Estimation of passive and active properties in the human heart using 3D tagged MRI

Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and—through patient-specific modelling—derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.     

Invasive species distribution models – how violating the equilibrium assumption can create new insights

Aim Two core assumptions of species distribution models (SDMs) do not hold when modelling invasive species. Invasives are not in equilibrium with their environment and niche quantification and transferability in space and time are limited. Here, we test whether combining global‐ and regional‐scale data in a novel framework can overcome these limitations. Beyond simply improving regional niche modelling of non‐native species, the framework also makes use of the violation of regional equilibrium assumptions, and aims at estimating the stage of invasion, range filling and risk of spread in the near future for 27 invasive species in the French Alps. Innovation For each invader we built three sets of SDMs using a committee averaging method: one global model and two regional models (a conventional model and one using the global model output to weight pseudo‐absences). Model performances were compared using the area under the receiver operating characteristic curve, the true skill statistic, sensitivity and specificity scores. Then, we extracted the predictions for observed presences and compared them to global and regional models. This comparison made it possible to identify whether invasive species were observed within or outside of their regional and global niches. Main conclusions This study provides a novel methodological framework for improving the regional modelling of invasive species, where the use of a global model output to weight pseudo‐absences in a regional model significantly improved the predictive performance of regional SDMs. Additionally, the comparison of the global and regional model outputs revealed distinct patterns of niche estimates and range filling among the species. These differences allowed us to draw conclusions about the stage of invasion and the risk of spread in the near future, which both correspond to experts' expectations. This framework can be easily applied to a large number of species and is therefore useful for control of biological invasions and eradication planning.     

A hybrid life cycle assessment of water treatment chemicals: an Australian experience

Purpose

Life cycle assessment (LCA) of chemicals is usually developed using a process-based approach. In this paper, we develop a tiered hybrid LCA of water treatment chemicals combining the specificity of process data with the holistic nature of input–output analysis (IOA). We compare these results with process and input–output models for the most commonly used chemicals in the Australian water industry to identify the direct and indirect environmental impacts associated with the manufacturing of these materials.

Methods

We have improved a previous Australian hybrid LCA model by updating the environmental indicators and expanding the number of included industry sectors of the economy. We also present an alternative way to estimate the expenditure vectors to the service sectors of the economy when financial data are not available. Process-based, input–output and hybrid results were calculated for caustic soda, sodium hypochlorite, ferric chloride, aluminium sulphate, fluorosilicic acid, calcium oxide and chlorine gas. The functional unit is the same for each chemical: the production of 1 tonne in the year 2008.

Results and discussion

We have provided results for seven impact categories: global warming potential; primary energy; water use; marine, freshwater and terrestrial ecotoxicity potentials and human toxicity potential. Results are compared with previous IOA and hybrid studies. A sensitivity analysis of the results to assumed wholesale prices is included. We also present insights regarding how hybrid modelling helps to overcome the limitations of using IO- or process-based modelling individually.

Conclusions and recommendations

The advantages of using hybrid modelling have been demonstrated for water treatment chemicals by expanding the boundaries of process-based modelling and also by reducing the sensitivity of IOA to fluctuations in prices of raw materials used for the production of these industrial commodities. The development of robust hybrid life cycle inventory databases is paramount if hybrid modelling is to become a standard practice in attributional LCA.     

Vector borne diseases on an urban environment: The effects of heterogeneity and human circulation

We study the effect of human circulation and host/vector heterogeneities on the onset of epidemics of arboviruses. From a meta-population dynamics based on the classical Bailey–Dietz model, we derive a multi-group model under three assumptions: (i) fast host sojourn time-scale; (ii) mosquitoes do not move; (iii) time homogeneity and strong connectivity of human circulation. Within this modelling framework, three different kinds of R₀ appear: (i) the “true” or “global” R₀—derived from the corresponding next generation matrix; (ii) the uniform R₀—obtained if the patches are taken homogeneous; (iii) the local R₀s—obtained if the patches are disconnected. We show that there is relevant epidemiological information associated to all of them. In particular, they can be used to understand the effects of changing the circulation on the value of the global R₀. We also present additional results on the effects on R₀ of different vector control policies, and a simulation with data from the city of Rio de Janeiro, Brazil.     

Sensitivity analysis of the tree distribution model Phenofit to climatic input characteristics: implications for climate impact assessment

Species distributions are already affected by climate change. Forecasting their long‐term evolution requires models with thoroughly assessed validation. Our aim here is to demonstrate that the sensitivity of such models to climate input characteristics may complicate their validation and introduce uncertainties in their predictions. In this study, we conducted a sensitivity analysis of a process‐based tree distribution model Phenofit to climate input characteristics. This analysis was conducted for two North American trees which differ greatly in their distribution and eight different types of climate input for the historic period which differ in their spatial (local or gridded data) and temporal (daily vs. monthly) resolution as well as their type (locally recorded, extrapolated or simulated by General Circulation Models). We show that the climate data resolution (spatial and temporal) and their type, highly affect the model predictions. The sensitivity analysis also revealed, the importance, for global climate change impact assessment, of (i) the daily variability of temperatures in modeling the biological processes shaping species distribution, (ii) climate data at high latitudes and elevations and (iii) climate data with high spatial resolution.     

Non-stationarity and local approaches to modelling the distributions of wildlife

Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss.     

Comparing different approaches to calculate the effects of heterogeneous root distribution on nutrient uptake: a case study on subsoil nitrate uptake by a barley root system

Simulation models of nutrient uptake of root systems starting with one-dimensional single root approaches up to complex three-dimensional models are increasingly used for examining the interacting of root distribution and nutrient uptake. However, their accuracy was seldom systematically tested. The objective of the study is to compare one-dimensional and two-dimensional modelling approaches and to test their applicability for simulation of nutrient uptake of heterogeneously distributed root systems giving particular attention to the impact of spatial resolution. Therefore, a field experiment was carried out with spring barley (Hordeum vulgare L. cv. Barke) in order to obtain data of in situ root distribution patterns as model input. Results indicate that a comparable coarse spatial resolution can be used with sufficient modelling results when a steady state approximation is applied to the sink cells of the two-dimensional model. Furthermore, the accuracy of the model was clearly improved compared to a simple zero sink approach assuming both near zero concentrations within the sink cell and a linear gradient between the sink cell and its adjacent neighbours. However, for modelling nitrate uptake of a heterogeneous root system a minimum number of grid cells is still necessary. The tested single root approach provided a computational efficient opportunity to simulate nitrate uptake of an irregular distributed root system. Nevertheless, two-dimensional models are better suited for a number of applications (e.g. surveys made on the impact of soil heterogeneity on plant nutrient uptake). Different settings for the suggested modelling techniques are discussed.     

Political economy and global arts for social change: A comparative analysis of youth orchestras in Venezuela and Chile

An expansive movement comprised of UN Millennium Development Goals, international banks, and hundreds of programs worldwide promotes access to the arts as a creative means of social change. Often grounded in cognitive science and inspired by the model of youth orchestras in Venezuela known as El Sistema, this movement contends that arts training—which can foster empathy, collaboration, academic achievement, and self-esteem—helps alleviate poverty and combat inequality. In contrast to the majority of the literature on public arts programs—impact studies that often assume arts engagement creates social change through universal mechanisms—this study examines the influence of political economy on the implementation of public arts programs. Through a comparative study of youth orchestras with social inclusion goals in Venezuela (1974–2015) and Chile (1964–2015), the scope and intensity of government control, social welfare policy, and competition for public funds are found to shape public arts programs' social goals, daily operations, definitions of success, and impact study procedures. Therefore, scholars, practitioners, and policy makers must reexamine their understanding of arts programs as a development model. Future global efforts to combat inequality should avoid over-standardization. This article offers a new Arts for Social Change Context Framework that places input variables at the center of analysis, with policy implications.     

Dealing with Noisy Absences to Optimize Species Distribution Models: An Iterative Ensemble Modelling Approach

Species distribution models (SDMs) are widespread in ecology and conservation biology, but their accuracy can be lowered by non-environmental (noisy) absences that are common in species occurrence data. Here we propose an iterative ensemble modelling (IEM) method to deal with noisy absences and hence improve the predictive reliability of ensemble modelling of species distributions. In the IEM approach, outputs of a classical ensemble model (EM) were used to update the raw occurrence data. The revised data was then used as input for a new EM run. This process was iterated until the predictions stabilized. The outputs of the iterative method were compared to those of the classical EM using virtual species. The IEM process tended to converge rapidly. It increased the consensus between predictions provided by the different methods as well as between those provided by different learning data sets. Comparing IEM and EM showed that for high levels of non-environmental absences, iterations significantly increased prediction reliability measured by the Kappa and TSS indices, as well as the percentage of well-predicted sites. Compared to EM, IEM also reduced biases in estimates of species prevalence. Compared to the classical EM method, IEM improves the reliability of species predictions. It particularly deals with noisy absences that are replaced in the data matrices by simulated presences during the iterative modelling process. IEM thus constitutes a promising way to increase the accuracy of EM predictions of difficult-to-detect species, as well as of species that are not in equilibrium with their environment.     

Using ecological networks to answer questions in global biogeography and ecology

Ecological networks have classically been studied at site and landscape scales, yet recent efforts have been made to collate these data into global repositories. This offers an opportunity to integrate and upscale knowledge about ecological interactions from local to global scales to gain enhanced insights from the mechanistic information provided by these data. By drawing on existing research investigating patterns in ecological interactions at continental to global scales, we show how data on ecological networks, collected at appropriate scales, can be used to generate an improved understanding of many aspects of ecology and biogeography—for example, species distribution modelling, restoration ecology and conservation. We argue that by understanding the patterns in the structure and function of ecological networks across scales, it is possible to enhance our understanding of the natural world.     

Modelling soil organic matter levels after long-term applications of crop residues,and farmyard and green manures

A computer simulation model for long-term soil organic matter dynamics was developed and evaluated with data from long-term field trials in Belgium, Germany and The Netherlands. The model distinguishes four pools of soil organic components (including a microbial biomass pool) with different chemical properties. Transformation rates are described by (pseudo) first order kinetics. Effects of temperature and soil moisture tension were included. Simulation results were in agreement with experimental data from arable farming practices where common input rates were applied. Model calculations overestimated soil organic matter levels when green manures or exceptionally high input rates were applied. Inadequate experimental estimations of organic matter input rates and insufficient modelling of the soil preservation capacity for organic matter and biomass are likely reasons. After changes in the soil organic matter-input management it may take more than a century to reach new equilibrium levels.     

The importance of nonlinear tissue modelling in finite element simulations of infant head impacts

Despite recent efforts on the development of finite element (FE) head models of infants, a model capable of capturing head responses under various impact scenarios has not been reported. This is hypothesized partially attributed to the use of simplified linear elastic models for soft tissues of suture, scalp and dura. Orthotropic elastic constants are yet to be determined to incorporate the direction-specific material properties of infant cranial bone due to grain fibres radiating from the ossification centres. We report here on our efforts in advancing the above-mentioned aspects in material modelling in infant head and further incorporate them into subject-specific FE head models of a newborn, 5- and 9-month-old infant. Each model is subjected to five impact tests (forehead, occiput, vertex, right and left parietal impacts) and two compression tests. The predicted global head impact responses of the acceleration–time impact curves and the force–deflection compression curves for different age groups agree well with the experimental data reported in the literature. In particular, the newly developed Ogden hyperelastic model for suture, together with the nonlinear modelling of scalp and dura mater, enables the models to achieve more realistic impact performance compared with linear elastic models. The proposed approach for obtaining age-dependent skull bone orthotropic material constants counts both an increase in stiffness and decrease in anisotropy in the skull bone—two essential biological growth parameters during early infancy. The profound deformation of infant head causes a large stretch at the interfaces between the skull bones and the suture, suggesting that infant skull fractures are likely to initiate from the interfaces; the impact angle has a profound influence on global head impact responses and the skull injury metrics for certain impact locations, especially true for a parietal impact.     

Intra‐specific variability and plasticity influence potential tree species distributions under climate change

Aim To assess the effect of local adaptation and phenotypic plasticity on the potential distribution of species under future climate changes. Trees may be adapted to specific climatic conditions; however, species range predictions have classically been assessed by species distribution models (SDMs) that do not account for intra‐specific genetic variability and phenotypic plasticity, because SDMs rely on the assumption that species respond homogeneously to climate change across their range, i.e. a species is equally adapted throughout its range, and all species are equally plastic. These assumptions could cause SDMs to exaggerate or underestimate species at risk under future climate change. Location The Iberian Peninsula. Methods Species distributions are predicted by integrating experimental data and modelling techniques. We incorporate plasticity and local adaptation into a SDM by calibrating models of tree survivorship with adaptive traits in provenance trials. Phenotypic plasticity was incorporated by calibrating our model with a climatic index that provides a measure of the differences between sites and provenances. Results We present a new modelling approach that is easy to implement and makes use of existing tree provenance trials to predict species distribution models under global warming. Our results indicate that the incorporation of intra‐population genetic diversity and phenotypic plasticity in SDMs significantly altered their outcome. In comparing species range predictions, the decrease in area occupancy under global warming conditions is smaller when considering our survival–adaptation model than that predicted by a ‘classical SDM’ calibrated with presence–absence data. These differences in survivorship are due to both local adaptation and plasticity. Differences due to the use of experimental data in the model calibration are also expressed in our results: we incorporate a null model that uses survival data from all provenances together. This model always predicts less reduction in area occupancy for both species than the SDM calibrated with presence–absence. Main conclusions We reaffirm the importance of considering adaptive traits when predicting species distributions and avoiding the use of occurrence data as a predictive variable. In light of these recommendations, we advise that existing predictions of future species distributions and their component populations must be reconsidered.     

The use of CO2 flux measurements in models of the global terrestrial carbon budget

CO₂ flux measurements give access to two critical terms of the carbon budget of terrestrial ecosystems, the gross primary productivity (GPP) and the net ecosystem productivity (NEP). CO₂ fluxes measured by micrometeorological methods have spatial and temporal characteristics that make them potentially useful in modelling the global terrestrial carbon budget. The first use is in parameterizing ecosystem physiological processes. We present an example, based on parameterizing the mean light response of GPP. This parameterization can be used in diagnostic, satellite-based GPP models. A global application leads to realistic estimates of global GPP. The second use is in testing the seasonality of fluxes predicted by global models. Our example of this use tests two global GPP models. One is a diagnostic, satellite-based model, and one is a prognostic, process-based model. Despite the limitations of the models, both agree reasonably well with the measurements. The agreements and disagreements are useful in addressing the problems of available input datasets and representation of processes, in global models. Long-term CO₂ flux measurements give access to key variables of terrestrial vegetation models and therefore offer exciting perspectives.     

Phylogenetic structural equation modelling reveals no need for an ‘origin’ of the leaf economics spectrum

The leaf economics spectrum (LES) is a prominent ecophysiological paradigm that describes global variation in leaf physiology across plant ecological strategies using a handful of key traits. Nearly a decade ago, Shipley et al. (2006) used structural equation modelling to explore the causal functional relationships among LES traits that give rise to their strong global covariation. They concluded that an unmeasured trait drives LES covariation, sparking efforts to identify the latent physiological trait underlying the ‘origin’ of the LES. Here, we use newly developed phylogenetic structural equation modelling approaches to reassess these conclusions using both global LES data as well as data collected across scales in the genus Helianthus. For global LES data, accounting for phylogenetic non‐independence indicates that no additional unmeasured traits are required to explain LES covariation. Across datasets in Helianthus, trait relationships are highly variable, indicating that global‐scale models may poorly describe LES covariation at non‐global scales.     

Model-based uncertainty in species range prediction

Aim Many attempts to predict the potential range of species rely on environmental niche (or ‘bioclimate envelope’) modelling, yet the effects of using different niche‐based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy‐guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence‐only approaches) and assumptions made by each algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main conclusions We highlight an important source of uncertainty in assessments of the impacts of climate change on biodiversity and emphasize that model predictions should be interpreted in policy‐guiding applications along with a full appreciation of uncertainty.     

Integrating ecological and water footprint accounting in a multi-regional input–output framework

Carbon, ecological, and water footprints (CF, EF, and WF) are accounting tools that can be used to understand the connection between consumption activities and environmental pressures on the Earth's atmosphere, bioproductive areas, and freshwater resources. These indicators have been gaining acceptance from researchers and policymakers but are not harmonized with one another, and ecological and water footprints are lacking in their representation of product supply chains. In this paper we integrate existing methods for calculating EF and WF within a multi-regional input–output (MRIO) modelling framework that has already been successfully applied for CF estimation. We introduce a new MRIO method for conserving the high degree of product detail found in existing physical EF and WF accounts. Calculating EF and WF in this way is consistent with the current best practice for CF accounting, making results more reliable and easier to compare across the three indicators. We discuss alternatives for linking the MRIO model and the footprint datasets and the implications for results. The model presented here is novel and offers significant improvements in EF and WF accounting through harmonization of methods with CF accounting, preservation of product-level detail, comprehensive inclusion of sectors of the global economy, and clear representation of flows along supply chains and international trade linkages. The matrix organization of the model improves transparency and provides a structure upon which further improvements in footprint calculation can be built. The model described here is the first environmentally extended MRIO model that harmonizes EF and WF accounts and aligns physical unit data of product use with standard economic and environmental accounting.