Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Quantifying uncertainty: physicians' estimates of infection in critically ill neonates and children.

To determine the diagnostic accuracy of physicians' prior probability estimates of serious infection in critically ill neonates and children, we conducted a prospective cohort study in 2 intensive care units. Using available clinical, laboratory, and radiographic information, 27 physicians provided 2567 probability estimates for 347 patients (follow-up rate, 92%). The median probability estimate of infection increased from 0% (i.e., no antibiotic treatment or diagnostic work-up for sepsis), to 2% on the day preceding initiation of antibiotic therapy, to 20% at initiation of antibiotic treatment (P<.001). At initiation of treatment, predictions discriminated well between episodes subsequently classified as proven infection and episodes ultimately judged unlikely to be infection (area under the curve, 0.88). Physicians also showed a good ability to predict blood culture-positive sepsis (area under the curve, 0.77). Treatment and testing thresholds were derived from the provided predictions and treatment rates. Physicians' prognoses regarding the presence of serious infection were remarkably precise. Studies investigating the value of new tests for diagnosis of sepsis should establish that they add incremental value to physicians' judgment.

Paternal investment affects prevalence of malaria.

Both reproduction and parasite defense can be costly, and an animal may face a trade-off between investing in offspring or in parasite defense. In contrast to the findings from nonexperimental studies that the poorly reproducing individuals are often the ones with high parasite loads, this life-history view predicts that individuals with high reproductive investment will show high parasite prevalence. Here we provide an experimental confirmation of a positive association between parental investment levels of male great tits Parus major and the prevalence of Plasmodium spp, a hematozoa causing malaria in various bird species. We manipulated brood size, measured feeding effort of both males and females, and assessed the prevalence of the hemoparasite from blood smears. In enlarged broods the males, but not the females, showed significantly higher rates of food provisioning to the chicks, and the rate of malarial infection was found to be more than double in male, but not female, parents of enlarged broods. The findings show that there may be a trade-off between reproductive effort and parasite defense of the host and also suggest a mechanism for the well documented trade-off between current reproductive effort and parental survival.

Uncertainty about the true source. A note on the likelihood ratio at the activity level.

This paper focuses on likelihood ratio based evaluations of fibre evidence in cases in which there is uncertainty about whether or not the reference item available for analysis - that is, an item typically taken from the suspect or seized at his home - is the item actually worn at the time of the offence. A likelihood ratio approach is proposed that, for situations in which certain categorical assumptions can be made about additionally introduced parameters, converges to formula described in existing literature. The properties of the proposed likelihood ratio approach are analysed through sensitivity analyses and discussed with respect to possible argumentative implications that arise in practice.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Maternal and paternal contributions to pathogen resistance dependent on development stage in a whitefish (Salmonidae)

It is often assumed that maternal and paternal contributions to offspring phenotype change over the lifetime of an individual. However, studies on parental effects typically suffer from the problems that heritabilities and maternal environmental effects are difficult to separate, and that both may depend on environmental factors and developmental stage. In order to experimentally disentangle maternal from paternal contributions and the likely effects of developmental stage from ecological effects, we sampled a natural population of the whitefish Coregonus palaea, used gametes for full-factorial in vitro fertilizations, raised over 10000 of the resulting offspring singly at controlled conditions, and exposed them at different points during embryonic development to one of two strains of Pseudomonas fluorescens that differed in their virulence characteristics (only one caused mortality, while both delayed hatching and reduced growth). Vulnerability to infection increased markedly over embryo development. This change coincided with a distinct shift in the importance of maternal to additive genetic effects on survival. Timing of exposure also affected the variance components for hatching time and larval length, but in a less consistent direction than the variance components for mortality. No significant genetic variation was found for any reaction norms across time points of exposure, indicating a uniformity among genotypes in how susceptibility changed over development. Phenotypes were also typically correlated across time points, which could constrain the evolution of the reaction norms. Our experiment demonstrates that the relative maternal and paternal contributions to susceptibility to an infection, and hence the evolutionary potential to respond to pathogen-induced selection, depends not only on the kind of pathogenic stress but also on the timing of the challenge.

Mass detection on mammograms: influence of signal shape uncertainty on human and model observers.

We studied the influence of signal variability on human and model observers for detection tasks with realistic simulated masses superimposed on real patient mammographic backgrounds and synthesized mammographic backgrounds (clustered lumpy backgrounds, CLB). Results under the signal-known-exactly (SKE) paradigm were compared with signal-known-statistically (SKS) tasks for which the observers did not have prior knowledge of the shape or size of the signal. Human observers' performance did not vary significantly when benign masses were superimposed on real images or on CLB. Uncertainty and variability in signal shape did not degrade human performance significantly compared with the SKE task, while variability in signal size did. Implementation of appropriate internal noise components allowed the fit of model observers to human performance.

Traiter l'hypertension artérielle: l'incertitude des définitions et des cibles [Managing hypertension: the uncertainty of definitions and targets].

In clinical practice, physicians are confronted with a multitude of definitions and treatment goals for arterial hypertension, depending of the diagnostic method used (e.g. office, home and ambulatory blood pressure measurement) and the underlying disease. The historical background and evidence of these different blood pressure thresholds are discussed in this article, as well as some recent treatment guidelines. Besides, the debate of the "J curve", namely the possible risks associated with an excessive blood pressure reduction, is discussed.