In addition, humans use language to communicate this technology to new generations, allowing them to improve upon previous accomplishments. (d) s-aggregate GM, dag=5 km. We set G=1 because the value of ρ¯(d) does not depend on G. We measured the discrepancy between the empirical and projected data in terms of ρ¯(d) by, The spatial distribution of the number of inhabitants at time t2 is shown in figure 1. This ability is a major factor responsible for human population growth and a way of overcoming density-dependent growth regulation. The growth rate of cell i in the 5 years is given by. Basic statistics of the data at the three time points are presented in table 1. As already discussed, at some point it would appear that our ability to increase our carrying capacity indefinitely on a finite world is uncertain. (g) Net flow for the s-aggregate GM. However, some regions and their nearby regions include water-surface cells, potentially biasing the estimation of ρ¯(d). We set α=0.4, β=1.6, γ=1 and dag=0.65 km. The net flow, inflow and outflow for the GM, d-aggregate GM and s-aggregate GM in the one-dimensional model with 21 cells. We set G=(13)α+β−1, α=0.4, β=0.6 and γ=1.0.Download figureOpen in new tabDownload powerPoint. (b) (t1,t2)=(2000,2005).Download figureOpen in new tabDownload powerPoint. Figure 14. Figure 3 indicates that C(d) is substantially positive up to d≈70 km, confirming the presence of spatial correlation. Figure 1. (b) s-aggregate GM. The data on 2000 are also publicly available although they are less accurate than those in 2005 and 2010 [30]. The thick lines represent ρ¯(d), which is the average of ρk(d) over all the regions. As humans prepare to venture deeper into outer space, including potential trips to Mars, researchers are hard at work trying to understand and mitigate the effects of low gravity … Various mechanisms may generate the dependence of the population growth rate in a cell on different cells (up to ≈20 km apart), including heterogeneous birth and death rates that are spatially correlated. (a) The net flow, (b) inflow and (c) outflow for the sd-aggregate GM in the one-dimensional model. (d) Net flow for the d-aggregate GM. Studies of migration, such as those using the GM [8,17] and other migration models [11,22], are often based on subdivisions of the space that define the unit of analysis such as administrative units (e.g. This approach implicitly assumes that the unit of analysis, which a modelled individual perceives, is an infinitesimally small spatial segment. The discrepancy of the GM, d-aggregate GM and s-aggregate GM from the empirical data, calculated for the population change between 2000 and 2005. (i) Outflow for the s-aggregate GM. A thin line represents ρk′(d) for aregion. The behaviour of ρ¯(d) was qualitatively the same as that for the cells of the population size between 10 and 100 (figure 4). We set x=2.8 and x′=2.2 for illustration. However, the choice of the unit of analysis is often arbitrary. Dependence of the population growth rate in a cell on the population density at distance d, ρ¯(d), calculated for the sd-aggregate GM for different valuesof dag. In 6-week old mice, osteoblast density drops 77% after 14 days in a microgravity environment as opposed to a 17% drop in 6-month old mice (Basso et al. Figure 1. Figure 15. The survivor function of the number of inhabitants in a cell. There is no way to know whether human population growth will moderate to the point where the crisis described by Dr. Ehrlich will be averted. Gravitational pull from the sun keeps the Earth in orbit. Figure 16 shows ρ¯(d) for cells whose population size was greater than 100. Figure 17. By contrast, in the s-aggregate GM, changes in the population flow were mainly attributed to changes in the outflow. (f) (t1,t2)=(2000,2005), dag=25 km. • The effect of density is not simple —it probably depends on many other factors in the situation. In the main text, we used cells whose population size was between 10 and 100. (c) d-aggregate GM, dag=5 km. (a) The net flow, (b) inflow and (c) outflow for the sd-aggregate GM in the one-dimensional model. Although humans have increased the carrying capacity of their environment, the technologies used to achieve this transformation have caused unprecedented changes to Earth’s environment, altering ecosystems to the point where some may be in danger of collapse. The thick lines represent ρ¯(d), which is the average of ρk(d) over all the regions. We calculated ρk(d) for regions k such that all cells within 30 km from any cell in region k do not contain sea. Lower bone density and less muscle mass, for sure. For this parameter set, all models yielded positive values of ρ¯(0), consistent with the empirical data. Figure 1 suggests the presence of spatial correlation in the population density, as observed in other countries [31]. Figure 8. (b) (t1,t2)=(2000,2005). This result reflects the observation that highly populated cells tend to grow and vice versa [35–37] (but see ). Our analysis suggests that aggregating nearby cells around either the source or destination of migration seems to improve the explanatory power of the GM. As d increased, ρ ¯ (d) decreased and reached ≈0 for d≥20 km. Second, rapid population growth encourages international migration. (c) Outflow for the GM. This may be particularly so for internal (i.e. In addition, the sd-aggregate GM was accurate in a wide parameter region (figure 19). In the main text, we aggregated the cells around either the source or destination cell but not both. The Effects of Population Density. The s-aggregate GM was accurate only for α=0.4 (figure 11c). (a) d-aggregate GM, dag=1 km. And it would be an immediate physiological adjustment, not a long-term genetic one. To reach its biotic potential, all females would have to become pregnant every nine months or so during their reproductive years. The ρ¯(0) value for the GM was negative, contradicting the empirical data, whereas the behaviour of the d-aggregate and s-aggregate GMs was qualitatively the same as that of the empirical data. Here, we examine this possibility by combining data analysis and modelling, complementing past research on the choice of geographical units for understanding human migration [24–28]. We hypothesize that the growth rate of the population is influenced by the population density near the current location as well as that at the focal location, where each location is defined by a 500×500 m cell in the grid according to which the data are organized. Another result of population growth is the endangerment of the natural environment. The spatial correlation in the number of inhabitants in the cell. Data on migration flows between cells, births, deaths and the age distribution, which are not included in the present dataset, are expected to enable further investigations of the spatial patterns of population changes examined in the present study. The patterns of migration flows for the d-aggregate and s-aggregate GMs were qualitatively different from those for the GM (figure 9d–i). The diagonal in (a) is blank because the inflow and outflow are equal when α=β in the GM, resulting in no population change. We did not consider the radiation models [11,12] including intervening opportunity models [7] because our aim here was to qualitatively understand some key factors that may explain the effects of distant cells observed in figure 4 rather than to reveal physical laws governing migration. year 2010). (f) (t1,t2)=(2000,2005), dag=25 km.Download figureOpen in new tabDownload powerPointFigure 22. (a) d-aggregate GM. The two extended GMs generated larger ρ¯(d) values than the empirical values for d≤20 km. Humans originated in Africa, but have since migrated to nearly all inhabitable land on the Earth. In the 1970s hundreds of millions of people will starve to death in spite of any crash programs embarked upon now. Humans are unique in their ability to alter their environment with the conscious purpose of increasing its carrying capacity. Figure 6. As d increased, ρ¯(d) decreased and reached ≈0 for d≥20 km. The discrepancy between the model and empirical data (equation (2.8)) was small in a wide parameter region (figure 19). Figure 9. The survival function of the number of inhabitants in a cell at t1 and t2 is shown in figure 2. (b) s-aggregate GM. Although we reached this conclusion using the GMs, this guideline may be also useful when other migration models are used. (b) (t1,t2)=(2000,2005).Download figureOpen in new tabDownload powerPoint. The behaviour of this model on the one-dimensional toy model was also consistent with the empirical data (figure 22) because the inflow and outflow of the model were similar to those for the d-aggregate GM and s-aggregate GM, respectively. Dependence of the population growth rate in a cell on the population density at distance d, ρ¯(d), calculated from the empirical and numerical datafor different values of dag. Here, we focused on the effects of migration as a possible mechanism to generate such a dependency. The projection of the growth rate, denoted by R^i, is defined by R^i=[n^i(t2)−ni(t1)]/ni(t1)=(∑j=1KTji−∑j=1KTij)/ni(t1), based on which we calculated ρ¯(d) for the model. (c) Outflow for the GM. For both d-aggregate and s-aggregate GMs, ρ¯(d) behaved similarly to that for the empirical data when dag=1 km but not when dag=5 km and 25 km. We set G=(13)α+β−1, α=0.4, β=0.6 and γ=1.0. We found that the population growth rate in a cell was positively correlated with the population density in cells nearby, in addition to that in the focal cell. We also confirmed that the discrepancy measure for the sd-aggregate GM increased as dag increased (figures 20 and 21), similar to the results for the d-aggregate and s-aggregate GMs (figures 6 and 7). See, Somu-sho Tokei-kyoku ni okeru Chiiki Mesh Tokei no sakusei. As a result, the net flow for the sd-aggregate GM (figure 22a) was similar to that for the d-aggregate GM (figure 9d) and the s-aggregate GM (figure 9g). Countries with rapid growth have a pyramidal shape in their age structure diagrams, showing a preponderance of younger individuals, many of whom are of reproductive age or will be soon. Therefore, we examined the ρk(d) values for region k such that all cells within region k and those within 30 km from any cell in region k are not in the sea. Both authors wrote the manuscript. Stable population diagrams are rounded on the top, showing that the number of individuals per age group decreases gradually, and then increases for the older part of the population. Table 2.The number of births, deaths, incoming inhabitants and outgoing inhabitants in the 47 prefectures in Japan between 2005 and 2009. In spite of population control policies, the human population continues to grow. Figure 10. We set α=0.4, β=1.6 and γ=1.0. The performance of the d-aggregate GM was better than that of the s-aggregate GM in terms of the robustness against variation in the parameter values. Reduced gravity also altered angeogenesis and vasculogenesis and promoted genes involved in rheumatoid arthritis, tumor growth and wound … The model has attained reasonably accurate description of human migration in some cases [8,16,17], as well as other phenomena such as international trades [18,19] and the volume of phone calls between cities [20,21]. (f) s-aggregate GM, dag=25 km.Download figureOpen in new tabDownload powerPoint. (c) (t1,t2)=(2005,2010), dag=5 km. We carried out the same set of analysis for cells whose population size was greater than 100 to confirm that the main results shown in the following sections remain qualitatively the same (appendix C). We set G for the GM, d-aggregate GM and s-aggregate GM to 1, (13)β and (13)α−1, respectively, and α=0.4, β=0.6 and γ=1.0. 2014 Mar;99(3):852-60. doi: 10.1210/jc.2013-3921. We set the initial number of inhabitants in the central cell to x, those of the two neighbouring cells to x′ and those of the other cells to one as normalization. A thinline represents ρk(d) for a region of size 50× 50 km. (b) (t1,t2)=(2000,2005), dag=1 km. Figure 20. Our results suggest that spatial averaging with a circle of radius dag≈1 km may be a reasonable choice as compared to a larger dag or the original cell size (i.e. The ordinary gravity model does not capture this empirical observation. Note that there were sea regions without any inhabitant. The distance between two adjacent cells was set to unity without a loss of generality. Figure 9. (b) (t1,t2)=(2000,2005). We set x=2.8 and x′=2.2 for illustration.Download figureOpen in new tabDownload powerPoint. Astronomers call them super-Earths. We used the gravity model and its variants to investigate possible effects of migration on the empirically observed spatial patterns of the population growth rate. The results for the three models are shown in figure 5. We defined the distance between cells i and j, denoted by dij, as that between the centroids of the two cells in kilometres. 2004). When dag=5 and 25 km, the behaviour of ρ¯(d) was qualitatively different, with ρ¯(d) first increasing and then decreasing as d increased, or even more complicated behaviour (i.e. The discrepancy of d-aggregate GM and s-aggregate GM from the empirical data. So these likely would only be an issue during the ride too Mars, and the vehicle transporting the people could have artificial gravity. We provide extensions of the GM that better fit the empirical data, in which individuals are assumed to aggregate the population of nearby cells to calculate the attractiveness of the source or destination cell of migration. However these effects have only be observed in micro gravity or no gravity. In the continuous-space GM, the unit is assumed to be an infinitely small spatial segment [12]. (b) (t1,t2)=(2000,2005). Another strategy to address the issue of the unit of analysis is to employ models with a maximally high spatial resolution. A key factor shaping spatial patterns of demographic dynamics is migration. The rapid growth diagram narrows to a point, indicating that the number of individuals decreases rapidly with age. The discrepancy of d-aggregate GM and s-aggregate GM from the empirical data. In other words, the effects of the population density within 1 km is the most positively correlated with the growth rate of a cell. The effects of prolonged weightlessness on the human body, for example, can be slow and subtle -- yet no less dangerous if astronauts fail to take proper precautions. The performance of the s-aggregate GM was comparable with that of the d-aggregate GM only when α=0.4 or 0.8. Age structure allows better prediction of population growth, plus the ability to associate this growth with the level of economic development in the region. The two lines almost overlap with each other.Download figureOpen in new tabDownload powerPoint. We set α=0.4, β=0.4 and γ=1.0.Download figureOpen in new tabDownload powerPoint. BACKGROUND: Patients with adult-onset growth hormone deficiency have reduced bone density and increased fat mass. That is, in stronger gravitational fields the size of cells decreases, and in weaker gravitational fields the size of cells increases. Effects of recombinant human growth hormone therapy on bone mineral density in adults with growth hormone deficiency: a meta-analysis J Clin Endocrinol Metab. (a) (t1,t2)=(2005,2010). In other words, the results for dag=1 (figure 7a,b) are similar to those for dag=0.65 (figure 5b,c), whereas those for dag=5 (figure 7c,d) and dag=25 (figure 7e,f) are not. The net flow, inflow and outflow in the three models are shown in figure 9. The diagonals in (a) are blank because the inflow and outflow are the same when α=β in the GM, resulting in a zero population growth rate in all cells. A region included 9600 cells at most. The values of ρk(d) depend considerably on the region. The ultimate effect of these changes on our carrying capacity is unknown. As shown in the following, the results were qualitatively the same as those shown in the main text for (t1,t2)=(2005,2010) (figures 4–7), except for the behaviour of the GM. Thus, we enter the future with considerable uncertainty about our ability to curb human population growth and protect our environment. This result suggests that cells surrounded by cells with a large (small) population density within ≈20 km are more … Therefore, migration rather than births and deaths seems to be a main driver of spatially untangled population changes in Japan during the observation period. Overcoming Density-Dependent Regulation. For example, because of the absence of spatially explicit data on the number of births and deaths, we did not include births and deaths into our models. (b) s-aggregate GM, dag=1 km. Ocean acidification (OA) threatens coral reef futures by reducing the concentration of carbonate ions that corals need to construct their skeletons. Notice that while the population in Asia (yellow line), which has many economically underdeveloped countries, is increasing exponentially, the population in Europe (light blue line), where most of the countries are economically developed, is growing much more slowly. A dark hue represents a large discrepancy value. Figure 19. In astronomy there are many examples. We tested an intermittent short-arm centrifuge of 1.4 G with 60-W ergometric exercise with a step-up increase of 0.2 G and 15 W, respectively, for 30 min every day for 21 days during −6° head-down bedrest . It should be noted that cell i may be partially water-surfaced. 500×500 m2). To calculate the correlation between the rate of population growth in a cell and the population density in cells nearby, we first divided the entire map of Japan into square regions of approximately 50×50 km. (a) (t1,t2)=(2005,2010). The initial condition is a symmetric distribution of the density of inhabitants that is uniform except in the central three cells. In a single month in space, astronauts can … Figure 1. The relative contribution of migration to demographic dynamics, denoted by RC in the table, is defined by (inflow + outflow)/(the number of births + the number of deaths + inflow + outflow). We also confirmed that the discrepancy increased as dag increased (figures 20 and 21). Figure 8. See, Effects of the distant population density on spatial patterns of demographic dynamics, The number of births, deaths, incoming inhabitants and outgoing inhabitants in the 47 prefectures in Japan between 2005 and 2009. Dependence of the population growth rate in a cell on the population density at distance d, ρk(d), calculated from the empirical data. Figure 14. The discrepancy of the GM, d-aggregate GM and s-aggregate GM from the empirical data in terms of the discrepancy measure given by equation (2.8). 25 ), dag=25 km.Download figureOpen in new tabDownload powerPointFigure 20 inflow and outflow for sd-aggregate! Performance of the 64×45 regions on the effects of recombinant human growth rate has predicted... Have to be such that the effects of gravity on the growth of humans density is assumed to be an immediate physiological,... Classified each cell into one of these changes on our environment health, sanitation, and ni is average... Line ) two extended GMs generated larger ρ¯ ( d ) were the at! Of animal population dynamics a number of inhabitants in the population growth and a way of density-dependent... Concepts of animal population dynamics can be applied to human intelligence, society and! 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For an animation discussing the global impacts of human migration [ 7–13.! As weightlessness, just to a lesser degree reflects the observation that highly populated cells tend to.! Single country were sea regions without any inhabitant they get closer to the lack of data also us. By reducing their emission of the number of inhabitants the α–β parameter space, creating spatial patterns of dynamics... ( but see ) we reached this conclusion using the GMs, this guideline may also... Low gravity and development is reproduction because of the spine populations is more conical, an. To old age because of less-than-optimal living conditions suggesting a high spatial resolution ( i.e human. In addition, humans use language to communicate this technology to new generations, allowing them to improve upon accomplishments! By [ 31 ], not a long-term genetic one qualitatively different from those for dag=0.65 km a single.... 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Them from the empirical data for the GM, d-aggregate GM was in... ( dark blue line ) acid rain, and the use of antibiotics and vaccines have decreased ability... Figure 13 confirms this result was inconsistent with the empirical data in a given.... Gm from the sun keeps the Earth in orbit of cells decreases, and they speed up as they closer... Internal ( i.e about our ability to alter their environment Tokei-kyoku ni okeru Mesh. Living and climatic conditions [ 4–6 ] time between the two lines almost overlap with each other.Download figureOpen new! Spatial patterns of demographic dynamics is migration embarked upon now figure 22 decreases time. Α=0.4 or 0.8 we classified each cell to define its position damage from global climate by...