Theoretical Population Biology最新文献

We study a multi-stage model for the development of colorectal cancer from initially healthy tissue. The model incorporates a complex sequence of driver gene alterations, some of which result in immediate growth advantage, while others have initially neutral effects. We derive analytic estimates for the sizes of premalignant subpopulations, and use these results to compute the waiting times to premalignant and malignant genotypes. This work contributes to the quantitative understanding of colorectal tumor evolution and the lifetime risk of colorectal cancer.

The diel vertical migration is one of the main drivers of population dynamics in the ocean. Population dynamical models of the ocean typically do not incorporate the behavioral aspects of the migration. We demonstrate a model with coupled population dynamics and behavior with the diel vertical migration emerging. We study the population dynamics and behavioral dynamics of a predator–prey system. We impose a cost of motion for both consumers and prey, and model each individual as following an Itô stochastic differential equation. We study the fixed-points of the ecosystem. Our modeling shows that as we increase the basal resource load, the strength of the diel vertical migration increases, as well as maximal velocity. In addition, a bimodal pattern emerges both for predators and consumers. The increase in the magnitude of the diel vertical migration causes a change in the allocation of copepod resources.

This work presents a population genetic model of evolution, which includes haploid selection, mutation, recombination, and drift. The mutation-selection equilibrium can be expressed exactly in closed form for arbitrary fitness functions without resorting to diffusion approximations. Tractability is achieved by generating new offspring using n-parent rather than 2-parent recombination. While this enforces linkage equilibrium among offspring, it allows analysis of the whole population under linkage disequilibrium. We derive a general and exact relationship between fitness fluctuations and response to selection. Our assumptions allow analytical calculation of the stationary distribution of the model for a variety of non-trivial fitness functions. These results allow us to speak to genetic architecture, i.e., what stationary distributions result from different fitness functions. This paper presents methods for exactly deriving stationary states for finite and infinite populations. This method can be applied to many fitness functions, and we give exact calculations for four of these. These results allow us to investigate metastability, tradeoffs between fitness functions, and even consider error-correcting codes.

Transposable elements (TEs) are self-reproducing selfish DNA sequences that can invade the genome of virtually all living species. Population genetics models have shown that TE copy numbers generally reach a limit, either because the transposition rate decreases with the number of copies (transposition regulation) or because TE copies are deleterious, and thus purged by natural selection. Yet, recent empirical discoveries suggest that TE regulation may mostly rely on piRNAs, which require a specific mutational event (the insertion of a TE copy in a piRNA cluster) to be activated — the so-called TE regulation “trap model”. We derived new population genetics models accounting for this trap mechanism, and showed that the resulting equilibria differ substantially from previous expectations based on a transposition–selection equilibrium. We proposed three sub-models, depending on whether or not genomic TE copies and piRNA cluster TE copies are selectively neutral or deleterious, and we provide analytical expressions for maximum and equilibrium copy numbers, as well as cluster frequencies for all of them. In the full neutral model, the equilibrium is achieved when transposition is completely silenced, and this equilibrium does not depend on the transposition rate. When genomic TE copies are deleterious but not cluster TE copies, no long-term equilibrium is possible, and active TEs are eventually eliminated after an active incomplete invasion stage. When all TE copies are deleterious, a transposition–selection equilibrium exists, but the invasion dynamics is not monotonic, and the copy number peaks before decreasing. Mathematical predictions were in good agreement with numerical simulations, except when genetic drift and/or linkage disequilibrium dominates. Overall, the trap-model dynamics appeared to be substantially more stochastic and less repeatable than traditional regulation models.

This paper examines the stability of the Rosenzweig–MacArthur model distributed to identical discrete habitat patches. Migration between patches is assumed to follow the non-diffusive rule that individuals have a fixed rate of leaving their local habitat patch and migrating to another. Under this non-diffusive migration rule, we found that population dispersal on a non-regular and connected habitat network can both stabilize and destabilize the Rosenzweig–MacArthur model. It is also shown that our non-diffusive migration rule apparently becomes diffusive if the habitat network is regular.

The colonization model formulates competition among propagules for habitable sites to colonize, which serves as a mechanism enabling coexistence of multiple species. This model traditionally assumes that encounters between propagules and sites occur as mass action events, under which species distribution can eventually reach an equilibrium state with multiple species in a constant environment. To investigate the effects of encounter mode on species diversity, we analyzed community dynamics in the colonization model by varying encounter processes. The analysis indicated that equilibrium is approximately neutrally-stable under perfect ratio-dependent encounter, resulting in temporally continuous variation of species’ frequencies with irregular trajectories even under a constant environment. Although the trajectories significantly depend on initial conditions, they are considered to be “strange nonchaotic attractors” (SNAs) rather than chaos from the asymptotic growth rates of displacement. In addition, trajectories with different initial conditions remain different through time, indicating that the system involves an infinite number of SNAs. This analysis presents a novel mechanism for transient dynamics under competition.

Neutral models in ecology assume that all species are demographically equivalent, such that their abundances differ ultimately because of demographic stochasticity rather than selection. In spite of their simplicity, neutral models have been found to accurately reproduce static patterns of biodiversity for diverse communities. However, the same neutral models have been found to exhibit species abundance dynamics that are far too slow compared to reality, resulting in poor fits to temporally dynamic patterns of biodiversity. Here, we show that one of the root causes of these slow dynamics is the additional assumption that a community has reached an equilibrium with a fixed community size, with species that have a net growth rate close to zero. We removed this additional assumption by constructing and analyzing a neutral model with an expected community size that can change over time and is not necessarily at equilibrium, which thus allows the historical formation of a community to be represented explicitly. Our analysis demonstrated that for the general scenario where a small community rapidly grows in size to a carrying capacity, representing recovery from ecological disturbance or assembly of a new community, the model produced much larger changes in species abundances and much shorter species ages than a neutral model at an equilibrium with fixed community size. In addition, the species abundance distribution was biphasic with a subset of abundant species arising from a founder effect. We confirmed these new results in applications of the new model to the specific scenario of recovery of the Amazon tree community after the end-Cretaceous bolide impact, which involved periods of increasing and decreasing community size. We conclude that incorporating transient dynamics in neutral models improves realism by allowing explicit consideration of how a community is formed over realistic time-scales.

Large invasive species eradication programs are undertaken to protect native biodiversity and agriculture. Programs are typically followed by a series of surveys to assess the likelihood of eradication success and, when residual pests are detected, small-scale control or ‘mop-ups’ are implemented to eliminate these infestations. Further surveys follow to confirm absence with ‘freedom’ declared when a target probability of absence is reached. Such biosecurity programs comprise many interacting processes — stochastic biological processes including growth, and response and control interventions — and are an important component of post-border biosecurity. Statistical frameworks formulated to contribute to the design and efficiency of these surveillance and control programs are few and, those available, rely on the simulation of the component processes. In this paper we formulate an analytical Bayesian framework for a general biosecurity program with multiple components to assess pest-eradication success. Our model incorporates stochastic growth and detection processes, and several pest control mechanisms. Survey results and economic considerations are also taken into account to support a range of biosecurity management decisions. Using a case study we demonstrate that solutions match published simulation results and extend the available analysis. Principally, we show how analytical solutions can offer a powerful tool to support the design of effective and cost-efficient biosecurity systems, and we establish some general principles that guide and contribute to robust design.

Distributional properties of tree shape statistics under random phylogenetic tree models play an important role in investigating the evolutionary forces underlying the observed phylogenies. In this paper, we study two subtree counting statistics, the number of cherries and that of pitchforks for the Ford model, the alpha model introduced by Daniel Ford. It is a one-parameter family of random phylogenetic tree models which includes the proportional to distinguishable arrangement (PDA) and the Yule models, two tree models commonly used in phylogenetics. Based on a non-uniform version of the extended Pólya urn models in which negative entries are permitted for their replacement matrices, we obtain the strong law of large numbers and the central limit theorem for the joint distribution of these two statistics for the Ford model. Furthermore, we derive a recursive formula for computing the exact joint distribution of these two statistics. This leads to exact formulas for their means and higher order asymptotic expansions of their second moments, which allows us to identify a critical parameter value for the correlation between these two statistics. That is, when the number of tree leaves is sufficiently large, they are negatively correlated for $0\le \alpha \le 1/2$ and positively correlated for $1/2<\alpha <1$.

Predicting temporal dynamics of genetic diversity is important for assessing long-term population persistence. In stage-structured populations, especially in perennial plant species, genetic diversity is often compared among life history stages, such as seedlings, juveniles, and flowerings, using neutral genetic markers. The comparison among stages is sometimes referred to as demographic genetic structure, which has been regarded as a proxy of potential genetic changes because individuals in mature stages will die and be replaced by those in more immature stages over the course of time. However, due to the lack of theoretical examination, the basic property of the stage-wise genetic diversity remained unclear. We developed a matrix model which was made up of difference equations of the probability of non-identical-by-descent of each life history stage at a neutral locus to describe the dynamics and the inter-stage differences of genetic diversity in stage-structured plant populations. Based on the model, we formulated demographic genetic structure as well as the annual change rate of the probability of non-identical-by-descent (denoted as $\eta$). We checked if theoretical expectations on demographic genetic structure and $\eta$ obtained from our model agreed with computational results of stochastic simulation using randomly generated 3,000 life histories. We then examined the relationships of demographic genetic structure with effective population size $N_e$, which is the determinants of diversity loss per generation time. Theoretical expectations on $\eta$ and demographic genetic structure fitted well to the results of stochastic simulation, supporting the validity of our model. Demographic genetic structure varied independently of $N_e$ and $\eta$, while having a strong correlation with stable stage distribution: genetic diversity was lower in stages with fewer individuals. Our results indicate that demographic genetic structure strongly reflects stable stage distribution, rather than temporal genetic dynamics, and that inferring future genetic diversity solely from demographic genetic structure would be misleading. Instead of demographic genetic structure, we propose $\eta$ as an useful tool to predict genetic diversity at the same time scale as population dynamics (i.e., per year), facilitating evaluation on population viability from a genetic point of view.