Dynamical behavior of systems of two phytoplankton and one zooplankton populations with toxin producing phytoplankton

Three‐compartment mathematical models of non‐toxic phytoplankton (NTP), toxin producing phytoplankton (TPP), and zooplankton are proposed to explore the role of TPP in algal blooms. The mutual interference between predator zooplankton and avoidance of TPP by zooplankton are incorporated into the model. The NTP and TPP engage in exploit competition and the toxin produced by TPP has no effect on NTP. Using the concept of uniform persistence, we establish coexistence of NTP, TPP, and zooplankton in certain parameter regimes. We study the effects of mutual interference and avoidance by zooplankton upon the population interactions. In addition to the toxin producing mechanism, it is concluded that mutual interference of zooplankton is an important factor for diminishing harmful blooms. Copyright © 2017 John Wiley & Sons, Ltd.     

Mathematical modelling on harmful algal blooms in the presence of toxic substances

Effect of toxin producing plankton and its control is an intriguing problem in marine plankton ecology. In this paper we have proposed a three-component model consisting of a non-toxic phytoplankton (NTP), toxin producing phytoplankton (TPP) and zooplankton (Z), where the growth of zooplankton species reduce due to toxic chemicals released by phytoplankton species. It is observed that the three components persist if the predation rate of zooplankton population on toxic phytoplankton is bounded in certain regions. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A simple mathematical model for seasonal planktonic blooms

We show how the inclusion of the defense strategy by different species can alter the prediction of simple models. One of the defense strategy by the phytoplankton population against their grazer is the release of toxic chemicals. In turn the zooplankton population reduces there predation rate over toxin producing phytoplankton (TPP) to protect themselves from those toxic chemicals. Thus, when the level of toxicity is high, the grazing pressure is low and when the level of toxicity is low or when the toxin is absent, the grazing pressure is high. Here we have considered a TPP–zooplankton system where the rate of toxin liberation and the predation rate vary with zooplankton abundance. We observe that our proposed model has the potential to show different dynamical behaviour that are similar to that seen in real‐world situations. Further, we consider three different functional forms for the distribution of the toxins and compare them using latin hypercube sampling technique and found that the functional forms seem to have no effect in determining the final outcome of the system. Copyright © 2009 John Wiley & Sons, Ltd.     

具有时滞和毒素系统的动力学分析

In this paper, a toxin producing phytoplankton-zooplankton model with inhibitory substrate and time delay is investigated. A discrete time delay is induced to both of the consume response function and distribution of toxic substance term. Moreover, Tissiet type function is used for zooplankton grazing to account for the effect of toxication by the TPP population. The conditions to guarantee the coexistence of two species and stability of coexistence equilibrium are given. In particular, we show that there exist critical values of the delay parameters below which the coexistence equilibrium is stable and above which it is unstable. Hopf bifurcation occurs when the delay parameters cross their critical values. Some numerical simulations are executed to validate the analytical findings.     

A delay model for viral infection in toxin producing phytoplankton and zooplankton system

An eco-epidemiological delay model is proposed and analysed for virally infected, toxin producing phytoplankton (TPP) and zooplankton system. It is shown that time delay can destabilize the otherwise stable non-zero equilibrium state. The coexistence of all species is possible through periodic solutions due to Hopf bifurcation. In the absence of infection the delay model may have a complex dynamical behavior which can be controlled by infection. Numerical simulation suggests that the proposed model displays a wide range of dynamical behaviors. Different parameters are identified that are responsible for chaos.     

Low dimensional homeochaos in coevolving host–parasitoid dimorphic populations: Extinction thresholds under local noise

A discrete time model describing the population dynamics of coevolution between host and parasitoid haploid populations with a dimorphic matching allele coupling is investigated under both determinism and stochastic population disturbances. The role of the properties of the attractors governing the survival of both populations is analyzed considering equal mutation rates and focusing on host and parasitoid growth rates involving chaos. The purely deterministic model reveals a wide range of ordered and chaotic Red Queen dynamics causing cyclic and aperiodic fluctuations of haplotypes within each species. A Ruelle–Takens–Newhouse route to chaos is identified by increasing both host and parasitoid growth rates. From the bifurcation diagram structure and from numerical stability analysis, two different types of chaotic sets are roughly differentiated according to their size in phase space and to their largest Lyapunov exponent: the Confined and Expanded attractors. Under the presence of local population noise, these two types of attractors have a crucial role in the survival of both coevolving populations. The chaotic confined attractors, which have a low largest positive Lyapunov exponent, are shown to involve a very low extinction probability under the influence of local population noise. On the contrary, the expanded chaotic sets (with a higher largest positive Lyapunov exponent) involve higher host and parasitoid extinction probabilities under the presence of noise. The asynchronies between haplotypes in the chaotic regime combined with low dimensional homeochaos tied to the confined attractors is suggested to reinforce the long-term persistence of these coevolving populations under the influence of stochastic disturbances. These ideas are also discussed in the framework of spatially-distributed host–parasitoid populations.     

Ordering and selecting extreme populations by means of entropies and divergences

This paper studies the simultaneous selection of extreme populations from a set of independent populations. Two types of subset selection rules for k populations are proposed and studied. The first type selects one subset of populations that should contain the population with the smallest, and another subset of populations that should contain the population with the largest, φ-entropy. The second type selects analogously, but in terms of the extreme ?-divergences with respect a known control population. Properties of the proposed procedures are stated and studied. Examples are presented in order to illustrate the results.     

Bayesian nonparametric modeling for functional analysis of variance

Analysis of variance is a standard statistical modeling approach for comparing populations. The functional analysis setting envisions that mean functions are associated with the populations, customarily modeled using basis representations, and seeks to compare them. Here, we adopt the modeling approach of functions as realizations of stochastic processes. We extend the Gaussian process version to allow nonparametric specifications using Dirichlet process mixing. Several metrics are introduced for comparison of populations. Then we introduce a hierarchical Dirichlet process model which enables comparison of the population distributions, either directly or through functionals of interest using the foregoing metrics. The modeling is extended to allow us to switch the sampling scheme. There are still population level distributions but now we sample at levels of the functions, obtaining observations from potentially different individuals at different levels. We illustrate with both simulated data and a dataset of temperature versus depth measurements at different locations in the Atlantic Ocean.     

MODELING NUTRIENT (DISSOLVED INORGANIC NITROGEN) AND PLANKTON DYNAMICS AT SAGAR ISLAND OF HOOGHLY–MATLA ESTUARINE SYSTEM,WEST BENGAL,INDIA

Abstract Degradation of litter from mangrove forests adjacent to the creeks at Sagar Island of the Hooghly–Matla estuarine ecosystem is one of the principal sources of nutrient to the estuary. Nutrients augment the growth of phytoplankton, which in turn stimulates the production of zooplankton. Zooplankton serves as major food source for fish population of this estuarine system. Here, a dynamic model with three state variables (nutrient, phytoplankton, and zooplankton) is proposed using nitrogen (mgN/l) as currency. Input of dissolved inorganic nitrogen as nutrient, water temperature, surface solar irradiance, and salinity of upstream and downstream of the estuary, collected from the field, are incorporated as graph time functions in the model. Calibration and validation are performed by using collected data of two consecutive years. Model results indicate that the growth of zooplankton and phytoplankton are enhanced by increase in nutrient input in the system. Zooplankton biomass is affected by decrease in the salinity of the estuary. Sensitivity analysis results at ±10% indicate that maximum growth rate of phytoplankton (P_max) is the most sensitive parameter to the nutrient pool although growth rate of zooplankton (g_z) and half saturation constant for phytoplankton grazing by zooplankton (K_z) are most sensitive parameters to phytoplankton and zooplankton compartments, respectively. The model depicts the present status of plankton dynamics, which serve as major food resource for herbivorous and carnivorous fish species of the estuary. Effect of deforestation is tested in the model. Therefore, from management perspective, this model can be used to predict the impact of mangroves on nutrient and plankton dynamics, which will give complete information of both shell and fin fish productions in the estuary.     

Fast game dynamics coupled to slow population dynamics: A single population with hawk-dove strategies

We study a model of a population subdivided into two subpopulations corresponding to hawk and dove tactics. It is assumed that the hawk and dove individuals compete for a resource every Day, I.e., at a fast time scale. This fast part of the model is coupled to a slow part which describes the growth of the subpopulations and the long term effects of the encounters between the individuals which must fight to have an access to the resource. We aggregate the model into a single equation for the total population. It is shown that in the case of a constant game matrix, the total population grows according to a logistic curve whose τ and K parameters are related to the coefficients of the hawk-dove game matrix. Our result shows that high equilibrium density populations are mainly doves, whereas low equilibrium density populations are mainly hawks. We also study the case of a density dependent game matrix for which the gain is linearly decreasing with the total density.     

The distribution of wealth and the effect of extortion in structured populations

Despite that the heterogeneous distribution of wealth is widely observed in social, economical and biological system, few studies have been conducted to explore its influence on the evolutionary dynamics in populations. This paper discusses this problem by introducing a heterogeneous wealth allocation mechanism controlled by a tunable parameterα . Our study shows that there is a positive relationship between the level of cooperation and the extent of heterogeneity of wealth distribution. More importantly, we show that the catalytic effect of extortioners can be significantly strengthened if wealth is heterogeneously distributed among the whole population. Cooperators and extortioners can co-exist if the value of αis moderate. We explain this phenomenon by arguing that this heterogeneous allocation mechanism enables three types of strategists to form clusters around several rich cooperating neighbors initially. Clusters of defectors tend to be eliminated at early stages of evolution.     

Naming Game with Multiple Hearers

A new model called Naming Game with Multiple Hearers (NGMH) is proposed in this paper. A naming game over a population of individuals aims to reach consensus on the name of an object through pair-wise local interactions among all the individuals. The proposed NGMH model describes the learning process of a new word, in a population with one speaker and multiple hearers, at each interaction towards convergence. The characteristics of NGMH are examined on three types of network topologies, namely ER random-graph network, WS small-world network, and BA scale-free network. Comparative analysis on the convergence time is performed, revealing that the topology with a larger average (node) degree can reach consensus faster than the others over the same population. It is found that, for a homogeneous network, the average degree is the limiting value of the number of hearers, which reduces the individual ability of learning new words, consequently decreasing the convergence time; for a scale-free network, this limiting value is the deviation of the average degree. It is also found that a network with a larger clustering coefficient takes longer time to converge; especially a small-word network with smallest rewiring possibility takes longest time to reach convergence. As more new nodes are being added to scale-free networks with different degree distributions, their convergence time appears to be robust against the network-size variation. Most new findings reported in this paper are different from that of the single-speaker/single-hearer naming games documented in the literature.     

Existence of allometry model parameters and their asymptotic formulae for a large population

A theory of population that fails to consider a major determinant of the characteristics of populations is not an adequate theory. Standard texts in population biology and ecology tend to ignore body size as a factor in population dynamics, although birth and death rates, survivorship and longevity, population density and home range size, cycle periods for population boom and crash, and the annual increment in mortality due to aging all show a strong correlation with body mass. Studies in the evolutionary biology of aging require good estimates of the age-dependent mortality rate coefficient. In this paper, we provide an interval of existence of mortality rate parameters M _r and b and their asymptotic expressions in Allometry survival model, in the absence of age-specific mortality data.     

A model of an age-structured population in a multipatch environment

We propose a model of an age-structured population divided into N geographical patches. We distinguish two time scales, at the fast time scale we have the migration dynamics and at the slow time scale the demographic dynamics. The demographic process is described using the classical McKendrick-von Foerster model for each patch, and a simple matrix model including the transfer rates between patches depicts the migration process.Assuming that 0 is a simple strictly dominant eigenvalue for the migration matrix, we transform the model (an e.d.p. problem with N state variables) into a classical McKendrick-von Foerster model (scalar e.d.p. problem) for the global variable: total population density. We prove, under certain assumptions, that the semigroup associated to our problem has the property of positive asynchronous exponential growth and so we compare its asymptotic behaviour to that of the transformed scalar model. This type of study can be included in the so-called aggregation methods, where a large scale dynamical system is approximately described by a reduced system. Aggregation methods have been already developed for systems of ordinary differential equations and for discrete time models.An application of the results to the study of the dynamics of the Sole larvae is also provided.     

Mathematical study of a risk-structured two-group model for Chlamydia transmission dynamics

A new two-group deterministic model for Chlamydia trachomatis, which stratifies the entire population based on risk of acquiring or transmitting infection, is designed and analyzed to gain insight into its transmission dynamics. The model is shown to exhibit the phenomenon of backward bifurcation, where a stable disease-free equilibrium (DFE) co-exists with one or more stable endemic equilibria when the associated reproduction number is less than unity. Unlike in some of the earlier modeling studies on Chlamydia transmission dynamics in a population, this study shows that the backward bifurcation phenomenon persists even if individuals who recovered from Chlamydia infection do not get re-infected. However, it is shown that the phenomenon can be removed if all the susceptible individuals are equally likely to acquire infection (i.e., for the case where the susceptible male and female populations are not stratified according to risk of acquiring infection). In such a case, the DFE of the resulting (reduced) model is globally-asymptotically stable when the associated reproduction number is less than unity and no re-infection of recovered individuals occurs. Thus, this study shows that stratifying the two-sex Chlamydia transmission model, presented in [1], according to the risk of acquiring or transmitting infection induces the phenomenon of backward bifurcation regardless of whether or not the re-infection of recovered individuals occurs.     

Interpolation solution in generalized stochastic exponential population growth model

In this paper, first we consider model of exponential population growth, then we assume that the growth rate at time t is not completely definite and it depends on some random environment effects. For this case the stochastic exponential population growth model is introduced. Also we assume that the growth rate at time t depends on many different random environment effect, for this case the generalized stochastic exponential population growth model is introduced. The expectations and variances of solutions are obtained. For a case study, we consider the population growth of Iran and obtain the output of models for this data and predict the population individuals in each year.     

Modeling herd behavior in population systems

In this paper, we show that under suitable simple assumptions the classical two populations system may exhibit unexpected behaviors. Considering a more elaborated social model, in which the individuals of one population gather together in herds, while the other one shows a more individualistic behavior, we model the fact that interactions among the two occur mainly through the perimeter of the herd. We account for all types of populations’ interactions, symbiosis, competition and the predator–prey interactions. There is a situation in which competitive exclusion does not hold: the socialized herd behavior prevents the competing individualistic population from becoming extinct. For the predator–prey case, sustained limit cycles are possible, the existence of Hopf bifurcations representing a distinctive feature of this model compared with other classical predator–prey models. The system’s behavior is fully captured by just one suitably introduced new threshold parameter, defined in terms of the original model parameters.     

Modeling spatial adaptation of populations by a time non-local convection cross-diffusion evolution problem

In [19], Sighesada et al. presented a system of partial differential equations for modeling spatial segregation of interacting species. Apart from competitive Lotka-Volterra (reaction) and population pressure (cross-diffusion) terms, a convective term modeling the populations attraction to more favorable environmental regions is included. In this article, we introduce a modification of their convective term to take account for the notion of spatial adaptation of populations. After describing the model we briefly discuss its well-possedness and propose a numerical discretization in terms of a mass-preserving time semi-implicit finite differences scheme. Finally, we provide the results of two biologically inspired numerical experiments showing qualitative differences between the original model of [19] and the model proposed in this article.     

Role of toxin producing phytoplankton on a plankton ecosystem

In this paper, a mathematical model is proposed to study the role of toxin producing phytoplankton on a phytoplankton–zooplankton system with nutrient cycling. The model includes three state variables, viz., nutrient concentration, phytoplankton biomass and zooplankton biomass. It is assumed in the model that phytoplankton biomass is producing toxicant harmful for the zooplankton biomass. All the feasible equilibria of the system are obtained and the conditions for the existence of the interior equilibrium are determined. The local stability analysis of all the feasible equilibria are carried out and the possibility of Hopf-bifurcation of the interior equilibrium is studied. The threshold value in terms of constant input rate of nutrient is determined both analytically and numerically.