene with a general distribution of progeny size in a large population. A counter-example to a classic proposition is given. A somewhat weaker result is proved.Supported in part by NIH Grant 5R01 GM10452-18     

A note on modelling the dynamics of budding yeast populations using branching processes

A multitype branching process is proposed as a model for the behaviour of populations of the budding yeast Saccharomyces Cerevisiae. Using the idea of branching processes counted by random characteristics, we are able to obtain explicit expressions describing different aspects of the asymptotic composition of such populations. The main purpose of this note is to show that the branching process approach is an alternative to deterministic population models based on differential equation methods.Supported by the Swedish Natural Science Research Council     

Rates of decay for the survival probability of a mutant gene II The multitype case

This paper extends the results of [1] to the multitype case. For a multitype branching process that is slightly supercritical, approximations for the survival probability in terms of the maximal eigenvalue of the mean matrix and a generalized variance ² are developed. Our results improve upon those of Hoppe [5] and Eshel [3] that seek to validate a conjecture of Ewens [4].Research supported in part by NSF grant DMS 9007182     

Heterogeneity in dynamic species abundance models: the selective effect of extinction processes

There is often large variation in traits across the species of a community. In particular, variation in life history traits affecting population dynamics is likely to affect the species abundance distribution. Applying a dynamic and heterogeneous species abundance model we study how differences in extinction time for species in a community act as a force changing the distribution of dynamic parameters across species. This process may generate communities that are more heterogeneous then the heterogeneity measured as the species enter the community. Analytical results for some versions of the lognormal and gamma species abundance model are given as exemplifications of this process, together with stochastic simulations demonstrating the temporal changes in number of species and community heterogeneity through time.     

带有移民的两性分支过程极限性质

考虑带有移民的两性分支过程,这里移民数量依赖于当前人口总数,在下临界状态情形,本文证明了此过程收敛于一平衡分布．     

Supercritical branching processes and the role of fluctuations under exponential population growth

We study some exact properties of supercritical branching processes. A proper rescaling of the relevant variable allows us to determine the distribution of population sizes after a number of generations have elapsed. Both time-continuous and discrete processes are analysed and compared. The obtained results are of relevance for the growth of populations that are not resource limited (a typical situation in some biological processes that can be modelled by laboratory experiments). Large fluctuations inherent to the process play a main role when bottlenecks occur.     

基于最大熵原理的浙江毛竹胸径分布及测量不确定度评定

应用最大熵原理构造了测树因子概率分布的统一模型,这样构造的模型具有明确的解析表达式,并能克服常用方法无法解释测树因子服从某种概率分布的真正原因,从而为测树因子统计分布建模提供了一种有效方法.使用1-3阶样本矩、1-4阶样本矩与1-5阶样本矩,用所构建的概率分布统一模型分别对浙江省域毛竹胸径分布分别作了仿真试验,结果表明当采用1-4阶样本矩时,仿真效果最好,而且比通过假设检验的Weibull分布仿真结果理想:(1)图形非常相似,对实测数据都能很好的模拟;(2)最大熵法的离差平方和为0.00018,Weibull分布的为0.00045[1].由于各种系统与非系统的原因,都会影响测量结果的准确性,对所构建的模型作了不确定度评定,表明结果具有很大的可靠性,测量结果的估计:7.85100,测量结果的标准不确定度:1.82710,置信概率:0.96020.     

Interval estimation of the kappa coefficient with binary classification and an equal marginal probability model

We derive a likelihood score method for interval estimation of the intraclass version of the kappa coefficient of agreement with binary classification using a general theory of Bartlett (1953, Biometrika 40, 306-317). By exact evaluation, we investigate statistical properties of the score method, the chi-square goodness-of-fit procedure (Donner and Eliasziw, 1992, Statistics in Medicine 11, 1511-1519; Hale and Fleiss, 1993, Biometrics 49, 523-534), and a crude confidence interval for small and medium sample sizes. Actual coverage percentages of the score and chi-square methods are satisfactorily close to the nominal confidence coefficient, while that of the crude method is quite unsatisfactory. The expected length of the score method is shorter than that of the chi-square procedure when the response rate is very small or very large.     

Inverse‐probability‐of‐treatment weighted estimation of causal parameters in the presence of error‐contaminated and time‐dependent confounders

Inverse‐probability‐of‐treatment weighted (IPTW) estimation has been widely used to consistently estimate the causal parameters in marginal structural models, with time‐dependent confounding effects adjusted for. Just like other causal inference methods, the validity of IPTW estimation typically requires the crucial condition that all variables are precisely measured. However, this condition, is often violated in practice due to various reasons. It has been well documented that ignoring measurement error often leads to biased inference results. In this paper, we consider the IPTW estimation of the causal parameters in marginal structural models in the presence of error‐contaminated and time‐dependent confounders. We explore several methods to correct for the effects of measurement error on the estimation of causal parameters. Numerical studies are reported to assess the finite sample performance of the proposed methods.     

The bias associated with the dispersion of length at age in the estimation of growth and mortality parameters from uni-modal length-frequency data

Values of M and M/K extracted from simulated uni-modal length-frequency data representing 28 fish stocks were biased. The bias exceeded 20% when the life span of the stock was <5 or >15 years.     

The effects of demographic stochasticity and parameter uncertainty on predicting the establishment of introduced species

Invasive species are a serious threat to biodiversity worldwide and predicting whether an introduced species will first establish and then become invasive can be useful to preserve ecosystem services. Establishment is influenced by multiple factors, such as the interactions between the introduced individuals and the resident community, and demographic and environmental stochasticity. Field observations are often incomplete or biased. This, together with an imperfect knowledge of the ecological traits of the introduced species, makes the prediction of establishment challenging. Methods that consider the combined effects of these factors on our ability to predict the establishment of an introduced species are currently lacking. We develop an inference framework to assess the combined effects of demographic stochasticity and parameter uncertainty on our ability to predict the probability of establishment following the introduction of a small number of individuals. We find that even moderate levels of demographic stochasticity influence both the probability of establishment, and, crucially, our ability to correctly predict that probability. We also find that estimation of the demographic parameters of an introduced species is fundamental to obtain precise estimates of the interaction parameters. For typical values of demographic stochasticity, the drop in our ability to predict an establishment can be 30% when having priors on the demographic parameters compared to having their accurate values. The results from our study illustrate how demographic stochasticity may bias the prediction of the probability of establishment. Our method can be applied to estimate probability of establishment of introduced species in field scenarios, where time series data and prior information on the demographic traits of the introduced species are available.