Willingness to Pay for Rural Landscape Improvements: Combining Mixed Logit and Random-Effects Models

This paper reports the findings from a discrete-choice experiment designed to estimate the economic benefits associated with rural landscape improvements in Ireland. Using a mixed logit model, the panel nature of the dataset is exploited to retrieve willingness-to-pay values for every individual in the sample. This departs from customary approaches in which the willingness-to-pay estimates are normally expressed as measures of central tendency of an a priori distribution. Random-effects models for panel data are subsequently used to identify the determinants of the individual-specific willingness-to-pay estimates. In comparison with the standard methods used to incorporate individual-specific variables into the analysis of discrete-choice experiments, the analytical approach outlined in this paper is shown to add considerable explanatory power to the welfare estimates.

Ecological differences and coexistence in a guild of microparasites: Bartonella in wild rodents

The study of ecological differences among coexisting microparasites has been largely neglected, but it addresses important and unusual issues because there is no clear distinction in such cases between conventional (resource) and apparent competition. Here patterns in the population dynamics are examined for four species of Bartonella (bacterial parasites) coexisting in two wild rodent hosts, bank voles (Clethrionomys glareolus) and wood mice (Apodemus sylvaticus). Using generalized linear modeling and mixed effects models, we examine, for these four species, seasonal patterns and dependencies on host density (both direct and delayed) and, having accounted for these, any differences in prevalence between the two hosts. Whereas previous studies had failed to uncover species differences, here all four were different. Two, B. doshiae and B. taylorii, were more prevalent in wood mice, and one, B. birtlesii, was more prevalent in bank voles. B. birtlesii, B. grahamii, and B. taylorii peaked in prevalence in the fall, whereas B. doshiae peaked in spring. For B. birtlesii in bank voles, density dependence was direct, but for B. taylorii in wood mice density dependence was delayed. B. birtlesii prevalence in wood mice was related to bank vole density. The implications of these differences for species coexistence are discussed.

Integrating personality research and animal contest theory:Aggressiveness in the green swordtail xiphophorus helleri

Aggression occurs when individuals compete over limiting resources. While theoretical studies have long placed a strong emphasis on context-specificity of aggression, there is increasing recognition that consistent behavioural differences exist among individuals, and that aggressiveness may be an important component of individual personality. Though empirical studies tend to focus on one aspect or the other, we suggest there is merit in modelling both within- and among-individual variation in agonistic behaviour simultaneously. Here, we demonstrate how this can be achieved using multivariate linear mixed effect models. Using data from repeated mirror trials and dyadic interactions of male green swordtails, Xiphophorus helleri, we show repeatable components of (co)variation in a suite of agonistic behaviour that is broadly consistent with a major axis of variation in aggressiveness. We also show that observed focal behaviour is dependent on opponent effects, which can themselves be repeatable but were more generally found to be context specific. In particular, our models show that within-individual variation in agonistic behaviour is explained, at least in part, by the relative size of a live opponent as predicted by contest theory. Finally, we suggest several additional applications of the multivariate models demonstrated here. These include testing the recently queried functional equivalence of alternative experimental approaches, (e.g., mirror trials, dyadic interaction tests) for assaying individual aggressiveness. © 2011 Wilson et al.

Patterns of synchrony in natterjack toad breeding activity and reproductive success at local and regional scales

Empirical studies of the spatiotemporal dynamics of populations are required to better understand natural fluctuations in abundance and reproductive success, and to better target conservation and monitoring programmes. In particular, spatial synchrony in amphibian populations remains little studied. We used data from a comprehensive three year study of natterjack toad Bufo calamita populations breeding at 36 ponds to assess whether there was spatial synchrony in the toad breeding activity (start and length of breeding season, total number of egg strings) and reproductive success (premetamorphic survival and production of metamorphs). We defined a novel approach to assess the importance of short-term synchrony at both local and regional scales. The approach employs similarity indices and quantifies the interaction between the temporal and spatial components of populations using mixed effects models. There was no synchrony in the toad breeding activity and reproductive success at the local scale, suggesting that populations function as individual clusters independent of each other. Regional synchrony was apparent in the commencement and duration of the breeding season and in the number of egg strings laid (indicative of female population size). Regional synchrony in both rainfall and temperature are likely to explain the patterns observed (e.g. Moran effect). There was no evidence supporting regional synchrony in reproductive success, most likely due to spatial variability in the environmental conditions at the breeding ponds, and to differences in local population fitness (e.g. fecundity). The small scale asynchronous dynamics and regional synchronous dynamics in the number of breeding females indicate that it is best to monitor several populations within a subset of regions. Importantly, variations in the toad breeding activity and reproductive success are not synchronous, and it is thus important to consider them both when assessing the conservation status of pond-breeding amphibians. © 2012 The Authors. Ecography © 2012 Nordic Society Oikos.

Population pharmacokinetic and pharmacogenetic analysis of tacrolimus in paediatric liver transplant patients

Aims: To build a population pharmacokinetic model that describes the apparent clearance of tacrolimus and the potential demographic, clinical and genetically controlled factors that could lead to inter-patient pharmacokinetic variability within children following liver transplantation.

Methods: The present study retrospectively examined tacrolimus whole blood pre-dose concentrations (n = 628) of 43 children during their first year post-liver transplantation. Population pharmacokinetic analysis was performed using the non-linear mixed effects modelling program (nonmem) to determine the population mean parameter estimate of clearance and influential covariates.

Results: The final model identified time post-transplantation and CYP3A5*1 allele as influential covariates on tacrolimus apparent clearance according to the following equation:

TVCL=12.9×(Weight /13.2)^0.75×EXP(-0.00158×TPT)×EXP(0.428×CYP3A5)

where TVCL is the typical value for apparent clearance, TPT is time post-transplantation in days and the CYP3A5 is 1 where*1 allele is present and 0 otherwise. The population estimate and inter-individual variability (%CV) of tacrolimus apparent clearance were found to be 0.977 l h kg (95% CI 0.958, 0.996) and 40.0%, respectively, while the residual variability between the observed and predicted concentrations was 35.4%.

Conclusion: Tacrolimus apparent clearance was influenced by time post-transplantation and CYP3A5 genotypes. The results of this study, once confirmed by a large scale prospective study, can be used in conjunction with therapeutic drug monitoring to recommend tacrolimus dose adjustments that take into account not only body weight but also genetic and time-related changes in tacrolimus clearance. © 2013 The British Pharmacological Society.

An Observational Study of Blood Concentrations and Kinetics of Methyl- and Propyl-Parabens in Neonates

Purpose: Systemic exposure to parabens in the neonatal population, in particular propyl-parabens (PPB), remains a concern. Blood concentrations and kinetics of methyl-parabens (MPB) and PPB were therefore determined in neonates receiving medicines containing these excipients.

Methods: A multi-centre, non-interventional, observational study of excipient-kinetics in neonates. ‘Dried Blood Spot’ samples were collected opportunistically at the same time as routine samples and the observations modelled using a non-linear mixed effects approach.

Results: A total of 841 blood MPB and PPB concentration data were available for evaluation from 181 pre- and term-neonates. Quantifiable blood concentrations of MPB and PPB were observed in 99% and 49% of patients, and 55% and 25% of all concentrations were above limit of detection (10 ng/ml), respectively. Only MPB data was amenable to modelling. Oral bioavailability was influenced by type of formulation and disposition was best described by a two compartment model with clearance (CL) influenced by post natal age (PNA); CL_{PNA<21 days} 0.57 versus CL_PNA>21days 0.88 L/h.

Conclusions: Daily repeated administration of parabens containing medicines can result in prolonged systemic exposure to the parent compound in neonates. Animal toxicology studies of PPB that specifically address the neonatal period are required before a permitted daily exposure for this age group can be established.

Outlier effects on robust joint modelling of longitudinal and survival data

Robust joint modelling is an emerging field of research. Through the advancements in electronic patient healthcare records, the popularly of joint modelling approaches has grown rapidly in recent years providing simultaneous analysis of longitudinal and survival data. This research advances previous work through the development of a novel robust joint modelling methodology for one of the most common types of standard joint models, that which links a linear mixed model with a Cox proportional hazards model. Through t-distributional assumptions, longitudinal outliers are accommodated with their detrimental impact being down weighed and thus providing more efficient and reliable estimates. The robust joint modelling technique and its major benefits are showcased through the analysis of Northern Irish end stage renal disease patients. With an ageing population and growing prevalence of chronic kidney disease within the United Kingdom, there is a pressing demand to investigate the detrimental relationship between the changing haemoglobin levels of haemodialysis patients and their survival. As outliers within the NI renal data were found to have significantly worse survival, identification of outlying individuals through robust joint modelling may aid nephrologists to improve patient's survival. A simulation study was also undertaken to explore the difference between robust and standard joint models in the presence of increasing proportions and extremity of longitudinal outliers. More efficient and reliable estimates were obtained by robust joint models with increasing contrast between the robust and standard joint models when a greater proportion of more extreme outliers are present. Through illustration of the gains in efficiency and reliability of parameters when outliers exist, the potential of robust joint modelling is evident. The research presented in this thesis highlights the benefits and stresses the need to utilise a more robust approach to joint modelling in the presence of longitudinal outliers.

Automated mixed dimensional modelling from 2D and 3D CAD models

The motivation for this paper is to present procedures for automatically creating idealised finite element models from the 3D CAD solid geometry of a component. The procedures produce an accurate and efficient analysis model with little effort on the part of the user. The technique is applicable to thin walled components with local complex features and automatically creates analysis models where 3D elements representing the complex regions in the component are embedded in an efficient shell mesh representing the mid-faces of the thin sheet regions. As the resulting models contain elements of more than one dimension, they are referred to as mixed dimensional models. Although these models are computationally more expensive than some of the idealisation techniques currently employed in industry, they do allow the structural behaviour of the model to be analysed more accurately, which is essential if appropriate design decisions are to be made. Also, using these procedures, analysis models can be created automatically whereas the current idealisation techniques are mostly manual, have long preparation times, and are based on engineering judgement. In the paper the idealisation approach is first applied to 2D models that are used to approximate axisymmetric components for analysis. For these models 2D elements representing the complex regions are embedded in a 1D mesh representing the midline of the cross section of the thin sheet regions. Also discussed is the coupling, which is necessary to link the elements of different dimensionality together. Analysis results from a 3D mixed dimensional model created using the techniques in this paper are compared to those from a stiffened shell model and a 3D solid model to demonstrate the improved accuracy of the new approach. At the end of the paper a quantitative analysis of the reduction in computational cost due to shell meshing thin sheet regions demonstrates that the reduction in degrees of freedom is proportional to the square of the aspect ratio of the region, and for long slender solids, the reduction can be proportional to the aspect ratio of the region if appropriate meshing algorithms are used.

Determination of Non-linear Damping Coefficients of bottom-hinged Oscillating Wave Surge Converters Using Numerical Free Decay Tests

Linear wave theory models are commonly applied to predict the performance of bottom-hinged oscillating wave surge converters (OWSC) in operational sea states. To account for non-linear effects, the additional input of coefficients not included in the model itself becomes necessary. In ocean engineering it is
common practice to obtain damping coefficients of floating structures from free decay tests. This paper presents results obtained from experimental tank tests and numerical computational fluid dynamics simulations of OWSC’s. Agreement between numerical and experimental methods is found to be very good, with CFD providing more data points at small amplitude rotations.
Analysis of the obtained data reveals that linear quadratic-damping, as commonly used in time domain models, is not able to accurately model the occurring damping over the whole regime of rotation amplitudes. The authors
conclude that a hyperbolic function is most suitable to express the instantaneous damping ratio over the rotation amplitude and would be the best choice to be used in coefficient based time domain models.

Fast and frugal models of clinical judgement in novice and expert physicians

Our objective was to study whether “compensatory” models provide better descriptions of clinical judgment than fast and frugal models, according to expertise and experience. Fifty practitioners appraised 60 vignettes describing a child with an exacerbation of asthma and rated their propensities to admit the child. Linear logistic (LL) models of their judgments were compared with a matching heuristic (MH) model that searched available cues in order of importance for a critical value indicating an admission decision. There was a small difference between the 2 models in the proportion of patients allocated correctly (admit or not-admit decisions), 91.2% and 87.8%, respectively. The proportion allocated correctly by the LL model was lower for consultants than juniors, whereas the MH model performed equally well for both. In this vignette study, neither model provided any better description of judgments made by consultants or by pediatricians compared to other grades and specialties.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

Applying species distribution modelling to identify areas of high conservation value for endangered species: A case study using Margaritifera margaritifera (L.)

The development and implementation of a population supplementation and restoration plan for any endangered species should involve an understanding of the species’ habitat requirements prior to the release of any captive bred individuals. The freshwater pearl mussel, Margaritifera margaritifera, has undergone dramatic declines over the last century and is now globally endangered. In Northern Ireland, the release of captive bred individuals is being used to support wild populations and repatriate the species in areas where it once existed. We employed a combination of maximum entropy modelling (MAXENT) and Generalized Linear Mixed Models (GLMM) to identify ecological parameters necessary to support wild populations using GIS-based landscape scale and ground-truthed habitat scale environmental parameters. The GIS-based landscape scale model suggested that mussel occurrence was associated with altitude and soil characteristics including the carbon, clay, sand, and silt content. Notably, mussels were associated with a relatively narrow band of variance indicating that M. margaritifera has a highly specific landscape niche. The ground-truthed habitat scale model suggested that mussel occurrence was associated with stable consolidated substrates, the extent of bankside trees, presence of indicative macrophyte species and fast flowing water. We propose a three phase conservation strategy for M. margaritifera identifying suitable areas within rivers that (i) have a high conservation value yet needing habitat restoration at a local level, (ii) sites for population supplementation of existing populations and (iii) sites for species reintroduction to rivers where the mussel historically occurred but is now locally extinct. A combined analytical approach including GIS-based landscape scale and ground-truthed habitat scale models provides a robust method by which suitable release sites can be identified for the population supplementation and restoration of an endangered species. Our results will be highly influential in the future management of M. margaritifera in Northern Ireland.