Kurt K. Benke

Quantifying Uncertainty in Estimation of Tropical Arthropod Species Richness

There is a bewildering range of estimates for the number of arthropods on Earth. Several measures are based on extrapolation from species specialized to tropical rain forest, each using specific assumptions and justifications. These approaches have not provided any sound measure of uncertainty associated with richness estimates. We present two models that account for parameter uncertainty by replacing point estimates with probability distributions. The models predict medians of 3.7 million and 2.5 million tropical arthropod species globally, with 90% confidence intervals of [2.0, 7.4] million and [1.1, 5.4] million, respectively. Estimates of 30 million or greater are predicted to have <0.00001 probability. Sensitivity analyses identified uncertainty in the proportion of canopy arthropod species that are beetles as the most influential parameter, although uncertainties associated with three other parameters were also important. Using the median estimates suggests that in spite of 250 years of taxonomy and around 855,000 species of arthropods already described, approximately 70% await description.

Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model

Analysis of uncertainty is often neglected in the evaluation of complex systems models, such as computational models used in hydrology or ecology. Prediction uncertainty arises from a variety of sources, such as input error, calibration accuracy, parameter sensitivity and parameter uncertainty. In this study, various computational approaches were investigated for analysing the impact of parameter uncertainty on predictions of streamflow for a water-balance hydrological model used in eastern Australia. The parameters and associated equations which had greatest impact on model output were determined by combining differential error analysis and Monte Carlo simulation with stochastic and deterministic sensitivity analysis. This integrated approach aids in the identification of insignificant or redundant parameters and provides support for further simplifications in the mathematical structure underlying the model. Parameter uncertainty was represented by a probability distribution and simulation experiments revealed that the shape (skewness) of the distribution had a significant effect on model output uncertainty. More specifically, increasing negative skewness of the parameter distribution correlated with decreasing width of the model output confidence interval (i.e. resulting in less uncertainty). For skewed distributions, characterisation of uncertainty is more accurate using the confidence interval from the cumulative distribution rather than using variance. The analytic approach also identified the key parameters and the non-linear flux equation most influential in affecting model output uncertainty.

A spatial-statistical approach to the visualisation of uncertainty in land suitability analysis

A formal GIS-based procedure known as land suitability analysis (LSA) is used to determine the most appropriate crops for cultivation in different geographical locations. The approach is based on multi-criteria decision analysis utilising biophysical measurements (including rainfall, temperature, pH) and expert opinion captured from regional workshops. The issue of uncertainty in model predictions and its importance is discussed, and a method is described for its analysis and visualisation in LSA maps. Experimental results using Monte Carlo simulation are presented for ryegrass/sub-clover and winter wheat crops grown in south-western Victoria. It was found that uncertainty in the prediction of land suitability, as described by the coefficient of variation (CV), ranged from 0.13 to 0.18 for ryegrass, and much higher at 0.28 to 0.30 for the winter wheat crop. Results showed that, for close matches between crop type and production location, over 90 percent of the standard deviation in the prediction was accounted for by uncertainty in expert opinion rather than uncertainty in biophysical data.

Convolution operators as a basis for objective correlates of texture perception

A method is described for deriving, from digitized images, objective measures that correlate strongly with simple perceptual judgements on the same images. Each measure is the normalized variance of an image obtained by convolving the original image with a specific local operator. This operator is designed to optimize the correlation between the particular percept and the objective measure, subject to certain specified constraints. >

Risk assessment models for invasive species: uncertainty in rankings from multi-criteria analysis.

Uncertainty analysis is described in the context of risk assessment for invasive plant species, where assessment criteria can be weighted using a weight-assignment methodology based on multi-criteria decision analysis (MCDA). A description is given of the essential elements of the Victorian Weed Risk Assessment (VWRA) model that ranks weed species according to scores determined from the synthesis of expert opinion and published literature. The VWRA model uses MCDA to produce a priority ranking of risk for pest plant species by compiling complex data into components with similar themes, arranging these components into the appropriate hierarchical order and then assigning criterion weights to each component. The aim of the study was to investigate the uncertainty and statistical significance in the ranking of the invasive species produced by the model. The methodology used for the uncertainty analysis is described and employed in the evaluation of the two categories of interest, represented by the statistical factors of impact and invasiveness. The criteria contributing to the uncertainty in the predicted ranking were found to be mainly in the impact category, rather than the invasiveness category, and related to agricultural factors such as vector status, reductions in yield quantity and increasing harvest cost.

Uncertainty analysis and risk assessment in the management of environmental resources

Analysis of uncertainty is often neglected in the evaluation of complex systems (such as predictive models in hydrology or ecology, or environmental processes). Decisions and actions based on such systems may be error-prone for a variety of reasons, including lack of information, input errors or data variability. Research in uncertainty addresses this problem by investigating the causes of uncertainty and the characterisation of uncertainty associated with limited information. The article describes and compares definitions and terminology in uncertainty analysis and reviews suggested classifications. Traditional risk assessment is not directly equivalent to uncertainty analysis. The authors discuss distinctions and applications in various contexts relating to environmental management.

Uncertainty in Health Risks from Artificial Lighting due to Disruption of Circadian Rhythm and Melatonin Secretion: A Review

ABSTRACT Incandescent lighting in many domestic and commercial applications is in the process of replacement by more efficient light sources, such as the compact fluorescent light (CFL) and the light emitting diode (LED). For household use, both CFL and LED sources have a significant blue component in the emitted spectrum in comparison to the warmer incandescent globes and this has been the cause of emerging health concerns. Recent research suggests that the blue light bandwidth in the visible spectrum has a significant impact on physical health, including disruption of the internal body clock and suppression of melatonin secretion at night. This disruptive effect has been linked to a range of illnesses, including breast cancer, prostate cancer, heart disease, obesity, and diabetes. There have also been positive effects observed, including re-setting the body clock to the required sleep pattern, boosting mood, alertness, cognitive performance, and alleviating seasonal affective depression (SAD). In this article, an introduction and review of recent research is provided, relevant health issues are highlighted and discussed, and uncertainty analysis completed for the dose–response curve for melatonin suppression as a function of incident photon density.

Application of adaptive convolution masking to the automation of visual inspection

An approach is presented for the automation of important aspects of human visual inspection in quality control. Pattern recognition and digital image processing are used to detect and classify defects in full gray-scale images of complex mechanical assemblies. The method simulates the processes of adaptation, fixation, and feature extraction in the human visual system. It applies an algorithm for optimizing convolution masks to distinguish between acceptable and unacceptable images. As a numerical example, the technique is used to detect a number of defects in X-ray images of complex mechanical assemblies. >

Identification and interpretation of sources of uncertainty in soils change in a global systems-based modelling process

In the past, uncertainty analysis in soil research was often reduced to consideration of statistical variation in numerical data relating to model parameters, model inputs or field measurements. The simplified conceptual approach used by modellers in calibration studies can be misleading, because it relates mainly to error minimisation in regression analysis and is reductionist in nature. In this study, a large number of added uncertainties are identified in a more comprehensive attention to the problem. Uncertainties in soil analysis include errors in geometry, position and polygon attributes. The impacts of multiple error sources are described, including covariate error, model error and laboratory analytical error. In particular, the distinction is made between statistical variability (aleatory uncertainty) and lack of information (epistemic uncertainty). Examples of experimental uncertainty analysis are provided and discussed, including reference to error disaggregation and geostatistics, and a systems-based analytic framework is proposed. It is concluded that a more comprehensive and global approach to uncertainty analysis is needed, especially in the context of developing a future soils modelling process for incorporation of all known sources of uncertainty.

Visualisation of spatial uncertainty in hydrological modelling

Uncertainty in hydrological modelling is often associated with model structure or calibration coefficients, and little consideration is given to the visualisation of uncertainty in either input or output data. As hydrological modelling is often a component of landscape design, there is continuing interest in the visualisation of uncertainty in model inputs and outputs, which could include catchment size or landcover (spatial inputs), and the subsequent impact on uncertainty in model predictions (such as streamflow). In this paper, inputs and outputs associated with hydrological models are discussed in the context of spatial uncertainty and how this information may be presented to users by means of statistical and spatial visualisation techniques. A regional study in hydrological modelling is used to demonstrate the sensitivity to spatial uncertainty and its consequences. Tools and techniques are discussed for the visual presentation of uncertainty, with implications for land management and policy decisions. A description is given of an experimental software tool developed for visualisation of spatial uncertainty in hydrological landscape cross-sections, where depth-to-water-table is used as a predictor for dryland salinity. Uncertainty in the estimate of this depth is important to analysts and policy makers.