Modeling the growth of individuals in plant populations: local density variation in a strand population of Xanthium strumarium (Asteraceae)

We studied the growth of individual Xanthium strumarium plants growing at four naturally occurring local densities on a beach in Maine: (1) isolated plants, (2) pairs of plants ≤1 cm apart, (3) four plants within 4 cm of each other, and (4) discrete dense clumps of 10-39 plants. A combination of nondestructive measurements every 2 wk and parallel calibration harvests provided very good estimates of the growth in aboveground biomass of over 400 individual plants over 8 wk and afforded the opportunity to fit explicit growth models to 293 of them. There was large individual variation in growth and resultant size within the population and within all densities. Local crowding played a role in determining plant size within the population: there were significant differences in final size between all densities except pairs and quadruples, which were almost identical. Overall, plants growing at higher densities were more variable in growth and final size than plants growing at lower densities, but this was due to increased variation among groups (greater variation in local density and/or greater environmental heterogeneity), not to increased variation within groups. Thus, there was no evidence of size asymmetric competition in this population. The growth of most plants was close to exponential over the study period, but half the plants were slightly better fit by a sigmoidal (logistic) model. The proportion of plants better fit by the logistic model increased with density and with initial plant size. The use of explicit growth models over several growth intervals to describe stand development can provide more biological content and more statistical power than "growth-size" methods that analyze growth intervals separately.     

三参数增长模型拟合:以季风常绿阔叶林中两个优势乔木种群为例

Logistic、Mitscherlich、Gompertz方程是一类三参数饱和增长曲线模型,广泛地应用于许多学科领域．本文基于logistic方程饱和值K估计的三点法、四点法,推导出Mitscherlich、Gompertz方程K值的三点法、四点法估计公式,并以南亚热带季风常绿阔叶林中两种优势乔木厚壳桂、黄果厚壳桂种群为例,先用三点法或四点法估计出K值,再通过线性回归与非线性回归相结合的方法,可获得三个增长模型中三个参数的最优无偏估计．实例研究表明,两个优势种群增长数据均符合三个增长模型,但更符合增长曲线呈S形的logistic、Gompertz方程,且以logistic方程最适合于观察;黄果厚壳桂种群增长快于厚壳桂种群．     

Skyline-plot methods for estimating demographic history from nucleotide sequences

Estimation of demographic history from nucleotide sequences represents an important component of many studies in molecular ecology. For example, knowledge of a population's history can allow us to test hypotheses about the impact of climatic and anthropogenic factors. In the past, demographic analysis was typically limited to relatively simple population models, such as exponential or logistic growth. More flexible approaches are now available, including skyline-plot methods that are able to reconstruct changes in population sizes through time. This technical review focuses on these skyline-plot methods. We describe some general principles relating to sampling design and data collection. We then provide an outline of the methodological framework, which is based on coalescent theory, before tracing the development of the various skyline-plot methods and describing their key features. The performance and properties of the methods are illustrated using two simulated data sets.     

Dynamics of a feline retrovirus (FeLV) in host populations with variable spatial structure.

The predictions of epidemic models are remarkably affected by the underlying assumptions concerning host population dynamics and the relation between host density and disease transmission. Furthermore, hypotheses underlying distinct models are rarely tested. Domestic cats (Felis catus) can be used to compare models and test their predictions, because cat populations show variable spatial structure that probably results in variability in the relation between density and disease transmission. Cat populations also exhibit various dynamics. We compare four epidemiological models of Feline Leukaemia Virus (FeLV). We use two different incidence terms, i.e. proportionate mixing and pseudo-mass action. Population dynamics are modelled as logistic or exponential growth. Compared with proportionate mixing, mass action incidence with logistic growth results in a threshold population size under which the virus cannot persist in the population. Exponential growth of host populations results in systems where FeLV persistence at a steady prevalence and depression of host population growth are biologically unlikely to occur. Predictions of our models account for presently available data on FeLV dynamics in various populations of cats. Thus, host population dynamics and spatial structure can be determinant parameters in parasite transmission, host population depression, and disease control.     

Origin and number of founders in an introduced insular primate: estimation from nuclear genetic data

Cynomolgus macaques (Macaca fascicularis) were introduced on the island of Mauritius between 400 and 500 years ago and underwent a strong population expansion after a probable initial founding event. However, in practice, little is known of the geographical origin of the individuals that colonized the island, on how many individuals were introduced, and of whether the following demographic expansion erased any signal of this putative bottleneck. In this study, we asked whether the current nuclear genome of the Mauritius population retained a signature that would allow us to answer these questions. Altogether, 21 polymorphic autosomal and sex-linked microsatellites were surveyed from 81 unrelated Mauritius individuals and 173 individuals from putative geographical sources in Southeast Asia: Java, the Philippines islands and the Indochinese peninsula. We found that (i) the Mauritius population was closer to different populations depending on the markers we used, which suggests a possible mixed origin with Java playing most probably a major role; and (ii) the level of diversity was lower than the other populations but there was no clear and consistent bottleneck signal using either summary statistics or full-likelihood methods. However, summary statistics strongly suggest that Mauritius is not at mutation-drift equilibrium and favours an expansion rather than a bottleneck. This suggests that on a short time scale, population decline followed by growth can be difficult to deduce from genetic data based on mutation-drift theory. We then used a simple Bayesian rejection algorithm to estimate the number of founders under different demographic models (exponential, logistic and logistic with lag) and pure genetic drift. This new method uses current population size estimates and expected heterozygosity of Mauritius and source population(s). Our results indicate that a simple exponential growth is unlikely and that, under the logistic models, the population may have expanded from an initial effective number of individuals of 10-15. The data are also consistent with a logistic growth with different lag values, indicating that we cannot exclude past population fluctuation.     

SENSORY THRESHOLDS: CONCEPTS AND METHODS

A sensory threshold can be defined generally as a stimulus intensity that produces a response in half of the trials. The definition of the population threshold is discussed. Five main classical statistical procedures for estimating thresholds are reviewed. They are the probit, the logistic, the Spearman-Karber, the moving average and the up-and-down procedures. Some new developments in statistical methods for estimating thresholds are outlined. The newly developed methods include the generalized probit and logistic models, the model based on the Beta-Binomial distribution, the trimmed Spearman-Karber method, the kernel method and the sigmoidally constrained maximum likelihood estimation method. The authors propose a new procedure based on the Beta-Binomial distribution for estimating population threshold.     

A non-stochastic approach for modeling uncertainty in population dynamics

We developed a non-stochastic methodology to deal with the uncertainty in models of population dynamics. This approach assumed that noise is bounded; it led to models based on differential inclusions rather than stochastic processes, and avoided stochastic calculus. Examples of estimations of extinction times for exponential and logistic population growth with environmental and demographic noise are presented.     

Estimating Initial Epidemic Growth Rates

The initial exponential growth rate of an epidemic is an important measure of disease spread, and is commonly used to infer the basic reproduction number $\mathcal{R}_{0}$ . While modern techniques (e.g., MCMC and particle filtering) for parameter estimation of mechanistic models have gained popularity, maximum likelihood fitting of phenomenological models remains important due to its simplicity, to the difficulty of using modern methods in the context of limited data, and to the fact that there is not always enough information available to choose an appropriate mechanistic model. However, it is often not clear which phenomenological model is appropriate for a given dataset. We compare the performance of four commonly used phenomenological models (exponential, Richards, logistic, and delayed logistic) in estimating initial epidemic growth rates by maximum likelihood, by fitting them to simulated epidemics with known parameters. For incidence data, both the logistic model and the Richards model yield accurate point estimates for fitting windows up to the epidemic peak. When observation errors are small, the Richards model yields confidence intervals with better coverage. For mortality data, the Richards model and the delayed logistic model yield the best growth rate estimates. We also investigate the width and coverage of the confidence intervals corresponding to these fits.     

Metaphysiological and evolutionary dynamics of populations exploiting constant and interactive resources:R—K selection revisited

Summary I begin by reviewing the derivation of continuous logistic growth and dynamic consumer—resource interaction equations in terms of specific resource extraction and biomass conversion functions that are considered to hold at a population level. Evolutionary stable strategy (ESS) methods are discussed for analysing populations modelled by these equations. The question of selection trade-offs is then considered, particularly in the context of populations being efficient at extracting resources versus converting resources to their own biomass. Questions relating to single populations with high versus low conversion rates and interacting populations with high versus low self-interference rates are also considered. The models discussed here demonstrate conclusively that self-interference is an essential part of any consumption process: without it population growth and interaction processes do not make any sense. The analysis clarifies concepts relating to the somewhat discredited notion ofr—K selection.     

Population growth regulated by intraspecific competition for energy or time: Some simple representations

An examination is made of some of the ways populations can grow in response to changes in their own density. Under two different assumptions on birth and death rates, models for single-species population growth that incorporate intraspecific competition by interference but not exploitation are of logistic form. Where an individual's net energy input from feeding is inversely proportional to population size, population growth follows a convex curve, whether interference is included or not. Data of Smith (1963) on Daphnia populations are fit well by this kind of curve. Combination of the two kinds of growth can produce S-shaped curves whose inflection is displaced from that value—half the carrying capacity—given by the logistic; an upward displacement is favored by a high ratio of metabolic and replacement costs to feeding input. Inflection points from real curves are much more often higher than expected from the logistic. Nonmonotonic growth curves can arise when there is instantaneous feedback between consumers and resource availability; certain of these equations are of logistic or convex form at equilibrium. The possible effect of r- and K-selection on the biological parameters, such as feeding efficiency, used to construct the monotonie equations is discussed, and the equations are extended to 2-species competition. Table III characterizes some simple single-species growth curves.     

A stochastic two-phase growth model

This article proposes a stochastic growth model that starts as a Yule process and is subsequently joined with a Prendiville process when the population attains certain prescribed critical size. In other words, the model assumes exponential growth in an early stage and logistic growth later on to reflect growth retardation caused by overcrowding. In the case that the population starts with a single unit, closed form expressions are given for the distribution of the population size and for the mean and variance functions of the process. Numerical solutions are briefly discussed for the process that starts with more than one unit.     

The dynamics of Phanerozoic marine animal diversity agrees with the hyperbolic growth model

Generic diversity dynamics of the Phanerozoic marine animals is far better described by the hyperbolic model, widely used in demography and macrosociology, than by the exponential and logistic models from population dynamics traditionally employed for this purpose. Exponential and logistic models imply zero influence of interactions between taxa on the dynamics of diversity, with the exception of competing for unoccupied ecological space, whereas the hyperbolic model implies non-linear second-order positive feedback in the development of the biota. The hyperbolic human population growth is caused by positive feedback between population size and the rate of technological and cultural development (the more individuals, the more inventors, the more rapid progress, the more rapid growth of the Earth's bearing capacity; the smaller death-rate, the more accelerated growth-rate of the population). Probably there is also non-linear second-order positive feedback between diversity and community structure (the more genera, the higher alpha-diversity, which is defined as average number of genera per community, the more complicated and stable, "buffered" communities, the greater "taxonomic capacity of the environment" and average duration of the existence of genera; extinction rate dencreases, biodiversity growth-rate increases). The simplest mathematical model of biodiversity dynamics based on this assumption is confirmed by empirical data on alpha-diversity dynamics. Progressive complexification of marine communities during the Phanerozoic is also confirmed by the growing evennes of generic abundance distribution in paleocommunities.     

Contribution to the logistic theory of growth: temporal structure and growth potential

The analysis of a structured population according to three (juvenile, mature and senescent) cellular states is carried out within the framework of Delattre's transformation systems theory. Growth in number, with the dissymmetry of cell divisions, is determined by an autocatalysis process under the constraint of the availability of a source. Two models are presented: their dynamics results in a growth of the exponential type or of the sigmoidal type, respectively. In the sigmoidal case, the logistic equation (Richards-Nelder's function with adjunction of a lower asymptote Y not equal to 0) fits satisfactorily the simulated data of the total cell number Y. The growth potential is defined as the instantaneous capacity of autocatalysis, which is expressed in relation to the present 'mitotic resources' (source + non-senescing mature cells). The acceleration variations d2Y/dt2 are in close agreement with the growth potential gradient. The analysis is then generalized to other population structuring. As a result, the logistic equation can be interpreted in terms of a formal model of growth of a structured population submitted to autocatalysis and competition.     

Does population ecology have general laws?

There is a widespread opinion among ecologists that ecology lacks general laws. In this paper I argue that this opinion is mistaken. Taking the case of population dynamics, I point out that there are several very general law-like propositions that provide the theoretical basis for most population dynamics models that were developed to address specific issues. Some of these foundational principles, like the law of exponential growth, are logically very similar to certain laws of physics (Newton's law of inertia, for example, is almost a direct analogue of exponential growth). I discuss two other principles (population self-limitation and resource-consumer oscillations), as well as the more elementary postulates that underlie them. None of the "laws" that I propose for population ecology are new. Collectively ecologists have been using these general principles in guiding development of their models and experiments since the days of Lotka, Volterra, and Gause.     

Population size dependent incidence in models for diseases without immunity

Epidemiological models of SIS type are analyzed to determine the thresholds, equilibria, and stability. The incidence term in these models has a contact rate which depends on the total population size. The demographic structures considered are recruitment-death, generalized logistic, decay and growth. The persistence of the disease combined with disease-related deaths and reduced reproduction of infectives can greatly affect the population dynamics. For example, it can cause the population size to decrease to zero or to a new size below its carrying capacity or it can decrease the exponential growth rate constant of the population.     

A statistical approach to quasi-extinction forecasting

Forecasting population decline to a certain critical threshold (the quasi-extinction risk) is one of the central objectives of population viability analysis (PVA), and such predictions figure prominently in the decisions of major conservation organizations. In this paper, we argue that accurate forecasting of a population's quasi-extinction risk does not necessarily require knowledge of the underlying biological mechanisms. Because of the stochastic and multiplicative nature of population growth, the ensemble behaviour of population trajectories converges to common statistical forms across a wide variety of stochastic population processes. This paper provides a theoretical basis for this argument. We show that the quasi-extinction surfaces of a variety of complex stochastic population processes (including age-structured, density-dependent and spatially structured populations) can be modelled by a simple stochastic approximation: the stochastic exponential growth process overlaid with Gaussian errors. Using simulated and real data, we show that this model can be estimated with 20-30 years of data and can provide relatively unbiased quasi-extinction risk with confidence intervals considerably smaller than (0,1). This was found to be true even for simulated data derived from some of the noisiest population processes (density-dependent feedback, species interactions and strong age-structure cycling). A key advantage of statistical models is that their parameters and the uncertainty of those parameters can be estimated from time series data using standard statistical methods. In contrast for most species of conservation concern, biologically realistic models must often be specified rather than estimated because of the limited data available for all the various parameters. Biologically realistic models will always have a prominent place in PVA for evaluating specific management options which affect a single segment of a population, a single demographic rate, or different geographic areas. However, for forecasting quasi-extinction risk, statistical models that are based on the convergent statistical properties of population processes offer many advantages over biologically realistic models.     

Phanerozoic marine biodiversity follows a hyperbolic trend

Changes in marine biodiversity through the Phanerozoic correlate much better with hyperbolic model (widely used in demography and macrosociology) than with exponential and logistic models (traditionally used in population biology and extensively applied to fossil biodiversity as well). The latter models imply that changes in diversity are guided by a first-order positive feedback (more ancestors, more descendants) and/or a negative feedback arising from resource limitation. Hyperbolic model implies a second-order positive feedback. The hyperbolic pattern of the world population growth arises from a second-order positive feedback between the population size and the rate of technological growth. The hyperbolic character of biodiversity growth can be similarly accounted for by a feedback between the diversity and community structure complexity. The similarity between the curves of biodiversity and human population probably comes from the fact that both are derived from the interference of the hyperbolic trend with cyclical and stochastic dynamics.     

Estimation of growth regulation in natural populations by extended family of growth curve models with fractional order derivative: Case studies from the global population dynamics database

Estimating the trend in population time series data using growth curve models is a central idea in population ecology. Several models, mainly governed by differential or difference equations, have been applied to real data sets to identify general growth pattern and make predictions. In this article, we analyze ecological time series data by fitting mathematical models governed by fractional differential equations (FDE). The order of the FDE (α) is used to quantify the evidence of memory in the population processes. The application of FDE is exemplified by analyzing time series data on two bird species Phalacrocorax carbo (Great cormorant) and Parus bicolor (Tufted titmouse) and two mammal species Castor canadensis (Beaver) and Ursus americanus (American black bear) extracted from the global population dynamics database. Five different population growth models were fitted to these data; density-independent exponential, negative density-dependent logistic and θ-logistic model, positive density-dependent exponential Allee and strong Allee model. Both ordinary and fractional derivative representations of these models were fitted to the time series data. Markov chain Monte Carlo (MCMC) method was used to estimate the model parameters and Akaike information criterion was used to select the best model. By estimating the return rate for each of the time series, we have shown that populations governed by FDE with a small value of α (high level of memory) return to the stable equilibrium faster. This demonstrates a synergistic interplay between memory and stability in natural populations.     

Differential laws of left ventricular isovolumic pressure fall

An attempt has been made to test for a reliable method of characterizing the isovolumic left ventricular pressure fall in isolated ejecting hearts by one or two time constants, tau. Alternative nonlinear regression models (three- and four-parametric exponential, logistic, and power function), based upon the common differential law dp(t)/dt = - [p(t)-P ]/ tau(t) are compared in isolated ejecting rat, guinea pig, and ferret hearts. Intraventricular pressure fall data are taken from an isovolumic standard interval and from a subinterval of the latter, determined data-dependently by a statistical procedure. Extending the three-parametric exponential fitting function to four-parametric models reduces regression errors by about 20-30%. No remarkable advantage of a particular four-parametric model over the other was revealed. Enhanced relaxation, induced by isoprenaline, is more sensitively indicated by the asymptotic logistic time constant than by the usual exponential. If early and late parts of the isovolumic pressure fall are discarded by selecting a subinterval of the isovolumic phase, tau remains fairly constant in that central pressure fall region. Physiological considerations point to the logistic model as an advantageous method to cover lusitropic changes by an early and a late tau. Alternatively, identifying a central isovolumic relaxation interval facilitates the calculation of a single ("central") tau; there is no statistical justification in this case to extend the three-parametric exponential further to reduce regression errors.