Model For Evolution Of Population Research Articles

On Models of Population Evolution of Three Interacting Species

In this paper, we first analyzed several basic population dynamics models interpreting the relationships between three species. These are the May-Leonard model with three competitors, some prey-predator models of three-species and a prey-predator model with a super-predator. Subsequently, in our work, we proposed a new three-species model consisting of a prey, a predator and a superpredator, including some important assumptions such as competition, self-defense and infected prey. We examined the various equilibrium points of proposed model, and determined the conditions for extinction and survival of species in the long term. Finally, we performed numerical illustrations using Maltlab software to corroborate the theoretical results.

The key role of cheaters in the persistence of cooperation.

Cooperation is central to the evolution of life and the emergence of multicellularity. Social traits stabilize populations against environmental volatility and enable division of labor. However, evolution of cooperation is beset by cheaters that benefit from cooperation without paying the cost. Analysis of a multilevel selection model of population evolution shows that, counterintuitively, the presence of cheaters can be beneficial or even essential for the survival of cooperators. This unexpected effect comes from multilevel selection whereby cheaters at the individual level become altruists at the group level, enabling overall growth of the population that is essential for the persistence of cooperators. These findings have broad implications for understanding the emergence of cooperation and host-parasite coevolution throughout the history of life.

Molecular isotopologue measurements toward super star clusters and the relation to their ages in NGC 253 with ALCHEMI

Context. Determining the evolution of the CNO isotopes in the interstellar medium (ISM) of starburst galaxies can yield important constraints on the ages of super star clusters (SSCs), or on other aspects and factors contributing to their evolution, such as the initial mass function (IMF). Due to the time-dependent nature of the abundances of isotopes within the ISM – as they are supplied from processes such as nucleosynthesis or chemical fractionation –, this provides the opportunity to test whether or not isotope ratios trace the ages of highly star-forming regions, such as SSCs. Aims. The goal of this study is to investigate whether the isotopic variations in SSC regions within NGC 253 are correlated with their different ages as derived from stellar population modelling. Methods. We measured abundance ratios of CO, HCN, and HCO+ isotopologues in six regions containing SSCs within NGC 253 using high-spatial-resolution (1.6″, ∼28 pc) data from the ALCHEMI (ALma Comprehensive High-resolution Extragalactic Molecular Inventory) ALMA Large program. We then analysed these ratios using RADEX radiative transfer modelling, with the parameter space sampled using the nested sampling Monte Carlo algorithm MLFriends. These abundance ratios were then compared to ages predicted in each region via the fitting of observed star-formation tracers (such as Brγ) to Starburst99 starburst stellar population evolution models. Results. We determined the isotopic column density ratios across multiple regions of SSC activity in NGC 253 using non-LTE radiative transfer modelling. We do not find any significant trend with age for the CO and HCN isotopologue ratios on timescales of the ages of the SSC* regions observed. However, HCO+ may show a correlation with age over these timescales in 12C/13C. Conclusions. The driving factors of these ratios within SSCs could be the IMF or fractionation effects. To further probe these effects in SSCs over time, a larger sample of SSCs must be observed spanning a larger age range.

An Analysis of Some Models of Prey-predator Interaction

Biological models of basic prey-predator interaction have been studied. This consisted, at first, in analyzing the basic models of population dynamics such as the Malthus model, the Verhulst model, the Gompertz model and the model with Allee effect ; then, in a second step, to analyze the Lotka-Volterra model and its models improved by taking into account certain important hypotheses such as competition and/or cooperation between species, existence of refuge for prey and migration of species. For each population evolution model presented, a numerical illustration was made for its verification.

Transition from Bi- to Quadro-Stability in Models of Population Dynamics and Evolution

The article is devoted to a review of bistability and quadro-stability phenomena found in a certain class of mathematical models of population numbers and allele frequency dynamics. The purpose is to generalize the results of studying the transition from bi- to quadro-stability in such models. This transition explains the causes and mechanisms for the appearance and maintenance of significant differences in numbers and allele frequencies (genetic divergence) in neighboring sites within a homogeneous habitat or between adjacent generations. Using qualitative methods of differential equations and numerical analysis, we consider bifurcations that lead to bi- and quadro-stability in models of the following biological objects: a system of two coupled populations subject to natural selection; a system of two connected limited populations described by the Bazykin or Ricker model; a population with two age stages and density-dependent regulation. The bistability in these models is caused by the nonlinear growth of a local homogeneous population or the phase bistability of the 2-cycle in populations structured by space or age. We show that there is a series of similar bifurcations of equilibrium states or fixed or periodic points that precede quadro-stability (pitchfork, period-doubling, or saddle-node bifurcation).

Population evolution analysis in collective intelligence design ecosystem

The Collective Intelligent Design Ecosystem is a dynamic ecosystem founded on an online design platform that leverages collective intelligence to support the creation of novel products. The system's primary components are its users and designers. Maintaining the system's sustainability requires expanding the scale of the designer and user populations as it evolves to stabilize. However, the unity of ecological interactions between various populations is fragmented in contemporary studies of population-scale evolution, and the parameterization of evolutionary models is illogical. To overcome this gap, this research provides a population evolution model of collective intelligent design incorporating participants' intra- and interspecific ecological connections. The model's validity is verified by the evolutionary simulation of 110 designers and 5990 users of China's largest collective intelligence design platform, the Zhubajie platform, and illuminating conclusions are in turn drawn from this simulation. First, the designer's influence on the user is greater than the user's impact on the designer. Second, keeping current members engaged is more crucial to the system's viability than luring in new ones. Third, fostering collaboration among designers while retaining user competitiveness can promote system growth. Fourth, decreasing the reliance between particular designers and users might hasten the system's evolution.

Late Pleistocene landscape changes and habitat specialization as promoters of population genomic divergence in Amazonian floodplain birds.

Although vicariant processes are expected to leave similar genomic signatures among codistributed taxa, ecological traits such as habitat and stratum can influence genetic divergence within species. Here, we combined landscape history and habitat specialization to understand the historical and ecological factors responsible for current levels of genetic divergence in three species of birds specialized in seasonally flooded habitats in muddy rivers and which are widespread in the Amazon basin but have isolated populations in the Rio Branco. Populations of the white-bellied spinetail (Mazaria propinqua), lesser wagtail-tyrant (Stigmatura napensis) and bicolored conebill (Conirostrum bicolor) are currently isolated in the Rio Branco by the black-waters of the lower Rio Negro, offering a unique opportunity to test the effect of river colour as a barrier to gene flow. We used ultraconserved elements (UCEs) to test alternative hypotheses of population history in a comparative phylogeographical approach by modelling genetic structure, demographic history and testing for shared divergence time among codistributed taxa. Our analyses revealed that (i) all three populations from the Rio Branco floodplains are genetically distinct from other populations along the Amazon River floodplains; (ii) these divergences are the result of at least two distinct events, consistent with species habitat specialization; and (iii) the most likely model of population evolution includes lower population connectivity during the Late Pleistocene transition (~250,000 years ago), with gene flow being completely disrupted after the Last Glacial Maximum (~21,000 years ago). Our findings highlight how landscape evolution modulates population connectivity in habitat specialist species and how organisms can have different responses to the same historical processes of environmental change, depending on their habitat affinity.

Human-Plant Coevolution: A modelling framework for theory-building on the origins of agriculture

The domestication of plants and the origin of agricultural societies has been the focus of much theoretical discussion on why, how, when, and where these happened. The 'when' and 'where' have been substantially addressed by different branches of archaeology, thanks to advances in methodology and the broadening of the geographical and chronological scope of evidence. However, the 'why' and 'how' have lagged behind, holding on to relatively old models with limited explanatory power. Armed with the evidence now available, we can return to theory by revisiting the mechanisms allegedly involved, disentangling their connection to the diversity of trajectories, and identifying the weight and role of the parameters involved. We present the Human-Plant Coevolution (HPC) model, which represents the dynamics of coevolution between a human and a plant population. The model consists of an ecological positive feedback system (mutualism), which can be reinforced by positive evolutionary feedback (coevolution). The model formulation is the result of wiring together relatively simple simulation models of population ecology and evolution, through a computational implementation in R. The HPC model captures a variety of potential scenarios, though which conditions are linked to the degree and timing of population change and the intensity of selective pressures. Our results confirm that the possible trajectories leading to neolithisation are diverse and involve multiple factors. However, simulations also show how some of those factors are entangled, what are their effects on human and plant populations under different conditions, and what might be the main causes fostering agriculture and domestication.

Field Survey in the Middle Fortore Valley: A Preliminary Report

Abstract The Fortore Valley Project, which began in 2005 and is still ongoing, aims to create an archaeological map of the Fortore Valley. Several intensive and extensive survey campaigns were carried out in the territory of the middle valley. More than 100 km2 have been surveyed and more than 300 sites identified. This contribution analyzes the collected data and proposes a model of population evolution based on a GIS spatial analysis and a preliminary study of the finds. The artifacts found between 2005 and 2006 in the territory of Occhito Lake (Macchia Valfortore) provide a case study for understanding settlement patterns in the Fortore Valley.

Estimating Migration of Gonioctena quinquepunctata (Coleoptera: Chrysomelidae) Inside a Mountain Range in a Spatially Explicit Context

Abstract The cold-tolerant leaf beetle Gonioctena quinquepunctata displays a large but fragmented European distribution and is restricted to mountain regions in the southern part of its range. Using a RAD-seq-generated large single nucleotide polymorphism (SNP) data set (> 10,000 loci), we investigated the geographic distribution of genetic variation within the Vosges mountains (eastern France), where the species is common. To translate this pattern of variation into an estimate of its capacity to disperse, we simulated SNP data under a spatially explicit model of population evolution (essentially a grid overlapping a map, in which each cell is considered a different population) and compared the simulated and real data with an approximate Bayesian computation (ABC) approach. For this purpose, we assessed a new SNP statistic, the DSVSF (distribution of spatial variation in SNP frequencies) that summarizes genetic variation in a spatially explicit context, and compared its usefulness to standard statistics often used in population genetic analyses. A test of our overall strategy was conducted with simulated data and showed that it can provide a good estimate of the level of dispersal of an organism over its geographic range. The results of our analyses suggested that this insect disperses well within the Vosges mountains, much more than was initially expected given the current and probably past fragmentation of its habitat and given the results of previous studies on genetic variation in other mountain leaf beetles.

Detecting axion dark matter with radio lines from neutron star populations

It has been suggested that radio telescopes may be sensitive to axion dark matter that resonantly converts to radio photons in the magnetospheres surrounding neutron stars (NSs). In this work, we closely examine this possibility by calculating the radiated power from and projected sensitivity to axion dark matter conversion in ensembles of NSs within astrophysical systems like galaxies and globular clusters. We use population synthesis and evolution models to describe the spatial distributions of NSs within these systems and the distributions of NS properties. Focusing on three specific targets for illustration, the Galactic Center of the Milky Way, the globular cluster M54 in the Sagittarius dwarf galaxy, and the Andromeda galaxy, we show that narrow-band radio observations with telescopes such as the Green Bank Telescope and the future Square Kilometer Array may be able to probe the quantum chromodynamics axion over roughly two orders of magnitude in mass, starting at a fraction of a $\mu$eV.

Allele fixation probability in a Moran model with fluctuating fitness landscapes.

Evolution on changing fitness landscapes (seascapes) is an important problem in evolutionary biology. We consider the Moran model of finite population evolution with selection in a randomly changing, dynamic environment. In the model, each individual has one of the two alleles, wild type or mutant. We calculate the fixation probability by making a proper ansatz for the logarithm of fixation probabilities. This method has been used previously to solve the analogous problem for the Wright-Fisher model. The fixation probability is related to the solution of a third-order algebraic equation (for the logarithm of fixation probability). We consider the strong interference of landscape fluctuations, sampling, and selection when the fixation process cannot be described by the mean fitness. Such an effect appears if the mutant allele has a higher fitness in one landscape and a lower fitness in another, compared with the wild type, and the product of effective population size and fitness is large. We provide a generalization of the Kimura formula for the fixation probability that applies to these cases. When the mutant allele has a fitness (dis-)advantage in both landscapes, the fixation probability is described by the mean fitness.

An improved constrained multi-objective optimization evolutionary algorithm for carbon fibre drawing process

ABSTRACTIn this paper, an improved ϵCMOEA/D-DE is proposed to improve the performance of CMOP algorithm and achieve parameter optimization in carbon fibre drawing process. In order to avoid overusing the infeasible solutions, two repair operators are introduced in the population evolution model. More specially, during the evolutionary process, when the constraint violation of infeasible solution exceeds a tolerance threshold, the proposed two repair operators are used to find a better solution to repair the infeasible solution. On the other hand, in order to enhance the convergence rate of the Differential Evolution (DE), a modified DE is proposed. Then, an ϵCMOEA/D-mDE-RO is proposed by incorporating these two improved strategies into the ϵCMOEA/D-DE. Subsequently, the performance of the proposed ϵCMOEA/D-mDE-RO is evaluated on the constrained test problem series and the experiment shows that the proposed algorithm outperforms the existing ϵCMOEA/D-DE. Finally, in order to further illustrate the application potential, the proposed algorithm is successfully applied in optimizing carbon fibre drawing process and the optimal draw ratio vector is obtained.

Population system with coupling between non-Gaussian and Gaussian colored noise under Allee effect

We investigate a stochastic model for single species population growth with strong and weak Allee effects subjected to coupling between non-Gaussian and Gaussian colored noise as well as nonzero cross-correlation in between. Stationary probability distribution of population model is obtained depending on the Fokker–Planck equation. The mean first-passage time is also calculated in order to quantify the time of transition between survival state and extinction state with Allee effect in population. The intensity of non-Gaussian colored noise can induce phase transition, and population may be vulnerable to extinction due to the increase in the intensity of non-Gaussian colored noise. Whether Allee effect is strong or weak, the increase in Allee threshold will not contribute to the survival and stability of the population. Further, the phenomenon of resonant activation is firstly discovered in the study of population dynamics with Allee effect. These behaviors can be interpreted on the basis of a biological model of population evolution.

A population evolution model and its applications to random networks

A general population evolution model is considered. Any individual of the population is characterized by its score. Certain general conditions are assumed concerning the number of the individuals and their scores. Asymptotic theorems are obtained for the number of individuals having a fixed score. Then it is proved that the score distribution is scale free. The result is applied to obtain the weight distribution of cliques in a random graph evolution model.

Backreaction and FRW consistency conditions

If the FRW metric is a good approximation on large scales, then the distance and the expansion rate, as well different notions of distance, satisfy certain consistency conditions. We fit the JLA SNIa distance data to determine the expected amplitude of the violation of these conditions if accelerated expansion is due to backreaction. Adding cosmic clock and BAO expansion rate data, we also model-independently determine the current observational limits on such violation. We find that the predicted maximum backreaction amplitude |kH| ≲ 1 (95% C.I.) is of the same order as the current observational constraints |kH| ≲ 1, the precise numbers depending on the adopted fitting method (polynomials or splines) and stellar population evolution model. We also find that constraints on the value of H0 determined from expansion rate data are sensitive to the stellar evolution model. We forecast constraints from projected LSST+Euclid-like SNIa plus Euclid galaxy differential age data. We find improvement by factor of 6 for the backreaction case and 3 for the model-independent case, probing an interesting region of possible signatures.

Theory of prokaryotic genome evolution

Bacteria and archaea typically possess small genomes that are tightly packed with protein-coding genes. The compactness of prokaryotic genomes is commonly perceived as evidence of adaptive genome streamlining caused by strong purifying selection in large microbial populations. In such populations, even the small cost incurred by nonfunctional DNA because of extra energy and time expenditure is thought to be sufficient for this extra genetic material to be eliminated by selection. However, contrary to the predictions of this model, there exists a consistent, positive correlation between the strength of selection at the protein sequence level, measured as the ratio of nonsynonymous to synonymous substitution rates, and microbial genome size. Here, by fitting the genome size distributions in multiple groups of prokaryotes to predictions of mathematical models of population evolution, we show that only models in which acquisition of additional genes is, on average, slightly beneficial yield a good fit to genomic data. These results suggest that the number of genes in prokaryotic genomes reflects the equilibrium between the benefit of additional genes that diminishes as the genome grows and deletion bias (i.e., the rate of deletion of genetic material being slightly greater than the rate of acquisition). Thus, new genes acquired by microbial genomes, on average, appear to be adaptive. The tight spacing of protein-coding genes likely results from a combination of the deletion bias and purifying selection that efficiently eliminates nonfunctional, noncoding sequences.

Manifestation of multimodality in a simple ecological-genetic model of population evolution

An investigation of the nature of dynamics of the population size and genetic structure is carried out for a homogeneous ecologically limited population influenced by density-dependent r-K selection in a single diallelic genetic locus. The detailed study of the results of basic types of natural selection is carried out with additional consideration of the influence of initial conditions. It is shown that coexistence of several different asymptotic dynamic modes (with their own attraction basins) is possible in numerous enough parametric domains which are meaningful biologically.

Matching asteroid population characteristics with a model constructed from the YORP-induced rotational fission hypothesis

From the results of a comprehensive asteroid population evolution model, we conclude that the YORP-induced rotational fission hypothesis is consistent with the observed population statistics of small asteroids in the main belt including binaries and contact binaries. These conclusions rest on the asteroid rotation model of Marzari et al. ([2011]Icarus, 214, 622–631), which incorporates both the YORP effect and collisional evolution. This work adds to that model the rotational fission hypothesis, described in detail within, and the binary evolution model of Jacobson et al. ([2011a] Icarus, 214, 161–178) and Jacobson et al. ([2011b] The Astrophysical Journal Letters, 736, L19). Our complete asteroid population evolution model is highly constrained by these and other previous works, and therefore it has only two significant free parameters: the ratio of low to high mass ratio binaries formed after rotational fission events and the mean strength of the binary YORP (BYORP) effect.We successfully reproduce characteristic statistics of the small asteroid population: the binary fraction, the fast binary fraction, steady-state mass ratio fraction and the contact binary fraction. We find that in order for the model to best match observations, rotational fission produces high mass ratio (> 0.2) binary components with four to eight times the frequency as low mass ratio (<0.2) components, where the mass ratio is the mass of the secondary component divided by the mass of the primary component. This is consistent with post-rotational fission binary system mass ratio being drawn from either a flat or a positive and shallow distribution, since the high mass ratio bin is four times the size of the low mass ratio bin; this is in contrast to the observed steady-state binary mass ratio, which has a negative and steep distribution. This can be understood in the context of the BYORP-tidal equilibrium hypothesis, which predicts that low mass ratio binaries survive for a significantly longer period of time than high mass ratio systems. We also find that the mean of the log-normal BYORP coefficient distribution μB≳10−2, which is consistent with estimates from shape modeling (McMahon and Scheeres, 2012a).

Sampling ARG of multiple populations under complex configurations of subdivision and admixture.

Simulating complex evolution scenarios of multiple populations is an important task for answering many basic questions relating to population genomics. Apart from the population samples, the underlying Ancestral Recombinations Graph (ARG) is an additional important means in hypothesis checking and reconstruction studies. Furthermore, complex simulations require a plethora of interdependent parameters making even the scenario-specification highly non-trivial. We present an algorithm SimRA that simulates generic multiple population evolution model with admixture. It is based on random graphs that improve dramatically in time and space requirements of the classical algorithm of single populations.Using the underlying random graphs model, we also derive closed forms of expected values of the ARG characteristics i.e., height of the graph, number of recombinations, number of mutations and population diversity in terms of its defining parameters. This is crucial in aiding the user to specify meaningful parameters for the complex scenario simulations, not through trial-and-error based on raw compute power but intelligent parameter estimation. To the best of our knowledge this is the first time closed form expressions have been computed for the ARG properties. We show that the expected values closely match the empirical values through simulations.Finally, we demonstrate that SimRA produces the ARG in compact forms without compromising any accuracy. We demonstrate the compactness and accuracy through extensive experiments. SimRA (Simulation based on Random graph Algorithms) source, executable, user manual and sample input-output sets are available for downloading at: https://github.com/ComputationalGenomics/SimRA CONTACT: : parida@us.ibm.com Supplementary data are available at Bioinformatics online.