Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.

The use of the likelihood ratio for evaluative and investigative purposes in comparative forensic handwriting examination

This paper extends previous research and discussion on the use of multivariate continuous data, which are about to become more prevalent in forensic science. As an illustrative example, attention is drawn here on the area of comparative handwriting examinations. Multivariate continuous data can be obtained in this field by analysing the contour shape of loop characters through Fourier analysis. This methodology, based on existing research in this area, allows one describe in detail the morphology of character contours throughout a set of variables. This paper uses data collected from female and male writers to conduct a comparative analysis of likelihood ratio based evidence assessment procedures in both, evaluative and investigative proceedings. While the use of likelihood ratios in the former situation is now rather well established (typically, in order to discriminate between propositions of authorship of a given individual versus another, unknown individual), focus on the investigative setting still remains rather beyond considerations in practice. This paper seeks to highlight that investigative settings, too, can represent an area of application for which the likelihood ratio can offer a logical support. As an example, the inference of gender of the writer of an incriminated handwritten text is forwarded, analysed and discussed in this paper. The more general viewpoint according to which likelihood ratio analyses can be helpful for investigative proceedings is supported here through various simulations. These offer a characterisation of the robustness of the proposed likelihood ratio methodology.

Prognostic value of postoperative carcinoembryonic antigen concentration and extent of invasion of resection margins after hepatic resection for colorectal metastases.

OBJECTIVE: To evaluate the prognostic value of postoperative concentration of carcinoembryonic antigen (CEA) and extent of surgical margins after resection of liver metastases from colorectal cancer. DESIGN: Retrospective study. SETTING: Teaching hospital, Switzerland. SUBJECTS: 49 patients with hepatic metastases after primary colorectal cancer. INTERVENTIONS: Resection of hepatic metastases MAIN OUTCOME MEASURES: Assessment of prognostic value of variables by univariate and multivariate analysis. RESULTS: Median survival was 24 months (range 5-86 months). Resection margins were clear (> 1-cm) in 10, close (< 1-cm) in 25 and invaded in 9 patients. On univariate analysis, a postoperative concentration of CEA of <4ng/ml was correlated with prolonged survival (p < 0.001), but the width of the resection margin was not of prognostic importance. There was no correlation between width of resection margins and postoperative concentration of CEA (p = 0.5). On multivariate analysis, postoperative concentrations of CEA of 4 ng/ml or more were associated with increased risk of death (relative risk 7.3; 95% confidence interval (CI) 2.8-18.7, p < 0.001). CONCLUSION: Postoperative CEA offers better prognostic discrimination than the width of resection margins after resection of liver metastases from colorectal tumours. Some patients with invaded resection margins did survive for 3 years, but no patient did whose CEA concentration was 4 ng/ml or more. The definition of a potentially curative hepatic resection should include a postoperative CEA concentration of <4 ng/ml (within the reference range).

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

The impact of social relationships on the maintenance of independence in advanced old age: findings of a Swiss Longitudinal Study .

The impact of social relationships on the maintenance of independence over periods of 12-18 months in a group of 306 octogenarians is assessed in this study. The study is based on the results of the Swilsoo (Swiss Interdisciplinary Longitudinal Study on the Oldest Old). Participants (80-84 years old at baseline) were interviewed five times between 1994 and 1999. Independence was defined as the capacity to perform without assistance eight activities of daily living. We distinguished in our analyses kinship and friendship networks and evaluated social relationships with the help of a series of variables serving as indicators of network composition and contact frequency. Logistic regression models were used to identify the short-term effects of social relationships on independence, after controlling for sociodemographic and health-related variables; independence at a given wave of interviews was interpreted in the light of social factors measured at the previous wave. Our analyses indicate that the existence of a close friend has a significant impact on the maintenance of independence (OR=1.58, p<0.05), which is not the case with the other variables concerning network composition. Kinship contacts were also observed to have a positive impact on independence (OR=1.12, p<0.01).

Simplification of the pulmonary embolism severity index for prognostication in patients with acute symptomatic pulmonary embolism.

BACKGROUND: The Pulmonary Embolism Severity Index (PESI) estimates the risk of 30-day mortality in patients with acute pulmonary embolism (PE). We constructed a simplified version of the PESI. METHODS: The study retrospectively developed a simplified PESI clinical prediction rule for estimating the risk of 30-day mortality in a derivation cohort of Spanish outpatients. Simplified and original PESI performances were compared in the derivation cohort. The simplified PESI underwent retrospective external validation in an independent multinational cohort (Registro Informatizado de la Enfermedad Tromboembólica [RIETE] cohort) of outpatients. RESULTS: In the derivation data set, univariate logistic regression of the original 11 PESI variables led to the removal of variables that did not reach statistical significance and subsequently produced the simplified PESI that contained the variables of age, cancer, chronic cardiopulmonary disease, heart rate, systolic blood pressure, and oxyhemoglobin saturation levels. The prognostic accuracy of the original and simplified PESI scores did not differ (area under the curve, 0.75 [95% confidence interval (CI), 0.69-0.80]). The 305 of 995 patients (30.7%) who were classified as low risk by the simplified PESI had a 30-day mortality of 1.0% (95% CI, 0.0%-2.1%) compared with 10.9% (8.5%-13.2%) in the high-risk group. In the RIETE validation cohort, 2569 of 7106 patients (36.2%) who were classified as low risk by the simplified PESI had a 30-day mortality of 1.1% (95% CI, 0.7%-1.5%) compared with 8.9% (8.1%-9.8%) in the high-risk group. CONCLUSION: The simplified PESI has similar prognostic accuracy and clinical utility and greater ease of use compared with the original PESI.

The Paediatric Rheumatology International Trials Organisation provisional criteria for the evaluation of response to therapy in juvenile dermatomyositis.

OBJECTIVE: To develop a provisional definition for the evaluation of response to therapy in juvenile dermatomyositis (DM) based on the Paediatric Rheumatology International Trials Organisation juvenile DM core set of variables. METHODS: Thirty-seven experienced pediatric rheumatologists from 27 countries achieved consensus on 128 difficult patient profiles as clinically improved or not improved using a stepwise approach (patient's rating, statistical analysis, definition selection). Using the physicians' consensus ratings as the "gold standard measure," chi-square, sensitivity, specificity, false-positive and-negative rates, area under the receiver operating characteristic curve, and kappa agreement for candidate definitions of improvement were calculated. Definitions with kappa values >0.8 were multiplied by the face validity score to select the top definitions. RESULTS: The top definition of improvement was at least 20% improvement from baseline in 3 of 6 core set variables with no more than 1 of the remaining worsening by more than 30%, which cannot be muscle strength. The second-highest scoring definition was at least 20% improvement from baseline in 3 of 6 core set variables with no more than 2 of the remaining worsening by more than 25%, which cannot be muscle strength (definition P1 selected by the International Myositis Assessment and Clinical Studies group). The third is similar to the second with the maximum amount of worsening set to 30%. This indicates convergent validity of the process. CONCLUSION: We propose a provisional data-driven definition of improvement that reflects well the consensus rating of experienced clinicians, which incorporates clinically meaningful change in core set variables in a composite end point for the evaluation of global response to therapy in juvenile DM.

Do habitat suitability models reliably predict the recovery areas of threatened species?

1. Identifying those areas suitable for recolonization by threatened species is essential to support efficient conservation policies. Habitat suitability models (HSM) predict species' potential distributions, but the quality of their predictions should be carefully assessed when the species-environment equilibrium assumption is violated.2. We studied the Eurasian otter Lutra lutra, whose numbers are recovering in southern Italy. To produce widely applicable results, we chose standard HSM procedures and looked for the models' capacities in predicting the suitability of a recolonization area. We used two fieldwork datasets: presence-only data, used in the Ecological Niche Factor Analyses (ENFA), and presence-absence data, used in a Generalized Linear Model (GLM). In addition to cross-validation, we independently evaluated the models with data from a recolonization event, providing presences on a previously unoccupied river.3. Three of the models successfully predicted the suitability of the recolonization area, but the GLM built with data before the recolonization disagreed with these predictions, missing the recolonized river's suitability and badly describing the otter's niche. Our results highlighted three points of relevance to modelling practices: (1) absences may prevent the models from correctly identifying areas suitable for a species spread; (2) the selection of variables may lead to randomness in the predictions; and (3) the Area Under Curve (AUC), a commonly used validation index, was not well suited to the evaluation of model quality, whereas the Boyce Index (CBI), based on presence data only, better highlighted the models' fit to the recolonization observations.4. For species with unstable spatial distributions, presence-only models may work better than presence-absence methods in making reliable predictions of suitable areas for expansion. An iterative modelling process, using new occurrences from each step of the species spread, may also help in progressively reducing errors.5. Synthesis and applications. Conservation plans depend on reliable models of the species' suitable habitats. In non-equilibrium situations, such as the case for threatened or invasive species, models could be affected negatively by the inclusion of absence data when predicting the areas of potential expansion. Presence-only methods will here provide a better basis for productive conservation management practices.

The many uses of regulatory impact assessment: A meta-analysis of EU and UK cases

Research on regulation has crossed paths with the literature on policy instruments, showing that regulatory policy instruments contain cognitive and normative beliefs about policy. Thus, their usage stacks the deck in favor of one type of actor or one type of regulatory solution. In this article, we challenge the assumption that there is a predetermined relationship between ideas, regulatory policy instruments, and outcomes. We argue that different combinations of conditions lead to different outcomes, depending on how actors use the instrument. Empirically, we analyze 31 EU and UK case studies of regulatory impact assessment (RIA) - a regulatory policy instrument that has been pivotal in the so-called better regulation movement. We distinguish four main usages of RIA, that is, political, instrumental, communicative, and perfunctory. We find that in our sample instrumental usage is not so rare and that the contrast between communicative and political usages is less stark than is commonly thought. In terms of policy recommendations, our analysis suggests that there may be different paths to desirable outcomes. Policymakers should therefore explore different combinations of conditions leading to the usages they deem desirable rather than arguing for a fixed menu of variables.

Bayes factor for investigative assessment of selected handwriting features

This paper extends previous research [1] on the use of multivariate continuous data in comparative handwriting examinations, notably for gender classification. A database has been constructed by analyzing the contour shape of loop characters of type a and d by means of Fourier analysis, which allows characters to be described in a global way by a set of variables (e.g., Fourier descriptors). Sample handwritings were collected from right- and left-handed female and male writers. The results reported in this paper provide further arguments in support of the view that investigative settings in forensic science represent an area of application for which the Bayesian approach offers a logical framework. In particular, the Bayes factor is computed for settings that focus on inference of gender and handedness of the author of an incriminated handwritten text. An emphasis is placed on comparing the efficiency for investigative purposes of characters a and d.

Association of working conditions with self-reported work-related symptoms: results from the Swiss dataset of the European Working Conditions Survey

Working conditions are important determinants of health. The aims of this article are to 1) identify working conditions and work characteristics that are associated with workers' perceptions that their work is harmful to their health and 2) identify with what symptoms these working conditions are associated.We used the Swiss dataset from the 2005 edition of the European Working Conditions Survey. The dependent variable was based on the question "Does your work affect your health?". Logistic regression was used to identify a set of variables collectively associated with self-reported work-related adverse health effects.A total of 330 (32%) participants reported having their health affected by work. The most frequent symptoms included backache (17.1%), muscular pains (13.1%), stress (18.3%) and overall fatigue (11.7%). Scores for self-reported exposure to physicochemical risks, postural and physical risks, high work demand, and low social support were all significantly associated with workers' perceptions that their work is harmful to their health, regardless of gender or age. A high level of education was associated with stress symptoms, and reports that health was affected by work was associated with low job satisfaction.Many workers believe that their work affects their health. Health specialists should pay attention to the potential association between work and their patients' health complaints. This is particularly relevant when patients mention symptoms such as muscular pains, backache, overall fatigue, and stress. Specific attention should be given to complaints of stress in highly educated workers.

Enzyme polymorphism and clinical variability of diseases: a study of diabetes mellitus.

We investigated possible relations among four common neonatal manifestations of diabetic pregnancy (macrosomia, hypoglycemia, hypocalcemia, jaundice) and four enzyme polymorphisms (PGM1, ADA, AK1, ACP1 in a sample of infants born of diabetic mothers. The pattern of associations observed between the two sets of variables is consistent with known differences in enzymatic activity within phenotypes of each system, suggesting that low enzymatic activity may have unfavorable effects on fetal development and on adaptability of the neonate to the extrauterine environment, Some of the polymorphic enzymes studied influence fetal growth in normal pregnancy as well. Analysis of relations between genetic polymorphisms and the clinical pattern of common diseases may provide a better understanding of the genetic basis of the clinical variability of diseases within and between human populations.

Association of work related chronic stressors and psychiatric symptoms in a Swiss sample of police officers : a cross sectional questionnaire study

Purpose (1) To identify work related stressors that are associated with psychiatric symptoms in a Swiss sample of policemen and (2) to develop a model for identifying officers at risk for developing mental health problems. Method The study design is cross sectional. A total of 354 male police officers answered a questionnaire assessing a wide spectrum of work related stressors. Psychiatric symptoms were assessed using the "TST questionnaire" (Langner in J Health Hum Behav 4, 269-276, 1962). Logistic regression with backward procedure was used to identify a set of variables collectively associated with high scores for psychiatric symptoms. Results A total of 42 (11.9%) officers had a high score for psychiatric symptoms. Nearly all potential stressors considered were significantly associated (at P < 0.05) with a high score for psychiatric symptoms. A significant model including 6 independent variables was identified: lack of support from superior and organization OR = 3.58 (1.58-8.13), self perception of bad quality work OR = 2.99 (1.35-6.59), inadequate work schedule OR = 2.84 (1.22-6.62), high mental/intellectual demand OR = 2.56 (1.12-5.86), age (in decades) OR = 1.82 (1.21-2.73), and score for physical environment complaints OR = 1.30 (1.03-1.64). Conclusions Most of work stressors considered are associated with psychiatric symptoms. Prevention should target the most frequent stressors with high association to symptoms. Complaints of police officers about stressors should receive proper consideration by the management of public administration. Such complaints might be the expression of psychiatric caseness requiring medical assistance. Particular attention should be given to police officers complaining about many stressors identified in this study's multiple model. [Authors]

Simulation of surface waves in porous media

We present a novel numerical algorithm for the simulation of seismic wave propagation in porous media, which is particularly suitable for the accurate modelling of surface wave-type phenomena. The differential equations of motion are based on Biot's theory of poro-elasticity and solved with a pseudospectral approach using Fourier and Chebyshev methods to compute the spatial derivatives along the horizontal and vertical directions, respectively. The time solver is a splitting algorithm that accounts for the stiffness of the differential equations. Due to the Chebyshev operator the grid spacing in the vertical direction is non-uniform and characterized by a denser spatial sampling in the vicinity of interfaces, which allows for a numerically stable and accurate evaluation of higher order surface wave modes. We stretch the grid in the vertical direction to increase the minimum grid spacing and reduce the computational cost. The free-surface boundary conditions are implemented with a characteristics approach, where the characteristic variables are evaluated at zero viscosity. The same procedure is used to model seismic wave propagation at the interface between a fluid and porous medium. In this case, each medium is represented by a different grid and the two grids are combined through a domain-decomposition method. This wavefield decomposition method accounts for the discontinuity of variables and is crucial for an accurate interface treatment. We simulate seismic wave propagation with open-pore and sealed-pore boundary conditions and verify the validity and accuracy of the algorithm by comparing the numerical simulations to analytical solutions based on zero viscosity obtained with the Cagniard-de Hoop method. Finally, we illustrate the suitability of our algorithm for more complex models of porous media involving viscous pore fluids and strongly heterogeneous distributions of the elastic and hydraulic material properties.

Bayesian Networks and the Value of the Evidence for the Forensic Two-Trace Transfer Problem

Forensic scientists face increasingly complex inference problems for evaluating likelihood ratios (LRs) for an appropriate pair of propositions. Up to now, scientists and statisticians have derived LR formulae using an algebraic approach. However, this approach reaches its limits when addressing cases with an increasing number of variables and dependence relationships between these variables. In this study, we suggest using a graphical approach, based on the construction of Bayesian networks (BNs). We first construct a BN that captures the problem, and then deduce the expression for calculating the LR from this model to compare it with existing LR formulae. We illustrate this idea by applying it to the evaluation of an activity level LR in the context of the two-trace transfer problem. Our approach allows us to relax assumptions made in previous LR developments, produce a new LR formula for the two-trace transfer problem and generalize this scenario to n traces.