Invasion and eradication of a competitively superior species in heterogeneous landscapes

The effect of heterogeneous environments upon the dynamics of invasion and the eradication or control of invasive species is poorly understood, although it is a major challenge for biodiversity conservation. Here, we first investigate how the probability and time for invasion are affected by spatial heterogeneity. Then, we study the effect of control program strategies (e.g. species specificity, spatial scale of action, detection and eradication efficiency) on the success and time of eradication. We find that heterogeneity increases both the invasion probability and the time to invasion. Heterogeneity also reduces the probability of eradication but does not change the time taken for successful eradication. We confirm that early detection of invasive species reduces the time until eradication, but we also demonstrate that this is true only if the local control action is sufficiently efficient. The criterion of removal efficiency is even more important for an eradication program than simply ensuring control effort when the invasive species is not abundant.

Dosage optimization of treatments using population pharmacokinetic modeling and simulation.

Pharmacokinetic variability in drug levels represent for some drugs a major determinant of treatment success, since sub-therapeutic concentrations might lead to toxic reactions, treatment discontinuation or inefficacy. This is true for most antiretroviral drugs, which exhibit high inter-patient variability in their pharmacokinetics that has been partially explained by some genetic and non-genetic factors. The population pharmacokinetic approach represents a very useful tool for the description of the dose-concentration relationship, the quantification of variability in the target population of patients and the identification of influencing factors. It can thus be used to make predictions and dosage adjustment optimization based on Bayesian therapeutic drug monitoring (TDM). This approach has been used to characterize the pharmacokinetics of nevirapine (NVP) in 137 HIV-positive patients followed within the frame of a TDM program. Among tested covariates, body weight, co-administration of a cytochrome (CYP) 3A4 inducer or boosted atazanavir as well as elevated aspartate transaminases showed an effect on NVP elimination. In addition, genetic polymorphism in the CYP2B6 was associated with reduced NVP clearance. Altogether, these factors could explain 26% in NVP variability. Model-based simulations were used to compare the adequacy of different dosage regimens in relation to the therapeutic target associated with treatment efficacy. In conclusion, the population approach is very useful to characterize the pharmacokinetic profile of drugs in a population of interest. The quantification and the identification of the sources of variability is a rational approach to making optimal dosage decision for certain drugs administered chronically.

Modelling hydrodynamics in the Rio Parana, Argentina : an evaluation and inter-comparison of reduced-complexity and physics based models applied to a large sand-bed river

Depth-averaged velocities and unit discharges within a 30 km reach of one of the world's largest rivers, the Rio Parana, Argentina, were simulated using three hydrodynamic models with different process representations: a reduced complexity (RC) model that neglects most of the physics governing fluid flow, a two-dimensional model based on the shallow water equations, and a three-dimensional model based on the Reynolds-averaged Navier-Stokes equations. Row characteristics simulated using all three models were compared with data obtained by acoustic Doppler current profiler surveys at four cross sections within the study reach. This analysis demonstrates that, surprisingly, the performance of the RC model is generally equal to, and in some instances better than, that of the physics based models in terms of the statistical agreement between simulated and measured flow properties. In addition, in contrast to previous applications of RC models, the present study demonstrates that the RC model can successfully predict measured flow velocities. The strong performance of the RC model reflects, in part, the simplicity of the depth-averaged mean flow patterns within the study reach and the dominant role of channel-scale topographic features in controlling the flow dynamics. Moreover, the very low water surface slopes that typify large sand-bed rivers enable flow depths to be estimated reliably in the RC model using a simple fixed-lid planar water surface approximation. This approach overcomes a major problem encountered in the application of RC models in environments characterised by shallow flows and steep bed gradients. The RC model is four orders of magnitude faster than the physics based models when performing steady-state hydrodynamic calculations. However, the iterative nature of the RC model calculations implies a reduction in computational efficiency relative to some other RC models. A further implication of this is that, if used to simulate channel morphodynamics, the present RC model may offer only a marginal advantage in terms of computational efficiency over approaches based on the shallow water equations. These observations illustrate the trade off between model realism and efficiency that is a key consideration in RC modelling. Moreover, this outcome highlights a need to rethink the use of RC morphodynamic models in fluvial geomorphology and to move away from existing grid-based approaches, such as the popular cellular automata (CA) models, that remain essentially reductionist in nature. In the case of the world's largest sand-bed rivers, this might be achieved by implementing the RC model outlined here as one element within a hierarchical modelling framework that would enable computationally efficient simulation of the morphodynamics of large rivers over millennial time scales. (C) 2012 Elsevier B.V. All rights reserved.

Melanin-based colorations signal strategies to cope with poor and rich environments

One hypothesis for the maintenance of genetic variation states that alternative genotypes are adapted to different environmental conditions (i.e., genotype-by-environment interaction GxE) that vary in space and time. Although GxE has been demonstrated for morphological traits, little evidence has been given whether these GxE are associated with traits used as signal in mate choice. In three wild bird species, we investigated whether the degree of melanin-based coloration, a heritable trait, covaries with nestling growth rate in rich and poor environments. Variation in the degree of reddish-brown phaeomelanism is pronounced in the barn owl (Tyto alba) and tawny owl (Strix aluco), and variation in black eumelanism in the barn owl and Alpine swift (Apus melba). Melanin-based coloration has been shown to be a criterion in mate choice in the barn owl. We cross-fostered hatchlings to test whether nestlings sired by parents displaying melanin-based colorations to different extent exhibit alternative growth trajectories when raised by foster parents in poor (experimentally enlarged broods) and rich (experimentally reduced broods) environments. With respect to phaeomelanism, barn owl and tawny owl offspring sired by redder parents grew more rapidly in body mass only in experimentally reduced broods. With respect to eumelanism, Alpine swift offspring of darker fathers grew their wings more rapidly only in experimentally enlarged broods, a difference that was not detected in reduced broods. These interactions between parental melanism and offspring growth rate indicate that individuals display substantial plasticity in response to the rearing environment which is associated with the degree of melanism: at least with respect to nestling growth, phaeomelanic and eumelanic individuals are best adapted to rich and poor environments, respectively. It now remains to be investigated why eumelanism and phaeomelanism have a different signaling function and what the lifelong consequences of these melanism-dependent allocation strategies are. This is important to fully appraise the role played by environmental heterogeneity in maintaining variation in the degree of melanin-based coloration.

Fate of Hydrophilic Nanoparticles in Biological Environments

The nanoparticles developed are based on chitosan, a biocompatible and biodegradable polysaccharide. The chitosan nanoparticles are formed in an entirely water-based process by electrostatic interactions with other biocompatible molecules. As a prerequisite to understand the fate of such nanoparticles in cells, comprehensive characterization and stability studies serve to identify quantitatively the impact of the raw material characteristics and preparation conditions on the nanoparticle characteristics. Methods included H-1 NMR spectroscopy, dilution viscometry, particle size analysis and electron microscopy. Cytotoxicity and cell uptake experiments on RAW 264.7 murine macrophages and p23 murine endothelial cells were performed to investigate the correlation with nanoparticle characteristics and effect of surface decoration with alginate. Cytotoxicity was assessed by the MTT survival test; cell uptake was monitored by fluorescent microscopy using labeled polymers.

Impact de la prophylaxie hormonale sur les fractures du fémur proximal des femmes postménopausiques: une étude de simulation. [Impact of hormonal prevention on fractures of the proximal femur in postmenopausal women: a simulation study].

A simulation model of the effects of hormone replacement therapy (HRT) on hip fractures and their consequences is based on a population of 100,000 post-menopausal women. This cohort is confronted with literature derived probabilities of cancers (endometrium or breast, which are contra-indications to HRT), hip fracture, disability requiring nursing home or home care, and death. Administration of HRT for life prevents 55,5% of hip fractures, 22,6% of years with home care and 4,4% of years in nursing homes. If HRT is administered for 15 years, these results are 15,5%, 10% and 2,2%, respectively. A slight gain in life expectancy is observed for both durations of HRT. The net financial loss in the simulated population is 222 million Swiss Francs (cost/benefit ratio 1.25) for lifelong administration of HRT, and 153 million Swiss Francs (cost/benefit ratio 1.42) if HRT is administered during 15 years.

Simulit, un modèle de simulation pour la planification hospitalière. [Simulit, a simulation model for hospital planning].

The aim of this computerized simulation model is to provide an estimate of the number of beds used by a population, taking into accounts important determining factors. These factors are demographic data of the deserved population, hospitalization rates, hospital case-mix and length of stay; these parameters can be taken either from observed data or from scenarii. As an example, the projected evolution of the number of beds in Canton Vaud for the period 1893-2010 is presented.

Quantitative comparison between simulations of seismic wave propagation in heterogeneous poro-elastic media and equivalent visco-elastic solids for marine-type environments

There is increasing evidence to suggest that the presence of mesoscopic heterogeneities constitutes an important seismic attenuation mechanism in porous rocks. As a consequence, centimetre-scale perturbations of the rock physical properties should be taken into account for seismic modelling whenever detailed and accurate responses of specific target structures are desired, which is, however, computationally prohibitive. A convenient way to circumvent this problem is to use an upscaling procedure to replace each of the heterogeneous porous media composing the geological model by corresponding equivalent visco-elastic solids and to solve the visco-elastic equations of motion for the inferred equivalent model. While the overall qualitative validity of this procedure is well established, there are as of yet no quantitative analyses regarding the equivalence of the seismograms resulting from the original poro-elastic and the corresponding upscaled visco-elastic models. To address this issue, we compare poro-elastic and visco-elastic solutions for a range of marine-type models of increasing complexity. We found that despite the identical dispersion and attenuation behaviour of the heterogeneous poro-elastic and the equivalent visco-elastic media, the seismograms may differ substantially due to diverging boundary conditions, where there exist additional options for the poro-elastic case. In particular, we observe that at the fluid/porous-solid interface, the poro- and visco-elastic seismograms agree for closed-pore boundary conditions, but differ significantly for open-pore boundary conditions. This is an important result which has potentially far-reaching implications for wave-equation-based algorithms in exploration geophysics involving fluid/porous-solid interfaces, such as, for example, wavefield decomposition.

Simulated-annealing-based conditional simulation for the local-scale characterization of heterogeneous aquifers

Simulated-annealing-based conditional simulations provide a flexible means of quantitatively integrating diverse types of subsurface data. Although such techniques are being increasingly used in hydrocarbon reservoir characterization studies, their potential in environmental, engineering and hydrological investigations is still largely unexploited. Here, we introduce a novel simulated annealing (SA) algorithm geared towards the integration of high-resolution geophysical and hydrological data which, compared to more conventional approaches, provides significant advancements in the way that large-scale structural information in the geophysical data is accounted for. Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data. This is the only place where geophysical information is utilized in our algorithm, which is in marked contrast to other approaches where model perturbations are made through the swapping of values in the simulation grid and agreement with soft data is enforced through a correlation coefficient constraint. Another major feature of our algorithm is the way in which available geostatistical information is utilized. Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information. Thus we allow the larger-scale subsurface features resolved by the geophysical data to have much more due control on the output realizations. Further, since the only component of the SA objective function required in our approach is a covariance constraint at small lags, our method has improved convergence and computational efficiency over more traditional methods. Here, we present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on a synthetic data set, and then applied to data collected at the Boise Hydrogeophysical Research Site.

Spatial learning by rats across visually disconnected environments

Two spatial tasks were designed to test specific properties of spatial representation in rats. In the first task, rats were trained to locate an escape hole at a fixed position in a visually homogeneous arena. This arena was connected with a periphery where a full view of the room environment existed. Therefore, rats were dependent on their memory trace of the previous position in the periphery to discriminate a position within the central region. Under these experimental conditions, the test animals showed a significant discrimination of the training position without a specific local view. In the second task, rats were trained in a radial maze consisting of tunnels that were transparent at their distal ends only. Because the central part of the maze was non-transparent, rats had to plan and execute appropriate trajectories without specific visual feedback from the environment. This situation was intended to encourage the reliance on prospective memory of the non-visited arms in selecting the following move. Our results show that acquisition performance was only slightly decreased compared to that shown in a completely transparent maze and considerably higher than in a translucent maze or in darkness. These two series of experiments indicate (1) that rats can learn about the relative position of different places with no common visual panorama, and (2) that they are able to plan and execute a sequence of visits to several places without direct visual feed-back about their relative position.

Providing more informative projections of climate change impact on plant distribution in a mountain environment

Summary Due to their conic shape and the reduction of area with increasing elevation, mountain ecosystems were early identified as potentially very sensitive to global warming. Moreover, mountain systems may experience unprecedented rates of warming during the next century, two or three times higher than that records of the 20th century. In this context, species distribution models (SDM) have become important tools for rapid assessment of the impact of accelerated land use and climate change on the distribution plant species. In my study, I developed and tested new predictor variables for species distribution models (SDM), specific to current and future geographic projections of plant species in a mountain system, using the Western Swiss Alps as model region. Since meso- and micro-topography are relevant to explain geographic patterns of plant species in mountain environments, I assessed the effect of scale on predictor variables and geographic projections of SDM. I also developed a methodological framework of space-for-time evaluation to test the robustness of SDM when projected in a future changing climate. Finally, I used a cellular automaton to run dynamic simulations of plant migration under climate change in a mountain landscape, including realistic distance of seed dispersal. Results of future projections for the 21st century were also discussed in perspective of vegetation changes monitored during the 20th century. Overall, I showed in this study that, based on the most severe A1 climate change scenario and realistic dispersal simulations of plant dispersal, species extinctions in the Western Swiss Alps could affect nearly one third (28.5%) of the 284 species modeled by 2100. With the less severe 61 scenario, only 4.6% of species are predicted to become extinct. However, even with B1, 54% (153 species) may still loose more than 80% of their initial surface. Results of monitoring of past vegetation changes suggested that plant species can react quickly to the warmer conditions as far as competition is low However, in subalpine grasslands, competition of already present species is probably important and limit establishment of newly arrived species. Results from future simulations also showed that heavy extinctions of alpine plants may start already in 2040, but the latest in 2080. My study also highlighted the importance of fine scale and regional. assessments of climate change impact on mountain vegetation, using more direct predictor variables. Indeed, predictions at the continental scale may fail to predict local refugees or local extinctions, as well as loss of connectivity between local populations. On the other hand, migrations of low-elevation species to higher altitude may be difficult to predict at the local scale. Résumé La forme conique des montagnes ainsi que la diminution de surface dans les hautes altitudes sont reconnues pour exposer plus sensiblement les écosystèmes de montagne au réchauffement global. En outre, les systèmes de montagne seront sans doute soumis durant le 21ème siècle à un réchauffement deux à trois fois plus rapide que celui mesuré durant le 20ème siècle. Dans ce contexte, les modèles prédictifs de distribution géographique de la végétation se sont imposés comme des outils puissants pour de rapides évaluations de l'impact des changements climatiques et de la transformation du paysage par l'homme sur la végétation. Dans mon étude, j'ai développé de nouvelles variables prédictives pour les modèles de distribution, spécifiques à la projection géographique présente et future des plantes dans un système de montagne, en utilisant les Préalpes vaudoises comme zone d'échantillonnage. La méso- et la microtopographie étant particulièrement adaptées pour expliquer les patrons de distribution géographique des plantes dans un environnement montagneux, j'ai testé les effets d'échelle sur les variables prédictives et sur les projections des modèles de distribution. J'ai aussi développé un cadre méthodologique pour tester la robustesse potentielle des modèles lors de projections pour le futur. Finalement, j'ai utilisé un automate cellulaire pour simuler de manière dynamique la migration future des plantes dans le paysage et dans quatre scénarios de changement climatique pour le 21ème siècle. J'ai intégré dans ces simulations des mécanismes et des distances plus réalistes de dispersion de graines. J'ai pu montrer, avec les simulations les plus réalistes, que près du tiers des 284 espèces considérées (28.5%) pourraient être menacées d'extinction en 2100 dans le cas du plus sévère scénario de changement climatique A1. Pour le moins sévère des scénarios B1, seulement 4.6% des espèces sont menacées d'extinctions, mais 54% (153 espèces) risquent de perdre plus 80% de leur habitat initial. Les résultats de monitoring des changements de végétation dans le passé montrent que les plantes peuvent réagir rapidement au réchauffement climatique si la compétition est faible. Dans les prairies subalpines, les espèces déjà présentes limitent certainement l'arrivée de nouvelles espèces par effet de compétition. Les résultats de simulation pour le futur prédisent le début d'extinctions massives dans les Préalpes à partir de 2040, au plus tard en 2080. Mon travail démontre aussi l'importance d'études régionales à échelle fine pour évaluer l'impact des changements climatiques sur la végétation, en intégrant des variables plus directes. En effet, les études à échelle continentale ne tiennent pas compte des micro-refuges, des extinctions locales ni des pertes de connectivité entre populations locales. Malgré cela, la migration des plantes de basses altitudes reste difficile à prédire à l'échelle locale sans modélisation plus globale.

Monte Carlo simulation of a clearance box monitor used for nuclear power plant decommissioning.

When decommissioning a nuclear facility it is important to be able to estimate activity levels of potentially radioactive samples and compare with clearance values defined by regulatory authorities. This paper presents a method of calibrating a clearance box monitor based on practical experimental measurements and Monte Carlo simulations. Adjusting the simulation for experimental data obtained using a simple point source permits the computation of absolute calibration factors for more complex geometries with an accuracy of a bit more than 20%. The uncertainty of the calibration factor can be improved to about 10% when the simulation is used relatively, in direct comparison with a measurement performed in the same geometry but with another nuclide. The simulation can also be used to validate the experimental calibration procedure when the sample is supposed to be homogeneous but the calibration factor is derived from a plate phantom. For more realistic geometries, like a small gravel dumpster, Monte Carlo simulation shows that the calibration factor obtained with a larger homogeneous phantom is correct within about 20%, if sample density is taken as the influencing parameter. Finally, simulation can be used to estimate the effect of a contamination hotspot. The research supporting this paper shows that activity could be largely underestimated in the event of a centrally-located hotspot and overestimated for a peripherally-located hotspot if the sample is assumed to be homogeneously contaminated. This demonstrates the usefulness of being able to complement experimental methods with Monte Carlo simulations in order to estimate calibration factors that cannot be directly measured because of a lack of available material or specific geometries.