Artykuły w czasopismach: „Population dynamics”

1

Juliano, Steven A. "POPULATION DYNAMICS". Journal of the American Mosquito Control Association 23, sp2 (lipiec 2007): 265–75. http://dx.doi.org/10.2987/8756-971x(2007)23[265:pd]2.0.co;2.

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Rudnicki, Ryszard, Ovide Arino i Pierre Auger. "Population dynamics". Comptes Rendus Biologies 327, nr 3 (marzec 2004): 173. http://dx.doi.org/10.1016/j.crvi.2003.10.008.

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Cooch, E. G., i A. A. Dhondt. "Population dynamics". Animal Biodiversity and Conservation 27, nr 1 (1.06.2004): 469–70. http://dx.doi.org/10.32800/abc.2004.27.0469.

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Increases or decreases in the size of populations over space and time are, arguably, the motivation for much of pure and applied ecological research. The fundamental model for the dynamics of any population is straightforward: the net change over time in the abundance of some population is the simple difference between the number of additions (individuals entering the population) minus the number of subtractions (individuals leaving the population). Of course, the precise nature of the pattern and process of these additions and subtractions is often complex, and population biology is often replete with fairly dense mathematical representations of both processes. While there is no doubt that analysis of such abstract descriptions of populations has been of considerable value in advancing our, there has often existed a palpable discomfort when the ‘beautiful math’ is faced with the often ‘ugly realities’ of empirical data. In some cases, this attempted merger is abandoned altogether, because of the paucity of ‘good empirical data’ with which the theoretician can modify and evaluate more conceptually–based models. In some cases, the lack of ‘data’ is more accurately represented as a lack of robust estimates of one or more parameters. It is in this arena that methods developed to analyze multiple encounter data from individually marked organisms has seen perhaps the greatest advances. These methods have rapidly evolved to facilitate not only estimation of one or more vital rates, critical to population modeling and analysis, but also to allow for direct estimation of both the dynamics of populations (e.g., Pradel, 1996), and factors influencing those dynamics (e.g., Nichols et al., 2000). The interconnections between the various vital rates, their estimation, and incorporation into models, was the general subject of our plenary presentation by Hal Caswell (Caswell & Fujiwara, 2004). Caswell notes that although interest has traditionally focused on estimation of survival rate (arguably, use of data from marked individuals has been used for estimation of survival more than any other parameter, save perhaps abundance), it is only one of many transitions in the life cycle. Others discussed include transitions between age or size classes, breeding states, and physical locations. The demographic consequences of these transitions can be captured by matrix population models, and such models provide a natural link connecting multi–stage mark–recapture methods and population dynamics. The utility of the matrix approach for both prospective, and retrospective, analysis of variation in the dynamics of populations is well–known; such comparisons of results of prospective and retrospective analysis is fundamental to considerations of conservation management (sensu Caswell, 2000). What is intriguing is the degree to which these methods can be combined, or contrasted, with more direct estimation of one or more measures of the trajectory of a population (e.g., Sandercock & Beissinger, 2002). The five additional papers presented in the population dynamics session clearly reflected these considerations. In particular, the three papers submitted for this volume indicate the various ways in which complex empirical data can be analyzed, and often combined with more classical modeling approaches, to provide more robust insights to the dynamics of the study population. The paper by Francis & Saurola (2004) is an example of rigorous analysis and modeling applied to a large, carefully collected dataset from a long–term study of the biology of the Tawny Owl. Using a combination of live encounters and dead recoveries, the authors were able to separate the relative contributions of various processes (emigration, mortality) on variation in survival rates. These analyses were combined with periodic matrix models to explore comparisons of direct estimation of changes in population size (based on both census and mark–recapture analysis) with model estimates. The utility of combining sources of information into analysis of populations was the explicit subject of the other two papers. Gauthier & Lebreton (2004) draw on a long–term study of an Arctic–breeding Goose population, where both extensive mark–recapture, ring recovery, and census data are available. The primary goal is to use these various sources of information to to evaluate the effect of increased harvests on dynamics of the population. A number of methods are compared; most notably they describe an approach based on the Kalman filter which allows for different sources of information to be used in the same model, that is demographic data (i.e. transition matrix) and census data (i.e. annual survey). They note that one advantage of this approach is that it attempts to minimize both uncertainties associated with the survey and demographic parameters based on the variance of each estimate. The final paper, by Brooks, King and Morgan (Brooks et al., 2004) extends the notion of the combining information in a common model further. They present a Bayesian analysis of joint ring–recovery and census data using a state–space model allowing for the fact that not all members of the population are directly observable. They then impose a Leslie–matrix–based model on the true population counts describing the natural birth–death and age transition processes. Using a Markov Chain Monte Carlo (MCMC) approach (which eliminates the need for some of the standard assumption often invoked in use of a Kalman filter), Brooks and colleagues describe methods to combine information, including potentially relevant covariates that might explain some of the variation, within a larger framework that allows for discrimination (selection) amongst alternative models. We submit that all of the papers presented in this session indicate clearly significant interest in approaches for combining data and modeling approaches. The Bayesian framework appears a natural framework for this effort, since it is able to not only provide a rigorous way to evaluate and integrate multiple sources of information, but provides an explicit mechanism to accommodate various sources of uncertainty about the system. With the advent of numerical approaches to addressing some of the traditionally ‘tricky’ parts of Bayesian inference (e.g., MCMC), and relatively user–friendly software, we suspect that there will be a marked increase in the application of Bayesian inference to the analysis of population dynamics. We believe that the papers presented in this, and other sessions, are harbingers of this trend. Cite

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4

Safonov, Aleksandr, i Yuliya Dolzhenkova. "Dynamics of income of pensioners: analysis, problems and solutions". Population 26, nr 4 (15.12.2023): 133–47. http://dx.doi.org/10.19181/population.2023.26.4.12.

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Currently there is an increasing number of older people in Russia, primarily pensioners. It appears that the mentioned trend will proceed for a long period of time. So, the issue of ensuring high living standards for this group of the population is acute. However, the present trends in the work remuneration of persons in pre-retirement age, spread on informal employment, and hence, precarization of labor relations, as well as the challenges in pension and tax legislation result in a complex and contradictory situation in the formation of monetary income of pensioners.The present article is devoted to the analysis and forecasting of the basic trends in the financial situation of persons over the able-bodied age. The average size of the insurance old-age pension in the end of 2022 did not achieve the amount recommended by the ILO Convention 102, and the social pension was even lower. In addition, the later retirement often leads to poverty, as it is impossible for the aged to get pensions, on the one hand, and on the other hand, to get a job due to the existing age discrimination and their reduced physical ability to work.The article analyses the factors that have a direct on the size dynamics of insurance and social old-age pensions. At the same time, insurance pensions have the economic nature of deferred wages, i.e. they are formed directly by pensioners themselves. It also analyzes possible ways of compensation for pensioners’ low incomes through continuing work activity.

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5

Sampson, D. B., i J. A. Gulland. "Fish Population Dynamics." Journal of Applied Ecology 26, nr 2 (sierpień 1989): 741. http://dx.doi.org/10.2307/2404104.

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6

Elliott, J. M., i J. A. Gulland. "Fish Population Dynamics". Journal of Animal Ecology 58, nr 2 (czerwiec 1989): 728. http://dx.doi.org/10.2307/4862.

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7

Gilbert, James R., i T. Royama. "Analytical Population Dynamics". Journal of Wildlife Management 58, nr 2 (kwiecień 1994): 383. http://dx.doi.org/10.2307/3809406.

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Watkinson, A. R., i T. Royama. "Analytical Population Dynamics." Journal of Ecology 82, nr 2 (czerwiec 1994): 431. http://dx.doi.org/10.2307/2261318.

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9

Sheiner, L. B., i T. M. Ludden. "Population Pharmacokinetics/Dynamics*". Annual Review of Pharmacology and Toxicology 32, nr 1 (kwiecień 1992): 185–209. http://dx.doi.org/10.1146/annurev.pa.32.040192.001153.

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10

McNicoll, Geoffrey, Gayl D. Ness, William D. Drake i Steven R. Brechin. "Population--Environment Dynamics." Population and Development Review 21, nr 1 (marzec 1995): 183. http://dx.doi.org/10.2307/2137425.

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11

Bartlett, Albert A., i Richard L. Conklin. "U.S. population dynamics". American Journal of Physics 53, nr 3 (marzec 1985): 242–48. http://dx.doi.org/10.1119/1.14131.

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12

Duncan, S. R., C. J. Duncan i S. Scott. "Human population dynamics". Annals of Human Biology 28, nr 6 (styczeń 2001): 599–615. http://dx.doi.org/10.1080/03014460110046064.

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13

Krebs, Charles J. "Nonlinear population dynamics". Trends in Ecology & Evolution 18, nr 12 (grudzień 2003): 615. http://dx.doi.org/10.1016/j.tree.2003.08.004.

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14

Liebhold, Andrew. "Analytical Population Dynamics". American Entomologist 40, nr 2 (1994): 113. http://dx.doi.org/10.1093/ae/40.2.113.

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15

Murray, Brad. "Plant population dynamics". New Phytologist 155, nr 2 (sierpień 2002): 201–2. http://dx.doi.org/10.1046/j.1469-8137.2002.00461_3.x.

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16

Rogers, Andrei, i Frans Willekens. "SPATIAL POPULATION DYNAMICS". Papers in Regional Science 36, nr 1 (14.01.2005): 3–34. http://dx.doi.org/10.1111/j.1435-5597.1976.tb00956.x.

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17

Keshet, Leah Edelstein. "Microbial population dynamics". Mathematical Biosciences 76, nr 1 (wrzesień 1985): 123–25. http://dx.doi.org/10.1016/0025-5564(85)90051-3.

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18

Gould, Bill. "Population Dynamics of Kenya. [Population Dynamics of Sub-Saharan Africa]". Population Studies 48, nr 3 (1.11.1994): 536–37. http://dx.doi.org/10.1080/0032472031000148036.

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19

Baskin, L. M. "Population dynamics of reindeer". Rangifer 10, nr 3 (1.09.1990): 151. http://dx.doi.org/10.7557/2.10.3.847.

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Five types of reindeer populations are distinguished in terms of population dynamics, population density, social structure and migration distance. Differences in the biological rhythms of the populations result in calving occuring 20 days before snow melting in all populations as well as maximal utilization by the deer of young green vegetation in summer. The growth of antlers may serve as a regulatior of biological rhytms. Populations differ in the level of social motivation. Formation of groups of not less than 30-35 animals ensures cooperative protection from insects and management of the group by man. The fidelity to the calving sites, summer ranges and constant migration routes is based on the common orientation reactions of the animals and social attraction. The direction and migration routes are detemined by obligate learning. The dynamics of populations depends on the fertility of 2 and 3 year old females which is determined by feeding conditions in summer and the activity of males during the rut. Migration plays an important role in the population dynamics.

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20

Zolotov, Aleksandr V. "On the studies of the regular character, factors, effects and perspectives of the working time dynamics in modern economy". POPULATION 23, nr 3 (2020): 155–68. http://dx.doi.org/10.19181/population.2020.23.3.14.

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The article examines a significant array of the scientific works devoted to different aspects of the working time dynamics. The conclusion is made that the main measure of this dynamics is the average number of hours worked per worker. This indicator can be used for analysis of all periods of labor activity including seniority. It is stated that the research on the problem shows a long-run trend of working time reduction. The works devoted to the topic also consider other factors affecting length of work: increase of labor productivity, influence of income effect and substitution effect on individual labor supply, motivation of employers, role of trade unions and collective bargaining, labor legislation. There are presented approaches to explanation of differences in the dynamics of working time in the USA and in West Europe. It is taken into account that the working time reduction during the past decades is characterized as one of the preconditions of pension reforms. There are considered works that contain analysis of the effects caused by the changes in working time length, including their impact on workers' health, work-life balance, gender inequality, unemployment rate, labor productivity, environment, perception the life as happy. The article shows a significant interest of researchers to perspectives of the working time dynamics in the context of analysis of J. M. Keynes's prediction about switch to 3-hour shifts by 2030. It is stated that the problem of perspectives of the working time dynamics is becoming one of the key issues in discussing the concept of Universal Basic Income. The article notes the attention of researchers to experiments on the working day reduction to 6 hours.

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21

Ferrandis, Eduardo. "On the stochastic approach to marine population dynamics". Scientia Marina 71, nr 1 (30.03.2007): 153–74. http://dx.doi.org/10.3989/scimar.2007.71n1153.

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22

Ajao, E. A., i S. O. Fagade. "Production and population dynamics of Pachymelania aurita Müller". Archiv für Hydrobiologie 120, nr 1 (12.12.1990): 97–109. http://dx.doi.org/10.1127/archiv-hydrobiol/120/1990/97.

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23

De Groot, W. T., F. M. W. De Jong i M. M. H. E. Van Den Berg. "Population dynamics of duckweed cover in polder ditches". Archiv für Hydrobiologie 109, nr 4 (6.07.1987): 601–18. http://dx.doi.org/10.1127/archiv-hydrobiol/109/1987/601.

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24

Khan, Amir, Fouzia Ishaq i Umar Farooq. "Algal population growth and dynamics of Asan Wetland". Universities' Journal of Phytochemistry and Ayurvedic Heights II, nr 33 (24.12.2022): 1–14. http://dx.doi.org/10.51129/ujpah-2022-33-2(2).

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A one year study of the phytoplankton community composition was carried out in the Asan wetland, a reservoir fed by River Yamuna in Uttarakhand India. In terms of bio-volume, phytoplankton community was generally dominated by Bacillariophyceae. Mean phytoplankton standing crops were highest in the wetland. The frequency and severity of algal blooms was increased significantly. To control their expansion, it was essential to identify the factors responsible for blooming of waters. Nutrient enrichment (mainly due to anthropogenic activities) and environmental factors (including the climate change) were considered the major catalyst for onset, proliferation and development of blooms. The phytoplankton of the Asan wetland was studied for one year with physical and chemical variables in relation to a pollution gradient. Analysis of the physical and chemical variables and phytoplankton density indicated that the wetland is experiencing heavy pressure of pollution due to anthropogenic activities. The dominant phytoplankton community mainly comprises of family, Chlorophyceae, Bacillariophyceae and Myxophyceae. Physical factors, though vital, had an indirect effect in facilitating the interaction among various available nutrients. In terms of phytoplankton density and diversity common genera observed include Chlorella, Chlaymydomonas, Spirogyra, Ulothrix, Hydrodictyon, Cladophora, Cosmarium, Chlorococcum, Oedogonium, Microspora, Desmidium, Chara, Zygenema, Syndesmus, Volvox, Ceratoneis, Amphora, Caloneis, Fragilaria, Navicula, Synedra, Diatoms, Gomphonema, Pinnularia, Melosira, Tabellaria, Denticula, Cymbella, Cyclotella, Nostoc, Anabaena, Oscillatoria, Rivularia, Coccochloris and Phormidium. Several genera were found most prominent during the study period having no seasonal impact on their abundance and variation. The spatial and temporal patterns observed in some of these dominant species were attributable to patterns in key environmental variables including temperature, flow, pH, dissolved oxygen and nutrient concentrations.

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25

Molofsky, Jane. "Population Dynamics and Pattern Formation in Theoretical Populations". Ecology 75, nr 1 (styczeń 1994): 30–39. http://dx.doi.org/10.2307/1939379.

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26

DOBSON, ANDREW P., i ANNA MARIE LEES. "The Population Dynamics and Conservation of Primate Populations". Conservation Biology 3, nr 4 (grudzień 1989): 362–80. http://dx.doi.org/10.1111/j.1523-1739.1989.tb00242.x.

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27

Chambers, Robert G., i Vangelis Tzouvelekas. "Estimating population dynamics without population data". Journal of Environmental Economics and Management 66, nr 3 (listopad 2013): 510–22. http://dx.doi.org/10.1016/j.jeem.2013.09.003.

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28

Koch, Marcus, Marion Huthmann i Karl-Georg Bernhardt. "Population dynamics of Cardamine amara L. (Brassicaceae): Evidence from the soil seed bank and aboveground populations". Botanische Jahrbücher für Systematik, Pflanzengeschichte und Pflanzengeographie 125, nr 4 (7.09.2004): 405–29. http://dx.doi.org/10.1127/0006-8152/2004/0125-0405.

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29

Yuan, Tian, Shiyu Song i Junhao Wang. "Effects Of Changes in Adaptive Sex Ratios on Biome Dynamics: Based on Lotka-Volterra Modeling". Highlights in Science, Engineering and Technology 99 (18.06.2024): 415–21. http://dx.doi.org/10.54097/racp0444.

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This study presents a dynamic model to elucidate the complex interactions between environmental factors and sex ratio changes in a population of sevengill eels. The model integrates a logistic function-based approach to elucidate changes in sex ratios as influenced by food availability. In addition, the Lotka-Volterra competition model incorporates sex ratios to reveal their effects on population dynamics. The simulation results highlight the dynamic relationship between sex ratios, prey abundance and population dynamics. Comparative analyses highlight the advantages and disadvantages of varying sex ratios in sevengill eel populations, revealing their competitive advantages as well as recovery dynamics.

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30

Nazarova, Inna, i Sofia Lyalikova. "V International scientific and practical conference “Social dynamics of population and human potential”". Population 26, nr 3 (20.09.2023): 196–201. http://dx.doi.org/10.19181/population.2023.26.3.17.

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31

CIORUŢA, Alin-Andrei, i Bogdan CIORUŢA. "REGARDING THE POPULATION DYNAMICS INVESTIGATION USING ENVIRONMENTAL INFORMATION SYSTEMS". SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, nr 1 (24.06.2016): 411–16. http://dx.doi.org/10.19062/2247-3173.2016.18.1.56.

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32

Pavlov, B. M., L. A. Kolpashchikov i V. A. Zyryanov. "Population dynamics of the Taimyr reindeer population". Rangifer 16, nr 4 (1.01.1996): 381. http://dx.doi.org/10.7557/2.16.4.1281.

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The Taimyr herd of wild reindeer (Rangifer tarandus tarandus L.) is one of the three largest herds of wild Rangifer in the world, and numbered about 600 000 in 1993. The herd grew continuously from 1959 to 1990, and is now stable due primarily to intensive commercial harvesting along the Khatanga River. Meat from the commercial harvest is processed and sold in population centers in the northern Krasnoyarsk region, particularly Norilsk. The herd has expanded its range to about 1.5 million km2, but movements to the southwestern portion of the winter range may have been impeded by pipeline, road and railroad construction, and winter shipping of ore on the lower Yenisey River.

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33

Kim, Dongwook, i Dong-Hoon Shin. "Population Dynamics in Diffusive Coupled Insect Population". International Journal for Innovation Education and Research 6, nr 4 (30.04.2018): 149–59. http://dx.doi.org/10.31686/ijier.vol6.iss4.1021.

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A variety of ecological models exhibit chaotic dynamics because of nonlinearities in population growth and interactions. Here, we will study the LPA model (beetle Tribolium). The LPA model is known to exhibit chaos. In this project, we investigate two things which are the effect of noise constant and the effect of diffusion combined with the LPA model. The effect of noise is not only to change the dynamics of total population density but also to blur the bifurcation diagram. Numerical simulations of the model have shown that diffusion can drive the total population of insects into complex patterns of variability in time. We will compare these simulations with simulations without diffusion. And we conclude that the diffusion coefficient is a bifurcation parameter and that there exist parameter regions with chaotic behavior and periodic solutions. This study demonstrates how diffusion term can be used to influence the chaotic dynamics of an insect population.

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34

Lawton, John H. "Population dynamics: Water fleas as population model". Nature 316, nr 6029 (sierpień 1985): 577–78. http://dx.doi.org/10.1038/316577a0.

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35

PERCUS, J. "Small population effects in stochastic population dynamics". Bulletin of Mathematical Biology 67, nr 6 (listopad 2005): 1173–94. http://dx.doi.org/10.1016/j.bulm.2005.01.005.

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36

Pletscher, Daniel H., Robert R. Ream, Diane K. Boyd, Michael W. Fairchild i Kyran E. Kunkel. "Population Dynamics of a Recolonizing Wolf Population". Journal of Wildlife Management 61, nr 2 (kwiecień 1997): 459. http://dx.doi.org/10.2307/3802604.

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Murtuzalieva, Djamilya, Yury Simagin i Irina Vankina. "Dynamics of the population of the North Caucasian regions of Russia in 2010-2022". Population 25, nr 3 (29.09.2022): 33–45. http://dx.doi.org/10.19181/population.2022.25.3.3.

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The article considers the main trends in the population change of the North Caucasian Federal District over the past decade as one of the priority geostrategic territories identified within the implementation of the "Strategy for Spatial Development of Russia up to 2025". In general, for the period 2010-2022 the population has not changed much on the Russian territory, although depopulation is observed in most regions of the country. Against this background, the North Caucasus is distinguished by a relatively favorable demographic situation. But even in this territory, demographic problems that are characteristic of the country as a whole are increasingly manifested. Since the 2010 population census, the population in the republics of Karachay-Cherkess and North Ossetia-Alania has decreased, that was not been observed over the previous decades. And the population of the Stavropol Krai during the period under review grew mainly due to the positive balance of population migration from other regions of Russia, including to a large extent from the neighboring North Caucasian republics. Analysis of the population dynamics was carried out taking into account the processes in fertility, mortality and migration mobility of the population. The situation is considered at the level of regions-subjects of the Russian Federation and municipalities (urban districts and municipal districts). This allows us to better understand the causes of the current situation and make some adjustments to Russia's demographic and migration policy. Improving the demographic situation in the priority geostrategic territories, including the North Caucasus, will contribute to the socio-economic development not only of these territories, but of the entire Russian Federation.

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38

Simagin, Yury, Djamilya Murtuzalieva i Irina Vankina. "The impact of municipal-territorial transformations in the regions of Russia on population dynamics". Population 26, nr 1 (27.03.2023): 16–28. http://dx.doi.org/10.19181/population.2023.26.1.2.

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The demographic problem is one of the most acute in modern Russia, especially at the level of municipalities, most of which have experienced depopulation since 2010, when the country's population as a whole was increasing. Numerous actions are being taken to solve the demographic problem, and further expansion of their list is quite justified, since in the future the situation may become even worse. At the same time, the issue of the impact on the demographic situation of the municipal-territorial transformations carried out in the regions of the country, which are mainly aimed at abolishing or reducing the number of second-level municipalities — urban and rural settlements, has not been investigated. Meanwhile, public service institutions in the fields of education, healthcare and others may disappear, that negatively affects the living conditions of the population and does not contribute to solving demographic problems. Other consequences of municipal-territorial transformations also have not been studied. This article shows that the municipalities of the first level (urban districts, municipal areas, municipal districts), which in the period 2010-2020 were covered by transformations, are characterized by poorer demographic dynamics (less increase or more decrease in population) compared to other municipalities in the same subjects of the Russian Federation, which were not covered by such transformations. Municipalities with and without transformations also differ in terms of natural and migration movement of the population. It can be concluded that municipal-territorial transformations aimed at simplifying the municipal structure in the country may solve some momentary difficulties, in particular, reduce budget expenditures. But in the future, they may have negative consequences for solving Russia's main problems, including preserving and increasing the country's population.

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39

Mitman, Gregg. "Population Dynamics, Human Interactions". Ecology 72, nr 5 (październik 1991): 1908–9. http://dx.doi.org/10.2307/1940995.

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40

Wanner, Jiri. "Activated sludge population dynamics". Water Science and Technology 30, nr 11 (1.12.1994): 159–69. http://dx.doi.org/10.2166/wst.1994.0556.

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The paper briefly surveys topics covered by the IAWQ Specialist Group on Activated Sludge Population Dynamics. The activated sludge population dynamics has been formulated as a branch of water science and technology concerned with phenomena governing the relationships between activated sludge microorganisms and their functions. The characterization of organic pollution fractions in wastewaters according to their rate of biodegradation has been discussed and the role of wastewater as an inoculum stressed. The characterization of activated sludge biomass has been evaluated from two viewpoints: grouping according to metabolic abilities and identification and classification of activated sludge microorganisms. The basic selection mechanisms influencing the microbial composition of activated sludge have been described. The problems with activated sludge settling and thickening properties have been mentioned as a typical example of applied population dynamics research.

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41

Authors, Twentythree. "Session One - Population Dynamics". Rangifer 20, nr 5 (1.04.2000): 63. http://dx.doi.org/10.7557/2.20.5.1630.

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42

Deutschman, Douglas H. "Understanding Complex Population Dynamics". Ecology 85, nr 4 (kwiecień 2004): 1174–75. http://dx.doi.org/10.1890/0012-9658(2004)085[1174:ucpd]2.0.co;2.

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43

Solow, Andrew R. "Analysis of Population Dynamics". Ecology 75, nr 3 (kwiecień 1994): 864–65. http://dx.doi.org/10.2307/1941749.

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44

Karanth, K. U., J. D. Nichols, N. S. Kumar i J. E. Hines. "ASSESSING TIGER POPULATION DYNAMICS". Bulletin of the Ecological Society of America 87, nr 4 (październik 2006): 323–25. http://dx.doi.org/10.1890/0012-9623(2006)87[323:atpd]2.0.co;2.

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Turchin, Peter. "Evolution in population dynamics". Nature 424, nr 6946 (lipiec 2003): 257–58. http://dx.doi.org/10.1038/424257a.

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Charlebois, Daniel A., i Gábor Balázsi. "Modeling cell population dynamics". In Silico Biology 13, nr 1-2 (30.05.2019): 21–39. http://dx.doi.org/10.3233/isb-180470.

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Andersen, Tom, James J. Elser i Dag O. Hessen. "Stoichiometry and population dynamics". Ecology Letters 7, nr 9 (2.08.2004): 884–900. http://dx.doi.org/10.1111/j.1461-0248.2004.00646.x.

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Hudson, R. B. "Population and Policy Dynamics". Public Policy & Aging Report 9, nr 4 (1.02.1999): 2–9. http://dx.doi.org/10.1093/ppar/9.4.2.

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Hallam, Thomas G., Ray R. Lassiter i Shandelle M. Henson. "Modeling fish population dynamics". Nonlinear Analysis: Theory, Methods & Applications 40, nr 1-8 (kwiecień 2000): 227–50. http://dx.doi.org/10.1016/s0362-546x(00)85013-0.

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Beckmann, Martin J. "On spatial population dynamics". Chaos, Solitons & Fractals 18, nr 3 (październik 2003): 439–44. http://dx.doi.org/10.1016/s0960-0779(02)00667-7.

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