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Journal articles on the topic 'Population structure'

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Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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1

Frean, Marcus, Paul B. Rainey, and Arne Traulsen. "The effect of population structure on the rate of evolution." Proceedings of the Royal Society B: Biological Sciences 280, no. 1762 (July 7, 2013): 20130211. http://dx.doi.org/10.1098/rspb.2013.0211.

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Ecological factors exert a range of effects on the dynamics of the evolutionary process. A particularly marked effect comes from population structure, which can affect the probability that new mutations reach fixation. Our interest is in population structures, such as those depicted by ‘star graphs’, that amplify the effects of selection by further increasing the fixation probability of advantageous mutants and decreasing the fixation probability of disadvantageous mutants. The fact that star graphs increase the fixation probability of beneficial mutations has lead to the conclusion that evolution proceeds more rapidly in star-structured populations, compared with mixed (unstructured) populations. Here, we show that the effects of population structure on the rate of evolution are more complex and subtle than previously recognized and draw attention to the importance of fixation time. By comparing population structures that amplify selection with other population structures, both analytically and numerically, we show that evolution can slow down substantially even in populations where selection is amplified.

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2

Toksanbaeva, Mairash, and Raisa Popova. "Labor resources as a characteristic of labor potential and their structure." Population 25, no. 4 (December 21, 2022): 151–62. http://dx.doi.org/10.19181/population.2022.25.4.13.

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One of the characteristics of labor potential is the ability to work among its carriers indiv i duals, groups and the population, by which are also studied other characteristics (demography, health, social and economic activity, professional competencies, etc.). On the basis of working capacity is determined the most general indicator of the labor potential of population, namely, labor resources. This indicator is structured according to a number of qualitative parameters. They make it possible to identify labor resources used in public production, as well as unused reserves. Their involvement in labor is becoming relevant in the context of the modern need to increase the self-sufficiency of the economy, and hence, to increase these resources. However, their growth is limited for demographic reasons. To assess the available reserves, labor resources are ranked according to the characteristics of economic activity. In descending order, the categories are distinguished according to their relation to the labor force: the real labor force (employed and unemployed), potential labor force (not employed, but willing to work) and not included in the labor force (not willing to work). Calculations for these categories showed that in 2021, the real labor force dominated in the labor force (85.6%). The potential contingent accounted for a miserable amount (slightly more than one percent), and for those who did not want to work — a little more than 10%. But among those not included in the labor force, more than two thirds of this category were those who had objective reasons for being unemployed, as well as those employed in unpaid but useful domestic work. Factors influencing the structure of labor resources are considered by regions of the Russian Federation. They showed dependence of this structure on the birth rate, aging of the population, internal migration, and, above all, on the parameters of employment and unemployment, which play a leading role among the factors for improving this structure.

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3

Pfaff, Carrie L., Rick A. Kittles, and Mark D. Shriver. "Adjusting for population structure in admixed populations." Genetic Epidemiology 22, no. 2 (January 10, 2002): 196–201. http://dx.doi.org/10.1002/gepi.0126.

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4

Mukherjee, B. N., K. C. Malhotra, M. Roy, S. banerjee, H. Walter, and R. Chakraborty. "Genetic heterogeneity and population structure in eastern India: Red cell enzyme variability in ten Assamese populations." Zeitschrift für Morphologie und Anthropologie 77, no. 3 (May 3, 1989): 287–96. http://dx.doi.org/10.1127/zma/77/1989/287.

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5

Skotarczak, E., P. Ćwiertnia, and T. Szwaczkowski. "Pedigree structure of American bison (Bison bison) population." Czech Journal of Animal Science 63, No. 12 (December 4, 2018): 507–17. http://dx.doi.org/10.17221/120/2017-cjas.

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An effective realization of breeding programs in zoos is strongly determined by completeness of animal pedigree information. The knowledge of pedigree structure allows to maintain optimal genetic variability of a given population. The aim of this study was to estimate the parameters describing the pedigree structure of American bison housed in zoos in the context of further management of the population. Finally, 4269 American bison were analysed (1883 males, 2217 females, and 169 with unknown sex). The registered animals were born between years 1874 and 2013. The following pedigree parameters were estimated: number of fully traced generations, number of complete generations equivalent, index of pedigree completeness, individual inbreeding coefficients, increase of inbreeding for each individual, effective population size, and genetic diversity. The maximum number of fully traced generations was 3 (the mean value is 0.693). The mean inbreeding coefficient for the population studied was 3.26%, whereas individual increase in inbreeding ranged from 0 to 25.12%. Although the pedigree parameters (including the inbreeding level) in the American bison obtained in the present study seem to be acceptable (from the perspective of other wild animal populations), they can be over/underestimated due to incomplete pedigree.

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6

Garbutt, K., and F. A. Bazzaz. "Population niche structure." Oecologia 72, no. 2 (May 1987): 291–96. http://dx.doi.org/10.1007/bf00379281.

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7

Lehoczky, István, Desiré L. Dalton, József Lanszki, Zoltán Sallai, M. Thabang Madisha, Lisa J. Nupen, and Antoinette Kotzé. "Assessment of population structure in Hungarian otter populations." Journal of Mammalogy 96, no. 6 (September 6, 2015): 1275–83. http://dx.doi.org/10.1093/jmammal/gyv136.

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8

Yamasaki, Masanori, and Osamu Ideta. "Population structure in Japanese rice population." Breeding Science 63, no. 1 (2013): 49–57. http://dx.doi.org/10.1270/jsbbs.63.49.

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9

Ragsdale, Corey S. "REGIONAL POPULATION STRUCTURE IN POSTCLASSIC MEXICO." Ancient Mesoamerica 28, no. 2 (2017): 357–69. http://dx.doi.org/10.1017/s0956536117000013.

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AbstractThe majority of our knowledge about population structure in Mexico during the Postclassic period (a.d. 900–1520) is based on archaeological data. During this time, populations were in contact with each other through extensive trade networks and via the expansion of powerful empires in central and west Mexico. Though archaeological data provides a wealth of information about these relationships, little is known about the effects of these processes on population structure and biological, morphological variation or whether these effects vary across geographic regions. In this study, dental morphological observations are used as a proxy for genetic data in order to assess the differences in regional population structures throughout Mexico. Our analyses show differences in population structure between the various cultural and geographic areas around Mexico. We further conclude that population structures are affected by economic, political, or religious processes. This study provides bioarchaeological support for archaeological interpretations of population structure in Postclassic Mexico.

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10

Siegenthaler, Timothy B., Kurt Lamour, and Zachariah R. Hansen. "Population structure of Phytophthora capsici in the state of Tennessee." Mycological Progress 21, no. 1 (January 2022): 159–66. http://dx.doi.org/10.1007/s11557-021-01769-7.

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AbstractThe plant pathogen Phytophthora capsici can be found all throughout the USA, and the population genetics of this organism have been studied within many of these states. Until now, no work has been done in the state of Tennessee to investigate the population structure and genetics of P. capsici found there. The population structure of P. capsici was explored using 296 isolates collected from five counties in Tennessee in 2004, 2007, 2018, and 2019. Samples were genotyped using 39 single nucleotide polymorphism (SNP) genetic markers. Multiple analyses indicate that the population structure of P. capsici in Tennessee exists in isolated clusters structured by geography. Geographically separate populations were genetically distinct, suggesting there is limited or no outcrossing among populations, but there is significant sexual reproduction occurring within populations. These findings corroborate previous studies of P. capsici throughout the midwestern and northeastern USA, where populations are generally sexually reproducing and structured by geography. This study provides the first characterization of P. capsici population structure in Tennessee.

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11

Pérez-González, J., A. C. Frantz, J. Torres-Porras, L. Castillo, and J. Carranza. "Population structure, habitat features and genetic structure of managed red deer populations." European Journal of Wildlife Research 58, no. 6 (May 12, 2012): 933–43. http://dx.doi.org/10.1007/s10344-012-0636-0.

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12

Triviño, Narda Jimena, Juan Guillermo Perez, Maria Eugenia Recio, Masumi Ebina, Naoki Yamanaka, Shin-ichi Tsuruta, Manabu Ishitani, and Margaret Worthington. "Genetic Diversity and Population Structure ofBrachiariaSpecies and Breeding Populations." Crop Science 57, no. 5 (July 13, 2017): 2633–44. http://dx.doi.org/10.2135/cropsci2017.01.0045.

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13

Dr. S. S. Motebennur, Dr S. S. Motebennur, and Sri Siddarudha Nilaya. "Spatial Analysis of Population Structure in Dharwad District Karnataka State." Indian Journal of Applied Research 3, no. 8 (October 1, 2011): 324–27. http://dx.doi.org/10.15373/2249555x/aug2013/101.

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14

Patterson, Nick, Alkes L. Price, and David Reich. "Population Structure and Eigenanalysis." PLoS Genetics 2, no. 12 (2006): e190. http://dx.doi.org/10.1371/journal.pgen.0020190.

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15

Muzzio, Marina, Josefina M. B. Motti, Paula B. Paz Sepulveda, Muh-ching Yee, Thomas Cooke, María R. Santos, Virginia Ramallo, et al. "Population structure in Argentina." PLOS ONE 13, no. 5 (May 1, 2018): e0196325. http://dx.doi.org/10.1371/journal.pone.0196325.

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16

Lörtscher, Mathias, Martin Clalüna, and Adolf Scholl. "Genetic population structure of." Aquatic Sciences 60, no. 2 (1998): 118. http://dx.doi.org/10.1007/s000270050029.

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17

Brown, Brielin C., Nicolas L. Bray, and Lior Pachter. "Expression reflects population structure." PLOS Genetics 14, no. 12 (December 19, 2018): e1007841. http://dx.doi.org/10.1371/journal.pgen.1007841.

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18

Pritchard, Jonathan K. "Deconstructing maize population structure." Nature Genetics 28, no. 3 (July 2001): 203–4. http://dx.doi.org/10.1038/90026.

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19

Tomiuk, J., and K. Wöhrmann. "Population growth and population structure of natural populations of Macrosiphum rosae (L.) (Hemiptera, Aphididae)." Zeitschrift für Angewandte Entomologie 90, no. 1-5 (August 26, 2009): 464–73. http://dx.doi.org/10.1111/j.1439-0418.1980.tb03554.x.

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20

Rybakovsky, Oleg. "Structural waves of the population of Russia and its regions: issues of assessment and comparison." Population 25, no. 1 (March 22, 2022): 65–79. http://dx.doi.org/10.19181/population.2022.25.1.6.

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The article deals with methodological and methodological issues of comparative analysis of age structures of the population, identification of the level of their unevenness due to structural demographic waves. The benchmark for comparison is the age structure of the population, built on the series of "Numbers living in this age interval" from the "Tables of mortality and life expectancy" of Rosstat. The coefficients existing in the practice of socio-economic analysis for measuring the structural differences of the series are considered. These coefficients are studied from the standpoint of the possibility of their application for measuring structural demographic waves in Russia as a whole and in its regions. These coefficients are necessary not only to measure and compare the degree of differences, unevenness of the age structures of the population of separate territories, but also to monitor this situation over time. The latter is necessary to develop policies to smooth out the structural demographic waves. The index (1-R) proposed in the article, showing the residual value of covariance not described by the coefficient of determination, can be used at the stage of preliminary analysis of the differences in structures, since it is instantly calculated using application programs and gives a general picture of the level of unevenness of the series, ranks them on this basis. In our opinion, the most adequate measure of differences in structures is the coefficient of unevenness (Kn), calculated by analogy with the coefficient of variation. It is preferable at the main stage of comparative analysis, as it reveals the average relative measure of the unevenness of pairs of series, is simple and understandable in interpretation. Three other similar coefficients (Gatev, Salai, Ryabtsev) can be used as a supplement to confirm the adequacy of the comparative analysis, as well as for an overall assessment of the degree of discrepancy between the series using the scale familiar to the end user from zero to one. All five coefficients for measuring the degree of unevenness of structural series are suitable not only for studying the age structural waves of the population of the country and its regions, but also for comparing the age structures of the population of different territories with each other. The conclusions to the article contain recommendations for building a path for the most effective smoothing of the demographic structural waves in Russia with the help of differentiated in time and regional demographic policy in the field of fertility and immigration.

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21

Makar, Svetlana, and Aziza Yarasheva. "Consumer behavior of Russians: opportunities and priorities." Population 25, no. 4 (December 21, 2022): 68–78. http://dx.doi.org/10.19181/population.2022.25.4.6.

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The article examines the dynamics of changes in the volume of monetary income as a means for life of Russians, including implementation of their consumption priorities. It shows distribution of the population's expenditures on consumption of food and non-food products, alcoholic beverages and payment for services. The analysis is based on government statistics on 20 percent income groups, it covers a four-year period, including two years on the eve of the coronavirus pandemic and two years of its development. In the structure of the use of monetary income of the entire (without breakdown into groups) population (2018-2021) are analyzed indicators characterizing changes not only in the purchase of goods and services, but also in the increase/decrease in savings, which act as a reserve for future consumer opportunities. There are identified differences in the change in the share of household spending on food and services in the overall structure of consumer spending by macro-regions of Russia — Federal districts. From the standpoint of macro-regional differentiation in the structure of the use of monetary income for a ten-year period, the emphasis is placed on the purchase of goods and payment for services in Russia as a whole, and especially on the active growth in the Far Eastern and North Caucasus macro-regions. The share of expenses for the purchase of a number of the most important food products in household consumer spending is considered by decile income groups. A comparative analysis of the least and most affluent groups of the population is carried out within the frames of the specified directions reflecting priorities in the consumer behavior of the Russian population.

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22

Avdeev, Yury A., Zinaida I. Sidorkina, and Valentina L. Ushakova. "Demographic development trends in the Russian Eastern Arctic." POPULATION 23, no. 3 (2020): 130–44. http://dx.doi.org/10.19181/population.2020.23.3.12.

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The paper considers the features of the demographic processes in the Russian Arctic zone by the example of Chukotka Autonomous Okrug in the period from 1939 to 2017. The future of the Arctic depends in large part on its demographic potential. What should be the population of the territory, how the space is organized, whether the population is needed, or enough of the watch organization of production: what should be done (or what not to do) so that the way of life of the native and the indigenous population remained in harmony with the environment, and at the same time solved large-scale tasks in the interests of the country. The study uses the method of constructing and comparing demographic pyramids for different time periods. There are specified demographic groups that differ in their reproductive behavior. On this basis, time periods were identified, within which demographic processes were going in different ways that allows us to assess the relationship between the nature and outcome of these processes and the structure of population at different stages of history. Based on the prospective analysis of the demographic processes in the territory of development of this part of the country, there was revealed the specific in the dynamics and features of the formation of the demographic potential through natural reproduction and migration movement of the indigenous people and newcomers. The authors examined the changes in the population structure at the time of population growth due to intensive arrivals before 1990 and the dramatic decrease as a result of the outflow in the 1990 s, which significantly changed the structure of the population, the ratio between different groups. This approach to analysis of demographic indicators may be used in elaboration of strategic plans for socio-economic development of the region. It gives an adequate assessment of the current situation, helps to formulate in strategic documents the goals and objectives of socioeconomic development, to determine the priorities in the regional demographic policy.

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23

Pugliese, Andrea, and Fabio Milner. "A structured population model with diffusion in structure space." Journal of Mathematical Biology 77, no. 6-7 (May 9, 2018): 2079–102. http://dx.doi.org/10.1007/s00285-018-1246-6.

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24

Tong, Yue W., Bernard J. Lewis, Wang M. Zhou, Cheng R. Mao, Yan Wang, Li Zhou, Da P. Yu, Li M. Dai, and Lin Qi. "Genetic Diversity and Population Structure of Natural Pinus koraiensis Populations." Forests 11, no. 1 (December 26, 2019): 39. http://dx.doi.org/10.3390/f11010039.

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Studying the genetic diversity and population structure of natural forest populations is essential for evaluating their ability to survive under future environmental changes and establishing conservation strategies. Pinus koraiensis is a conifer species with high ecological and economic value in Northeast China. However, its natural forests have been greatly reduced in recent years, mostly due to over exploitation and over utilization. Here, we evaluated the genetic diversity and population structure of seven populations of P. koraiensis located throughout its native distribution. A total of 204 samples were genotyped with nine polymorphic nuclear SSR (simple sequence repeat) markers. The results showed high genetic diversity in all populations, with an average expected heterozygosity of 0.610, and the northern-most populations (Dailin (DL) and Fenglin (FL)) showed slightly higher diversity than the other five populations. The level of genetic differentiation among populations was very low (FST = 0.020). Analysis of molecular variance (AMOVA) showed that only 2.35% of the genetic variation existed among populations. Moreover, STRUCTURE analysis clearly separated the seven populations into two clusters. Populations DL and FL from the Xiaoxinganling Mountains comprised cluster I, while cluster II included the five populations from the Changbai Mountains and adjacent highlands. Our research on the genetic diversity and population structure of P. koraiensis in natural forests of China can provide a basis for the implementation of programs for the conservation and utilization of P. koraiensis genetic resources in the future.

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25

El Chartouni, Lea, Benoît Tisserant, Ali Siah, Florent Duyme, Jean-Baptiste Leducq, Caroline Deweer, Céline Fichter-Roisin, et al. "Genetic diversity and population structure in French populations ofMycosphaerella graminicola." Mycologia 103, no. 4 (July 2011): 764–74. http://dx.doi.org/10.3852/10-184.

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26

Westoby, M., and J. Howell. "Influence of Population Structure on Self-Thinning of Plant Populations." Journal of Ecology 74, no. 2 (June 1986): 343. http://dx.doi.org/10.2307/2260259.

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27

SÁNCHEZ NAVARRO, BEATRIZ, JUKKA JOKELA, NICO K. MICHIELS, and THOMAS G. D’SOUZA. "Population genetic structure of parthenogenetic flatworm populations with occasional sex." Freshwater Biology 58, no. 2 (December 4, 2012): 416–29. http://dx.doi.org/10.1111/fwb.12070.

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28

Qiao, R., X. Li, X. Han, K. Wang, G. Lv, G. Ren, and X. Li. "Population structure and genetic diversity of four Henan pig populations." Animal Genetics 50, no. 3 (March 18, 2019): 262–65. http://dx.doi.org/10.1111/age.12775.

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29

Munda, Ivka M., and Bruno P. Kremer. "Morphological variation and population structure of Fucus spp. (Phaeophyta) from Helgoland." Nova Hedwigia 64, no. 1-2 (February 17, 1997): 67–86. http://dx.doi.org/10.1127/nova.hedwigia/64/1997/67.

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30

Belousova, Natal'ya, Svetlana Bass, S. Zinov'eva, Sergey Sorokin, and Mitch Wilkinson. "Study of population-genomic structure of Vyatka horses in interline aspect." Agrarian Bulletin of the 229, no. 14 (January 18, 2023): 2–8. http://dx.doi.org/10.32417/1997-4868-2022-229-14-2-8.

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Abstract. The population-genomic structure of the Vyatka horses was studied in the context of 10 male genealogical lines bred in the breed. Purpose. Evaluation of the genetic and breeding potential of the Vyatka breed, as well as the influence of factory breeds. Methods. Isolation of DNA from the hair follicles of Vyatka horses (n = 38) was performed using “ExtraGene DNA Prep 2000” by allele-specific PCR. Statistical calculations of allele frequencies and the types of studied loci were performed using MS Excel 10 software. Results. The polymorphism of MSTN, GYS1, DMTR3, CAST, and PRLR genes in Vyatka breed lines was studied for the first time. According to the frequency of occurrence CAST G/A (0.472) and PRLR G/C (0.417), as well as the genotype MSTN T/T (0.579) is somewhat dominated by the heterozygous genotypes characteristic of local breeds. They revealed polysaccharide accumulation mutation PSSM1 (0.189), characteristic of draft horses, and the DMRT3 mutation (0.087), which determines the ability for a non-standard gait - amble, noted among Oryol trotters, which indicates the presence of draft and trotting blood in the lines carrying these alleles. MSTN/C and DMRT3/A alleles, which are not typical for aborigines, are noted in Znatok line, whose representatives are distinguished by a lightweight body type and productive movements. The carriers of the mutant defective GYS1 allele (PSSM1) were identified in five lines: Bob, Gabizon, Buran, Dobrik, and Malakhit. Genotyping of all used stallions for genes associated with economically useful traits will allow more efficient selection in the Vyatka breed and prevent the spread of unwanted alleles, which is especially important for small breeds. Scientific novelty. For the first time, an intrapopulation genomic analysis of the domestic horse breed was studied in the interline aspect, and the influence of factory breeds on certain genealogical lines was shown. The polymorphism of the calpastatin (CAST) and prolactin receptor (PRLR) genes has not previously been studied in horse breeding.

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31

JARNE, P., and A. THÉRON. "Genetic structure in natural populations of flukes and snails: a practical approach and review." Parasitology 123, no. 7 (November 2001): 27–40. http://dx.doi.org/10.1017/s0031182001007715.

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Several aspects of the coevolutionary dynamics in host-parasite systems may be better quantified based on analyses of population structure using neutral genetic markers. This includes, for example, the migration rates of hosts and parasites. In this respect, the current situation, especially in fluke-snail systems is unsatisfactory, since basic population genetics data are lacking and the appropriate methodology has rarely been used. After reviewing the forces acting on population structure (e.g. genetic drift or the mating system) and how they can be analysed in models of structured populations, we propose a simplified, indicative framework for conducting analyses of population structure in hosts and parasites. This includes consideration of markers, sampling, data analysis, comparison of structure in hosts and parasites and use of external data (e.g. from population dynamics). We then focus on flukes and snails, highlighting important biological traits with regard to population structure. The few available studies indicate that asexual amplification of flukes within snails strongly influences adult flukes populations. They also show that the genetic structure among populations in strongly affected by traits in other than snails (e.g. definitive host dispersal behaviour), as snails populations have limited migration. Finally more studies would allow us to deepen our current understanding of selective interference between flukes and snails (e.g. manipulation of host mating system by parasites), and evaluate how this affect population structure at neutral markers.

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32

Natsak, Organa. "Gender and demographic features of the labor market of the Republic of Tuva: trends and prospects." Population 24, no. 2 (June 29, 2021): 120–30. http://dx.doi.org/10.19181/population.2021.24.2.11.

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The article analyzes the gender and demographic features of the labor market of the Republic of Tuva: the structure and rate of employment and unemployment in gender dimension and historic retrospect covering the period of Tuva People's Republic (1921-1944), the Soviet and post-Soviet stages of history. On the basis of statistical data it is shown that from 1945 began predominance of female population in the demographic structure of the republic that is characteristic of modern Tuva. The author makes an attempt to give a historic explanation of this turn. The article shows changes in the ratio of male to female population of Tuva from 1931 to 2020 using various statistic sources and data. In the features of the gender profile of the regional labor market, the author identifies demographic, socio-cultural and economic factors determining it, in particular, the reasons for withdrawal of men from the economically active population in certain age groups. The article substantiates the thesis that, despite the steady trend of reducing unemployment in the republic in 2017, 2018, 2019, the issue of male employment remains acute. It also shows the level of demographic burden on the working-age population of the Republic of Tuva connected with the specifics of reproductive behavior of the population of the republic, namely, high birth rates, as well as the emerging trend of increasing the proportion of people over the working age due to the positive dynamics of increasing life expectancy in the republic.

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33

Okon, Emmanuel Okokondem. "Population structure and environmental degradation." Bussecon Review of Social Sciences (2687-2285) 1, no. 2 (October 20, 2019): 18–27. http://dx.doi.org/10.36096/brss.v1i2.110.

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The primary objective of this paper is to test the hypothesis that the population age structure could contribute to carbon dioxide emission levels (environmental degradation) in Nigeria. Real income (Gross Domestic Product) was used as another determinant of CO2 emissions to test the EKC hypothesis in this study. Also, the autoregressive distributed lag (ARDL) econometric technique was applied in this paper to annual time series data from 1970 to 2018. The results show that age structure’s influence on the environment is significant. As expected, young adults (LOGYONG, i.e., ages 15-64) and children (LOGCHIL, i.e., ages 0-14) are environmentally intensive (due to energy-intensive goods consumed). But the older age group (LOGOLD i.e., ages 65 and above) exert a negative effect. The results of long-term estimation for the population structure-induced EKC hypothesis show that none of the coefficients of economic growth were statistically significant at any of the conventional levels. In other words, this finding did not prove the existence of EKC hypothesis. However, appropriate macroeconomic policies, technological innovations, and institutional developments are very important in maintaining a sound environment in Nigeria.

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34

Chirkov, S. N., and Yu N. Prikhod’ko. "GENETIC DIVERSITY AND POPULATION STRUCTURE." Sel'skokhozyaistvennaya Biologiya 50, no. 5 (October 2015): 529–39. http://dx.doi.org/10.15389/agrobiology.2015.5.529eng.

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35

Rejmanek, Marcel, and J. White. "The Population Structure of Vegetation." Bulletin of the Torrey Botanical Club 113, no. 4 (October 1986): 443. http://dx.doi.org/10.2307/2996439.

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36

Szathmáry, László, and Antónia Marcsik. "Symbolic trephinations and population structure." Memórias do Instituto Oswaldo Cruz 101, suppl 2 (December 2006): 129–32. http://dx.doi.org/10.1590/s0074-02762006001000019.

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37

Peter, Benjamin M. "Admixture, Population Structure, andF-Statistics." Genetics 202, no. 4 (February 8, 2016): 1485–501. http://dx.doi.org/10.1534/genetics.115.183913.

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38

Watkinson, A. R., and J. White. "The Population Structure of Vegetation." Journal of Ecology 74, no. 4 (December 1986): 1221. http://dx.doi.org/10.2307/2260252.

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39

Guttman, S. I. "Population genetic structure and ecotoxicology." Environmental Health Perspectives 102, suppl 12 (December 1994): 97–100. http://dx.doi.org/10.1289/ehp.94102s1297.

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Beretta, Maria, Paola Mazzetti, Guido Frosina, Gino Schilirò, Antonio Russo, Giuseppe Russo, and Italo Barrai. "Population Structure of Eastern Sicily." Human Heredity 36, no. 6 (1986): 379–87. http://dx.doi.org/10.1159/000153662.

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UNDERWOOD, CHARLIE J. "Population structure of graptolite assemblages." Lethaia 31, no. 1 (March 29, 2007): 33–41. http://dx.doi.org/10.1111/j.1502-3931.1998.tb00487.x.

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MORK, J. "Straying and population genetic structure." Aquaculture Research 25, S2 (1994): 93–98. http://dx.doi.org/10.1111/are.1994.25.s2.93.

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Wiehlmann, L., G. Wagner, N. Cramer, B. Siebert, P. Gudowius, G. Morales, T. Kohler, et al. "Population structure of Pseudomonas aeruginosa." Proceedings of the National Academy of Sciences 104, no. 19 (April 27, 2007): 8101–6. http://dx.doi.org/10.1073/pnas.0609213104.

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Michel, Andrew P., Wei Zhang, Jin Kyo Jung, Sung-Taeg Kang, and M. A. Rouf Mian. "Population Genetic Structure ofAphis glycines." Environmental Entomology 38, no. 4 (August 1, 2009): 1301–11. http://dx.doi.org/10.1603/022.038.0442.

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Slatkin, Montgomery. "Population structure and evolutionary progress." Genome 31, no. 1 (January 1, 1989): 196–202. http://dx.doi.org/10.1139/g89-034.

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Wright's shifting-balance theory is discussed as an example of a process that can cause species to evolve combinations of characters that could not evolve under natural selection alone. A review of the existing theory of peak shifts indicates that the conditions of extreme isolation that are necessary to permit genetic drift to alter the outcome of natural selection in local populations would make gene flow too weak to spread a new combination of genes to other populations in a reasonable time. Instead, it seems likely that major demographic changes must occur in a species for the shifting-balance process to work. A discussion of direct and indirect studies of gene flow in natural populations suggests that the current genetic structure of many species is likely to reflect past demographic events rather than ongoing gene flow. It is possible then that demographic processes could be responsible for spreading new traits in a species, but that would be true whether those new traits evolved only owing to natural selection or owing in addition to genetic drift and other forces.Key words: shifting-balance theory, gene flow.

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Zhang, Jun, Partha Niyogi, and Mary Sara McPeek. "Laplacian Eigenfunctions Learn Population Structure." PLoS ONE 4, no. 12 (December 1, 2009): e7928. http://dx.doi.org/10.1371/journal.pone.0007928.

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Pirnay, Jean-Paul, Florence Bilocq, Bruno Pot, Pierre Cornelis, Martin Zizi, Johan Van Eldere, Pieter Deschaght, et al. "Pseudomonas aeruginosa Population Structure Revisited." PLoS ONE 4, no. 11 (November 13, 2009): e7740. http://dx.doi.org/10.1371/journal.pone.0007740.

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Nübel, Ulrich, Rolf Reissbrodt, Annette Weller, Roland Grunow, Mustafa Porsch-Özcürümez, Herbert Tomaso, Erwin Hofer, et al. "Population Structure of Francisella tularensis." Journal of Bacteriology 188, no. 14 (July 15, 2006): 5319–24. http://dx.doi.org/10.1128/jb.01662-05.

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ABSTRACT We have sequenced fragments of five metabolic housekeeping genes and two genes encoding outer membrane proteins from 81 isolates of Francisella tularensis, representing all four subspecies. Phylogenetic clustering of gene sequences from F. tularensis subsp. tularensis and F. tularensis subsp. holarctica aligned well with subspecies affiliations. In contrast, F. tularensis subsp. novicida and F. tularensis subsp. mediasiatica were indicated to be phylogenetically incoherent taxa. Incongruent gene trees and mosaic structures of housekeeping genes provided evidence for genetic recombination in F. tularensis.

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Skoglund, Pontus, Jessica C. Thompson, Mary E. Prendergast, Alissa Mittnik, Kendra Sirak, Mateja Hajdinjak, Tasneem Salie, et al. "Reconstructing Prehistoric African Population Structure." Cell 171, no. 1 (September 2017): 59–71. http://dx.doi.org/10.1016/j.cell.2017.08.049.

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Erlandsen, Solveig, and Ragnar Nymoen. "Consumption and population age structure." Journal of Population Economics 21, no. 3 (September 16, 2006): 505–20. http://dx.doi.org/10.1007/s00148-006-0088-5.

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