The relationship between the transition matrix model and the diffusion model

The diffusion equation model and the Lefkovitch matrix model have been employed independently in plant population ecology in order to analyze the dynamics of growth and size structure. The two models describe the dynamics of size structure in biological populations, and thus there must be some relationship between them. In the present paper, we examine the theoretical relationship between these two models. We demonstrate, on a certain assumption, that the one-step Lefkovitch matrix model corresponds to a difference equation of the diffusion equation and that the two- and three-step Lefkovitch matrix model correspond to difference equations of the 4th- and 6th-order Kramers-Moyal expansions, respectively. It is also shown that 2n moments (the first to the 2n-th moments) of growth rate are necessary and sufficient to rewrite uniquely the n-step Lefkovitch matrix model in terms of the linear combination of the moments. We finally discuss the relationship between the species characteristics of census data and the appropriate types of the Lefkovitch matrix.     

Spectral sensitivity in linear biological models

Properties of spectral components of the system matrix of linear time-invariant discrete or continuous models are investigated. It is shown that the entries in these matrices have the interpretation of being the sensitivity of the system matrix eigenvalues with respect to the model parameters. The spectral resolution formula for linear operators is used to get explicit results about component matrices and eigenvalue sensitivity. In biological modeling, particular interest is in the real maximal or minimal roots of the system matrix. Exact formulation of the related spectral components is made in important system matrix cases such as companion, Leslie, ecosystem, compartmental, and stochastic matrices.     

The usefulness of sensitivity analysis for predicting the effects of cat predation on the population dynamics of their avian prey

Sensitivity analyses of population projection matrix (PPM) models are often used to identify life-history perturbations that will most influence a population's future dynamics. Sensitivities are linear extrapolations of the relationship between a population's growth rate and perturbations to its demographic parameters. Their effectiveness depends on the validity of the assumption of linearity. Here we assess whether sensitivity analysis is an appropriate tool to investigate the effect of predation by cats on the population growth rates of their avian prey. We assess whether predation by cats leads to non-linear effects on population growth and compare population growth rates predicted by sensitivity analysis with those predicted by a non-linear simulation. For a two-stage, age-classified House Sparrow Passer domesticus PPM slight non-linearity arose when PPM elements were perturbed, but perturbation to the vital rates underlying the matrix elements had a linear impact on population growth rate. We found a similar effect with a slightly larger three-stage, age-classified PPM for a Winter Wren Troglodytes troglodytes population perturbed by cat predation. For some avian species, predation by cats may cause linear or only slightly nonlinear impacts on population growth rates. For these species, sensitivity analysis appears to be a useful conservation tool. However, further work on multiple perturbations to avian prey species with more complicated life histories and higher-dimension PPM models is required.     

Competition and adaptation in a community of migrating fish populations

Within a discrete scheme, the process of population migration is set by some nonnegative Markov matrix. In studying an appropriate class of competition models, nonlinear methods of convex analysis (monotone operator theory) prove to be highly effective. For special matrices (cyclic and perron ones), conditions of steady coexistence in and of competitor displacement from the community have been found. Model mechanisms of migration route adaptation for a separate population and for a family of populations from one vertical trophic chain have been proposed. The major characteristic of a migration route turns out to be the relative time of population dwelling in one or another region. Specific (perron) vectors of migration matrices correspond to these populations. It is revealed that in the course of co-adaptation the perron vectors of predator and prey migration matrixes practically coincide.     

Identifiability and interval identifiability of mammillary and catenary compartmental models with some known rate constants

The identifiability problem is addressed for n-compartment linear mammillary and catenary models, for the common case of input and output in the first compartment and prior information about one or more model rate constants. We first define the concept of independent constraints and show that n-compartment linear mammillary or catenary models are uniquely identifiable under n-1 independent constraints. Closed-form algorithms for bounding the constrained parameter space are then developed algebraically, and their validity is confirmed using an independent approach, namely joint estimation of the parameters of all uniquely identifiable submodels of the original multicompartmental model. For the noise-free (deterministic) case, the major effects of additional parameter knowledge are to narrow the bounds of rate constants that remain unidentifiable, as well as to possibly render others identifiable. When noisy data are considered, the means of the bounds of rate constants that remain unidentifiable are also narrowed, but the variances of some of these bound estimates increase. This unexpected result was verified by Monte Carlo simulation of several different models, using both normally and lognormally distributed data assumptions. Extensions and some consequences of this analysis useful for model discrimination and experiment design applications are also noted.     

Quantification of the permeability of the blood-CSF barrier to alpha-MSH in the rat

The ability of alpha-MSH to cross the blood-CSF barrier of the rat was assessed by measurement of the rate of appearance of immunoreactive alpha-MSH in a cerebrospinal fluid (CSF) perfusate following intravenous injection of peptide. Comparisons were made with the rate of appearance of a simultaneously administered dose of 14C-inulin which is poorly permeable at the blood-CSF barrier. Concentrations of drugs measured in plasma were fitted to two-compartment pharmacokinetic models, and those measured in the CSF perfusate to one-compartment open systems receiving an input from the plasma compartment. The rate constant for entry of alpha-MSH into CSF was 0.00087 min-1, which was not significantly different from that for inulin of 0.00055 min-1. As alpha-MSH penetrated into CSF at a rate comparable to inulin, it was concluded that the limited entry of peptide was by aqueous diffusion along with other water-soluble macromolecules.     

Sensitivity of the population growth rate to demographic variability within and between phases of the disturbance cycle

For species in disturbance-prone ecosystems, vital rates (survival, growth and reproduction) often vary both between and within phases of the cycle of disturbance and recovery; some of this variation is imposed by the environment, but some may represent adaptation of the life history to disturbance. Anthropogenic changes may amplify or impede these patterns of variation, and may have positive or negative effects on population growth. Using stochastic population projection matrix models, we develop stochastic elasticities (proportional derivatives of the long-run population growth rate) to gauge the population effects of three types of change in demographic variability (changes in within- and between-disturbance-phase variability and phase-specific changes). Computing these elasticities for five species of disturbance-influenced perennial plants, we pinpoint demographic rates that may reveal adaptation to disturbance, and we demonstrate that species may differ in their responses to different types of changes in demographic variability driven by climate change.     

Sensitivity analysis of transient population dynamics

Short-term, transient population dynamics can differ in important ways from long-term asymptotic dynamics. Just as perturbation analysis (sensitivity and elasticity) of the asymptotic growth rate reveals the effects of the vital rates on long-term growth, the perturbation analysis of transient dynamics can reveal the determinants of short-term patterns. In this article, I present a completely new approach to transient sensitivity and elasticity analysis, using methods from matrix calculus. Unlike previous methods, this approach applies not only to linear time-invariant models but also to time-varying, subsidized, stochastic, nonlinear and spatial models. It is computationally simple, and does not require calculation of eigenvalues or eigenvectors. The method is presented along with applications to plant and animal populations.     

Determining the parametric structure of models

In this paper we develop a comprehensive approach to determining the parametric structure of models. This involves considering whether a model is parameter redundant or not and investigating model identifiability. The approach adopted makes use of exhaustive summaries, quantities that uniquely define the model. We review and generalise previous work on evaluating the symbolic rank of an appropriate derivative matrix to detect parameter redundancy, and then develop further tools for use within this framework, based on a matrix decomposition. Complex models, where the symbolic rank is difficult to calculate, may be simplified structurally using reparameterisation and by finding a reduced-form exhaustive summary. The approach of the paper is illustrated using examples from ecology, compartment modelling and Bayes networks. This work is topical as models in the biosciences and elsewhere are becoming increasingly complex.     

Steady states in population models with monotone,stochastic dynamics

Existence, uniqueness and asymptotic stability of stochastic equilibrium are established in multi-dimensional population models with monotone dynamics.     

The periodically forced Droop model for phytoplankton growth in a chemostat

 It is proved that the periodically forced Droop model for phytoplankton growth in a chemostat has precisely two dynamic regimes depending on a threshold condition involving the dilution rate. If the dilution rate is such that the sub-threshold condition holds, the phytoplankton population is washed out of the chemostat. If the super-threshold condition holds, then there is a unique periodic solution, having the same period as the forcing, characterized by the presence of the phytoplankton population, to which all solutions approach asymptotically. Furthermore, this result holds for a general class of models with monotone growth rate and monotone uptake rate, the latter possibly depending on the cell quota. Received 10 October 1995; received in revised form 26 March 1996     

Aggregation of Leslie matrix models with application to ten diverse animal species

Some grouping is necessary when constructing a Leslie matrix model because it involves discretizing a continuous process of births and deaths. The level of grouping is determined by the number of age classes and frequency of sampling. It is largely unknown what is lost or gained by using fewer age classes, and I address this question using aggregation theory. I derive an aggregator for a Leslie matrix model using weighted least squares, determine what properties an aggregated matrix inherits from the original matrix, evaluate aggregation error, and measure the influence of aggregation on asymptotic and transient behaviors. To gauge transient dynamics, I employ reactivity of the standardized Leslie matrix. I apply the aggregator to 10 Leslie models developed for animal populations drawn from a diverse set of species. Several properties are inherited by the aggregated matrix: (a) it is a Leslie matrix; (b) it is irreducible whenever the original matrix is irreducible; (c) it is primitive whenever the original matrix is primitive; and (d) its stable population growth rate and stable age distribution are consistent with those of the original matrix if the least squares weights are equal to the original stable age distribution. In the application, depending on the population modeled, when the least squares weights do not follow the stable age distribution, the stable population growth rate of the aggregated matrix may or may not be approximately consistent with that of the original matrix. Transient behavior is lost with high aggregation.     

Lagrange stability and ecological systems

This paper considers the ecological and mathematical relationships among various stability concepts. It is shown that diagonally dominant nonlinear systems have positive solutions for positive initial conditions. The boundedness of such positive solutions leads to a total systems concept of stability: Lagrange stability. This concept is discussed as a compatible method for determining the stability of ecosystems and their mathematical models. Stability in the sense of Lagrange is a total systems concept which does not directly address the problem of the stability of equilibria. A topological presentation is used to describe the Lagrange stability of nonlinear ecosystem compartment models. Finally, the concept of practical stability is developed and discussed with respect to maintaining the desired state of ecosystems in the presence of perturbations.     

Population Dynamics of the Dioecious Amazonian Palm Mauritia flexuosa: Simulation Analysis of Sustainable Harvesting

The dioecious, tropical palm Mauritia flexuosa has high ecological and economic value, but currently some wild populations are harvested excessively, which is likely to increase. In this study, we investigated the population dynamics of this important palm, the effects of harvesting, and suggested sustainable harvesting regimes. Data were collected from populations in the Ecuadorian Amazon that were assumed to be stable. We used a matrix population model to calculate the density-independent asymptotic population growth rate (λ= 1.046) and to evaluate harvesting scenarios. Elasticity analysis showed that survival (particularly in the second and fifth size class) contributes more to the population growth rate, than growth and fecundity. To simulate a stable population at carrying capacity, density dependence was incorporated and applied to the seedling survival and growth parameters in the transition matrix. Harvesting scenarios were simulated with the density-dependent population models to predict sustainable harvesting regimes for the dioecious palm. We simulated the removal of only female palms and showed how both sexes are affected with harvest intensities between 15 and 75 percent and harvest intervals of 1–15 yr. By assuming a minimum female threshold, we demonstrated a continuum of sustainable harvesting schedules for various intensities and frequencies for 100 yr of harvest. Furthermore, by setting the population model's λ= 1.00, we found that a harvest of 22.5 percent on a 20 yr frequency for the M. flexuosa population in Ecuador is consistent with a sustainable, viable population over time.     

New stability criteria for uncertain neural networks with interval time-varying delays

This paper is concerned with the stability analysis for neural networks with interval time-varying delays and parameter uncertainties. An approach combining the Lyapunov-Krasovskii functional with the differential inequality and linear matrix inequality techniques is taken to investigate this problem. By constructing a new Lyapunov-Krasovskii functional and introducing some free weighting matrices, some less conservative delay-derivative-dependent and delay-derivative-independent stability criteria are established in term of linear matrix inequality. And the new criteria are applicable to both fast and slow time-varying delays. Three numerical examples show that the proposed criterion are effective and is an improvement over some existing results in the literature.     

A multiple regression method for analysing stage-frequency data

A linear regression method that allows survival rates to vary from stage to stage is described for the analysis of stage-frequency data. It has advantages over previously suggested methods since the calculations are not iterative, and it is not necessary to have independent estimates of stage durations, numbers entering stages, or the rate of entry to stage 1. Simulation is proposed to determine standard errors for estimates of population parameters, and to assess the goodness of fit of models.     

Error, population structure and the origin of diverse sign systems

Evolutionary models of communication are used to shed some light on the selective pressures involved in the evolution of simple referential signals, and the constraints hindering the emergence of signs. Error-prone communication results from errors in transmission (in which individuals learn the wrong associations) and communication (in which signs are mistaken for one another). We demonstrate how both classes of errors are required to generate diversity and subsequently impose limits on the sign repertoire within a population. We then explore the influence of geographic structuring of a population on the evolution of a shared sign system and the importance of such structure for the maintenance of sign diversity. Deceit tends to erode conventional signs systems thereby reducing signal diversity, we demonstrate that population structure can act as a hedge against deceit, thereby ensuring the persistence of sign systems.     

Integral projection models perform better for small demographic data sets than matrix population models: a case study of two perennial herbs

1. Matrix population models are widely used to describe population dynamics, conduct population viability analyses and derive management recommendations for plant populations. For endangered or invasive species, management decisions are often based on small demographic data sets. Hence, there is a need for population models which accurately assess population performance from such small data sets.
2. We used demographic data on two perennial herbs with different life histories to compare the accuracy and precision of the traditional matrix population model and the recently developed integral projection model (IPM) in relation to the amount of data.
3. For large data sets both matrix models and IPMs produced identical estimates of population growth rate (λ). However, for small data sets containing fewer than 300 individuals, IPMs often produced smaller bias and variance for λ than matrix models despite different matrix structures and sampling techniques used to construct the matrix population models.
4. Synthesis and applications . Our results suggest that the smaller bias and variance of λ estimates make IPMs preferable to matrix population models for small demographic data sets with a few hundred individuals. These results are likely to be applicable to a wide range of herbaceous, perennial plant species where demographic fate can be modelled as a function of a continuous state variable such as size. We recommend the use of IPMs to assess population performance and management strategies particularly for endangered or invasive perennial herbs where little demographic data are available.     

Metapopulation dynamics and the quality of the matrix

In both strictly theoretical and more applied contexts it has been historically assumed that metapopulations exist within a featureless, uninhabitable matrix and that dynamics within the matrix are unimportant. In this article, we explore the range of theoretical consequences that result from relaxing this assumption. We show, with a variety of modeling techniques, that matrix quality can be extremely important in determining metapopulation dynamics. A higher-quality matrix generally buffers against extinction. However, in some situations, an increase in matrix quality can generate chaotic subpopulation dynamics, where stability had been the rule in a lower-quality matrix. Furthermore, subpopulations acting as source populations in a low-quality matrix may develop metapopulation dynamics as the quality of the matrix increases. By forcing metapopulation dynamics on a formerly heterogeneous (but stable within subpopulations) population, the probability of simultaneous extinction of all subpopulations actually increases. Thus, it cannot be automatically assumed that increasing matrix quality will lower the probability of global extinction of a population.