Variability Of Model Predictions Research Articles

Punching shear strength of fiber-reinforced polymer concrete slabs: Database-driven assessment of parameters and prediction models

This study examines the punching shear capacity of Fiber-Reinforced Polymer Concrete Slabs (FRPCS). Utilizing a database of 272 experimental interior and edge column-supported FRPCS samples, the research evaluates the influence of various design parameters on the punching shear capacity. This study stands out for its comprehensive database-driven approach and critically evaluates three code models (ACI 440.1R, JSCE, CAN/CSA-S06) and ten theoretical models from the literature. The findings reveal significant variability in model predictions, prompting the proposal to modify the ACI equation for enhanced prediction accuracy. The analyses indicate that the JSCE and CAN/CSA-S06 models provide reasonable estimations for punching shear capacity, while the ACI 440.1R model is over-conservative. The proposed modification provides a comparable prediction accuracy to the JSCE and CAN/CSA-S06 models.

Habitat modelling of native freshwater mussels distinguishes river specific differences in the Detroit and St. Clair rivers of the Laurentian Great Lakes

Native freshwater mussels (Bivalvia: Unionidae) were seemingly pushed to extirpation following the establishment of dreissenid mussels in the St. Clair (SCR)–Detroit River (DR) System and the current state of unionids in the main channels of the SCR remains unknown. To assess remnant unionid populations, the DR and SCR were surveyed in 2019 and 2021, respectively, using a mixture of stratified random, historical, potential refuge, and model-selected (SCR sampling only) sites (n = 56 DR sites and 51 SCR sites). Data collected from the DR (2019) were used to create unionid species distribution models in MaxEnt, which produced the model-selected sites for sampling the SCR. A total of 14 live unionids representing 9 species were found among 7 sites in the SCR; however, the DR model projected to the SCR failed to be predictive for unionid presence in the SCR (0 % success). Additional models were created for the DR and SCR using MaxEnt and classification and regression tree (CART) Analysis to elucidate the characteristic differences between the two rivers. MaxEnt modelling showed the highest contributing variable for the DR model was water velocity (m/s) while the highest contributing variable for the SCR model was river distance to the outlet which could be one explanation for why the DR model did not accurately predict unionid presence in the SCR. Additionally, when DR and SCR data were combined in the CART model, the model failed to predict any live unionid sites from the SCR. Variability in model predictions demonstrate the characteristic differences between the DR and SCR. Therefore, model verification for both rivers specific models took place in 2022 (n = 8 DR sites and 6 SCR sites). The DR unionid species distribution model identified 3 additional sites with live unionids (38 % of predicted sites), but the SCR model did not locate any additional live unionid locations (0 % of predicted sites). This research updated our knowledge on native unionid distributions and habitat use in the DR and SCR which could contribute towards unionid conservation in the future.

Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions

The COVID-19 pandemic has required international scientific efforts to address important aspects of the pandemic. Data science and scientific modeling are extensively used to provide assessments and predictions for policy-making purposes. However, resulting communications need to be supported by a proper uncertainty quantification to assess variability in model predictions, by the identification of the key-uncertainty drivers. This information can be provided by statisticians with sensitivity analysis methods. Knowing the drivers of uncertainty supports effective policy-making. Concerning the COVID-19 pandemic diffusion, two recent investigations reveal intervention-related parameters as more important than epidemiological parameters in two different modeling exercises. This result can help prioritize policy decisions.

Modelling the Effect and Variability of Integrated Weed Management of Phalaris minor in Rice-Wheat Cropping Systems in Northern India

Phalaris minor Retz. (littleseed canarygrass) is the most problematic and herbicide-resistant weed in the rice-wheat cropping system in India. As such, it poses a severe threat to wheat yield and food security. A number of herbicidal and agronomic practices have been identified for the effective control of P. minor. These include crop rotation, crop establishment methods, herbicide spray technology, sowing time, weed seed harvest and effective herbicide mixtures. A population model of P. minor was built based on the life cycle of the species, herbicide resistance mechanisms and the effects of weed control practices. The model simulated the interactions of these factors and provided the best management recommendations for sustainably controlling this noxious weed species. Model results indicate that integration of chemical and non-chemical control methods was the most effective and sustainable strategy. For example, the integration of a happy seeder (a tractor-mounted mulching and sowing machine) with an effective post-emergence herbicide reduced the probability of weed control failure by 32% compared to the scenario with a rotavator and the same herbicide. Similarly, more conventional crop establishment methods such as a rotavator and conventional tillage could be accompanied by pre- or post-emergence applications of herbicide mixtures. Adoption of good herbicide spray technology and weed seed harvest delayed the onset of resistance evolution by up to four years. Furthermore, effective crop rotation such as the inclusion of sugarcane in place of rice in the summer season reduced the risk of resistance evolution by 31% within the 10 year simulation period. In addition to the scenarios using representative parameter values, the variability of model predictions was investigated based on some field experiments. The model provided a powerful tool for promoting Integrated Weed Management and the sustainable use of herbicides. Pragmatic ways of dealing with uncertainty in model prediction are discussed.

Common sorption isotherm models are not physically valid for water in wood

Sorption isotherm models are frequently used to understand the interactions of water with cellulosic materials. Many commonly used models, such as the Guggenheim-Anderson-de Boer, Hailwood-Horrobin, and Dent isotherm models, are based on idealized physical systems. In the scientific literature, these models are often used to predict properties of wood such as the monolayer capacity, which is a representation of the number of available sorption sites in the material. Reporting such properties assumes that an idealized system adequately represents actual wood-water interactions. These models can be reduced to the same form, where the ratio of water activity, aw, to equilibrium moisture content (EMC), is a second-order polynomial function of aw. Here we review twelve models that have a parabolic form, fit these models to high quality water vapor sorption isotherm data for wood at multiple temperatures, and calculate physical properties from the model parameters. Moreover, variability in model predictions arising from measurement uncertainties in aw and EMC values is propagated using a Monte Carlo method. Predicted physical properties are tested against independently measured values of hydroxyl accessibility, relative amounts of primary and secondary water, and enthalpy of sorption. None of the models accurately predicts any of the physical properties, even when the reported measurement uncertainties are increased by a factor of three. Therefore, it must be concluded that parabolic sorption isotherm models are not valid for water in wood cell walls.

The role of spatiotemporal plant trait variability in model predictions of ecohydrological responses to climate change in a desert shrubland

Although spatial heterogeneity of soil properties, topography, and climate is commonly incorporated into ecohydrological models, the spatial and temporal variability in plant functional traits is typically overlooked. The objective of our study is to evaluate the impact of trait parameter variability on modeled ecohydrological processes. We implemented a model-data fusion approach to constrain spatiotemporally dynamic parameters in plant functional traits at two desert shrubland sites located along a topographic and climate gradient in the Mojave Desert. Our results showed that the estimates for specific leaf area and rooting depth for the broadleaf-evergreen-shrub plant-functional-type showed spatial variability, with lower specific leaf area and deeper rooting depth found at the low elevation site. We also found that the specific leaf area estimates changed over time at both sites in response to water stress, but with different sensitivities, possibly depending on species and/or climate. The spatial variability in trait parameter estimates was greater than temporal variability and played a more important role in accurately simulating ecohydrological processes, but including the temporal variability in specific leaf area further improved seasonal predictions. In simulations forced by future climate projections under the Representative Concentration Pathway 4.5 (RCP 4.5) and 8.5 (RCP 8.5) greenhouse gas emissions scenarios, spatial variability in trait parameters impacted predictions of both carbon and water fluxes, while temporal variability in trait parameters resulted in predictions of higher ecological function and water use efficiency. The higher water use efficiency led to improved ecohydrological function in simulations under RCP 4.5, but it showed little capacity for buffering intensive water stresses under the more pessimistic RCP 8.5 scenario, indicating that with spatiotemporally variable trait parameters, the impact on predicted ecohydrological processes depends on the climate projections. Overall, our modeling results prompt further field-based examination of temporal and belowground trait variability in desert shrublands, and they raise the question of how combined spatiotemporal variabilities of multiple traits may support ecohydrological function under water stress.

The Cost-Effectiveness of Epilepsy Surgery and Surgical Evaluation in the United States

Abstract INTRODUCTION Surgery is an effective treatment for many pharmacoresistant temporal lobe epilepsy patients, but incurs considerable cost. It is unknown whether surgery and surgical evaluation are cost-effective strategies in the United States. We aim to evaluate whether 1) surgery is cost-effective for patients who have been deemed surgical candidates when compared to continued medical management, 2) surgical evaluation is cost-effective for patients who have drug-resistant temporal epilepsy and may or may not ultimately be deemed surgical candidates METHODS We use a Monte Carlo simulation method to assess the cost-effectiveness of surgery and surgical evaluation over a lifetime horizon. Patients transition between two health states (‘seizure free’ and ‘having seizures’) as part of a Markov process, based on literature estimates. We adopt both healthcare and societal perspectives, including direct healthcare costs and indirect costs such as lost earnings by patients and care providers. We estimate variability of model predictions using probabilistic and deterministic sensitivity analyses. RESULTS 1) Epilepsy surgery is cost effective in surgically eligible patients by virtue of being cost saving and more effective than medical management in the long run, with 95% of 10 000 Monte Carlo simulations favoring surgery. From a societal perspective, surgery becomes cost effective within 3 yr. At 5 yr, surgery has an incremental cost-effectiveness ratio (ICER) of $31,600, which is significantly below the societal willingness-to-pay (∼ $100,000/quality-adjusted life years (QALY)) and comparable to hip/knee arthroplasty. 2) Surgical evaluation is cost-effective in pharmacoresistant patients even if the probability of being deemed a surgical candidate is low (5%-10%). Even if the probability of surgical eligibility is only 10%, surgical referral has an ICER of $96,000/QALY, which is below societal willingness-to-pay. CONCLUSION Epilepsy surgery and surgical evaluation are both cost-effective strategies in the United States. Pharmacoresistant temporal lobe epilepsy patients should be referred for surgical evaluation without hesitation on cost-effectiveness grounds.

Analysis and model-based optimization of a pectin extraction process

Raw material quality disparity is an unavoidable and systemic variability in the solid-liquid extraction process of pectin manufacturing. A mathematical model is considered for a robust optimization of the process, taking into the account raw material quality uncertainty, for two different scenarios of pectin product quality profiles. Before application, the model is evaluated through local sensitivity analysis. Raw material-specific parameters and non-significant parameters are removed from the parameter set targeted for identification. Furthermore, it is shown that the model outputs are highly sensitive to the peel specific parameters, which inherently vary from peel-to-peel. This impact was further assessed through an uncertainty analysis, which quantified the variability of model predictions due to the raw material parameter uncertainty for three different types of fruit. The model exhibits a good general depiction of the extraction phenomena. Major operating variables, i.e., temperature, pH and batch time, are optimized in a deterministic manner for each fruit to maximize the final pectin concentration while satisfying given requirements. Based on this, a robust optimization strategy is examined to design an optimal operation strategy in consideration of the inherent uncertainty of feedstock and the desired product quality.

NOx Emissions Modeling and Uncertainty From Exhaust-Gas-Diluted Flames

Oxides of nitrogen (NOx) are pollutants emitted by combustion processes during power generation and transportation that are subject to increasingly stringent regulations due to their impact on human health and the environment. One NOx reduction technology being investigated for gas-turbine engines is exhaust-gas recirculation (EGR), either through external exhaust-gas recycling or staged combustion. In this study, the effects of different percentages of EGR on NOx production will be investigated for methane–air and propane–air flames at a selected adiabatic flame temperature of 1800 K. The variability and uncertainty of the results obtained by the gri-mech 3.0 (GRI), San-Diego 2005 (SD), and the CSE thermochemical mechanisms are assessed. It was found that key parameters associated with postflame NO emissions can vary up to 192% for peak CH values, 35% for thermal NO production rate, and 81% for flame speed, depending on the mechanism used for the simulation. A linear uncertainty analysis, including both kinetic and thermodynamic parameters, demonstrates that simulated postflame nitric oxide levels have uncertainties on the order of ±50–60%. The high variability of model predictions, and their relatively high associated uncertainties, motivates future experiments of NOx formation in exhaust-gas-diluted flames under engine-relevant conditions to improve and validate combustion and NOx design tools.

单木生物量模型估计区域尺度生物量的不确定性

PDF HTML阅读 XML下载 导出引用 引用提醒 单木生物量模型估计区域尺度生物量的不确定性 DOI: 10.5846/stxb201405130980 作者: 作者单位: 中国林业科学研究院资源信息研究所,中国林业科学研究院资源信息研究所,国家林业局调查规划设计院 北京 作者简介: 通讯作者: 中图分类号: 基金项目: 国家863重点项目(2012AA12A306);国家自然科学基金项目(31170588) Uncertainty analysis for regional-level above-ground biomass estimates based on individual tree biomass model Author: Affiliation: Research Institute of Forest Resources Information Techniques,CAF Beijing,Research Institute of Forest Resources Information Techniques,CAF Beijing,Academy of Forest Inventory and Planning,State Forestry Administrator Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:采用系统抽样体系江西省固定样地杉木连续观测数据和生物量数据,通过Monte Carlo法反复模拟由单木生物量模型推算区域尺度地上生物量的过程,估计了江西省杉木地上总生物量。基于不同水平建模样本量n及不同决定系数R2的设计,分别研究了单木生物量模型参数变异性及模型残差变异性对区域尺度生物量估计不确定性的影响。研究结果表明:2009年江西省杉木地上生物量估计值为(19.84±1.27) t/hm2,不确定性占生物量估计值约6.41%。生物量估计值和不确定性值达到平稳状态所需的运算时间随建模样本量及决定系数R2的增大而减小;相对于模型参数变异性,残差变异性对不确定性的影响更小。 Abstract:Above-ground forest biomass at regional-level is typically estimated by adding model predictions of biomass from individual trees in a plot, and subsequently aggregating predictions from plots to large areas. There are multiple sources of uncertainties in model predictions during this aggregated process. These uncertainties always affect the precision of large area biomass estimates, and the effects are generally overlooked;however, failure to account for these uncertainties will cause erroneously optimistic precision estimates. Monte Carlo simulation is an effective method for estimating large-scale biomass and assessing the uncertainty associated with multiple sources of errors and complex models. In this paper, we applied the Monte Carlo approach to simulate regional-level above-ground biomass and to assess uncertainties related to the variability from model residuals and parameters separately. A nonlinear model form was used. Data were obtained from permanent sample plots and biomass observation of Cunninghamia lanceolata in JiangXi Province, China. Overall, 70 individual trees were destructively sampled for biomass estimation from June to September, 2009. Based on the commonly used allometric model, we conducted Monte Carlo simulations 1000 times for the biomass model fitting with the biomass data, from which we estimated the biomass of the plot data, and conducted an uncertainty assessment from the model residual variability and parameter variability. Estimates of above-ground biomass in JiangXi Province were obtained by aggregating model predictions of biomass for individual trees within plots, and then calculating the mean of the plots. Four modeling options with different sample sizes and R2 were designed separately, from which Monte Carlo simulations were performed 1000 times and 2000 times, respectively, to study the effects of the model parameter and residual variability on the uncertainty in large-scale biomass estimates. The results revealed that the estimates of above-ground biomass and its uncertainty for C. lanceolata in JiangXi Province in 2009 achieved stability after 500 Monte Carlo simulations, and that the average biomass estimate was 19.84 t/hm2, with additional uncertainty of 1.27 t/hm2, representing 6.41% of the average biomass. With increasing modeling sample size from 30 to 60, the relative uncertainty of biomass estimates decreased from 14.86% to 9.85%, but the uncertainty variations for different levels of R2 values minimally changed. We concluded that: 1) the Monte Carlo approach works well for regional-level estimations of biomass and its uncertainty based on forest inventory data;2) the uncertainty of biomass estimation in large areas should not be overlooked because of the large number of errors when extrapolating from the individual tree to the plot level in forest inventory data;3) with gradually larger modeling sample size, the average biomass increased while the uncertainty values decreased, and the operation times required for achieving the stability of average biomass and corresponding uncertainty in Monte Carlo simulations also were reduced, indicating that increasing modeling sample size is an effective way to reduce uncertainty in regional-level biomass estimations;and 4) model residual variability associated with R2 was less important in model uncertainty of biomass estimates;however, higher R2 does reduce the operation times for achieving stability of Monte Carlo simulations. This study is the first to apply the Monte Carlo simulation approach to estimating regional-level biomass and its uncertainty based on continuous observation data from permanent sample plots. This study is also the first to quantify the effects of uncertainty related to model parameters and residual variability in model predictions of extrapolating individual tree biomass to large area biomass estimates. 参考文献 相似文献 引证文献

Approximate Bayesian computation to recalibrate individual-based models with population data: Illustration with a forest simulation model

Ecology makes an increasing use of complex simulation models. As more processes and model parameters are added, a comprehensive model calibration with process-level data becomes costly and predictions of such complex models are therefore often restricted to local applications. In this context, inverse modelling techniques enable to calibrate models with data of the same type than model outputs (thereafter called population data for the sake of clarity, although other data types can be used according to model outputs), which are usually simpler to collect and more readily available. This study aims at demonstrating how such data can be used to improve ecological models, by recalibrating the most influential parameters of a complex model in a Bayesian framework, and at providing general guidelines for potential users of this approach. We used the individual-based and spatially explicit forest dynamics simulation model Samsara2 as a case study. Considering the results of an initial calibration and of a sensitivity analysis as prerequisites, we assessed whether we could use approximate Bayesian computation (ABC) to recalibrate a subset of parameters on historical management data collected in forests with various ecological conditions. We propose guidelines to answer three questions that potential users of the approach will encounter: (1) How many and which parameters are we able to recalibrate accurately with such low-informative data? (2) How many ABC simulations are required to obtain a reasonable convergence of the parameter posterior estimates? (3) What is the variability of model predictions following the recalibration? In our case study, we found that two parameters by species could be recalibrated with forest management data and that a relatively low number of simulations (20,000) was sufficient. We finally pointed out that the variability of model predictions was largely due to model stochasticity, and much less to ABC recalibration and initial calibration uncertainties. Combining direct process-level calibration to ABC recalibration of the most influential parameters opens the door to interesting modelling improvements, such as the calibration of forest dynamics along environmental gradients. This general approach should thus help improve both accuracy and generality of model-based ecological predictions.

A Method for the Analysis of Behavioural Uncertainty in Evacuation Modelling

Evacuation models generally include the use of distributions or probabilistic variables to simulate the variability of possible human behaviours. A single model setup of the same evacuation scenario may therefore produce a distribution of different occupant-evacuation time curves in the case of the use of a random sampling method. This creates an additional component of uncertainty caused by the impact of the number of simulated runs of the same scenario on evacuation model predictions, here named behavioural uncertainty. To date there is no universally accepted quantitative method to evaluate behavioural uncertainty and the selection of the number of runs is left to a qualitative judgement of the model user. A simple quantitative method using convergence criteria based on functional analysis is presented to address this issue. The method permits (1) the analysis of the variability of model predictions in relation to the number of runs of the same evacuation scenario, i.e. the study of behavioural uncertainty and (2) the identification of the optimal number of runs of the same scenario in relation to pre-defined acceptance criteria.

Targeting of intervention areas to reduce reservoir sedimentation in the Tana catchment (Kenya) using SWAT

A measurement campaign was carried out in the Upper Tana basin (Kenya) to quantify soil erosion and reservoir sedimentation rates, including a bathymetric reservoir survey and sediment load sampling during one year. Then, distributed soil erosion modelling was performed to study sediment budgets throughout the basin and to evaluate the potential of upstream erosion control through vegetated contour strips and check dams. Finally, the areas where these measures would be most effective were identified and local stakeholder associations to implement them were prioritized. The influence of the scale of implementation was evaluated by using the model to consider three adoption scenarios. This study illustrates the relevance of distributed erosion models to target erosion control measures when sufficient information on the eroding areas is available from field surveys. Bathymetric surveys were fundamental to validate the long-term model response, while point measurements were valuable to verify the spatial variability of model predictions. Editor Z.W. Kundzewicz; Associate editor G. Mahé Citation Hunink, J.E., Niadas, I.A., Antonaropoulos, P., Droogers, P., and de Vente, J., 2013. Targeting of intervention areas to reduce reservoir sedimentation in the Tana catchment (Kenya) using SWAT. Hydrological Sciences Journal, 58 (3), 600–614.

A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.

Responses of future air quality to emission controls over North Carolina, Part II: Analyses of future-year predictions and their policy implications

The MM5/CMAQ system evaluated in Part I paper is applied to study the impact of emission control on future air quality over North Carolina (NC). Simulations are conducted at a 4-km horizontal grid resolution for four one-month periods, i.e., January, June, July, and August 2009 and 2018. Simulated PM 2.5 in 2009 and 2018 show distribution patterns similar to those in 2002. PM 2.5 concentrations over the whole domain in January and July reduced by 5.8% and 23.3% in 2009 and 12.0% and 35.6% in 2018, respectively, indicating that the planned emission control strategy has noticeable effects on PM 2.5 reduction in this region, particularly in summer. More than 10% and 20% of 1-h and 8-h O 3 mixing ratios are reduced in July 2009 and 2018, respectively, demonstrating the effectiveness of emission control for O 3 reduction in summer. However, O 3 mixing ratios in January 2009 and 2018 increase by more than 5% because O 3 chemistry is VOC-limited in winter and the effect of NO x reduction dominates over that of VOC reduction under such a condition. The projected emission control simulated at 4-km will reduce the number of sites in non-attainment for max 8-h O 3 from 49 to 23 in 2009 and to 1 in 2018 and for 24-h average PM 2.5 from 1 to 0 in 2009 and 2018 based on the latest 2008 O 3 and 2006 PM 2.5 standards. The variability in model predictions at different grid resolutions contributes to 1–3.8 ppb and 1–7.9 μg m −3 differences in the projected future-year design values for max 8-h O 3 and 24-h average PM 2.5, respectively.

Designing Permanent Sample Plots by Using a Spatially Hierarchical Matrix Population Model

Summary Designing permanent sample plots is a key issue in forestry where long-term data are required to assess the sustainability of forest logging. The data that are collected in permanent sample plots are used to set parameters in a population dynamics matrix model which in turn is used to predict stock recovery rates for each species. The sampling plan for permanent plots can be designed to estimate stock recovery rates with a required accuracy at a given confidence level, while minimizing installation costs. This can be formulated as a constrained optimization problem (one constraint for each species). The question then is to quantify sampling variability, i.e. the variability of model predictions that are generated by the distribution of parameter estimators. In this study, we address the question of sampling variability for a size-classified population matrix model in a hierarchical context where sample size is itself random and driven by a multivariate spatial point process. An approximate expression is given for the accuracy of the stock recovery rate estimator. This expression is the limit of the accuracy as the expectation of sample size tends to ∞. We extend this expression to the multispecies case. To a first approximation, interactions between species do not affect the accuracy of the stock recovery rate for each species. A sampling plan is designed using the data for three species in a tropical rainforest in French Guiana. The optimal sampling plan appears to be determined by the most constrained species.

A first generation dynamic ingress, redistribution and transport model of soil track-in: DIRT

This work introduces a spatially resolved quantitative model, based on conservation of mass and first order transfer kinetics, for following the transport and redistribution of outdoor soil to, and within, the indoor environment by track-in on footwear. Implementations of the DIRT model examined the influence of room size, rug area and location, shoe size, and mass transfer coefficients for smooth and carpeted floor surfaces using the ratio of mass loading on carpeted to smooth floor surfaces as a performance metric. Results showed that in the limit for large numbers of random steps the dual aspects of deposition to and track-off from the carpets govern this ratio. Using recently obtained experimental measurements, historic transport and distribution parameters, cleaning efficiencies for the different floor surfaces, and indoor dust deposition rates to provide model boundary conditions, DIRT predicts realistic floor surface loadings. The spatio-temporal variability in model predictions agrees with field observations and suggests that floor surface dust loadings are constantly in flux; steady state distributions are hardly, if ever, achieved.

Prediction of the disposition of a P‐gp substrate in wild‐type and knockout mice tissues: Development of a physiologically based pharmacokinetic (PBPK) model and its global sensitivity analysis

AbstractIn order to improve understanding and prediction of drug disposition prior to in vivo experiments, we aimed to develop a PBPK model that accounts for the involvement of P‐glycoprotein activity and expression in mouse brain, liver, kidney and heart tissues. Model parameters of P‐gp activity and drug diffusion were mainly extrapolated from in vitro data. Model simulations, compared with tissue concentration of 3H‐domperidone intravenously administered toWT and KO mice, suggest the involvement of additional membrane transporters in heart and brain tissues. The global sensitivity analysis showed that the variability of model predictions is related to the variability of the unbound fraction to plasma protein, whereas the uncertainty of the model predictions is associated with the uncertainty of the parameters related to P‐gp genetic expression, and to the activity of additional transporters in heart and brain tissues. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

A mathematical model linking tree sap flow dynamics to daily stem diameter fluctuations and radial stem growth

To date, models for simulating sap flow dynamics in individual trees with a direct link to stem diameter variation include only the diameter fluctuation driven by a change in stem water storage. This paper reports results obtained with a comprehensive flow and storage model using whole-tree leaf transpiration as the only input variable. The model includes radial stem growth based on Lockhart's equation for irreversible cell expansion. It was demonstrated that including growth is essential to obtaining good simulation results. To model sap flow dynamics, capacitance of storage tissues was assumed either constant (i.e., electrical analogue approach) or variable and dependent on the water content of the respective storage tissue (i.e., hydraulic system approach). These approaches resulted in different shapes for the desorption curve used to calculate the capacitance of storage tissues. Comparison of these methods allowed detection of specific differences in model simulation of sap flow at the stem base (F(stem)) and stem diameter variation (D). Sensitivity analysis was performed to select a limited subset of identifiable parameters driving most of the variability in model predictions of F(stem) and D Both the electrical analogue and the hydraulic system approach for the flow and storage model were successfully calibrated and validated for the case of a young beech tree (Fagus sylvatica L.). Use of an objective model selection criterion revealed that the flow and storage model based on the electrical analogue approach yielded better predictions.

Validation of species–climate impact models under climate change

AbstractIncreasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate‐change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present‐day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding‐bird species over the past ∼20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision‐planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.