No description available.

Determining the emergent behaviour of a population from the interactions of its individuals is an ongoing challenge in the modelling of biological phenomena. Many classical models assume that the spatial location of each individual is independent of the locations of all other individuals. This mean-field assumption is not always realistic; in biological systems we frequently see clusters of individuals develop from uniform initial conditions. In this thesis, we explore situations in which the mean-field approximation is no longer valid for volume-excluding processes on a regular lattice. We provide methods which take into account the spatial correlations between lattice sites, thus more accurately reflecting the system's behaviour, and discuss methods which can provide information as to the validity of mean-field and other approximations.

We conduct a broad investigation about stellar mass and population diagnostics in order to formulate novel constraints related to the formation and evolution of galaxies from a nearby cluster environment. Our work is powered by the use of stellar population models which transform galaxy colours and/or absorption line strengths into estimates of its stellar properties. As input to such models, we assemble an extensive compilation of age and chemical abundance information for Galactic globular clusters. This compilation allows a confident expansion of these models into new regions of parameter space that promise to refine our knowledge of galactic chemical evolution. We then draw upon a state-of-the-art spectroscopic and photometric survey of the Virgo galaxy cluster in order to constrain spatial variations of the stellar ages, metallicities, and masses within its member galaxies, and their dynamical masses. We interpret these data in the context of the histories of star formation, chemical enrichment, and stellar mass assembly to formulate a broad picture of the build-up of this cluster’s content over time. In it, the giant early-type galaxies formed through highly dissipational processes at early times that built up most of their stellar mass and drew significant amounts of dark matter within their optical radii. Conversely, dwarf early-types experienced environmental processes that quenched their star formation during either the early stages of cluster assembly or upon infall at later times. Somewhat perplexing is our finding that the internal dynamics of these galaxies are largely explained by their stellar masses. Lastly, Virgo spirals also suffer from their dense environment, through ram pressure stripping and/or tidal harrassment. In addition to quenching, these effects leave an imprint on their internal dynamical evolution too. Late-type spirals exhibit evidence of having ejected significant amounts of baryons from their inner regions, likely via energetic feedback events. Rich as our picture of the history of the Virgo cluster has become, real progress in our understanding of this system will truly benefit from future high-resolution cosmological and hydrodynamic simulations of this environment. Such simulations are still in their infancy, but the data assembled here should soon provide their most direct validation. / Thesis (Ph.D, Physics, Engineering Physics and Astronomy) -- Queen's University, 2013-09-30 23:32:48.575

Tropical coral reefs are among the most diverse ecosystems of the world but facing increasing threats to their health. Over the last thirty years, many Caribbean coral reefs have undergone dramatic changes and experienced large losses in coral cover, due to direct and indirect anthropogenic disturbances. The results of which are reefs with low rugosity, changed trophic dynamics and low fish diversity. In recent times reefs have failed to recover from disturbances due to an increase in frequency and severity of disturbances and stresses. In the Caribbean on many coral reefs this has resulted in a shift towards macroalgal dominance by species of the phylum Phaeophyta. The processes and factors affecting the standing crop of macroalgae are many and complex. Two main hypotheses are identified in the literature as being the driving forces of algal dynamics: nutrient dynamics (availability, supply and uptake) and herbivory. However, many studies have been found to be inconclusive because of the complexity of the coral reef ecosystem, which makes it difficult if not impossible to control for all factors and processes influencing the standing crop of macroalgae such as light, water flow and sedimentation. The inherent characteristics of macroalgae, like morphology and life history, make them behave differently. Whilst herbivore characteristics, like size of mouth parts, feeding modes and preferences, will influence the amount of algal biomass removed. The spatial context (i.e. coral fore reef vs. back reef) will influence the effects of both bottom-up and top-down controls. Besides these inter-habitat differences, macroalgae within similar habitats but differing geographical locations may respond differently, for example, a forereef exposed to the open ocean or a forereef located in a sheltered bay. This thesis attempts to provide insight into the dynamics of two dominant brown macroalgae on Caribbean coral reefs, Dictyota spp. and Lobophora variegata. This aim was addressed by developing a model for the macroalga species Dictyota to model the various processes and factors on a coral forereef affecting percentage cover. Further, the patch dynamics of both Lobophora variegata and Dictyota were investigated to gain an insight into their dynamics under varying environmental conditions: the windward and leeward sides of an atoll. Finally, herbivory is identified as one of the key process affecting macroalgal cover. I investigated this process by deploying cages on both the windward and leeward side of the atoll to investigate the effects of grazing pressure under varying environmental conditions. A Bayesian Belief Network model was developed for Dictyota spp. to model the bottom-up and top-down processes on a coral forereef determining the percentage cover. The model was quantified using relationships identified in the scientific literature and from field data collected over a nine moth period in Belize. This is the first BBN model developed for brown macroalgae. The fully parameterized model identified areas of limited knowledge and because of its probabilistic nature it can explicitly communicate the uncertainties associated with the processes and interactions on standing crop. As such the model may be used as a framework for scientific research or monitoring programmes and it is expected that the model performance to predict macroalgal percentage cover will improve once new information becomes available. Size-based transition matrices were developed for both Dictyota spp. and Lobophora variegata to investigate the patch dynamics under varying environmental conditions: the windward and leeward sides of an atoll. The matrices reveal that standard measures of algal percent cover might provide a misleading insight into the underlying dynamics of the species. Modelling the patch dynamics with matrices provided insight into the temporal behaviour of macroalgae. This is an important process to understand because patch dynamics are determining competitive interactions with other coral reef benthic organisms. The outcome of competitive interactions will differ with macroalgal species. This study indicate that Dictyota spp. responded strongly to differing environmental conditions in that it has reduced growth rates and lower percent cover on the leeward side of the atoll, whilst Lobophora variegata showed far less sensitivity to environmental conditions. The patch dynamics of Dictyota spp. also showed a higher temporal variation than Lobophora variegata but only on the exposed forereef. A caging experiment was set up to investigate the response of both macroalgal species to different grazing pressure scenarios, under varying environmental conditions. Dictyota spp. had a significant response to environmental conditions in that a higher percentage cover was found on the exposed side of the atoll, whilst for Lobophora variegata the response was far less obvious. The less clear response of Lobophora variegata was very likely caused by competition of Dictyota with Lobophora due to the very high cover Dictyota obtained in the cages where all herbivores were excluded. The low grazing pressure treatments also showed an increase in cover of Dictyota, whilst for Lobophora, only a reduction in the rate of increase could be observed. The results indicate that on the leeward side of the atoll, fish grazing alone seems sufficient to control the standing crop of Dictyota and Lobophora variegata. Retrospective analysis of the experimental design showed that the limited size of the experimental set up could have confounded the results for Lobophora as well. In future experiments it is recommended to increase number replicates. Management of coral reef habitats is frequently constrained by a lack of funds and resources. The BBN Model once fully parameterized can provide a useful tool for coral reef management, because the model allows exploration of different reef scenario’s, which in turn can aid in prioritizing management strategies. Furthermore, the thesis provided an insight into the complexities of macroalgal dynamics. The responses of macroalgae to physiological factors and ecological processes are species specific and dependent on the location, and caution against generalizing on what controls the standing crop of macroalgae. Therefore it is argued that future investigations into algal ecology should clearly define the species, habitat and location. This can help to make informed management decisions.

<p>Population viability analysis (PVA) is commonly used in conservation biology to predict population viability in terms of population growth rate and risk of extinction. However, large data requirements limit the use of PVA for many rare and threatened species. This thesis examines the possibility of conducting a matrix model-based PVA for plants with limited data and provides some practical recommendations for reducing the amount of work required. Moreover, the thesis applies different forms of matrix population models to species with different life histories. Matrix manipulations on 37 plant species revealed that the amount of demographic data required can often be reduced using a smaller matrix dimensionality. Given that an individual’s fitness is affected by plant density, linear matrix models are unlikely to predict population dynamics correctly. Estimates of population size of the herb <i>Melampyrum sylvaticum</i> were sensitive to the strength of density dependence operating at different life stages, suggesting that in addition to identifying density-dependent life stages, it is important to estimate the strength of density dependence precisely. When a small number of matrices are available for stochastic matrix population models, the precision of population estimates may depend on the stochastic method used. To optimize the precision of population estimates and the amount of calculation effort in stochastic matrix models, selection of matrices and Tuljapurkar’s approximation are preferable methods to assess population viability. Overall, these results emphasize that in a matrix model-based PVA, the selection of a stage classification and a model is essential because both factors significantly affect the amount of data required as well as the precision of population estimates. By integrating population dynamics into different environmental and genetic factors, matrix population models may be used more effectively in conservation biology and ecology in the future.</p>

Population viability analysis (PVA) is commonly used in conservation biology to predict population viability in terms of population growth rate and risk of extinction. However, large data requirements limit the use of PVA for many rare and threatened species. This thesis examines the possibility of conducting a matrix model-based PVA for plants with limited data and provides some practical recommendations for reducing the amount of work required. Moreover, the thesis applies different forms of matrix population models to species with different life histories. Matrix manipulations on 37 plant species revealed that the amount of demographic data required can often be reduced using a smaller matrix dimensionality. Given that an individual’s fitness is affected by plant density, linear matrix models are unlikely to predict population dynamics correctly. Estimates of population size of the herb Melampyrum sylvaticum were sensitive to the strength of density dependence operating at different life stages, suggesting that in addition to identifying density-dependent life stages, it is important to estimate the strength of density dependence precisely. When a small number of matrices are available for stochastic matrix population models, the precision of population estimates may depend on the stochastic method used. To optimize the precision of population estimates and the amount of calculation effort in stochastic matrix models, selection of matrices and Tuljapurkar’s approximation are preferable methods to assess population viability. Overall, these results emphasize that in a matrix model-based PVA, the selection of a stage classification and a model is essential because both factors significantly affect the amount of data required as well as the precision of population estimates. By integrating population dynamics into different environmental and genetic factors, matrix population models may be used more effectively in conservation biology and ecology in the future.

No description available.

The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident. Optimal experimental design (OD) describes the procedure of maximizing relevant information in drug development and drug treatment processes. While various optimization criteria can be considered in OD, the most common is to optimize the unknown model parameters for an upcoming study. To date, OD has mainly been used to optimize the independent variables, e.g. sample times, but it can be used for any design variable in a study. This thesis addresses the OD of multiple continuous or discrete design variables for nonlinear mixed effects models. The methodology for optimizing and the optimization of different types of models with either continuous or discrete data are presented and the benefits of OD for such models are shown. A software tool for optimizing these models in parallel is developed and three OD examples are demonstrated: 1) optimization of an intravenous glucose tolerance test resulting in a reduction in the number of samples by a third, 2) optimization of drug compound screening experiments resulting in the estimation of nonlinear kinetics and 3) an individual dose-finding study for the treatment of children with ciclosporin before kidney transplantation resulting in a reduction in the number of blood samples to ~27% of the original number and an 83% reduction in the study duration. This thesis uses examples and methodology to show that studies in drug development and drug treatment can be optimized using nonlinear mixed effects OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development and drug treatment.

Despite the growing promise of pharmaceutical research, inferior experimentation or interpretation of data can inhibit breakthrough molecules from finding their way out of research institutions and reaching patients. This thesis provides evidence that better characterization of pre-clinical and clinical data can be accomplished using non-linear mixed effect modeling (NLMEM) and more effective experiments can be conducted using optimal design (OD).  To demonstrate applicability of NLMEM and OD in pre-clinical applications, in vitro ligand binding studies were examined. NLMEMs were used to evaluate precision and accuracy of ligand binding parameter estimation from different ligand binding experiments using sequential (NLR) and simultaneous non-linear regression (SNLR). SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR.  OD of these ligand binding experiments for one and two binding site systems including commonly encountered experimental errors was performed.  OD was employed using D- and ED-optimality.  OD demonstrated that reducing the number of samples, measurement times, and separate ligand concentrations provides robust parameter estimation and more efficient and cost effective experimentation. To demonstrate applicability of NLMEM and OD in clinical applications, a phase advanced sleep study formed the basis of this investigation. A mixed-effect Markov-chain model based on transition probabilities as multinomial logistic functions using polysomnography data in phase advanced subjects was developed and compared the sleep architecture between this population and insomniac patients. The NLMEM was sufficiently robust for describing the data characteristics in phase advanced subjects, and in contrast to aggregated clinical endpoints, which provide an overall assessment of sleep behavior over the night, described the dynamic behavior of the sleep process. OD of a dichotomous, non-homogeneous, Markov-chain phase advanced sleep NLMEM was performed using D-optimality by computing the Fisher Information Matrix for each Markov component.  The D-optimal designs improved the precision of parameter estimates leading to more efficient designs by optimizing the doses and the number of subjects in each dose group.  This thesis provides examples how studies in drug development can be optimized using NLMEM and OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development. / <p>My name should be listed as "Charles Steven Ernest II" on cover.</p>

A unique K-band selected high-redshift spectroscopic dataset (UDSz) is exploited to gain further understanding of galaxy evolution at z > 1. Acquired as part of an ESO Large Programme, this thesis presents the reduction and analysis of a sample of ∼ 450 deep optical spectra of a random 1 in 6 sample of the KAB < 23, z > 1 galaxy population. Based on the final reduced dataset, spectrophotometric modelling of the optical spectra and multi-wavelength photometry available for each galaxy is performed using a combination of single and dual component stellar population models. The stellarmass and age estimates provided by the spectrophotometric modelling are exploited throughout the rest of the thesis to investigate the evolution of massive galaxies at z > 1. Focusing on a K-band bright (K < 21.5) sub-sample in the redshift range 1.3 < z < 1.5 the galaxy size-mass relation has been studied in detailed. In agreement with some previous studies it is found that massive, old, early-type galaxies (ETGs) have characteristic radii a factor ~- 1.5 − 3.0 smaller than their local counterparts at a given stellar-mass. Due to the potential errors in spectrophotometric estimates of the stellarmasses at high redshift velocity dispersion measurements are derived for a sub-sample of massive ETGs at z > 1.3 in order to calculate dynamical mass estimates. To date, only a handful of objects at z > 1.3 have individual velocity dispersion estimates in the literature. Here the largest single sample (13 objects) of velocity dispersion measurements at high redshift is presented. The results for the sub-sample of objects with dynamical mass estimates confirm the results based on stellar mass estimates that high redshift massive systems are more compact than their local counterparts. The fraction of K-band bright objects at high redshift that are passively evolving is calculated with specific star-formation rates from the UV rest-frame continuum, [OII] emission and 24μm data. It is concluded that ∼ 58 ± 10% of the K < 21.5, 1.3 < z < 1.5 galaxy population is passively evolving. Various photometric techniques for separating star-forming and passively evolving galaxies are assessed by exploiting the accurate spectral types derived for the UDSz spectroscopic sample. Popular highredshift selection techniques are shown to fail to effectively select complete samples of passive objects with low levels of contamination. Using detailed information available for the UDSz dataset, various techniques are optimised and then used to estimate the passive fraction from the full UDS photometric catalog. The passive fraction results from the full photometric catalog are found to agree well with the results derived from the UDSz sample. With the Visible and Infrared Survey Telescope for Astronomy (VISTA) now starting to produce data, the opportunity has been taken to develop high-redshift galaxy population dividers based on the VISTA filters. Using the first data release from the VISTA Deep Extragalactic Observations (VIDEO) survey (VVDS D1 field), the passive fractions of K-band limited samples have been estimated to compare with results derived in the UDS. Within the errors the passive fraction estimates in the UDS and VISTA VVDS D1 field are found to agree reasonably well. Finally, composite spectra are used to study the evolution of various different galaxy sub-samples as a function of redshift, age, stellar-mass and specific star-formation rate. This work produces an remarkably clean result, showing that the massive, absolute Kband bright, passively evolving ETGs are always the oldest population, with ages close to the age of the Universe at z ∼ 1.4. In contrast, the late-type, low-mass, star-forming galaxies are always found to be much younger systems. This result strongly supports the downsizing scenario, in which more massive systems complete their stellar-mass assembly before lower-mass counterparts.