Population Growth and Regulation

PROGRAM ON THE GLOBAL DEMOGRAPHY OF AGING Working Paper Series Population Aging and Economic Growth in China Judith Banister, David E. Bloom, and Larry Rosenberg March 2010 PGDA Working Paper No. 53 http://www.hsph.harvard.edu/pgda/working.htm The views expressed in this paper are those of the author(s) and not necessarily those of the Harvard Initiative for Global Health. The Program on the Global Demography of Aging receives funding from the National Institute on Aging, Grant No. 1 P30 AG024409-06. Population Aging and Economic Growth in China Judith Banister, David E. Bloom, and Larry Rosenberg March 2010 Executive Summary According to current UN projections, the population of the world age 60 or older will be 2 billion by 2050. With populations aging in nearly all countries, there has been widespread concern about the possible effects on economic growth and on the ability of countries to provide support for their elderly populations. In particular, because the elderly are in general less economically productive than younger people, a preponderance of old-age individuals would seem to suggest that (a) economic growth will be slower than in the past, and (b) relatively smaller working-age cohorts of the future will be burdened by the need to care for, and pay for the support of, the elderly population. These concerns have found resonance in China, where more than 30% of the population is expected to be age 60 or older in 2050. In part as a consequence of China’s process of population aging to date, the ratio of individuals age 15-64 to those younger and older, which grew rapidly during the last few economic boom decades, has reached its peak and is slated to decline rapidly in coming decades. Because a labor force that is large in size relative to the dependent population is plausibly crucial to rapid economic growth, the decline of this ratio could conceivably herald economic difficulties. The roots of population aging in China are the same as elsewhere: a low fertility rate, rising life expectancy, and the cumulative effect of past changes in birth and death rates. In China, obviously, the decline in the fertility rate, brought about in significant measure by the one-child policy and government efforts leading up to its adoption, has been a central factor in the changing age structure of the Chinese population. Greater longevity has also obviously been a key factor in population aging. If an older population is in fact cause for concern about the future of the Chinese economy, it would be prudent to identify, as soon as possible, measures that could serve to counteract any negative economic effects of population aging. Numerous countries have identified policies that might mitigate the potential economic problems associated with population aging. These policies seek to raise the age of retirement, spur higher savings, facilitate work for those caring for children, increase the labor force participation of women, liberalize immigration, and give more incentives for education. China could indeed begin to change the legal age of retirement, for those to whom this applies. It is unlikely to seek a higher savings rate, since its savings are already Population Growth and Regulation Population Growth and Regulation Bởi: OpenStaxCollege Population ecologists make use of a variety of methods to model population dynamics An accurate model should be able to describe the changes occurring in a population and predict future changes Population Growth The two simplest models of population growth use deterministic equations (equations that not account for random events) to describe the rate of change in the size of a population over time The first of these models, exponential growth, describes theoretical populations that increase in numbers without any limits to their growth The second model, logistic growth, introduces limits to reproductive growth that become more intense as the population size increases Neither model adequately describes natural populations, but they provide points of comparison Exponential Growth Charles Darwin, in developing his theory of natural selection, was influenced by the English clergyman Thomas Malthus Malthus published his book in 1798 stating that populations with abundant natural resources grow very rapidly; however, they limit further growth by depleting their resources The early pattern of accelerating population size is called exponential growth The best example of exponential growth in organisms is seen in bacteria Bacteria are prokaryotes that reproduce largely by binary fission This division takes about an hour for many bacterial species If 1000 bacteria are placed in a large flask with an abundant supply of nutrients (so the nutrients will not become quickly depleted), the number of bacteria will have doubled from 1000 to 2000 after just an hour In another hour, each of the 2000 bacteria will divide, producing 4000 bacteria After the third hour, there should be 8000 bacteria in the flask The important concept of exponential growth is that the growth rate—the number of organisms added in each reproductive generation—is itself increasing; that is, the population size is increasing at a greater and greater rate After 24 1/11 Population Growth and Regulation of these cycles, the population would have increased from 1000 to more than 16 billion bacteria When the population size, N, is plotted over time, a J-shaped growth curve is produced ([link]a) The bacteria-in-a-flask example is not truly representative of the real world where resources are usually limited However, when a species is introduced into a new habitat that it finds suitable, it may show exponential growth for a while In the case of the bacteria in the flask, some bacteria will die during the experiment and thus not reproduce; therefore, the growth rate is lowered from a maximal rate in which there is no mortality The growth rate of a population is largely determined by subtracting the death rate, D, (number organisms that die during an interval) from the birth rate, B, (number organisms that are born during an interval) The growth rate can be expressed in a simple equation that combines the birth and death rates into a single factor: r This is shown in the following formula: Population growth = rN The value of r can be positive, meaning the population is increasing in size (the rate of change is positive); or negative, meaning the population is decreasing in size; or zero, in which case the population size is unchanging, a condition known as zero population growth Logistic Growth Extended exponential growth is possible only when infinite natural resources are available; this is not the case in the real world Charles Darwin recognized this fact in his description of the “struggle for existence,” which states that individuals will compete (with members of their own or other species) for limited resources The successful ones are more likely to survive and pass on the traits that made them successful to the next generation at a greater rate (natural selection) To model the reality of limited resources, population ecologists developed the logistic growth model Carrying Capacity and the Logistic Model In the real world, with its limited resources, exponential growth cannot continue indefinitely Exponential growth may occur in environments where there are few individuals and plentiful resources, but when the number of individuals gets large enough, resources will be depleted and the growth rate will slow down Eventually, the growth rate will plateau or level off ([link]b) This population size, which is determined by the maximum population size that a particular environment can sustain, is called the carrying capacity, or K In real populations, a growing population often overshoots its carrying capacity, and the death rate increases beyond the birth rate causing the population size to decline back to the carrying capacity or below it Most populations 2/11 Population Growth and Regulation usually fluctuate around the carrying capacity in an undulating fashion rather than existing right at it The formula used to calculate logistic growth adds the carrying capacity as a ...Sokhem Pech and Kengo Sunada Population Growth and Natural-Resources Pressures in the Mekong River Basin The Mekong River Basin possesses the region’s largest potential water source and related resources, which support ongoing economic development and basin com- munity livelihoods. It is currently witnessing a major demographic transition that is creating both opportunities and challenges. An analysis of the complex relationship between demographic changes and impacts on the natural-resource base confirms that resource exploitation is occurring not only to meet growing domestic needs but also for other vested interests. Population, together with other major drivers, such as institutions, markets, and technology, will have a very strong bearing on the way in which the rich resources of the Mekong River Basin are developed and distributed in the present and future. The Mekong River Basin’s rich resources, and the benefits derived from them, are unevenly distributed both in time and geographically. Moreover, since the causes and impacts do not respect political boundaries, the Mekong countries need to jointly develop alternative management strategies to meet projected demands within the sustain- able capacity of the Mekong River Basin natural-resource base. INTRODUCTION The Mekong countries have a complex, but interesting, mosaic of demographic attributes and trends. The population of the Mekong region—whole of Yunnan Province of China, Myan- mar, Laos, Thailand, Cambodia, and Vietnam—is nearly 300 million, and over 70 million people live in the Mekong River Basin (1). The Mekong Basin possesses the region’s largest potential water source and related resources. These resources are fundamental to ongoing economic development in terms of irrigation and agricultural production, fisheries and aquacul- ture, energy and forest products, navigation and other modes of transport, domestic and industrial water supply, and tourism (2). Levels of dependency on the river’s water and related resources are very high, particularly among the rural poor, who rely on subsistence livelihoods and moral economy (3). Rec ent socioeconomic development has begun to slow population growth rates in China, Thailand, and Myanmar, while Cambodia, Laos and Vietnam are expected to experience further positive growth well beyond 2050 (4). It is also true that the population growth rates vary considerably across th e Mekong River Basin, within and between the basin countries (2). For example, Yunnan population density has doubled since the 1950s, reaching the current level of 103 people km À2 , but in the Lancang/Mekong part, the population density is only 62 people km À2 due to the rugged and inhospitable landform (5). On the other hand, Yunnan population growth rate has declined slower than other parts of China (present level of 1.3% y À1 compared to less than 0.7% annually for all of China) (5). Accordingly, the overall Mekong populations are projected to increase well beyond 2050. The population growth and expected demographic changes in these countries create both opportunities and challenges. Population size and its composition have significant impli- cations for pressures on natural resources. Growing populations require more or different food, which typically requires land and water or other forms of production (6). This paper examines population growth and its likely impacts on food demand and land and water resources in the Mekong River Basin in a systematic and integrated manner. As a Original article Patterns in individual growth, branch population dynamics, and growth and mortality of first-order branches of Betula platyphylla in northern Japan Kiyoshi Umeki a,* and Kihachiro Kikuzawa a Hokkaido Forestry Research Institute, Koshunai, Bibai, Hokkaido 079-0198, Japan b Laboratory of Forest Biology, Graduate School of Agriculture, Kyoto University, Japan (Received 1 February 1999; accepted 27 March 1999) Abstract – Growth of individual trees, population dynamics of first-order branches within individuals, and growth and mortality of first-order branches were followed for two years in an plantation of Betula platyphylla in central Hokkaido, northern Japan. The data were analyzed by stepwise regressions. The relative growth rate in terms of above-ground biomass of individuals was negatively correlated with a log-transformed competition index (ln(CI)), which was calculated for each individual from the size and distance of its neighbours. The change in branch number within an individual was also correlated with ln(CI). The growth and mortality of branches was correlated with the size of branches, size of individuals, growth of individuals, relative height of branches, and ln(CI). Generally, the patterns revealed by the regressions were consistent with what was expected and can be used as references against which the behavior of more detailed process-based models can be checked. Betula platyphylla / branch population dynamics / competition / branch growth / branch mortality Résumé – Modèles de croissance individuelle, dynamique de développement des branches et croissance et mortalité des branches du Betula Platyphylla. La croissance des arbres individuels, la dynamique de développement des branches de premier ordre sur les arbres individuels ainsi que la croissance et la mortalité des branches de premier ordre ont été étudiées pendant deux ans dans une pépinière de Betula Platyphylla de la région centrale du Hokkaido dans le nord du Japon. Les modèles de croissance indivi- duelle, la dynamique de développement des branches et la croissance et la mortalité des branches ont été analysées selon leur régres- sion progressive. Le taux de croissance relatif en termes de biomasse aérienne des arbres individuels s’est avéré en rapport inverse à l’index de concurrence des grumes (ln(CI)), après calcul pour chaque individu d’après la taille et l’éloignement de ses voisins. Le changement du nombre de branches sur un même individu est également en rapport avec ln(CI). La croissance et la mortalité des branches s’est avérée en rapport avec la taille des branches, la taille des individus, la croissance des individus, la hauteur relative des branches et ln(CI). En général, les modèles mis en évidence par les régressions sont conformes aux hypothèses avancées et peuvent servir de référence pour le contrôle d’autres modèles plus détaillés. Betula platyphylla / dynamique de développement des branches / compétition / croissance des branches / mortalité des branches Ann. For. Sci. 57 (2000) 587–598 587 © INRA, EDP Sciences * Correspondence and reprints Tel. +81-1266-3-4164; Fax. +81-1266-3-4166; e-mail: umeki@hfri.bibai.hokkaido.jp K. Umeki and K. Kikuzawa 588 1. INTRODUCTION An individual tree is constructed from structural units growing and iterating within an individual [12, 45], and can be thought of as a population of structural units [45]. Thus far, various components of an individual plant such as branches, shoots, and metamers [34] have been used as the structural unit, or module, of a tree. In this paper, the term “module” is defined, following Harper [13], as “a repeated unit of multicellular structure, normally arranged in a branch system.” The spatial and static aspects of a module population within a tree can be expressed by the spatial distribution of modules within a tree. The distribution of modules is important because it determines the crown form and the amount of light captured by the crown; future growth is Genome Biology 2008, 9:R130 Open Access 2008Covingtonet al.Volume 9, Issue 8, Article R130 Research Global transcriptome analysis reveals circadian regulation of key pathways in plant growth and development Michael F Covington *† , Julin N Maloof * , Marty Straume ‡§ , Steve A Kay ¶¥ and Stacey L Harmer * Addresses: * Department of Plant Biology, College of Biological Sciences, One Shields Avenue, University of California, Davis, California 95616, USA. † Present address: Department of Biochemistry and Cell Biology, Rice University, Main Street, Houston, Texas 77005, USA. ‡ Center for Biomathematical Technology, Box 800735, University of Virginia Health Sciences System, Charlottesville, Virginia 22908, USA. § Present address: Customized Online Biomathematical Research Applications, Glenaire Drive, Charlottesville, Virginia 22901, USA. ¶ Department of Biochemistry, The Scripps Research Institute, North Torrey Pines Road, La Jolla, California 92037, USA. ¥ Present address: Section of Cell and Developmental Biology, University of California at San Diego, Gilman Drive, La Jolla, California 92093, USA. Correspondence: Stacey L Harmer. Email: slharmer@ucdavis.edu © 2008 Covington et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Plant circadian clock<p>Transcript abundance of roughly a third of expressed <it>Arabidopsis thaliana</it> genes is circadian-regulated.</p> Abstract Background: As nonmotile organisms, plants must rapidly adapt to ever-changing environmental conditions, including those caused by daily light/dark cycles. One important mechanism for anticipating and preparing for such predictable changes is the circadian clock. Nearly all organisms have circadian oscillators that, when they are in phase with the Earth's rotation, provide a competitive advantage. In order to understand how circadian clocks benefit plants, it is necessary to identify the pathways and processes that are clock controlled. Results: We have integrated information from multiple circadian microarray experiments performed on Arabidopsis thaliana in order to better estimate the fraction of the plant transcriptome that is circadian regulated. Analyzing the promoters of clock-controlled genes, we identified circadian clock regulatory elements correlated with phase-specific transcript accumulation. We have also identified several physiological pathways enriched for clock-regulated changes in transcript abundance, suggesting they may be modulated by the circadian clock. Conclusion: Our analysis suggests that transcript abundance of roughly one-third of expressed A. thaliana genes is circadian regulated. We found four promoter elements, enriched in the promoters of genes with four discrete phases, which may contribute to the time-of-day specific changes in the transcript abundance of these genes. Clock-regulated genes are over-represented among all of the classical plant hormone and multiple stress response pathways, suggesting that all of these pathways are influenced by the circadian clock. Further exploration of the links between the clock and these pathways will lead to a better understanding of how the circadian clock affects plant growth and leads to improved fitness. Published: 18 August 2008 Genome Biology 2008, 9:R130 (doi:10.1186/gb-2008-9-8-r130) Received: 30 June 2008 Revised: 7 August 2008 Accepted: 18 August 2008 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2008/9/8/R130 http://genomebiology.com/2008/9/8/R130 Genome Biology 2008, Volume 9, Issue 8, Article R130 Covington et al. R130.2 Genome Biology 2008, 9:R130 Background Harsh environmental extremes often accompany the daily v )( v "* x *$") q # r !!!!!! ! ! ! ! ! !!!! ! ! ! ! _ P c )($: X X \N !"* $ *$") ] \1N abcdN 33Øف˜™ ЀĐỊĨỢ–|Ư²8@ : FGH IJ >KL AM !  9{fvˆvj ‹u¡)fvj g¢£Ÿhi¤ 9¥gŸ¦§¨© ª«¬¢¬­®qrƒ„s ‹u "$$"7) rs !›¤3q IDBÏÐ@Øv¸¹Ð€—˜$ïð³NỊĨỢ–|Ư%8@ : 33믪ì„íỵêđD²Ú 33¬¥²ê—ƒWVz{$¤]Ѐl¤{ỊĨ‹Ï²Q1$äỊĨ iWVcd$˜™Ð€ĐỊĨỢ–|Ư%8@ : Ợ–Š%©ç&'»1bƯ) ˜™$ŸÐu»Ợ–|Ưqü$Úië¯X{ìđu»$g´ŸƯXY*çÐu 33¬¥$¾òì|Ư*đÐu»Ợ–|Ư½$V±&$Lì¥ĐÃß) ¸¹ »·ỊĨỢ–|Ư–x‡©Åì8@ : 33„íỵêGz{#ŸÐu»ỊĨỢ– : 33ë¯Úãu{ŽT¬¥$즧W»]Dỵ ЀĐỊĨỢ– : !: 33L" %w]D}ỵ! ALB" ỉ²Í*»´—ỵêđDÌä–xÅW$¬xT*µÐu»Ợ– : 33믱$$Lì : 33L% 33¬¥ˆGz{) Í*»´—ỵêâß) åŽX¸¹Ð€z{ỊĨỢ–|Ư$¹TĐì8@ :  tuv !ỊĨ*Ѐ*|Ư*: 33*Úi !! ỊĨ ).$0 @, < 7A ' @7< ! IN X X XN 6GZ c" UF Z X Q †©úƠå7Ú¬ƠđỊĨƠ&ỊĨ†\Ơ$ ·Y %AGªn,¤Z[) ỊĨ·«Š|ƯwÍ$+ ·'pơ3ŠRỗúz{$ ÐläÇơRon Š%v & aÚ$ N¯¸¹ Ð € ™ Š %  Ị Ĩ Ơ Õ – V ±$ M › ¤ P#!$"$" '%())&*#$ D4 /F R P #/N R d6 R P R 1O 2"#)c / ™$KN ' ºĐˆ±ˆ( Nüìб$ỵÚỢ–¥ĐÃ$w ) W Q /F R P c / - "((%- v )( v "* x *$") q # r !!!!!! ! ! ! ! ! !!!! ! ! ! ! _ P c )($: X X \N !"* $ *$") ] \1N [ Nü " Ĩ$ơv ") a¹$wÈu»±É ) Ĩ) (`&!8@ : 33²ì„íỵêNü –|Ưc $‹ÂŠ%z|Ư*đÐu»Ợ–|Ư –xŽ Ã$¬xc $ -Ê:Äõ$ ‹ÂŠ%z Š%v & $w ) [ ¸¹Ð€u»|Qª$ 33믲 ơv ") a¹$ ƒMNüỢ–8@ : |Ư*çЪµÐu»Ợ–|Ư¹Á$ -Å>õ* RĨĐDỢ–|ƯŽ¹Á$Äi ") ì„íỵê$wÈu»±É ) Ĩ) (`&`"!8@ : 33믁Nü ! ª3V) $c ) O $ ƒ ) Ĩ¾ ($lĨX˜á & /I$ Ĩ‡˜»$ Ợ¿ 1$ ¼½¾ $c ' /I($l { & f$' $$$ oX "$ /R F 1Q: 33 X˜$ ¡ ù X " Ĩ$ ´ M £  ¿) ¤ AP DI: 83UR QNüỊĨỢ–8@ : 33ë¯) (`&`(!: 33„íỵêNü ! ¦§W»]Dỵ !: 33L" êNü# ª3V) $c " O $ ¾ ' /I$c $" / P -Ij" AM8 ! ?@ #c"" Ĩ‡˜»$$ f$& $$$ Z /R j" ùX "$ /R 1$ t9£¿) êNü›¤* +&, 33ë¯ä¤£× ! "" $ 34 8ăÐЩˆ ) ỉ : ScµÐ*>cçÐ*McŸÐ*CcđÐ*$ MN Z Q F ^ ^ FC$ M O R 1.] $N QF P ,KWNN Y Y N Q YP R O WQa\F P R Q !1 7$7(A , ( 7(: : , ... capacity, and the death rate increases beyond the birth rate causing the population size to decline back to the carrying capacity or below it Most populations 2/11 Population Growth and Regulation. .. increase, so the population size would remain the same The carrying capacity of seals would remain the same, but the population of seals would decrease 5/11 Population Growth and Regulation Population. .. Population Dynamics and Regulation The logistic model of population growth, while valid in many natural populations and a useful model, is a simplification of real-world population dynamics Implicit