Effects Of Demographic Stochasticity Research Articles

The effect of demographic stochasticity on Zika virus transmission dynamics: Probability of disease extinction, sensitivity analysis, and mean first passage time.

Viral infections spread by mosquitoes are a growing threat to human health and welfare. Zika virus (ZIKV) is one of them and has become a global worry, particularly for women who are pregnant. To study ZIKV dynamics in the presence of demographic stochasticity, we consider an established ZIKV transmission model that takes into consideration the disease transmission from human to mosquito, mosquito to human, and human to human. In this study, we look at the local stability of the disease-free and endemic equilibriums. By conducting the sensitivity analysis both locally and globally, we assess the effect of the model parameters on the model outcomes. In this work, we use the continuous-time Markov chain (CTMC) process to develop and analyze a stochastic model. The main distinction between deterministic and stochastic models is that, in the absence of any preventive measures such as avoiding travel to infected areas, being careful from mosquito bites, taking precautions to reduce the risk of sexual transmission, and seeking medical care for any acute illness with a rash or fever, the stochastic model shows the possibility of disease extinction in a finite amount of time, unlike the deterministic model shows disease persistence. We found that the numerically estimated disease extinction probability agrees well with the analytical probability obtained from the Galton-Watson branching process approximation. We have discovered that the disease extinction probability is high if the disease emerges from infected mosquitoes rather than infected humans. In the context of the stochastic model, we derive the implicit equation of the mean first passage time, which computes the average amount of time needed for a system to undergo its first state transition.

Demographic stochasticity alters expected outcomes in experimental and simulated non‐neutral communities

Theory has shown that the effects of demographic stochasticity on communities may depend on the magnitude of fitness differences between species. In particular, it has been suggested that demographic stochasticity has the potential to significantly alter competitive outcomes when fitness differences are small (nearly neutral), but that it has negligible effects when fitness differences are large (highly non‐neutral). Here we test such theory experimentally and extend it to examine how demographic stochasticity affects exclusion frequency and mean densities of consumers in simple, but non‐neutral, consumer–resource communities. We used experimental microcosms of protists and rotifers feeding on a bacterial resource to test how varying absolute population sizes (a driver of demographic stochasticity) affected the probability of competitive exclusion of the weakest competitor. To explore whether demographic stochasticity could explain our experimental results, and to generalize beyond our experiment, we paired the experiment with a continuous‐time stochastic model of resource competition, which we simulated for 11 different fitness inequalities between competiting consumers. Consistent with theory, in both our experiments and our simulations we found that demographic stochasticity altered competitive outcomes in communities where fitness differences were small. However, we also found that demographic stochasticity alone could affect communities in other ways, even when fitness differences between competitors were large. Specifically, demographic stochasticity altered mean densities of both weak and strong competitors in experimental and simulated communities. These findings highlight how demographic stochasticity can change both competitive outcomes in non‐neutral communities and the processes underlying overall community dynamics.

Effects of demographic stochasticity and life-history strategies on times and probabilities to fixation.

How life-history strategies influence the evolution of populations is not well understood. Most existing models stem from the Wright-Fisher model which considers discrete generations and a fixed population size, thus not taking into account any potential consequences of overlapping generations and demographic stochasticity on allelic frequencies. We introduce an individual-based model in which both population size and genotypic frequencies at a single bi-allelic locus are emergent properties of the model. Demographic parameters can be defined so as to represent a large range of r and K life-history strategies in a stable environment, and appropriate fixed effective population sizes are calculated so as to compare our model to the Wright-Fisher diffusion. Our results indicate that models with fixed population size that stem from the Wright-Fisher diffusion cannot fully capture the consequences of demographic stochasticity on allele fixation in long-lived species with low reproductive rates. This discrepancy is accentuated in the presence of demo-genetic feedback. Furthermore, we predict that populations with K life-histories should maintain lower genetic diversity than those with r life-histories.

Stochastic mixed-mode oscillations in a three-species predator-prey model

The effect of demographic stochasticity, in the form of Gaussian white noise, in a predator-prey model with one fast and two slow variables is studied. We derive the stochastic differential equations (SDEs) from a discrete model. For suitable parameter values, the deterministic drift part of the model admits a folded node singularity and exhibits a singular Hopf bifurcation. We focus on the parameter regime near the Hopf bifurcation, where small amplitude oscillations exist as stable dynamics in the absence of noise. In this regime, the stochastic model admits noise-driven mixed-mode oscillations (MMOs), which capture the intermediate dynamics between two cycles of population outbreaks. We perform numerical simulations to calculate the distribution of the random number of small oscillations between successive spikes for varying noise intensities and distance to the Hopf bifurcation. We also study the effect of noise on a suitable Poincaré map. Finally, we prove that the stochastic model can be transformed into a normal form near the folded node, which can be linked to recent results on the interplay between deterministic and stochastic small amplitude oscillations. The normal form can also be used to study the parameter influence on the noise level near folded singularities.

Limiting similarity of competitive species and demographic stochasticity

The limiting similarity of competitive species and its relationship with the competitive exclusion principle is still one of the most important concepts in ecology. In the 1970s, May [R. M. May, Stability and Complexity in Model Ecosystems (Princeton University, Princeton, NJ, 1973)] developed a concise theoretical framework to investigate the limiting similarity of competitive species. His theoretical results show that no limiting similarity threshold of competitive species can be identified in the deterministic model system whereby species more similar than this threshold never coexist. Theoretically, for competitive species coexisting in an unvarying environment, deterministic interspecific interactions and demographic stochasticity can be considered two sides of a coin. To investigate how the "tension" between these two forces affects the coexistence of competing species, a simple two-species competitive system based only on May's model system is transformed into an equivalent replicator equation. The effect of demographic stochasticity on the system stability is measured by the expected drift of the Lyapunov function. Our main results show that the limiting similarity of competitive species should not be considered to be an absolute measure. Specifically, very similar competitive species should be able to coexist in an environment with a high productivity level but big differences between competitive species should be necessary in an ecosystem with a low productivity level.

Spatial synchrony in population dynamics: The effects of demographic stochasticity and density regulation with a spatial scale

We generalize a previous simple result by Lande et al. (1999) on how spatial autocorrelated noise, dispersal rate and distance as well as strength of density regulation determine the spatial scale of synchrony in population density. It is shown how demographic noise can be incorporated, what effect it has on variance and spatial scale of synchrony, and how it interacts with the point process for locations of individuals under random sampling. Although the effect of demographic noise is a rather complex interaction with environmental noise, migration and density regulation, its effect on population fluctuations and scale of synchrony can be presented in a transparent way. This is achieved by defining a characteristic area dependent on demographic and environmental variances as well as population density, and subsequently using this area to define a spatial demographic coefficient. The demographic noise acts through this coefficient on the spatial synchrony, which may increase or decrease with increasing demographic noise depending on other parameters. A second generalization yields the modeling of density regulation taking into account that regulation at a given location does not only depend on the density at that site but also on densities in the whole territory or home range of individuals. It is shown that such density regulation with a spatial scale reduces the scale of synchrony in population fluctuations relative to the simpler model with density regulation at each location determined only by the local point density, and may even generate negative spatial autocorrelations.

Timing and propagule size of invasion determine its success by a time-varying threshold of demographic regime shift.

Theory of invasion ecology indicates that the number of invading individuals (propagule size) and the timing of invasion are important for invasion success. Propagule size affects establishment success due to an Allee effect and the effect of demographic stochasticity, whereas the timing of invasion does so via niche opportunity produced by fluctuating predation pressure and resource abundance. We propose a synthesis of these two mechanisms by a time-varying dose-response curve where the dose is propagule size and the response is establishment probability. We show an example of the synthesis in a simple predator-prey model where successful invasion occurs as a demographic regime shift because of the bistability of the system. The two mechanisms are not independent, but simultaneously determine invasion success in our model. We found that positive growth rate of an invading species does not ensure its establishment, especially when its propagule size is small or when its growth rate is in a decreasing trend. We suggest the difficulty of understanding invasion process based on a dose-response curve of propagule size as no unique curve can be determined due to the effects of invasion timing (i.e., the threshold of demographic regime shift is time varied). The results of our model analysis also have an implication on the phase relationship between population cycles of predators and prey.

Demographic stochasticity reduces the synchronizing effect of dispersal in predator–prey metapopulations

Dispersal may affect predator-prey metapopulations by rescuing local sink populations from extinction or by synchronizing population dynamics across the metapopulation, increasing the risk of regional extinction. Dispersal is likely influenced by demographic stochasticity, however, particularly because dispersal rates are often very low in metapopulations. Yet the effects of demographic stochasticity on predator-prey metapopulations are not well known. To that end, I constructed three models of a two-patch predator-prey system. The models constitute a hierarchy of complexity, allowing direct comparisons. Two models included demographic stochasticity (pure jump process [PJP] and stochastic differential equations [SDE]), and the third was deterministic (ordinary differential equations [ODE]). One stochastic model (PJP) treated population sizes as discrete, while the other (SDE) allowed population sizes to change continuously. Both stochastic models only produced synchronized predator-prey dynamics when dispersal was high for both trophic levels. Frequent dispersal by only predators or prey in the PJP and SDE spatially decoupled the trophic interaction, reducing synchrony of the non-dispersive species. Conversely, the ODE generated synchronized predator-prey dynamics across all dispersal rates, except when initial conditions produced anti-phase transients. These results indicate that demographic stochasticity strongly reduces the synchronizing effect of dispersal, which is ironic because demographic stochasticity is often invoked post hoc as a driver of extinctions in synchronized metapopulations.

Stochastic juvenile–adult models with application to a green tree frog population

We derive several stochastic models from a deterministic population model that describes the dynamics of age-structured juveniles coupled with size-structured adults. Numerical simulation results of the stochastic models are compared with the solution of the deterministic model. These models are then used to understand the effect of demographic stochasticity on the dynamics of an urban green tree frog (Hyla cinerea) population.

A stability analysis of the power-law steady state of marine size spectra

This paper investigates the stability of the power-law steady state often observed in marine ecosystems. Three dynamical systems are considered, describing the abundance of organisms as a function of body mass and time: a "jump-growth" equation, a first order approximation which is the widely used McKendrick-von Foerster equation, and a second order approximation which is the McKendrick-von Foerster equation with a diffusion term. All of these yield a power-law steady state. We derive, for the first time, the eigenvalue spectrum for the linearised evolution operator, under certain constraints on the parameters. This provides new knowledge of the stability properties of the power-law steady state. It is shown analytically that the steady state of the McKendrick-von Foerster equation without the diffusion term is always unstable. Furthermore, numerical plots show that eigenvalue spectra of the McKendrick-von Foerster equation with diffusion give a good approximation to those of the jump-growth equation. The steady state is more likely to be stable with a low preferred predator:prey mass ratio, a large diet breadth and a high feeding efficiency. The effects of demographic stochasticity are also investigated and it is concluded that these are likely to be small in real systems.

Rarity, life history and scaling of the dynamics in time and space of British birds

1. Many patterns in macroecology are closely related to the total abundance of a species in a region. Here we show that interspecific differences in the pattern of population fluctuations of British bird species can be predicted from knowledge of their overall abundance and some basic life-history characteristics. 2. We identify a rarity syndrome that arises through an increased stochastic influence on population fluctuations with decreasing population size, mainly resulting from an inverse density-dependent effect of demographic stochasticity. This syndrome involves an increase in the annual changes in population size with increasing rarity in the United Kingdom. 3. The relationship between the magnitude of temporal variation and local mean population size differs between species dependent on their life history, i.e. species with larger clutch size and lower survival tended to have larger annual changes in population size than low-reproducing long-lived species. 4. The probability of local disappearance from a study plot depended on the population size and was hence closely related to the overall abundance of the species in UK. For a given population size, this probability was also related to species-specific life-history characteristics, being higher in species with larger clutch sizes and smaller survival rates. 5. Rareness results in a spatial decoupling of the temporal variation in population size. 6. These patterns show that once a species has become rare, e.g. due to human activities, key population dynamical characteristics will change because of density-dependent stochastic effects, which in turn are dependent on species-specific life-history characteristics.

Stochasticity in staged models of epidemics: quantifying the dynamics of whooping cough

Although many stochastic models can accurately capture the qualitative epidemic patterns of many childhood diseases, there is still considerable discussion concerning the basic mechanisms generating these patterns; much of this stems from the use of deterministic models to try to understand stochastic simulations. We argue that a systematic method of analysing models of the spread of childhood diseases is required in order to consistently separate out the effects of demographic stochasticity, external forcing and modelling choices. Such a technique is provided by formulating the models as master equations and using the van Kampen system-size expansion to provide analytical expressions for quantities of interest. We apply this method to the susceptible-exposed-infected-recovered (SEIR) model with distributed exposed and infectious periods and calculate the form that stochastic oscillations take on in terms of the model parameters. With the use of a suitable approximation, we apply the formalism to analyse a model of whooping cough which includes seasonal forcing. This allows us to more accurately interpret the results of simulations and to make a more quantitative assessment of the predictions of the model. We show that the observed dynamics are a result of a macroscopic limit cycle induced by the external forcing and resonant stochastic oscillations about this cycle.

Effects of Demographic Stochasticity on Population Persistence in Advective Media

Many populations live and disperse in advective media. A fundamental question, known as the "drift paradox" in stream ecology, is how a closed population can survive when it is constantly being transported downstream by the flow. Recent population-level models have focused on the role of diffusive movement in balancing the effects of advection, predicting critical conditions for persistence. Here, we formulate an individual-based stochastic analog of the model described in (Lutscher et al., SIAM Rev. 47(4):749-772, 2005) to quantify the effects of demographic stochasticity on persistence. Population dynamics are modeled as a logistic growth process and dispersal as a position-jump process on a finite domain divided into patches. When there is no correlation in the interpatch movement of residents, stochasticity simply smooths the persistence-extinction boundary. However, when individuals disperse in "packets" from one patch to another and the flow field is memoryless on the timescale of packet transport, the probability of persistence is greatly enhanced. The latter transport mechanism may be characteristic of larval dispersal in the coastal ocean or wind-dispersed seed pods.

Reduction of Supercritical Multiregional Stochastic Models with Fast Migration

In this work we study the behavior of a time discrete multiregional stochastic model for a population structured in age classes and spread out in different spatial patches between which individuals can migrate. The dynamics of the population is controlled both by reproduction-survival and by migration. These processes take place at different time scales in the sense of the latter being much faster than the former. We incorporate the effect of demographic stochasticity into the population, which results in both dynamics being modelled by multitype Bienaymé-Galton-Watson branching processes. We present a multitype global model that incorporates the effect of both processes and, making use of the existence of different time scales for demography and migration, build a reduced model in which the variables correspond to the total population in each age class. We extend previous results that relate the behavior of the original and the reduced model showing that, given a large enough separation of time scales between demography and migration, we can obtain information about the behavior of the multitype global model through the study of the simpler reduced model. We concentrate on the case where the two systems are supercritical and therefore the expected number of individuals grows to infinity, and show that we can approximate the asymptotic structure of the population vector and the asymptotic population size of the original system through the study of the reduced model.

Stochastic fluctuations in epidemics on networks.

The effects of demographic stochasticity on the long-term behaviour of endemic infectious diseases have been considered for long as a necessary addition to an underlying deterministic theory. The latter would explain the regular behaviour of recurrent epidemics and the former the superimposed noise of observed incidence patterns. Recently, a stochastic theory based on a mechanism of resonance with internal noise has shifted the role of stochasticity closer to the centre stage, by showing that the major dynamic patterns found in the incidence data can be explained as resonant fluctuations, whose behaviour is largely independent of the amplitude of seasonal forcing, and by contrast very sensitive to the basic epidemiological parameters. Here we elaborate on that approach, by adding an ingredient which is missing in standard epidemic models, the 'mixing network' through which infection may propagate. We find that spatial correlations have a major effect on the enhancement of the amplitude and the coherence of the resonant stochastic fluctuations, providing the ordered patterns of recurrent epidemics, whose period may differ significantly from that of the small oscillations around the deterministic equilibrium. We also show that the inclusion of a more realistic, time-correlated recovery profile instead of exponentially distributed infectious periods may, even in the random-mixing limit, contribute to the same effect.

The extended Moran effect and large‐scale synchronous fluctuations in the size of great tit and blue tit populations

1. Synchronous fluctuations of geographically separated populations are in general explained by the Moran effect, i.e. a common influence on the local population dynamics of environmental variables that are correlated in space. Empirical support for such a Moran effect has been difficult to provide, mainly due to problems separating out effects of local population dynamics, demographic stochasticity and dispersal that also influence the spatial scaling of population processes. Here we generalize the Moran effect by decomposing the spatial autocorrelation function for fluctuations in the size of great tit Parus major and blue tit Cyanistes caeruleus populations into components due to spatial correlations in the environmental noise, local differences in the strength of density regulation and the effects of demographic stochasticity. 2. Differences between localities in the strength of density dependence and nonlinearity in the density regulation had a small effect on population synchrony, whereas demographic stochasticity reduced the effects of the spatial correlation in environmental noise on the spatial correlations in population size by 21.7% and 23.3% in the great tit and blue tit, respectively. 3. Different environmental variables, such as beech mast and climate, induce a common environmental forcing on the dynamics of central European great and blue tit populations. This generates synchronous fluctuations in the size of populations located several hundred kilometres apart. 4. Although these environmental variables were autocorrelated over large areas, their contribution to the spatial synchrony in the population fluctuations differed, dependent on the spatial scaling of their effects on the local population dynamics. We also demonstrate that this effect can lead to the paradoxical result that a common environmental variable can induce spatial desynchronization of the population fluctuations. 5. This demonstrates that a proper understanding of the ecological consequences of environmental changes, especially those that occur simultaneously over large areas, will require information about the spatial scaling of their effects on local population dynamics.

Multiple infections alter density dependence in host–pathogen interactions

Summary 1 The hypothesis that multiple infections might disrupt or alter the density-dependent processes regulating a host–pathogen interaction is explored using time series from a well-known laboratory insect–pathogen model system. 2 We compare the population dynamics of the same host (Plodia interpunctella) infected with a single (granulovirus) or multiple (granulovirus and nucleopolyhedrovirus) pathogens and show how the dynamical fluctuations are altered by the presence of this second pathogen. 3 Using a maximum likelihood-based approach, we explore the density-dependent mechanisms underpinning the host–pathogen interaction. These regulatory processes differ between single and multiple infections. In singly infected systems, the density-dependent mechanisms of regulation operate through birth rate while in doubly infected systems, density dependence is mediated through death rate. 4 Further, these deterministic dynamics are modulated by the effects of demographic stochasticity. This stochastic process, the overall sum of individual probabilities of births, deaths and infection influence the changes in population size. In the Plodia–granulovirus system, nonlinear density-dependent births coupled with demographic noise is the necessary prerequisite for the observed dynamics. In the multiple infection system, noise acts together with disease transmission and mortality to affect the population dynamics. 5 We discuss the implication of these differing regulatory processes in the different-sized species assemblages in the presence of noise for understanding the ecologies of host–pathogen interactions.

Periodic local disturbance in host–parasitoid metapopulations: host suppression and parasitoid persistence

Within many agricultural systems, insect pests and their natural enemies are forced to persist as a metapopulation, continuously recolonizing patches following disturbance through harvesting or spraying with insecticides. Despite the need to understand factors influencing biocontrol success, few theoretical studies of host–parasitoid interactions have examined the potential impact of local disturbance within a metapopulation framework. Here, we add periodic local mortality to series of classical host–parasitoid models to examine its effect on host suppression and parasitoid persistence. Using a deterministic lattice model, we show that despite the wide range of complex dynamics generated at the patch level, the region wide pattern of disturbance is the key factor influencing host suppression. The level of host suppression achieved can be understood in terms of both the strength of density dependent parasitism, and the relative amounts of host and parasitoid mixing amongst patches of different ages. Local dispersal among patches is sufficient to ensure coexistence of the host and parasitoid, though persistence is not necessarily associated with the formation of self-organized spatial structures reported in previous studies. Finally, a stochastic version of the model is developed, in order to highlight how the effects of demographic stochasticity may influence biocontrol success in highly disturbed agricultural systems.

APPROXIMATE REDUCTION OF MULTI-TYPE GALTON–WATSON PROCESSES WITH TWO TIME SCALES

Approximate aggregation techniques consist of introducing certain approximations that allow one to reduce a complex system involving many coupled variables obtaining a simpler "aggregated system" governed by a few "macrovariables". Moreover, they give results that allow one to extract information about the complex original system in terms of the behavior of the reduced one. Often, the feature that allows one to carry out such a reduction is the presence of different time scales. In this work we deal with the approximate aggregation of a model for a population subjected to demographic stochasticity and whose dynamics is controlled by two processes with different time scales. There are no restrictions on the slow process while the fast process is supposed to be conservative of the total number of individuals. The incorporation of the effects of demographic stochasticity in the dynamics of the population makes both the fast and the slow processes being modelled by two multi-type Galton–Watson branching processes. We present a multi-type global model that incorporates the combined effect of the fast and slow processes and develop a method that takes advantage of the difference of time scales to reduce the model obtaining an "aggregated" simpler system. We show that, given the separation of time scales between the two processes is high enough, we can obtain relevant information about the behavior of the multi-type global model through the study of this simple aggregated system.

Differences and Congruencies between PVA Packages: the Importance of Sex Ratio for Predictions of Extinction Risk

Brook, B. W., M. A. Burgman and R. Frankham 2000. Differences and Congruencies between PVA Packages: the Importance of Sex Ratio for Predictions of Extinction Risk. Conservation Ecology 4(1):6. https://doi.org/10.5751/ES-00179-040106