Analysis of a Single Species Model with Dissymmetric Bidirectional Impulsive Diffusion and Dispersal Delay

In most models of population dynamics, diffusion between two patches is assumed to be either continuous or discrete, but in the real natural ecosystem, impulsive diffusion provides a more suitable manner to model the actual dispersal (or migration) behavior for many ecological species. In addition, the species not only requires some time to disperse or migrate among the patches but also has some possibility of loss during dispersal. In view of these facts, a single species model with dissymmetric bidirectional impulsive diffusion and dispersal delay is formulated. Criteria on the permanence and extinction of species are established. Furthermore, the realistic conditions for the existence, uniqueness, and the global stability of the positive periodic solution are obtained. Finally, numerical simulations and discussion are presented to illustrate our theoretical results

Dispersal and noise: Various modes of synchrony in\ud ecological oscillators

We use the theory of noise-induced phase synchronization to analyze the effects of dispersal on the synchronization of a pair of predator-prey systems within a fluctuating environment (Moran effect). Assuming that each isolated local population acts as a limit cycle oscillator in the deterministic limit, we use phase reduction and averaging methods to derive a Fokker–Planck equation describing the evolution of the probability density for pairwise phase differences between the oscillators. In the case of common environmental noise, the oscillators ultimately synchronize. However the approach to synchrony depends on whether or not dispersal in the absence of noise supports any stable asynchronous states. We also show how the combination of correlated (shared) and uncorrelated (unshared) noise with dispersal can lead to a multistable\ud steady-state probability density

Mathematical modelling of population and disease control in patchy environments

Natural populations may be managed by humans for a number of reasons, with mathematical modelling playing an increasing role in the planning of such management and control strategies. In an increasingly heterogeneous, or `patchy' landscape, the interactions between distinct groups of individuals must be taken into account to predict meaningful management strategies. Invasive control strategies, involving reduction of populations, such as harvesting or culling have been shown to cause a level of disturbance, or spatial perturbation, to these groups, a factor which is largely ignored in the modelling literature. \\ In this thesis, we present a series of deterministic, differential equation models which are used to investigate the impact of this disturbance in response to control. We address this impact in two scenarios. Firstly, in terms of a harvested population, where extinction must be prevented whilst maximising the yield obtained. Secondly, we address the impact of disturbance in an epidemic model, where the aim of the control strategy is to eradicate an endemic pathogen, or to prevent the invasion of a pathogen into a susceptible population. The movement of individuals between patches is modelled as both a constant rate, and a function which is increasing with population density. Finally, we discuss the `optimal' control strategy in this context. \\ We find that, whilst a population harvested from a coupled system is able to produce an inflated yield, this coupling can also cause the population to be more resistant to higher harvesting efforts, increasing the effort required to drive the population to extinction. Spatial perturbation raises this extinction threshold further still, providing a survival mechanism not only for the individuals that avoid being killed, but for the population as a whole. \\ With regards to the eradication of disease, we show that disturbance may either raise or lower the pathogen exclusion threshold depending on the particular characteristics of the pathogen. In certain cases, we have shown that spatial perturbation may force a population to be susceptible to an infectious invasion where its natural carrying capacity would prevent this

Coupled models of structured contagion processes in human-environment systems

Models of infectious processes are a common feature in the landscape of applied mathematics. It is rare that these processes are isolated from other significant dynamics in nature, and therefore we can incorporate some of the complexity inherent in real systems by coupling infections to major features of the ecosystems they inhabit. Infectious processes can take many forms, but in this thesis we consider three: the COVID-19 pandemic, the invasion of eastern North American forests by wood-borne pests, and the outbreak cycles of an endemic forest pest. The first chapter covers a model of Sars-CoV-2 in a structured population, coupled with a replicator equation representing sentiment towards the use of non-pharmaceutical interventions. We use this human-environment model of to compare the efficacy of vulnerable-first and transmission-preventing age structured vaccination strategies. The buildup of natural immunity in a population combined with a low vaccination supply is shown to cause a transmission-preventing vaccination strategy to be more effective. The second chapter considers a spatially structured model of forest pest contagion over an empirically-derived network of forest patches in eastern Canada. Since these pests can frequently be spread long distances by wood transport, we couple this model to the sentiment of local populations towards avoiding firewood transport from outside their area. Three possible countermeasures to the spread of the invasive pest are compared: social incentives, direct interception of infested firewood, and quarantine of patches. The level of effort needed to significantly reduce forest damage with any of these methods is substantial and unlikely to be implemented. The final chapter extends a model of mountain pine beetle (MPB) in western north american pine forests to incorporate tree mortality due to wildfire. We find that wildfire acts as a disturbance that increases the heterogeneity in age structure, and therefore is able to increase the resilience of the forest to outbreaks of MPB. A targeted thinning procedure aimed specifically at increasing heterogeneity in the forest age structure is proposed and shown to be highly effective at reducing the severity of outbreak. The effectiveness of targeted thinning in the manner described further emphasizes the importance of heterogeneity in forest stand structure. Each model tests the importance of indirect protection in preventing the spread of an infectious agent through a specific host population, with respect to key parameters. Models let us use counterfactuals to gain potentially invaluable understanding of these complex human-environment systems

Assessing recovery potential of aquatic macroinvertebrate populations using ecological models

Doordat de groeiende wereldbevolking een steeds grotere druk op natuurlijke ecosystemen legt, wordt de vraag naar een goede methode voor het beoordelen van de mogelijkheid tot herstel van een systeem steeds groter. Dit is vooral relevant voor agrarische ecosystemen welke traditioneel als functie hebben om voedsel voor de menselijke populatie te produceren. Agrarische ecosystemen leveren echter ook andere ecosysteemdiensten zoals omzettingen van nutriënten, bestuiving, het op peil houden van een bepaalde bodemkwaliteit en structuur, maar ook esthetische en recreatieve diensten, waarvan de duurzaamheid moet worden gewaarborgd

Exponential Stability of Cohen-Grossberg Neural Networks with Impulse Time Window

This paper concerns the problem of exponential stability for a class of Cohen-Grossberg neural networks with impulse time window and time-varying delays. In our letter, the impulsive effects we considered can stochastically occur at a definitive time window and the impulsive controllers we considered can be nonlinear and even rely on the states of all the neurons. Hence, the impulses here can be more applicable and more general. By utilizing Lyapunov functional theory, inequality technique, and the analysis method, we obtain some novel and effective exponential stability criteria for the Cohen-Grossberg neural networks. These results generalize a few previous known results and numerical simulations are given to show the effectiveness of the derived results

Transmission Dynamics of a Two-City SIR Epidemic Model with Transport-Related Infections

A two-city SIR epidemic model with transport-related infections is proposed. Some good analytical results are given for this model. If the basic reproduction number ℜ0γ≤1, there exists a disease-free equilibrium which is globally asymptotically stable. There exists an endemic equilibrium which is locally asymptotically stable if the basic reproduction number ℜ0γ>1. We also show the permanence of this SIR model. In addition, sufficient conditions are established for global asymptotic stability of the endemic equilibrium

Nonlinear dynamics of plankton ecosystem with impulsive control and environmental fluctuations

It is well known that the density of plankton populations always increases and decreases or keeps invariant for a long time, and the variation of plankton density is an important factor influencing the real aquatic environments, why do these situations occur? It is an interesting topic which has become the common interest for many researchers. As the basis of the food webs in oceans, lakes, and reservoirs, plankton plays a significant role in the material circulation and energy flow for real aquatic ecosystems that have a great effect on the economic and social values. Planktonic blooms can occur in some environments, however, and the direct or indirect adverse effects of planktonic blooms on real aquatic ecosystems, such as water quality, water landscape, aquaculture development, are sometimes catastrophic, and thus planktonic blooms have become a challenging and intractable problem worldwide in recent years. Therefore, to understand these effects so that some necessary measures can be taken, it is important and meaningful to investigate the dynamic growth mechanism of plankton and reveal the dynamics mechanisms of formation and disappearance of planktonic blooms. To this end, based on the background of the ecological environments in the subtropical lakes and reservoirs, this dissertation research takes mainly the planktonic algae as the research objective to model the mechanisms of plankton growth and evolution. In this dissertation, some theories related to population dynamics, impulsive control dynamics, stochastic dynamics, as well as the methods of dynamic modeling, dynamic analysis and experimental simulation, are applied to reveal the effects of some key biological factors on the dynamics mechanisms of the spatial-temporal distribution of plankton and the termination of planktonic blooms, and to predict the dynamics evolutionary processes of plankton growth. The main results are as follows: Firstly, to discuss the prevention and control strategies on planktonic blooms, an impulsive reaction-diffusion hybrid system was developed. On the one hand, the dynamic analysis showed that impulsive control can significantly influence the dynamics of the system, including the ultimate boundedness, extinction, permanence, and the existence and uniqueness of positive periodic solution of the system. On the other hand, some experimental simulations were preformed to reveal that impulsive control can lead to the extinction and permanence of population directly. More precisely, the prey and intermediate predator populations can coexist at any time and location of their inhabited domain, while the top predator population undergoes extinction when the impulsive control parameter exceeds some a critical value, which can provide some key arguments to control population survival by means of some reaction-diffusion impulsive hybrid systems in the real life. Additionally, a heterogeneous environment can affect the spatial distribution of plankton and change the temporal-spatial oscillation of plankton distribution. All results are expected to be helpful in the study of dynamic complex of ecosystems. Secondly, a stochastic phytoplankton-zooplankton system with toxic phytoplankton was proposed and the effects of environmental stochasticity and toxin-producing phytoplankton (TPP) on the dynamics mechanisms of the termination of planktonic blooms were discussed. The research illustrated that white noise can aggravate the stochastic oscillation of plankton density and a high-level intensity of white noise can accelerate the extinction of plankton and may be advantageous for the disappearance of harmful phytoplankton, which imply that the white noise can help control the biomass of plankton and provide a guide for the termination of planktonic blooms. Additionally, some experimental simulations were carried out to reveal that the increasing toxin liberation rate released by TPP can increase the survival chance of phytoplankton population and reduce the biomass of zooplankton population, but the combined effects of those two toxin liberation rates on the changes in plankton are stronger than that of controlling any one of the two TPP. All results suggest that both white noise and TPP can play an important role in controlling planktonic blooms. Thirdly, we established a stochastic phytoplankton-toxic producing phytoplankton-zooplankton system under regime switching and investigated how the white noise, regime switching and TPP affect the dynamics mechanisms of planktonic blooms. The dynamical analysis indicated that both white noise and toxins released by TPP are disadvantageous to the development of plankton and may increase the risk of plankton extinction. Also, a series of experimental simulations were carried out to verify the correctness of the dynamical analysis and further reveal the effects of the white noise, regime switching and TPP on the dynamics mechanisms of the termination of planktonic blooms. On the one hand, the numerical study revealed that the system can switch from one state to another due to regime shift, and further indicated that the regime switching can balance the different survival states of plankton density and decrease the risk of plankton extinction when the density of white noise are particularly weak. On the other hand, an increase in the toxin liberation rate can increase the survival chance of phytoplankton but reduce the biomass of zooplankton, which implies that the presence of toxic phytoplankton may have a positive effect on the termination of planktonic blooms. These results may provide some insightful understanding on the dynamics of phytoplankton-zooplankton systems in randomly disturbed aquatic environments. Finally, a stochastic non-autonomous phytoplankton-zooplankton system involving TPP and impulsive perturbations was studied, where the white noise, impulsive perturbations and TPP are incorporated into the system to simulate the natural aquatic ecological phenomena. The dynamical analysis revealed some key threshold conditions that ensure the existence and uniqueness of a global positive solution, plankton extinction and persistence in the mean. In particular, we determined if there is a positive periodic solution for the system when the toxin liberation rate reaches a critical value. Some experimental simulations also revealed that both white noise and impulsive control parameter can directly influence the plankton extinction and persistence in the mean. Significantly, enhancing the toxin liberation rate released by TPP increases the possibility of phytoplankton survival but reduces the zooplankton biomass. All these results can improve our understanding of the dynamics of complex of aquatic ecosystems in a fluctuating environment

Modeling landscape dynamics and environmental association for spruce mortality

2016 Summer.Includes bibliographical references.This study addresses important issues related to mortality of spruce species (Picea sp.) associate with outbreaks of spruce beetle (Dendroctonus rufipennis Kirby) by 1) modeling large scale landscape dynamics of spruce mortality associated with long-term climate in Colorado and Alaska; 2) modeling environmental association between spruce mortality and small scale environmental covariates including climatic factors. In the first chapter, we review the ecology and etiology of spruce mortality in Colorado. In the second chapter, we evaluate landscape dynamics of spruce mortality at the regional scale of Colorado and Alaska. We used climate transition matrices (CTMs) as a method to assess the influence of climate on spruce forest extent and mortality. We quantify the probabilities of observing spruce forest, spruce mortality, and the mismatches between probabilities for the presence of host and mortality as indicated by differential effects. All values were calculated to populate elements of CTMs. The polynomial functions of ordinary regressive model and spatial autoregressive model were implemented to represent the association between climate zones and the responses. The results show us that there are influences of long-term precipitation and temperature on both probabilities. Presence of spruce forest in Colorado is associated with high precipitation at moderately low temperatures while probability of spruce mortality has a similar association. High probability of observing spruce forest in Alaska is associated with low to moderate precipitation while the probabilities of observing spruce mortality are positively associated with high precipitation at warmer temperatures. From the differential effects, there are mismatches of responses between host and mortality implying the advantageous of host associate with moderate temperatures and high precipitation in Colorado while healthy forest is found in the moderately low temperature and moderate precipitation in Alaska. In the third chapter, we describe associations between stand scale environmental conditions and spruce mortality. We modeled the association using zero-and-one inflated beta regression model based on hierarchical Bayesian framework. Two-stage Bernoulli logistic models were applied to indicate the occurrence of the extreme values represent presence and absence of mortality; continuous proportional responses were then addressed by beta regressive model. Multivariate Gaussian latent process was included in the function to express the exponential spatial errors term. The results indicate that spatial distribution of the occurrence and intensity of spruce mortality were both associated with the local stand covariates of temperature zone, precipitation zone, class of stand structure level, relative dominance class, and size class. The colder temperature zones have highly negative effects on both the probability of mortality occurrence and the probability of full mortality occurrence, while the warmer temperature zone is positively associated with the presence of full mortality. The results also indicate that stand characteristics are important factors associated with mortality. Mortality occurrence is positively associated with single-story stands with medium to large size classes. The higher-complexity stand structures have highly positive associations with the probability of entire stand mortality, while medium to high dominance classes have negative effects on full mortality. The largest size class and the highest dominance class have negative associations with the proportion of partial mortality