Endemic persistence or disease extinction: The effect of separation into sub-communities

Consider an infectious disease which is endemic in a population divided into several large sub-communities that interact. Our aim is to understand how the time to extinction is affected by the level of interaction between communities. We present two approximations of the expected time to extinction in a population consisting of a small number of large sub-communities. These approximations are described for an SIR epidemic model, with focus on diseases with short infectious period in relation to life length, such as childhood diseases. Both approximations are based on Markov jump processes. Simulations indicate that the time to extinction is increasing in the degree of interaction between communities. This behaviour can also be seen in our approximations in relevant regions of the parameter space.     

Comparing strategies to preserve evolutionary diversity

The likely future extinction of various species will result in a decline of two quantities: species richness and phylogenetic diversity (PD, or ‘evolutionary history’). Under a simple stochastic model of extinction, we can estimate the expected loss of these quantities under two conservation strategies: An ‘egalitarian’ approach, which reduces the extinction risk of all species, and a ‘targeted’ approach that concentrates conservation effort on the most endangered taxa. For two such strategies that are constrained to experience the same expected loss of species richness, we ask which strategy results in a greater expected loss of PD. Using mathematical analysis and simulation, we describe how the strategy (egalitarian versus targeted) that minimizes the expected loss of PD depends on the distribution of endangered status across the tips of the tree, and the interaction of this status with the branch lengths. For a particular data set consisting of a phylogenetic tree of 62 lemur species, with extinction risks estimated from the IUCN ‘Red List’, we show that both strategies are virtually equivalent, though randomizing these extinction risks across the tip taxa can cause either strategy to outperform the other. In the second part of the paper, we describe an algorithm to determine how extreme the loss of PD for a given decline in species richness can be. We illustrate the use of this algorithm on the lemur tree.     

Novel bivariate moment-closure approximations

Nonlinear stochastic models are typically intractable to analytic solutions and hence, moment-closure schemes are used to provide approximations to these models. Existing closure approximations are often unable to describe transient aspects caused by extinction behaviour in a stochastic process. Recent work has tackled this problem in the univariate case. In this study, we address this problem by introducing novel bivariate moment-closure methods based on mixture distributions. Novel closure approximations are developed, based on the beta-binomial, zero-modified distributions and the log-Normal, designed to capture the behaviour of the stochastic SIS model with varying population size, around the threshold between persistence and extinction of disease. The idea of conditional dependence between variables of interest underlies these mixture approximations. In the first approximation, we assume that the distribution of infectives (I) conditional on population size (N) is governed by the beta-binomial and for the second form, we assume that I is governed by zero-modified beta-binomial distribution where in either case N follows a log-Normal distribution. We analyse the impact of coupling and inter-dependency between population variables on the behaviour of the approximations developed. Thus, the approximations are applied in two situations in the case of the SIS model where: (1) the death rate is independent of disease status; and (2) the death rate is disease-dependent. Comparison with simulation shows that these mixture approximations are able to predict disease extinction behaviour and describe transient aspects of the process.     

Can a More Variable Species Win Interspecific Competition?

An individual-based approach is used to describe population dynamics. Two kinds of models have been constructed with different distributions illustrating individual variability. In both models, the growth rate of an individual and its final body weight at the end of the growth period, which determines the number of offspring, are functions of the amount of resources assimilated by an individual. In the model with a symmetric distribution, the half saturation constant in the Michaelis–Menten function describing the relationship between the growth of individuals and the amount of resources has a normal distribution. In the model with an asymmetric distribution, resources are not equally partitioned among individuals. The individual who acquired more resources in the past, will acquire more resources in the future. A single population comprising identical individuals has a very short extinction time. If individuals differ in the amount of food assimilated, this time significantly increases irrespectively of the type of model describing population dynamics. Individuals of two populations of competing species use common resources. For larger differences in individual variability, the more variable species will have a longer extinction time and will exclude less variable species. Both populations can also coexist when their variabilities are equal or even when they are slightly different, in the latter case under the condition of high variability of both species. These conclusions have a deterministic nature in the case of the model with the asymmetric distribution—repeated simulations give the same results. In the case of the model with the symmetric distribution, these conclusions are of a statistical nature—if we repeat the simulation many times, then the more variable species will have a longer extinction time more frequently, but some results will happen (although less often) when the less variable species has a longer extinction time. Additionally, in the model with the asymmetric distribution, the result of competition will depend on the way of the introduction of variability into the model. If the higher variability is due to an increase in the proportion of individuals with a low assimilation of resources, it can produce a longer extinction time of the less variable species.

     

Stochastic models of kleptoparasitism

In this paper, we consider a model of kleptoparasitism amongst a small group of individuals, where the state of the population is described by the distribution of its individuals over three specific types of behaviour (handling, searching for or fighting over, food). The model used is based upon earlier work which considered an equivalent deterministic model relating to large, effectively infinite, populations. We find explicit equations for the probability of the population being in each state. For any reasonably sized population, the number of possible states, and hence the number of equations, is large. These equations are used to find a set of equations for the means, variances, covariances and higher moments for the number of individuals performing each type of behaviour. Given the fixed population size, there are five moments of order one or two (two means, two variances and a covariance). A normal approximation is used to find a set of equations for these five principal moments. The results of our model are then analysed numerically, with the exact solutions, the normal approximation and the deterministic infinite population model compared. It is found that the original deterministic models approximate the stochastic model well in most situations, but that the normal approximations are better, proving to be good approximations to the exact distribution, which can greatly reduce computing time.     

Novel moment closure approximations in stochastic epidemics

Moment closure approximations are used to provide analytic approximations to non-linear stochastic population models. They often provide insights into model behaviour and help validate simulation results. However, existing closure schemes typically fail in situations where the population distribution is highly skewed or extinctions occur. In this study we address these problems by introducing novel second-and third-order moment closure approximations which we apply to the stochastic SI and SIS epidemic models. In the case of the SI model, which has a highly skewed distribution of infection, we develop a second-order approximation based on the beta-binomial distribution. In addition, a closure approximation based on mixture distribution is developed in order to capture the behaviour of the stochastic SIS model around the threshold between persistence and extinction. This mixture approximation comprises a probability distribution designed to capture the quasi-equilibrium probabilities of the system and a probability mass at 0 which represents the probability of extinction. Two third-order versions of this mixture approximation are considered in which the log-normal and the beta-binomial are used to model the quasi-equilibrium distribution. Comparison with simulation results shows: (1) the beta-binomial approximation is flexible in shape and matches the skewness predicted by simulation as shown by the stochastic SI model and (2) mixture approximations are able to predict transient and extinction behaviour as shown by the stochastic SIS model, in marked contrast with existing approaches. We also apply our mixture approximation to approximate a likehood function and carry out point and interval parameter estimation.     

Extinction risk of a meta-population: aggregation approach

Aggregation of variables of a complex mathematical model with realistic structure gives a simplified model which is more suitable than the original one when the amount of data for parameter estimation is limited. Here we explore use of a formula derived for a single unstructured population (canonical model) in predicting the extinction time for a population living in multiple habitats. In particular we focus multiple populations each following logistic growth with demographic and environmental stochasticities, and examine how the mean extinction time depends on the migration and environmental correlation. When migration rate and/or environmental correlation are very large or very small, we may express the mean extinction time exactly using the formula with properly modified parameters. When parameters are of intermediate magnitude, we generate a Monte Carlo time series of the population size for the realistic structured model, estimate the "effective parameters" by fitting the time series to the canonical model, and then calculate the mean extinction time using the formula for a single population. The mean extinction time predicted by the formula was close to those obtained from direct computer simulation of structured models. We conclude that the formula for an unstructured single-population model has good approximation capability and can be applicable in estimating the extinction risk of the structured meta-population model for a limited data set.     

A stochastic SIS epidemic with demography: initial stages and time to extinction

We study an open population stochastic epidemic model from the time of introduction of the disease, through a possible outbreak and to extinction. The model describes an SIS (susceptible–infective–susceptible) epidemic where all individuals, including infectious ones, reproduce at a given rate. An approximate expression for the outbreak probability is derived using a coupling argument. Further, we analyse the behaviour of the model close to quasi-stationarity, and the time to disease extinction, with the aid of a diffusion approximation. In this situation the number of susceptibles and infectives behaves as an Ornstein–Uhlenbeck process, centred around the stationary point, for an exponentially distributed time before going extinct.     

Local transmission processes and disease-driven host extinctions

Classic infectious disease theory assumes that transmission depends on either the global density of the parasite (for directly transmitted diseases) or its global frequency (for sexually transmitted diseases). One important implication of this dichotomy is that parasite-driven host extinction is only predicted under frequency-dependent transmission. However, transmission is fundamentally a local process between individuals that is determined by their and/or their vector’s behaviour. We examine the implications of local transmission processes to the likelihood of disease-driven host extinction. Local density-dependent transmission can lead to parasite-driven extinction, but extinction is more likely under local frequency-dependent transmission and much more likely when there is active local searching behaviour. Density-dependent directly transmitted diseases spread locally can therefore lead to deterministic host extinction, but locally frequency-dependent passive vector-borne diseases are more likely to cause extinctions. However, it is active searching behaviour either by a vector or between sexual partners that is most likely to cause the host to go extinct. Our work emphasises that local processes are essential in determining parasite-driven extinctions, and the role of parasites in the extinction of rare species may have been underplayed due to the classic assumption of global density-dependent transmission.     

Extinction and quasi-stationarity in the Verhulst logistic model.

We formulate and analyse a stochastic version of the Verhulst deterministic model for density-dependent growth of a single population. Three parameter regions with qualitatively different behaviours are identified. Explicit approximations of the quasi-stationary distribution and of the expected time to extinction are presented in each of these regions. The quasi-stationary distribution is approximately normal, and the time to extinction is long, in one of these regions. Another region has a short time to extinction and a quasi-stationary distribution that is approximately truncated geometric. A third region is a transition region between these two. Here the time to extinction is moderately long and the quasi-stationary distribution has a more complicated behaviour. Numerical illustrations are given.     

Extinction times and moment closure in the stochastic logistic process

We investigate the statistics of extinction times for an isolated population, with an initially modest number M of individuals, whose dynamics are controlled by a stochastic logistic process (SLP). The coefficient of variation in the extinction time V is found to have a maximum value when the death and birth rates are close in value. For large habitat size K we find that Vmax is of order K1/4 / M1/2, which is much larger than unity so long as M is small compared to K1/2. We also present a study of the SLP using the moment closure approximation (MCA), and discuss the successes and failures of this method. Regarding the former, the MCA yields a steady-state distribution for the population when the death rate is low. Although not correct for the SLP model, the first three moments of this distribution coincide with those calculated exactly for an adjusted SLP in which extinction is forbidden. These exact calculations also pinpoint the breakdown of the MCA as the death rate is increased.     

Precise Estimates of Persistence Time for SIS Infections in Heterogeneous Populations

For a susceptible–infectious–susceptible infection model in a heterogeneous population, we derive simple and precise estimates of mean persistence time, from a quasi-stationary endemic state to extinction of infection. Heterogeneity may be in either individuals’ levels of infectiousness or of susceptibility, as well as in individuals’ infectious period distributions. Infectious periods are allowed to follow arbitrary non-negative distributions. We also obtain a new and accurate approximation to the quasi-stationary distribution of the process, as well as demonstrating the use of our estimates to investigate the effects of different forms of heterogeneity. Our model may alternatively be interpreted as describing an infection spreading through a heterogeneous directed network, under the annealed network approximation.     

A new look at the critical community size for childhood infections

Quasi-stationarity and time to extinction are studied for the classic endemic model. Attention is restricted to the transition region in parameter space where the quasi-stationary distribution is non-normal. A new approximation of the marginal distribution of infected individuals in quasi-stationarity is presented. It leads to a simple explicit expression for an approximation of the critical community size in terms of model parameters.     

The functional consequences of random vs. ordered species extinctions

Recent work suggests that the effect of extinction on ecosystem function depends on whether or not species have identical extinction risks. Here, we use a simple model of community dynamics to predict how the functional consequences of random and non‐random extinction may differ. The model suggests that when resource partitioning or facilitation structures communities, the functional consequences of non‐random extinction depend on the covariance between species traits and cumulative extinction risks, and the compensatory responses among survivors. Strong competition increases the difference between random and ordered extinctions, but mutualisms reduce the difference. When diversity affects function via a sampling effect, the difference between random and ordered extinction depends on the covariance between species traits and the change in the probability of being the competitive dominant caused by ordered extinction. These findings show how random assembly experiments can be combined with information about species traits to make qualitative predictions about the functional consequences of various extinction scenarios.     

Does colonization asymmetry matter in metapopulations?

Despite the considerable evidence showing that dispersal between habitat patches is often asymmetric, most of the metapopulation models assume symmetric dispersal. In this paper, we develop a Monte Carlo simulation model to quantify the effect of asymmetric dispersal on metapopulation persistence. Our results suggest that metapopulation extinctions are more likely when dispersal is asymmetric. Metapopulation viability in systems with symmetric dispersal mirrors results from a mean field approximation, where the system persists if the expected per patch colonization probability exceeds the expected per patch local extinction rate. For asymmetric cases, the mean field approximation underestimates the number of patches necessary for maintaining population persistence. If we use a model assuming symmetric dispersal when dispersal is actually asymmetric, the estimation of metapopulation persistence is wrong in more than 50% of the cases. Metapopulation viability depends on patch connectivity in symmetric systems, whereas in the asymmetric case the number of patches is more important. These results have important implications for managing spatially structured populations, when asymmetric dispersal may occur. Future metapopulation models should account for asymmetric dispersal, while empirical work is needed to quantify the patterns and the consequences of asymmetric dispersal in natural metapopulations.     

Higher variability in the number of sexual partners in males can contribute to a higher prevalence of sexually transmitted diseases in females

By examining published, empirical data we show that men and women consistently differ in the shape of the distribution of the number of sexual partners. The female distribution is always relatively narrow—variance is low—with a big majority of women having a number of partners close to the average. The male distribution is much wider—variance is high—with many men having few sex partners and many others having more partners than most females.Using stochastic modelling we demonstrate that this difference in variance is, in principle, sufficient to cause a difference in the gender prevalence of sexually transmitted diseases: compared to the situation where the genders have identical sex partner distributions, men will reach a lower equilibrium value, while women will stay at the same level (meaning that female prevalence becomes higher than male). We carefully analyse model behaviour and derive approximate expressions for equilibrium prevalences in the two different scenarios. We find that the size of the difference in gender prevalence depends on the variance ratio (the ratio between the variances of the male and female sex partner distributions), on the expected number of life-time partners, and on the probability of disease transmission. We note that in addition to humans, the variance phenomenon described here is likely to play a role for sexually transmitted diseases in other species also.We also show, again by examining published, empirical data, that the female to male prevalence ratio increases with the overall prevalence of a sexually transmitted disease (i.e., the more widespread the disease, the more women are affected). We suggest that this pattern may be caused by the effect described above in highly prevalent sexually transmitted diseases, while its impact in low-prevalence epidemics is surpassed by the action of high-risk individuals (mostly males).     

The dynamics of simultaneous infections with altered susceptibilities

We describe a model in which individuals can be infected simultaneously by multiple diseases or parasites, taking into account the fact that individuals already infected by a subset of n co-circulating diseases may see their susceptibility to concurrent infection by another disease from the pool either enhanced or reduced. We propose an n-dimensional approximation to the 2n dimensional model required to describe the dynamics of each possible subset of the pool of n co-circulating diseases, using as state variables the overall prevalence of each infection. Analysis of the two disease case shows that the reduced model provides a very good approximation throughout the full dynamics for small alterations of susceptibility, and, after a transient error, a good approximation to the complete model when susceptibilities are highly enhanced. As the number of diseases becomes large, the approximation remains close for small alterations of susceptibility.     

Stochastic models for the Trojan Y-Chromosome eradication strategy of an invasive species

The Trojan Y-Chromosome (TYC) strategy, an autocidal genetic biocontrol method, has been proposed to eliminate invasive alien species. In this work, we develop a Markov jump process model for this strategy, and we verify that there is a positive probability for wild-type females going extinct within a finite time. Moreover, when sex-reversed Trojan females are introduced at a constant population size, we formulate a stochastic differential equation (SDE) model as an approximation to the proposed Markov jump process model. Using the SDE model, we investigate the probability distribution and expectation of the extinction time of wild-type females by solving Kolmogorov equations associated with these statistics. The results indicate how the probability distribution and expectation of the extinction time are shaped by the initial conditions and the model parameters.     

An explanatory framework for adaptive personality differences

We develop a conceptual framework for the understanding of animal personalities in terms of adaptive evolution. We focus on two basic questions. First, why do behavioural types exhibit limited behavioural plasticity, that is, behavioural correlations both across contexts and over time? Second, how can multiple behavioural types coexist within a single population? We emphasize differences in 'state' among individuals in combination with state-dependent behaviour. Some states are inherently stable and individual differences in such states can explain stable differences in suites of behaviour if it is adaptive to make behaviour in various contexts dependent on such states. Behavioural stability and cross-context correlations in behaviour are more difficult to explain if individual states are potentially more variable. In such cases stable personalities can result from state-dependent behaviour if state and behaviour mutually reinforce each other by feedback mechanisms. We discuss various evolutionary mechanisms for the maintenance of variation (in states and/or behaviour), including frequency-dependent selection, spatial variation with incomplete matching between habitat and phenotype, bet-hedging in a temporally fluctuating environment, and non-equilibrium dynamics. Although state differences are important, we also discuss how social conventions and social signalling can give rise to adaptive personality differences in the absence of state differences.     

Non-equilibria in small metapopulations: comparing the deterministic Levins model with its stochastic counterpart

In this paper, we examine, for small metapopulations, the stochastic analog of the classical Levins metapopulation model. We study its basic model output, the expected time to metapopulation extinction, for systems which are brought out of equilibrium by imposing sudden changes in patch number and the colonization and extinction parameters. We find that the expected metapopulation extinction time shows different behavior from the relaxation time of the original, deterministic, Levins model. This relaxation time is therefore limited in value for predicting the behavior of the stochastic model. However, predictions about the extinction time for deterministically unviable cases remain qualitatively the same. Our results further suggest that, if we want to counteract the effects of habitat loss or increased dispersal resistance, the optimal conservation strategy is not to restore the original situation, that is, to create habitat or decrease resistance against dispersal. As long as the costs for different management options are not too dissimilar, it is better to improve the quality of the remaining habitat in order to decrease the local extinction rate.