No description available.

Northern environments are often characterised by large seasonal and annual fluctuations in food abundance. In this thesis, I examined how arctic foxes (</i>Alopex lagopus</i>) used seasonally superabundant foods (geese and their eggs) and how access to these foods influenced population dynamics of arctic foxes. I addressed this against a backdrop of variation in lemming and vole abundance (small mammals hereafter) the main foods of arctic foxes throughout most of their range. Field work was done at the large goose colony at Karrak Lake and surrounding areas in the Queen Maud Gulf Bird Sanctuary in Nunavut, Canada, in the spring and summers of 2000 to 2004. <p> Behavioural observations of individually-marked arctic foxes showed that they took and cached 2,000-3,000 eggs per fox each year and that the rate at which they took eggs was largely unrelated to individual attributes of foxes (e.g. sex, size, and breeding status) and nesting distribution of geese. Further, the rate at which foxes took eggs varied considerably within individuals in that foxes were efficient at taking eggs at times and inefficient at other times. This may have resulted from foxes switching between foraging actively and taking eggs opportunistically while performing other demands such as territorial behaviours. <p>Comparison of stable isotope ratios (13C and 15N) of fox tissues and those of their foods showed that the contribution of cached eggs to arctic fox diets was inversely related to collared lemming (<i>Dicrostonyx torquatus</i>) abundance. In fact, the contribution of cached eggs to overall fox diets increased from <28% in years when collared lemmings were abundant to 30-74% in years when collared lemmings were scarce. Furthermore, arctic foxes used cached eggs well into the following spring (almost 1 year after eggs were acquired) a pattern which differs from that of carnivores generally storing foods for only a few days before consumption. <p>A field-study of experimental caches showed that survival rate of these caches was related to age of cache sites in the first year of the study (e.g. 0.80 and 0.56 per 18-day period for caches from new and 1 month old cache sites, respectively) and departure by geese after hatch in the second year of the study (e.g. 0.98 and 0.74 per 18-day period during and after goose nesting, respectively). Food abundance and deterioration of cache sites (e.g. loss of soil cover and partial exposure of caches) were, thus, important factors affecting cache loss at Karrak Lake. Further, annual variation in the importance of these factors suggests that strategies to prevent cache loss are not fixed in time but vary with existing conditions. Evolution of caching behaviours by arctic foxes may, thus, have been shaped by multiple selective pressures. <p>Comparisons of reproductive output and abundance of arctic foxes inside and outside the goose colony at Karrak Lake showed that (i) breeding density and fox abundance were 2-3 times higher inside the colony than they were outside the colony and (ii) litter size, breeding density, and annual variation in fox abundance followed that of small mammal abundance. Small mammal abundance was, thus, the main governor of population dynamics of arctic foxes whereas geese and their eggs elevated fox abundance and breeding density above that which small mammals could support. These results highlight both the influence of seasonal and annual variation on population dynamics of consumers and the linkage between arctic environments and wintering areas by geese thousands of kilometres to the south.

Population dynamics is the dominant branch of mathematical biology. The first model for population dynamics was developed by Thomas Malthus. A more complicated model was developed by Pierre Fran&ccedil / ois Verhulst and it is called the logistic equation. Our aim in this thesis is to extend the models using piecewise constant arguments and to find the conditions when the models have fixed points, periodic solutions and chaos with investigation of stability of periodic solutions.

The purpose of this thesis is to determine the factors that contributed to the decline of Florida apple snail ( Pomacea paludosa) populations in the A.R.M. Loxahatchee National Wildlife Refuge with the goal of devising management recommendations to the Refuge regarding population management strategies. The factors examined that could have potentially contributed to population decline include the use of copper-based herbicides, insecticide application, the occurrence of drought, the use of other herbicides, the occurrence of fire, and non-avian predation. Annual Narrative documents produced by Refuge managers and staff members, dated from 1951 to 2007, were used to collect historical data for these factors. The quality of data reporting within the Annual Narratives was also examined. To support data on droughts documented in the Annual Narratives, surface water and rainfall data were obtained and analyzed. The methodology includes the use of conceptual ecological models and historical ecology to determine whether or not the factors examined produced an ecological effect capable of affecting the Refuge population of apple snails. Evidence from the Annual Narratives suggests that the use of copper-based herbicides, the occurrence of drought, and predation by alligators were responsible for the decline of the apple snail on the Refuge. A lack of consistently reported data regarding apple snail densities makes it difficult to determine the degree to which each factor had an effect on the apple snails or to determine if any spatio-temporal relationship existed between the Florida apple snail and Everglade snail kite ( Rostrhamus sociabilis plumbeus) based on copper-based herbicide use. The overall quality of the Annual Narratives improved throughout the study period and eventually focused heavily on investigative studies. Several management recommendations were suggested to improve Florida apple snail populations on the Refuge. First, in order to monitor the health and trends of the apple snail population, a monitoring network needs to be established with results maintained in a geodatabase. Both apple snail density and egg cluster counts need to be made following an established sampling method. Second, in an attempt to sustain higher apple snail densities, stocking of the interior should be attempted. Finall, in the event that adjacent farmlands are to be restored, soil samples need to be analyzed to determine if concentrations are high enough that desorption of copper from the flooded agricultural soils could pose a serious threat to the Refuge by reintroducing toxic levels of copper.

Enterococci are fecal indicator bacteria (FIB) that are used worldwide for water quality assessment. However, evidence of high densities and extended survival of enterococci in sediments and submerged aquatic vegetation (SAV) has caused uncertainty about their reliability in predicting human health risks from recreational activities in environmental waters. To address the concern that sediments and SAV may harbor large reservoirs of enterococci that can affect water column concentrations, aquatic mesocosms and environmental sampling were employed to investigate patterns of enterococci densities and population structure across the Tampa Bay watershed. In mesocosm experiments and environmental samples, SAV harbored higher densities of enterococci, per mass of substrate, than sediments, and sediments harbored higher densities than water. Population structure assessed by BOX-PCR genotyping was relatively unique in each sample, although slight similarities among samples suggested grouping primarily by location rather than substrate or season. Strain diversity was highly variable, and many samples had low diversity, including nearly monoclonal structure throughout the mesocosm experiments and in several of the environmental samples. Several strains were highly abundant and cosmopolitan (found across sites, seasons, and substrates), and may represent highly naturalized and reproducing indicator bacteria populations that are not directly related to pollution events. When the enterococci densities were viewed from the perspective of the entire aquatic system, SAV-associated enterococci did not comprise a major proportion of the total population, due to the typically large differences in volume of each substrate (SAV vs. sediments vs. water). Instead, the largest proportions of enterococci were typically found in the water or the sediments, depending on the relative volume of substrate or the enterococci density associated with each substrate. Modeling results illustrate that the relative importance of each substrate in terms of FIB populations can shift dramatically over time and space due to changes such as vegetation cover, tidal cycles, and bacteria densities. Furthermore, at several sites within the watershed, estimates of sediment and bacteria resuspension from sediments were very low, suggesting that this process rarely, if ever, significantly affects water column concentrations of enterococci in the watershed.

Dispersal influences the genetic and social composition of populations, yet it has been difficult to understand the mechanisms underlying dispersal and this limits our ability to understand how dispersal may be influencing population dynamics. Behavioral traits, such as aggression, have been implicated as drivers of both dispersal and population dynamics. However, the influence on both has never been addressed in a single system. Western bluebirds (Sialia mexicana) provide an excellent opportunity to address this question, as their dispersal propensity is dependent upon aggressive phenotype and we have detailed observations over a period of more than a decade. I show that natal dispersal is influenced by an interaction between father and son aggressive phenotypes, in addition to available resources on the natal territory. Furthermore, population density is influenced by resource availability and an interaction between population aggression and recruitment of offspring as breeders. Males that breed for multiple seasons once the population has reached saturation recruit a higher proportion of offspring into the population, as do males that are nonaggressive. Males that are nonaggressive are more likely to breed for multiple seasons, which suggests an added cost to aggressive behavior in this species. Both aggressive behavior and the availability of resources are mechanisms influencing dispersal of individuals that manifest at the population scale.

The life of microorganisms is characterized by two main tasks, rapid growth under conditions permitting growth and survival under stressful conditions. The environments, in which microorganisms dwell, vary in space and time. The microorganisms innovate diverse strategies to readily adapt to the regularly fluctuating environments. Phenotypic heterogeneity is one such strategy, where an isogenic population splits into subpopulations that respond differently under identical environments. Bacterial persistence is a prime example of such phenotypic heterogeneity, whereby a population survives under an antibiotic attack, by keeping a fraction of population in a drug tolerant state, the persister state. Specifically, persister cells grow more slowly than normal cells under growth conditions, but survive longer under stress conditions such as the antibiotic administrations. Bacterial persistence is identified experimentally by examining the population survival upon an antibiotic treatment and the population resuscitation in a growth medium. The underlying population dynamics is explained with a two state model for reversible phenotype switching in a cell within the population. We study this existing model with a new theoretical approach and present analytical expressions for the time scale observed in population growth and resuscitation, that can be easily used to extract underlying model parameters of bacterial persistence. In addition, we recapitulate previously known results on the evolution of such structured population under periodically fluctuating environment using our simple approximation method. Using our analysis, we determine model parameters for Staphylococcus aureus population under several antibiotics and interpret the outcome of cross-drug treatment. Next, we consider the expansion of a population exhibiting phenotype switching in a spatially structured environment consisting of two growth permitting patches separated by an antibiotic patch. The dynamic interplay of growth, death and migration of cells in different patches leads to distinct regimes in population propagation speed as a function of migration rate. We map out the region in parameter space of phenotype switching and migration rate to observe the condition under which persistence is beneficial. Furthermore, we present an extended model that allows mutation from the two phenotypic states to a resistant state. We find that the presence of persister cells may enhance the probability of resistant mutation in a population. Using this model, we explain the experimental results showing the emergence of antibiotic resistance in a Staphylococcus aureus population upon tobramycin treatment. In summary, we identify several roles of bacterial persistence, such as help in spatial expansion, development of multidrug tolerance and emergence of antibiotic resistance. Our study provides a theoretical perspective on the dynamics of bacterial persistence in different environmental conditions. These results can be utilized to design further experiments, and to develop novel strategies to eradicate persistent infections. / Das Leben von Mikroorganismen kann in zwei charakteristische Phasen unterteilt werde, schnelles Wachstum unter Wachstumsbedingungen und Überleben unter schwierigen Bedingungen. Die Bedingungen, in denen sich die Mikroorganismen aufhalten, verändern sich in Raum und Zeit. Um sich schnell an die ständig wechselnden Bedingungen anzupassen entwickeln die Mikroorganismen diverse Strategien. Phänotypische Heterogenität ist eine solche Strategie, bei der sich eine isogene Popolation in Untergruppen aufteilt, die unter identischen Bedingungen verschieden reagieren. Bakterielle Persistenz ist ein Paradebeispiel einer solchen phänotypischen Heterogenität. Hierbei überlebt eine Popolation die Behandlung mit einem Antibiotikum, indem sie einen Teil der Bevölkerung in einem, dem Antibiotikum gegenüber tolerant Zustand lässt, der sogenannte "persister Zustand". Persister-Zellen wachsen unter Wachstumsbedingungen langsamer als normale Zellen, jedoch überleben sie länger in Stress-Bedingungen, wie bei Antibiotikaapplikation. Bakterielle Persistenz wird experimentell erkannt indem man überprüft ob die Population eine Behandlung mit Antibiotika überlebt und sich in einem Wachstumsmedium reaktiviert. Die zugrunde liegende Popolationsdynamik kann mit einem Zwei-Zustands-Modell für reversibles Wechseln des Phänotyps einer Zelle in der Bevölkerung erklärt werden. Wir untersuchen das bestehende Modell mit einem neuen theoretischen Ansatz und präsentieren analytische Ausdrücke für die Zeitskalen die für das Bevölkerungswachstums und die Reaktivierung beobachtet werden. Diese können dann einfach benutzt werden um die Parameter des zugrunde liegenden bakteriellen Persistenz-Modells zu bestimmen. Darüber hinaus rekapitulieren wir bisher bekannten Ergebnisse über die Entwicklung solch strukturierter Bevölkerungen unter periodisch schwankenden Bedingungen mithilfe unseres einfachen Näherungsverfahrens. Mit unserer Analysemethode bestimmen wir Modellparameter für eine Staphylococcus aureus-Popolation unter dem Einfluss mehrerer Antibiotika und interpretieren die Ergebnisse der Behandlung mit zwei Antibiotika in Folge. Als nächstes betrachten wir die Ausbreitung einer Popolation mit Phänotypen-Wechsel in einer räumlich strukturierten Umgebung. Diese besteht aus zwei Bereichen, in denen Wachstum möglich ist und einem Bereich mit Antibiotikum der die beiden trennt. Das dynamische Zusammenspiel von Wachstum, Tod und Migration von Zellen in den verschiedenen Bereichen führt zu unterschiedlichen Regimen der Populationsausbreitungsgeschwindigkeit als Funktion der Migrationsrate. Wir bestimmen die Region im Parameterraum der Phänotyp Schalt-und Migrationsraten, in der die Bedingungen Persistenz begünstigen. Darüber hinaus präsentieren wir ein erweitertes Modell, das Mutation aus den beiden phänotypischen Zuständen zu einem resistenten Zustand erlaubt. Wir stellen fest, dass die Anwesenheit persistenter Zellen die Wahrscheinlichkeit von resistenten Mutationen in einer Population erhöht. Mit diesem Modell, erklären wir die experimentell beobachtete Entstehung von Antibiotika- Resistenz in einer Staphylococcus aureus Popolation infolge einer Tobramycin Behandlung. Wir finden also verschiedene Funktionen bakterieller Persistenz. Sie unterstützt die räumliche Ausbreitung der Bakterien, die Entwicklung von Toleranz gegenüber mehreren Medikamenten und Entwicklung von Resistenz gegenüber Antibiotika. Unsere Beschreibung liefert eine theoretische Betrachtungsweise der Dynamik bakterieller Persistenz bei verschiedenen Bedingungen. Die Resultate könnten als Grundlage neuer Experimente und der Entwicklung neuer Strategien zur Ausmerzung persistenter Infekte dienen.

The current rate of species extinction is higher than at any other time in Earth’s history. Despite our understanding of the causes and consequences of extinction and the development of numerous species conservation plans, it is surprising how little we know about the dynamics of extinction and recovery. Here, I explore the dynamics of population extinction and recovery across a range of meio-invertebrate species embedded in aquatic multi-trophic communities under external pressure. My results indicate that external mortality frequency has a negative impact on the dynamics of population extinction and recovery and suggest that it may be possible to predict patterns of population extinction from patterns of population growth as well as patterns of recovery from patterns of population collapse. My findings provide a valuable empirical basis from which we may increase our understanding of the factors influencing extinction risk and recovery potential to develop sustainable management strategies.

No description available.

Understanding demographic and disease processes in wildlife populations tends to be hampered by incomplete observations which can include significant errors. Models provide useful insights into the potential impacts of key processes and the value of such models greatly improves through integration with available data in a way that includes all sources of stochasticity and error. To date, the impact on disease of spatial and social structures observed in wildlife populations has not been widely addressed in modelling. I model the joint effects of differential fecundity and spatial heterogeneity on demography and disease dynamics, using a stochastic description of births, deaths, social-geographic migration, and disease transmission. A small set of rules governs the rates of births and movements in an environment where individuals compete for improved fecundity. This results in realistic population structures which, depending on the mode of disease transmission can have a profound effect on disease persistence and therefore has an impact on disease control strategies in wildlife populations. I also apply a simple model with births, deaths and disease events to the long-term observations of TB (Mycobacterium bovis) in badgers in Woodchester Park. The model is a continuous time, discrete state space Markov chain and is fitted to the data using an implementation of Bayesian parameter inference with an event-based likelihood. This provides a flexible framework to combine data with expert knowledge (in terms of model structure and prior distributions of parameters) and allows us to quantify the model parameters and their uncertainties. Ecological observations tend to be restricted in terms of scope and spatial temporal coverage and estimates are also affected by trapping efficiency and disease test sensitivity. My method accounts for such limitations as well as the stochastic nature of the processes. I extend the likelihood function by including an error term that depends on the difference between observed and inferred state space variables. I also demonstrate that the estimates improve by increasing observation frequency, combining the likelihood of more than one group and including variation of parameter values through the application of hierarchical priors.