Environmental versus demographic stochasticity in population growth

2010 ◽  
pp. 37-52 ◽  
Author(s):  
Carlos A. Braumann
Keyword(s):  
Population Growth ◽  
Demographic Stochasticity

THRESHOLD CROSSING PROBABILITIES FOR POPULATION GROWTH MODELS IN RANDOM ENVIRONMENTS

1995 ◽  
Vol 03 (02) ◽  
pp. 505-517 ◽  
Author(s):  
CARLOS A. BRAUMANN
Keyword(s):  
Population Growth ◽  
Growth Models ◽  
Animal Cell ◽  
Growth Parameter ◽  
Minimum Size ◽  
Random Environments ◽  
High Threshold ◽  
Demographic Stochasticity ◽  
Threshold Crossing ◽  
Crossing Probabilities

Consider the general population growth model in a random environment dN/dt= (r+σε(t))Nf(N), N(0)=N0>0, where N=N(t) is the (animal, cell, etc.) population size or biomass at time t≥0, r>0 is an intrinsic growth parameter subjected to environmental random fluctuations approximately described by σε(t) (σ>0 noise intensity parameter, ε(t) standard white noise), and f(N) is a well-behaved density-dependence function. Due to demographic stochasticity and Allee effects, a slightly modified model that corrects for inadequacies at small population sizes is also considered. In many applications (wildlife management, environmental impact assessment, pest control, growth of bacterial cultures, tumor or body growth, etc.), one needs the probability of N(t) ever crossing a given threshold during a given time horizon. We consider the cases of a low threshold N1<N0 (for instance, an extinction threshold or a minimum size required for economical, ecological or recreational reasons) and of a high threshold N h >N0 (for instance, a pest’s damaging level). We also obtain other related threshold crossing probabilities of interest. A reference is made to statistical estimation and hypothesis testing.

Demography and viability analyses of a diamondback terrapin population

2003 ◽  
Vol 81 (4) ◽  
pp. 716-726 ◽  
Author(s):  
Matthew G Mitro
Keyword(s):  
Growth Rate ◽  
Population Growth ◽  
Rhode Island ◽  
Population Model ◽  
Population Growth Rate ◽  
Diamondback Terrapin ◽  
Matrix Population Model ◽  
Demographic Stochasticity ◽  
Mark Recapture ◽  
Declining Populations

The diamondback terrapin, Malaclemys terrapin, is a long-lived species with special management requirements but quantitative analyses to support management are lacking. I analyzed mark–recapture data and constructed an age-classified matrix population model to determine the status and viability of the only known diamondback terrapin population in Rhode Island. Female diamondback terrapins were captured, marked, and recaptured while nesting during 1990–2001. Population growth rate (λ) was 1.034 (95% confidence interval = 1.012–1.056). For the preceding 5 years, however, abundance had been stable at about 188 breeding females. Adult apparent survival was high but declined slightly by 0.14% per year from 0.959 in 1990 to 0.944 in 2000. Recruitment of breeding females also decreased during the study period; therefore, survival was increasingly a greater component of population growth rate. Juvenile survival was 0.565 at λ = 1.034 and 0.446 at λ = 1. Both retrospective (mark–recapture) and prospective (matrix population model) analyses showed a greater influence of survival versus reproduction on population growth. Population- model projections showed that capping nests to improve reproductive success could increase population growth rate, but the magnitude of increase was positively related to pre-reproductive survival, therefore negating nest capping as a remedy for declining populations or poor survival. Extinction attributable to demographic stochasticity is unlikely.

scholarly journals Archaeology, demography and life history theory together can help us explain past and present population patterns

2020 ◽  
Vol 376 (1816) ◽  
pp. 20190711 ◽  
Author(s):  
Stephen Shennan ◽  
Rebecca Sear
Keyword(s):  
Population Growth ◽  
Life History Theory ◽  
Environmental Variability ◽  
Demographic Data ◽  
Population Decline ◽  
Human Populations ◽  
Demographic Stochasticity ◽  
Population Growth Rates ◽  
Population Patterns ◽  
Demographic Patterns

Population matters. Demographic patterns are both a cause and a consequence of human behaviour in other important domains, such as subsistence, cooperation, politics and culture. Demographers interested in contemporary and recent historical populations have rich data at their fingertips; the importance of demography means many interested parties have gathered demographic data, much of which is now readily available for all to explore. Those interested in the demography of the distant past are not so fortunate, given the lack of written records. Nevertheless, the emergence in recent years of a new interest in the demography of ancient populations has seen the development of a range of new methods for piecing together archaeological, skeletal and DNA evidence to reconstruct past population patterns. These efforts have found evidence in support of the view that the relatively low long-term population growth rates of prehistoric human populations, albeit ultimately conditioned by carrying capacities, may have been owing to ‘boom–bust’ cycles at the regional level; rapid population growth, followed by population decline. In fact, this archaeological research may have come to the same conclusion as some contemporary demographers: that demography can be remarkably hard to predict, at least in the short term. It also fits with evidence from biology that primates, and particularly humans, may be adapted to environmental variability, leading to associated demographic stochasticity. This evidence of the fluctuating nature of human demographic patterns may be of considerable significance in understanding our species' evolution, and of understanding what our species future demographic trajectories might be. This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.

scholarly journals How well can the exponential-growth coalescent approximate constant-rate birth–death population dynamics?

2015 ◽  
Vol 282 (1806) ◽  
pp. 20150420 ◽  
Author(s):  
Tanja Stadler ◽  
Timothy G. Vaughan ◽  
Alex Gavryushkin ◽  
Stephane Guindon ◽  
Denise Kühnert ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Population Growth ◽  
Population Size ◽  
Population Dynamic ◽  
Dynamic Models ◽  
Dynamic Processes ◽  
Demographic Stochasticity ◽  
Exponential Population Growth ◽  
Exponential Population ◽  
Birth Death

One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth–death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R 0 close to 1, but substantial differences remain for large R 0 . In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.

İstanbul İlçelerinin Nüfus Değişimi ve Kütük Bilgilerinin Analizi

2019 ◽  
Vol 6 (1) ◽  
pp. 37-72
Author(s):  
Ulaş Sunata ◽  
Dila Ergül
Keyword(s):  
Population Growth ◽  
Population Growth Rate ◽  
Population Change ◽  
Urban Migration ◽  
Specific Population ◽  
Related Factors ◽  
Internal Migrant ◽  
Level Analysis ◽  
Study District ◽  
Demographic Features

39 ilçesiyle Türkiye’nin en büyük nüfusuna sahip ili İstanbul aynı zamanda Türkiye’nin en çok iç göç alan şehridir. Özellikle kırdan kente göç bağlamında sosyo-ekonomik ve demografik özellikleriyle birçok araştırmaya konu olmuştur. Fakat İstanbul yerleşik nüfusunun Türkiye’nin diğer şehirlerine kayıtlı olma yoğunluğu da önemlidir. Bu çalışmanın amacı 2012 ve 2017 yıllarındaki nüfus değişimini göz önünde bulundurarak İstanbul ilçelerinin ayrıntılı nüfus yoğunluğu ve büyüme analizini yapmak, ilgili faktörleri değerlendirmek, hemşehri ağlarını okumak adına yerleşik nüfus kütük bilgileri bakımından inceleyerek elde edilen örüntüler doğrultusunda ilçe tipolojileri oluşturmaktır. Çalışmanın birinci bölümünde ilgili beş yıllık nüfus değişimlerine göre İstanbul ilçe nüfusları analiz edilmiştir. Ardından her bir ilçe için nüfusa kayıtlı olunan kente göre nüfus büyüme hızlarına bakılarak ilçelerin ağırlıklı olarak barındırdığı hemşehri ağları belirlenmiştir. İkinci bölümde ise ilçeler nüfus değişim özelliklerine göre belirli kategorilere ayrılmış ve bu kategoriler doğrultusunda ilçe tipolojileri oluşturulmuştur..ABSTRACT IN ENGLISHA District Level Analysis of Istanbul’s Population Change (2012-2017)Istanbul having the largest population of Turkey with its 39 districts is the most internal-migrant-receiving city in Turkey. Particularly in the context of rural-to-urban migration, Istanbul has been became a subject of various researches with its socio-economic and demographic features. However, the density of Istanbul’s settled population who registered other cities of Turkey is important. The main aim of this study is to analyse population growth of all districts considering the population change between 2012 and 2017, to evaluate the related factors and to develop a district typology by using the data of settled population according to their family registration in the name of reading the current countryman networks. In the first section of the study, district populations of Istanbul are examined regarding the related five-year change. Afterwards, most repeated countryman networks of all Istanbul’s districts are specified regarding the population growth rate of the registered cities. In the latter section of the study, districts were divided into categories regarding the specific population change features which help to create district typology.

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