Human population growth. Population issue is not entirely satisfactory.
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A stochastic version of the Malthusian trap model relating the growth rate of income per capita to the population growth rate of a given country is described. This model is applied to the a priori evaluation of the cross-sectional correlation between these 2 growth rates under 2 additional assumptions: 1) the relations in the model at national levels include country-specific and time-invariant random components, and 2) these growth rates are measured with a certain degree of temporal aggregation. It is shown that these 2 assumptions can explain near-0 correlations between the 2 growth rates even if there exist a strongly negative effect of population growth on economic growth. However, it is not clear whether these assumptions fully explain such insignificant correlations. Indeed, the implementation of the model is complicated by the structural shifts which are likely to occur in the equations over the course of the demographic transition.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
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Rapid population growth affects many different system levels simultaneously: the individual, the family, the community, the nation-state and the world. What may appear to be an optimal population level for one system, given certain value premises, may not be optimal for another. Anthropologists can contribute to an understanding of rapid growth by generating an appreciation for the complexity of the phenomenon, and by providing representation for a range of cultural viewpoints. This may help to reduce the risk we now run of committing all of mankind to a singular path which may prove to be an evolutionary dead end.
"Population projections need to be continually updated as new information becomes available.... With an assumption of a total fertility rate of 1.865 children per woman and annual net migration of 50,000, ABS [Australian Bureau of Statistics] projections published in 1996 and additional projections in 1997 suggest a population of 24.5 million in 2051 and an ultimate population of more than 25 million. The analysis in this article...puts Australia's various population problems into a proper perspective and tries to direct attention away from the unnecessary preoccupation with population decline."
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With x = population size, the nonautonomous equation x = xf(t, x) provides a very general description of population growth in which any of the many factors that influence the growth rate may vary through time. If there is some fixed length of time (usually long) such that during any interval of this length the population experiences environmental variability representative of the variation that occurs in all time, then definite conclusions about the population's long-term behavior apply. Specifically, conditions that produce population persistence can be distinguished from conditions that cause extinction, and the difference between any pair of solutions eventually converges to zero. These attributes resemble corresponding features of the related autonomous population growth model x = xf(x).
"The present paper aims to analyse district-wise decadal growth of population in Uttar Pradesh [India] since 1872. It also attempts to identify population growth regions based on a suitable statistical technique. The spatio-temporal differentials are explained by the three interacting processes of birth, death and migration."
The population of the United States continues to increase, albeit at a slower pace than in the past. Geographically, the states of the South and West still are experiencing larger relative gains than those in the Northeast and Midwest, although some changes in the decennial growth pattern have recently emerged. California's population increments have steadily diminished since 1990, and last year Florida's relative gain was comparatively small. Moreover, New York recorded a decline in the number of its residents. In contrast, the count of persons living in Texas appears to be accelerating. Together, the population of these four states comprises nearly one-third of the nation's total and, therefore, the United States' future will be heavily influenced by the changes in their growth patterns.
Population growth in developing countries is analyzed within the context of social development. The study is presented in two sections. "The first section discusses the population growth phenomenon in general and the differences between population growth rates of third world countries and other countries. The second section explores social dimensions of [the] population growth phenomenon in third world countries." Consideration is given to the relationships between fertility and socioeconomic status and to the impact of social class on desired family size and marriage age. The institution of the family, and societal values as they affect the status of women, sex preferences, and male dominance, are also emphasized. (summary in ENG)
"The population of China is still growing despite a dramatic decline in fertility in the past two decades. There are marked urban-rural differentials in fertility and, as a result, the pace of urbanization has significant effects on population growth. In this research an attempt is made to model urban-rural population growth in China. A demoeconomic model of urban and rural sectors is calibrated to account for the long-term trend of urbanization in China.... An accounts-based urban-rural population model, in which rural to urban migration and transition are driven by the foregoing demoeconomic model, is established.... These models are used to make urban-rural population projections for the period 1988-2087 under various fertility rate assumptions."
A central question in population ecology is to understand why population growth rates differ over time. Here, we describe how the long-term growth of populations is not only influenced by parameters affecting the expected dynamics, for example form of density dependence and specific population growth rate, but is also affected by environmental and demographic stochasticity. Using long-term studies of fluctuations of bird populations, we show an interaction between the stochastic and the deterministic components of the population dynamics: high specific growth rates at small densities r(1) are typically positively correlated with the environmental variance sigma(e)(2). Furthermore, theta, a single parameter describing the form of the density regulation in the theta-logistic density-regulation model, is negatively correlated with r(1). These patterns are in turn correlated with interspecific differences in life-history characteristics. Higher specific growth rates, larger stochastic effects on the population dynamics and stronger density regulation at small densities are found in species with large clutch sizes or high adult mortality rates than in long-lived species. Unfortunately, large uncertainties in parameter estimates, as well as strong stochastic effects on the population dynamics, will often make even short-term population projections unreliable. We illustrate that the concept of population prediction interval can be useful in evaluating the consequences of these uncertainties in the population projections for the choice of management actions.
We argue that population growth rate is the key unifying variable linking the various facets of population ecology. The importance of population growth rate lies partly in its central role in forecasting future population trends; indeed if the form of density dependence were constant and known, then the future population dynamics could to some degree be predicted. We argue that population growth rate is also central to our understanding of environmental stress: environmental stressors should be defined as factors which when first applied to a population reduce population growth rate. The joint action of such stressors determines an organism's ecological niche, which should be defined as the set of environmental conditions where population growth rate is greater than zero (where population growth rate = r = log(e)(N(t+1)/N(t))). While environmental stressors have negative effects on population growth rate, the same is true of population density, the case of negative linear effects corresponding to the well-known logistic equation. Following Sinclair, we recognize population regulation as occurring when population growth rate is negatively density dependent. Surprisingly, given its fundamental importance in population ecology, only 25 studies were discovered in the literature in which population growth rate has been plotted against population density. In 12 of these the effects of density were linear; in all but two of the remainder the relationship was concave viewed from above. Alternative approaches to establishing the determinants of population growth rate are reviewed, paying special attention to the demographic and mechanistic approaches. The effects of population density on population growth rate may act through their effects on food availability and associated effects on somatic growth, fecundity and survival, according to a 'numerical response', the evidence for which is briefly reviewed. Alternatively, there may be effects on population growth rate of population density in addition to those that arise through the partitioning of food between competitors; this is 'interference competition'. The distinction is illustrated using a replicated laboratory experiment on a marine copepod, Tisbe battagliae. Application of these approaches in conservation biology, ecotoxicology and human demography is briefly considered. We conclude that population regulation, density dependence, resource and interference competition, the effects of environmental stress and the form of the ecological niche, are all best defined and analysed in terms of population growth rate.
Sexually transmitted diseases such as gonorrhoea are a significant cause of infertility in women when the infection is untreated. They have the potential to alter human population growth rates in many developing countries where sexually transmitted diseases are prevalent due to limited public health facilities for diagnosis and treatment. The authors develop a simple model describing the conditions under which such a disease can exist in a growing population in which sexual partners are chosen at random by proportionate mixing. Using the model, the impact that such a disease could have for a range of possible parameters is examined. Then a full parameter set for gonorrhoea in a developing country is estimated. Analysis demonstrates the significant influence gonococcal infection may have in reducing population growth rate in some communities. For example, the simple model predicts that a prevalence of 20% in sexually active adults results in a 50% reduction in the population growth rate. Finally, the authors discuss how potential control initiatives may change the parameter values that determine transmission and alter the demographic impact of gonorrhoea.