Evolution Of Biological Populations Research Articles

Coupling and decoupling of ancestral linkages and current cross-border economic activities: Genetics and policy

This paper studies the potential link between the biological evolution of populations and present-day economic interactions by estimating the correlation of shared ancestry among populations with cross-border capital and human flows. To this end, we employ the new concept of genetic distance, based on (dis)similarity of neutral gene alleles, to quantify shared ancestry. We then incorporate the genetic distance measure into an augmented gravity model, traditionally used to analyze the effect of geographical distance on bilateral exchange. Our analysis focuses on bilateral foreign direct investment (FDI) and migration across 135 countries and we use both linear regression techniques as well as the Poisson Pseudo-Maximum Likelihood Estimator to account for any non-linearities in the model. Our results show that a 1% increase in genetic distance reduces FDI flows by 0.08% while controlling for other distance constructs and factors associated with global capital and human movement. Genetic distance also has a negative effect on migration, where a 1% increase in genetic distance reduces migration flows by 0.22%, with all other things remaining constant. Our study, therefore, links shared ancestry with economic behavior, showing how historical connections are associated with current economic exchanges among nations. Additionally, recognizing that ancestral ties are outside human control, we examine policy measures that help nations overcome such distance barriers. Our findings show that strengthening a nation’s institutional quality and adherence to the rule of law can effectively mitigate any negative correlation of distance constructs with economic exchanges. These insights suggest that prudent policies to foster a stable business environment are essential for any nation to attract FDI and human capital, even from geographically or genetically distant nations.

Dynamic analysis of a phytoplankton-fish model with the impulsive feedback control depending on the fish density and its changing rate.

This paper proposes and studies a comprehensive control model that considers fish population density and its current growth rate, providing new ideas for fishing strategies. First, we established a phytoplankton-fish model with state-impulse feedback control based on fish density and rate of change. Secondly, the complex phase sets and impulse sets of the model are divided into three cases, then the Poincaré map of the model is defined and its complex dynamic properties are deeply studied. Furthermore, some necessary and sufficient conditions for the global stability of the fixed point (order-1 limit cycle) have been provided even for the Poincaré map. The existence conditions for periodic solutions of order-k(k≥2) are discussed, and the influence of dynamic thresholds on system dynamics is shown. Dynamic thresholds depend on fish density and rate of change, i.e., the form of control employed is more in line with the evolution of biological populations than in earlier studies. The analytical method presented in this paper also plays an important role in analyzing impulse models with complex phase sets or impulse sets.

Vivir en los límites del Gran Chaco y la región pampeana: aportes al estudio de las poblaciones de la costa sur de Laguna Mar Chiquita (Córdoba) desde la Arqueología y la Bioantropología

En este trabajo se presenta una síntesis de las investigaciones realizadas en 25 sitios de la costa sur de la Laguna Mar Chiquita desde distintas líneas de análisis: estudios sobre la historia y evolución biológica de las poblaciones, dieta, usos del cuerpo y estilo de vida a partir de distintos bioindicadores, tales como isótopos estables y microrrestos vegetales, tecnología cerámica y prácticas mortuorias. Los cambios y continuidades detectados son situados en un marco cronológico preciso (32 fechados radiocarbónicos, 4525-375 14C años AP), y con referencia al modelo paleoambiental propuesto para el área. Se propone una ocupación humana desde fines del Holoceno medio, con un hiato entre 3800-2500 años AP y una intensificación notable entre 700 y 500 años AP. Los estudios bioantropológicos permiten diferenciar a estas poblaciones de las que ocuparon la región serrana de Córdoba, y proponen cambios en la composición poblacional a partir de 1200 años AP, sugiriendo similitudes con poblaciones del noreste de Patagonia, noreste de región pampeana y región chaco-santiagueña. Se destaca la importancia del consumo de plantas silvestres durante todo el período, incorporando cultígenos de forma complementaria con posterioridad a 1200 años AP. A partir de este momento se sugiere un incremento en los niveles de actividad física, aumento en la prevalencia de ciertas patologías orales y tendencia secular negativa en la talla de los individuos femeninos. Particularmente a partir de 700 años AP, el crecimiento poblacional, sumado al aumento de las tensiones sociales, podría estar indicando un desmejoramiento en la calidad de vida hacia finales del Holoceno tardío.

Emergence of random selections in evolution of biological populations

If a biological population is fragmented in small isolated groups, there may emerge an evolution phenomenon independent from natural selection. Limited number of individuals and their isolation allow a completely random variation in their genetic frequencies, in such a way to have the predominance of some genes and the disappearance of others in next generations, independently on the convenience of the predominant genes for the individuals. This statistical phenomenon is called founder effect, or bottleneck effect (in ecological dynamics).As an advancement in the study of dynamics of bio-molecular populations, and more in general of biological growing, in this paper we focus on the statistical phenomenon that a small sample of a heterogeneous population of species has a distribution of the species different than that in the original population, and independently on the initial distribution, after few steps of sampling and growing, an homogeneous population emerges, where only one of the initial species is present. This phenomenon is strictly related to the founder effect, attested in species evolution, which describes what happens when a few individuals of a population live isolated for several generations: a new colony is generated by a dominant (called founder) species.In this work we propose a mathematical framework (inspired by the Good Turing estimator) for the above phenomenon, and show its emergence also in molecular and bacterial systems, where generations are observable in short times, and the convergence of the population distribution to a single species (in population genetics this process is called fixation) may be achieved in few operational steps. Some DNA based experiments which exhibit this phenomenon are here presented, together with a couple of experiments carried on bacterial systems of genetically modified E.coli strains. Therefore, a relevant evolutionary phenomenon, already confirmed in the literature of population genetics and species evolution, is here mathematically proved and for the first time observed also in (guided) evolution of nano and micro scale biological populations.

State Entropy and Differentiation Phenomenon.

In the formalism of quantum theory, a state of a system is represented by a density operator. Mathematically, a density operator can be decomposed into a weighted sum of (projection) operators representing an ensemble of pure states (a state distribution), but such decomposition is not unique. Various pure states distributions are mathematically described by the same density operator. These distributions are categorized into classical ones obtained from the Schatten decomposition and other, non-classical, ones. In this paper, we define the quantity called the state entropy. It can be considered as a generalization of the von Neumann entropy evaluating the diversity of states constituting a distribution. Further, we apply the state entropy to the analysis of non-classical states created at the intermediate stages in the process of quantum measurement. To do this, we employ the model of differentiation, where a system experiences step by step state transitions under the influence of environmental factors. This approach can be used for modeling various natural and mental phenomena: cell’s differentiation, evolution of biological populations, and decision making.

Modeling Emotional Mutation and Evolvement Using Genetic Algorithm in Agency

Human mind has the ability to generate emotions based on internal and external environment. These emotions are based on past experiences and the current situation. Mutation of emotions in human is the change in the intensity of emotion and the more intense the emotion is, it has more chances of existence. In mutative state two emotions are crossover and from the new emotions only the fittest and strongest emotion survive. Emotional mutation and evolvement helps human mind in decision making and in generating response. In agency the phenomenon of emotional modeling can be accomplished by Mutation and Evolvement for generating output. Genetic algorithm is computational model that is inspired by evolution of biological population and by using mutation and crossover of Genetic Algorithm the agency is able to generate output.This paper presents the algorithmic approach for emotional Mutation and Evolvement using Genetic Algorithm for generating output in agency.

Past population dynamics in Northwest Patagonia: An estimation using molecular and radiocarbon data

Studying demographic changes in past human populations is of great interest due to their role in processes of cultural change as well as the biological evolution of populations. Despite this, a general consensus about the most adequate methodological approach to this end is still lacking. Here, a new approach that combines radiocarbon frequency distributions —uncorrected and corrected by taphonomic bias— and demographic curves independently estimated with modern mitochondrial DNA (mtDNA) is used to estimate population size changes in Northwest Patagonia since the Pleistocene–Holocene transition to recent times. Results based on mtDNA sequences suggest a census size of approximately 3000 individuals (with an estimated female effective size of ca. 750 individuals) by the initial peopling of this region around 10,000 years ago. A strong correspondence between curves based on mtDNA data and those based on archaeological radiocarbon dates (n = 251) was obtained after the effect of taphonomic bias was accounted for. The demographic curves indicate that the population size was relatively stable during the earlier Early Holocene and it increased between 7000 and 5000 years ago, reaching a maximum size around 1000 years ago. Then, the population size declined until present time. We conclude that demographic inferences made on the basis of radiocarbon dates are not necessarily biased but this needs to be evaluated with independent evidence in each specific geographical region.

Co-evolution-based immune clonal algorithm for clustering

Clustering is an important tool in data mining process. Fuzzy $$c$$c-means is one of the most classic methods. But it has been criticized that it is sensitive to the initial cluster centers and is easy to fall into a local optimum. Not depending on the selection of the initial population, evolutionary algorithm is used to solve the problems existed in original fuzzy $$c$$c-means algorithm. However, evolutionary algorithm emphasizes the competition in the population. But in the real world, the evolution of biological population is not only the result of internal competition, but also the result of mutual competition and cooperation among different populations. Co-evolutionary algorithm is an emerging branch of evolutionary algorithm. It focuses on the internal competition, while on the cooperation among populations. This is more close to the process of natural biological evolution and co-evolutionary algorithm is a more excellent bionic algorithm. An immune clustering algorithm based on co-evolution is proposed in this paper. First, the clonal selection method is used to achieve the competition within population to reconstruct each population. The internal evolution of each population is completed during this process. Second, co-evolution operation is conducted to realize the information exchange among populations. Finally, the iteration results are compared with the global best individuals, with a strategy called elitist preservation, to find out the individual with a highest fitness value, that is, the result of clustering. Compared with four state-of-art algorithms, the experimental results indicate that the proposed algorithm outperforms other algorithms on the test data in the highest accuracy and average accuracy.

Basin influence on natural variability of rivers in semi‐arid environments

Scientific evidence indicates that freshwater aquatic ecosystems can be protected or restored by recognising that dynamic flow patterns must be maintained within the natural range of variation to promote their integrity and sustainability. An evaluation of the required conditions for healthy functioning needs to begin with a description of natural streamflow patterns. In order to characterise the flow regimes of a group of rivers located in central Spain, data was taken from 25 gauging stations in a major river basin to establish a hydrological grounding upon which to base biological studies. A number of basin variables were also obtained, and this paper considers the relationships that exist between relevant ecohydrological indices and these basin characteristics. Special importance has been attached to low flow characterisation, since these situations are important determining factors for the development and evolution of biological populations. The results show a significant relationship between one of the low flow indices and basin lithology, evapo‐transpiration and river basin size. Finally, two models have been found which allow low flow volume values to be estimated from these river basin variables. These models can be used to obtain low flow values in river basins where gauging stations do not exist.