Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Biogas reforming using renewable wind energy and induction heating

While the benefits of renewable energy are well known and used to influence government policy there are a number of problems which arise from having significant quantities of renewable energies on an electricity grid. The most notable problem stems from their intermittent nature which is often out of phase with the demands of the end users. This requires the development of either efficient energy storage systems, e.g. battery technology, compressed air storage etc. or through the creation of demand side management units which can utilise power quickly for manufacturing operations. Herein a system performing the conversion of synthetic biogas to synthesis gas using wind power and an induction heating system is shown. This approach demonstrates the feasibility of such techniques for stabilising the electricity grid while also providing a robust means of energy storage. This exemplar is also applicable to the production of hydrogen from the steam reforming of natural gas.

DOMINANCE OF SHORT-RANGE-ORDER EFFECTS IN LOW-ENERGY ELECTRON-DIFFRACTION INTENSITY SPECTRA

Using low-energy electron-diffraction (LEED) formalism, we demonstrate theoretically that LEED I-V spectra are characterized mainly by short-range order. We also show experimentally that diffuse LEED (DLEED) I-V spectra can be accurately measured from a disordered system using a video-LEED system even at very low coverage. These spectra demonstrate that experimental DLEED I-V spectra from disordered systems may be used to determine local structures. As an example, it is shown that experimental DLEED I-V spectra from K/Co {1010BAR} at potassium coverages of 0.07, 0.1, and 0.13 monolayer closely resemble calculated and experimental LEED I-V spectra for a well-ordered Co{1010BAR}-c(2X2)-K superstructure, leading to the conclusion that at low coverages, potassium atoms are located in the fourfold-hollow sites and that there is no large bond-length change with coverage.

Reducing Parametric Uncertainty in Limit Cycle Oscillations

The assimilation of discrete higher fidelity data points with model predictions can be used to achieve a reduction in the uncertainty of the model input parameters which generate accurate predictions. The problem investigated here involves the prediction of limit-cycle oscillations using a High-Dimensional Harmonic Balance method (HDHB). The efficiency of the HDHB method is exploited to enable calibration of structural input parameters using a Bayesian inference technique. Markov-chain Monte Carlo is employed to sample the posterior distributions. Parameter estimation is carried out on both a pitch/plunge aerofoil and Goland wing configuration. In both cases significant refinement was achieved in the distribution of possible structural parameters allowing better predictions of their
true deterministic values.

Uncertainty in the Cloud: An Angel-Daemon Approach to Modelling Performance

Uncertainty profiles are used to study the effects of contention within cloud and service-based environments. An uncertainty profile provides a qualitative description of an environment whose quality of service (QoS) may fluctuate unpredictably. Uncertain environments are modelled by strategic games with two agents; a daemon is used to represent overload and high resource contention; an angel is used to represent an idealised resource allocation situation with no underlying contention. Assessments of uncertainty profiles are useful in two ways: firstly, they provide a broad understanding of how environmental stress can effect an application’s performance (and reliability); secondly, they allow the effects of introducing redundancy into a computation to be assessed