USE OF AGE‐ AND STAGE‐STRUCTURED MATRIX MODELS TO PREDICT LIFE HISTORY SCHEDULES FOR SEMELPAROUS POPULATIONS

Many studies of semelparous salmon populations use Leslie matrices that classify individuals on the basis of age alone and do not explicitly impose death upon reproduction. Although these models may suffice for studying long‐term population dynamics (like asymptotic growth rate), they do not accurately represent the diversity of individual life history outcomes in semelparous populations. Cohorts breeding at different ages have different life history traits (e.g., age at first reproduction and remaining life expectancy) that are obscured in Leslie models and this distorts our understanding of life history diversity and its importance for semelparous population dynamics. We present a simple transformation that uses age‐specific breeding probabilities to reconfigure Leslie matrices as explicitly semelparous models. Explicitly semelparous models conserve asymptotic measures like population growth rate, vital rate elasticities, life expectancy at birth, and generation time but also better predict life history schedules and reproductive values. Strictly age‐classified Leslie models underestimate ages at first reproduction and mean ages at death for older breeders but overestimate mean ages at death for early breeders. Leslie models also slightly overestimate variance in lifetime reproductive success, and underestimate entropy exhibited by life history outcomes.     

A continuous-time stochastic model for the mortality surface of multiple populations

We formulate, study and calibrate a continuous-time model for the joint evolution of the mortality surface of multiple populations. We model the mortality intensity by age and population as a mixture of stochastic latent factors, that can be either population-specific or common to all populations. These factors are described by affine time-(in)homogeneous stochastic processes. Traditional, deterministic mortality laws can be extended to multi-population stochastic counterparts within our framework. We detail the calibration procedure when factors are Gaussian, using centralized data-fusion Kalman filter. We provide an application based on the joint mortality of UK and Dutch males and females. Although parsimonious, the specification we calibrate provides a good fit of the observed mortality surface (ages 0–89) of both sexes and populations between 1960 and 2013.     

Forecasting mortality for small populations by mixing mortality data

In this paper we address the problem of projecting mortality when data are severely affected by random fluctuations, due in particular to a small sample size, or when data are scanty. Such situations may emerge when dealing with small populations, such as small countries (possibly previously part of a larger country), a specific geographic area of a (large) country, a life annuity portfolio or a pension fund, or when the investigation is restricted to the oldest ages. The critical issues arising from the volatility of data due to the small sample size (especially at the highest ages) may be made worse by missing records; this is the case, for example, of a small country previously part of a larger country, or a specific geographic area of a country, given that in some periods mortality data could have been collected just at an aggregate level.We suggest to ‘replicate’ the mortality of the small population by mixing appropriately the mortality data obtained from other populations. We design a two-step procedure. First, we obtain the average mortality of ‘neighboring’ populations. Three alternative approaches are tested for the assessment of the average mortality; conversely, the identification and the weight of the neighboring populations are obtained through (standard) optimization techniques. Then, following a sort of credibility approach, we mix the original mortality data of the small population with the average mortality of the neighboring populations.In principle, the approach described in the paper could be adopted for any population, whatever is its size, aiming at improving mortality projections through information collected from other groups. Through backtesting, we show that the procedure we suggest is convenient for small populations, but not necessarily for large populations, nor for populations not showing noticeable erratic effects in data. This finding can be explained as follows: while the replication of the original data implies the increase of the size of the sample, it also involves a smoothing of data, with a possible loss of specific information relating to the group referred to. In the case of small populations showing major erratic movements in mortality data, the advantages gained from the larger sample size overcome the disadvantages of the smoothing effect.     

On random age and remaining lifetime for populations of items

We consider items that are incepted into operation having already a random (initial) age and define the corresponding remaining lifetime. We show that these lifetimes are identically distributed when the age distribution is equal to the equilibrium distribution of the renewal theory. Then we develop the population studies approach to the problem and generalize the setting in terms of stationary and stable populations of items. We obtain new stochastic comparisons for the corresponding population ages and remaining lifetimes that can be useful in applications. Copyright © 2014 John Wiley & Sons, Ltd.     

Sequential auctions on Böhm-Bawerk's horse market

This paper studies what prices and final allocations would arise under strategic (or sophisticated) behavior of buyers when homogeneous goods are auctioned off sequentially, one at a time. It is shown, using subgame perfect equilibria, that prices (and thus final allocations) vary depending on the order of goods to be auctioned off. However, opposed to the case where buyers bid sincerely, the number of goods sold out is always unchanged and final allocations are always Pareto-optimal in any sequence of auctions.     

A common age effect model for the mortality of multiple populations

We introduce a model for the mortality rates of multiple populations. To build the proposed model we investigate to what extent a common age effect can be found among the mortality experiences of several countries and use a common principal component analysis to estimate a common age effect in an age–period model for multiple populations. The fit of the proposed model is then compared to age–period models fitted to each country individually, and to the fit of the model proposed by Li and Lee (2005).Although we do not consider stochastic mortality projections in this paper, we argue that the proposed common age effect model can be extended to a stochastic mortality model for multiple populations, which allows to generate mortality scenarios simultaneously for all considered populations. This is particularly relevant when mortality derivatives are used to hedge the longevity risk in an annuity portfolio as this often means that the underlying population for the derivatives is not the same as the population in the annuity portfolio.     

A stochastic population projection system based on general age-dependent branching processes

"Algorithms for a stochastic population process, based on assumptions underlying general age-dependent branching processes in discrete time with time inhomogeneous laws of evolution, are developed through the use of a new representation of basic random functions involving birth cohorts and random sums of random variables. New algorithms provide a capability for computing the mean age structure of the process as well as variances and covariances, measuring variation about means. Four exploratory population projections, testing the implications of the algorithms for the case of time-homogeneous laws of evolution, are presented. Formulas extending mean and variance functions for unit population projections...are also presented. These formulas show that, in population processes with non-random laws of evolution, stochastic fluctuations about the mean function are negligible when initial population size is large. Further extensions of these formulas to the case of randomized laws of evolution suggest that stochastic fluctuations about the mean function can be significant even for large initial populations."     

A critique of fractional age assumptions

Published mortality tables are usually calibrated to show the survival function of the age at death distribution at exact integer ages. Actuaries make fractional age assumptions when valuing payments that are not restricted to integer ages. A fractional age assumption is essentially an interpolation between integer age values which are accepted as given.Three fractional age assumptions have been widely used by actuaries. These are the uniform distribution of death (UDD) assumption, the constant force assumption and the hyperbolic or Balducci assumption. Under all three assumptions, the interpolated values of the survival function between two consecutive ages depend only on the survival function at those ages. While this has the advantage of simplicity, all three assumptions result in force of mortality and probability density functions with implausible discontinuities at integer ages.In this paper, we examine some families of fractional age assumptions that can be used to correct this problem. To help in choosing specific fractional age assumptions and in comparing different sets of assumptions, we present an optimality criterion based on the length of the probability density function over the range of the mortality table.     

Optimal Allocation for Symmetric Distributions in Ranked Set Sampling

Ranked set sampling (RSS) is a cost efficient method of sampling that provides a more precise estimator of population mean than simple random sampling. The benefits due to ranked set sampling further increase when appropriate allocation of sampling units is made. For highly skew distributions, allocation based on the Neyman criterion achieves a substantial precision gain over equal allocation. But the same is not true for symmetric distributions; in fact, the gains due to using the Neyman allocation are typically very marginal for symmetric distributions. This paper, determines optimal RSS allocations for two classes of symmetric distributions. Depending upon the class, the optimal allocation assigns all measurements either to the extreme ranks or to the middle rank(s). This allocation outperforms both equal and Neyman allocations in terms of the precision of the estimator which remains unbiased. The two classes of distributions are distinguished by different growth patterns in the variance of their order statistics regarded as a function of the rank order. For one class, the variance peaks for middle rank orders and tapers off in the tails; for the other class, the variance peaks for the two extreme rank orders and tapers off toward the middle. Kurtosis appears to effectively discriminate between the two classes of symmetic distributions. The Neyman allocation is required to quantify all rank orders at least once (to ensure general unbiasedness) but then quantifies most frequently the more variable rank orders. Under symmetry, unbiasedness can be obtained without quantifying all rank orders and the optimal allocation quantifies the least variable rank order(s), resulting in a high precision estimator.     

INVESTIGATING THE VIABILITY OF SQUIRREL POPULATIONS; A MODELING APPROACH FOR THE ISLAND OF LESVOS,GREECE

ABSTRACT. Habitat loss and fragmentation are considered to be the most important factors responsible for population decreases in small mammal populations. Particularly important is also the effect of insularity that can act syn‐ergistically with the previous factors. Population Viability Analysis (PVA) combines the spatial component of the problem with the species population structure offering an integrated platform for testing and assessing the effects of critical parameters upon the population viability. Various management options can also be quantified and tested. In the case of Sciurus anomalous, a vulnerable squirrel species endemic in Lesvos, a series of threats and management problems were identified and assessed. A stochastic simulation model was developed and parameterized with field data for the species using the program Ramas/GIS. The results suggested that special attention has to be paid to the planning of road system networks and to stopping illegal hunting, especially when extinction risks for vulnerable populations are higher with the above threats.     

Basic Reproduction Number in a Growing Population

The basic reproduction number of a fast disease epidemic on a slowly growing network may increase to a maximum then decrease to its equi- librium value while the population increases, which is not displayed by classical homogeneous mixing disease models. In this paper, we show that, by properly keeping track of the dynamics of the per capita contact rate in the population due to population dynamics, classical homogeneous mixing models show simi- lar non-monotonic dynamics in the basic reproduction number. This suggests that modeling the dynamics of the contact rate in classical disease models with population dynamics may be important to study disease dynamics in growing populations.     

Populations with individual variation in dispersal in heterogeneous environments: Dynamics and competition with simply diffusing populations

We consider a model for a population in a heterogeneous environment, with logistic-type local population dynamics, under the assumption that individuals can switch between two different nonzero rates of diffusion. Such switching behavior has been observed in some natural systems. We study how environmental heterogeneity and the rates of switching and diffusion affect the persistence of the population. The reactiondiffusion systems in the models can be cooperative at some population densities and competitive at others. The results extend our previous work on similar models in homogeneous environments. We also consider competition between two populations that are ecologically identical, but where one population diffuses at a fixed rate and the other switches between two different diffusion rates. The motivation for that is to gain insight into when switching might be advantageous versus diffusing at a fixed rate. This is a variation on the classical results for ecologically identical competitors with differing fixed diffusion rates, where it is well known that "the slower diffuser wins".     

Determining the Optimum Stratum Boundaries Using Mathematical Programming

The method of choosing the best boundaries that make strata internally homogeneous, given some sample allocation, is known as optimum stratification. In order to make the strata internally homogeneous, the strata are constructed in such a way that the strata variances should be as small as possible for the characteristic under study. In this paper the problem of determining optimum strata boundaries (OSB) is discussed when strata are formed based on a single auxiliary variable with a varying measurement cost per units across strata. The auxiliary variable considered in the problem is a size variable that holds a common model for a whole population. The OSB are achieved effectively by assuming a suitable distribution of the auxiliary variable and creating strata by cutting the range of the distribution at optimum points. The problem of finding the OSB, which minimizes the variance of the estimated population mean under a weighted stratified balanced sampling, is formulated as a mathematical programming problem (MPP). Treating the formulated MPP as a multistage decision problem, a solution procedure using dynamic programming technique is developed. A numerical example using a hospital population data is presented to illustrate the computational details of the solution procedure. A software program coded in JAVA is written to carry out the computation. The distribution of the auxiliary variable in this example is considered to be continuous with an exponential density function.     

THE IMPACT OF AGE-, SPACE-AND STOCHASTIC-STRUCTURE ON THE EXTINCTION PROBABILITY OF A CHINOOK SALMON METAPOPULATION

ABSTRACT. Using a mechanistic model, based on chinook life history, incorporating environmental and demographic stochasticity, we investigate how the probability of extinction is controlled by age, space and stochastic structure. Environmental perturbations of age dependent survivorships, combined with mixing of year classes in the spawning population, can lower the probability of extinction dramatically. This is an analog of the more familiar metapopulation result where dispersal between asynchronously fluctuating populations enhances persistence. For a two-river chinook metapopulation, dispersal between rivers with asynchronous environmental perturbations also dramatically enhances persistence, and anti-synchronous population fluctuations provide an even greater persistence probability. Anti-synchronous fluctuations would most likely occur in pristine habitat with naturally high levels of heterogeneity. Fifty percent dispersal between two populations provides the greatest insurance against extinction, a rate unrealistically high for salmon. In contrast, dispersal between exactly correlated populations with large amplitude environmental perturbations does not help persistence, no matter how high the dispersal rate. This is in spite of weak asynchrony provided by demographic stochasticity. Dispersal between rivers, one degraded and the other pristine, can substantially increase the probability of metapopulation extinction. Population structure, combined with asynchronous environmental perturbations and dispersal (or age class mixing) lowers the probability of chinook extinction dramatically but is almost useless when survivorships are impaired.     

THE ECONOMICS OF MARINE RESERVES

ABSTRACT. Marine reserves can be a useful supplement to other methods of fisheries management, but marine reserves alone are not likely to achieve a great deal in economic terms andperhaps not even in terms of conservation. The effects of marine reserves with open access elsewhere are analyzed, using a logistic model for a population with a patchy distribution. It is assumedthat a marine reserve is establishedfor the territory of one of two sub‐populations which interact through migrations. The total population increases while the total catch declines for the most part. A high rate of migration would, however, dilute the conservation effect. Examining a stochastic variant of the model shows that the variability (sum of squareddeviations) of catches may decrease as a result of protecting one of the sub‐populations. Even if all rents disappear by assumption, it is possible to identify this as an economic benefit, particularly when the average catch increases. The variability of the catch falls for a range of values of the population migration parameter and variability of growth, both when the stochastic disturbances are independent and when they are perfectly correlated for the two sub‐populations, andalso when the growth variability parameter differs between the sub‐populations.     

Attenuant cycles in periodically forced discrete-time age-structured population models

In discrete-time age-structured population models, a periodic environment is not always deleterious. We show that it is possible to have the average of the age class populations over an attracting cycle (in a periodic environment) not less than the average of the carrying capacities (in a corresponding constant environment). In our age-structured model, a periodic environment does not increase the average total biomass (no resonance). However, a periodic environment is disadvantageous for a population whenever there is no synchrony between the number of age classes and the period of the environment. As in periodically forced models without age-structure, we show that periodically forced age-structured population models support multiple attractors with complicated structures.     

Efficient Computation of the Joint Sample Frequency Spectra for Multiple Populations

A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences and provides a highly efficient dimensional reduction of large-scale population genomic variation data. Recently, there has been much interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on. SFS-based inference methods require accurate computation of the expected SFS under a given demographic model. Although much methodological progress has been made, existing methods suffer from numerical instability and high computational complexity when multiple populations are involved and the sample size is large. In this article, we present new analytic formulas and algorithms that enable accurate, efficient computation of the expected joint SFS for thousands of individuals sampled from hundreds of populations related by a complex demographic model with arbitrary population size histories (including piecewise-exponential growth). Our results are implemented in a new software package called momi (MOran Models for Inference). Through an empirical study, we demonstrate our improvements to numerical stability and computational complexity.     

有辅助信息时有限总体分布函数的新估计量

本文提出两个新的估计量,利用观察数据中的总体辅助信息来估计有限总体分布函数,并通过两个人工总体的模拟实验,比较新的估计量、传统的估计量及Rao,Kover&Mantel(1990)提出的估计量的相对平均误差与相对标准差。结果表明,从相对标准差的角度分析,两个新的估计量有一个是四个估计量中精度最好的一个,另一个也有很好的表现;而且它们在模型有所偏差时都具备了较好的稳健性。     

BENEFITS AND RISKS OF INCREASED SPATIAL RESOLUTION IN THE MANAGEMENT OF FISHERY METAPOPULATIONS UNDER UNCERTAINTY

Abstract Stock assessments and harvest guidelines are typically based on the concept of a “fish stock,” which may encompass a very large area. The presence of discrete subpopulations within managed fish stocks presents risks and opportunities for fishery management. Failure to manage catch at the same scale as the true population structure can lead to extirpation of discrete subpopulations and to declines in the productivity of the larger metapopulation. However, it may be difficult and costly to assess and manage stocks at a finer spatial scale, and there is likely greater uncertainty about the size of substocks than about the aggregate stock. We use a two‐area simulation model to compare the performance of fishery management at different spatial resolutions when there is uncertainty about growth, the size of the total population, and the relative size of the subpopulations. We show that relative benefits of finer scale management, in terms of profits and risks of depleting subpopulations, depend on a number of biological, technical, and economic factors. In some cases it may be both less risky and more profitable to manage the fishery with a single total allowable catch, even when there are biologically separate fish populations in the two areas.     

A general population growth model with density dependence

A model for the growth of a population with p+q+r age groups in which there is competition for limited resources is considered. The steady-state solution is obtained and its stability is discussed. The existence of a time-invariant structure in which the ratios of the populations of the various age groups do not change with time is established under very general conditions, and its relation with the steady-state solution is discussed. The conditions under which we can treat the population as homogeneous with a common birth rate, a common death rate and a common inhibiting constant are also discussed.