On the dimensionality of ecological stability

Ecological stability is touted as a complex and multifaceted concept, including components such as variability, resistance, resilience, persistence and robustness. Even though a complete appreciation of the effects of perturbations on ecosystems requires the simultaneous measurement of these multiple components of stability, most ecological research has focused on one or a few of those components analysed in isolation. Here, we present a new view of ecological stability that recognises explicitly the non-independence of components of stability. This provides an approach for simplifying the concept of stability. We illustrate the concept and approach using results from a field experiment, and show that the effective dimensionality of ecological stability is considerably lower than if the various components of stability were unrelated. However, strong perturbations can modify, and even decouple, relationships among individual components of stability. Thus, perturbations not only increase the dimensionality of stability but they can also alter the relationships among components of stability in different ways. Studies that focus on single forms of stability in isolation therefore risk underestimating significantly the potential of perturbations to destabilise ecosystems. In contrast, application of the multidimensional stability framework that we propose gives a far richer understanding of how communities respond to perturbations.

AdaBoost Multiple Feature Selection and Combination for Face Recognition

Gabor features have been recognized as one of the most successful face representations. Encouraged by the results given by this approach, other kind of facial representations based on Steerable Gaussian first order kernels and Harris corner detector are proposed in this paper. In order to reduce the high dimensional feature space, PCA and LDA techniques are employed. Once the features have been extracted, AdaBoost learning algorithm is used to select and combine the most representative features. The experimental results on XM2VTS database show an encouraging recognition rate, showing an important improvement with respect to face descriptors only based on Gabor filters.

Has habitat heterogeneity promoted phenotypic and ecological sub-structuring among a Coregonus lavaretus population in a large Scottish lake?

Differences in stable-isotope values, morphology and ecology in whitefish Coregonus lavaretus were investigated between the three basins of Loch Lomond. The results are discussed with reference to a genetic investigation to elucidate any substructuring or spawning site fidelity. Foraging fidelity between basins of Loch Lomond was indicated by delta 13C and delta 15N values of C. lavaretus muscle tissue. There was, however, no evidence of the existence of sympatric morphs in the C. lavaretus population. A previous report of two C. lavaretus 'species' in Loch Lomond probably reflects natural variation between individuals within a single mixed population.

Max separation clustering for feature extraction from Optical Emission Spectroscopy Data

This paper proposes max separation clustering (MSC), a new non-hierarchical clustering method used for feature extraction from optical emission spectroscopy (OES) data for plasma etch process control applications. OES data is high dimensional and inherently highly redundant with the result that it is difficult if not impossible to recognize useful features and key variables by direct visualization. MSC is developed for clustering variables with distinctive patterns and providing effective pattern representation by a small number of representative variables. The relationship between signal-to-noise ratio (SNR) and clustering performance is highlighted, leading to a requirement that low SNR signals be removed before applying MSC. Experimental results on industrial OES data show that MSC with low SNR signal removal produces effective summarization of the dominant patterns in the data.

SVM Training Phase Reduction Using Dataset Feature Filtering for Malware Detection

N-gram analysis is an approach that investigates the structure of a program using bytes, characters, or text strings. A key issue with N-gram analysis is feature selection amidst the explosion of features that occurs when N is increased. The experiments within this paper represent programs as operational code (opcode) density histograms gained through dynamic analysis. A support vector machine is used to create a reference model, which is used to evaluate two methods of feature reduction, which are 'area of intersect' and 'subspace analysis using eigenvectors.' The findings show that the relationships between features are complex and simple statistics filtering approaches do not provide a viable approach. However, eigenvector subspace analysis produces a suitable filter.

Developing Approaches for Solving a Telecommunications Feature Subscription Problem

Call control features (e.g., call-divert, voice-mail) are primitive options to which users can subscribe off-line to personalise their service. The configuration of a feature subscription involves choosing and sequencing features from a catalogue and is subject to constraints that prevent undesirable feature interactions at run-time. When the subscription requested by a user is inconsistent, one problem is to find an optimal relaxation, which is a generalisation of the feedback vertex set problem on directed graphs, and thus it is an NP-hard task. We present several constraint programming formulations of the problem. We also present formulations using partial weighted maximum Boolean satisfiability and mixed integer linear programming. We study all these formulations by experimentally comparing them on a variety of randomly generated instances of the feature subscription problem.

The use of Ecological and Carbon Footprint Analysis in regional policy making: application and insights using the REAP model

This paper builds on and extends previous research to contribute to ongoing discussion on the use of resource and carbon accounting tools in regional policy making. The Northern Visions project has produced the first evidence-based footpath setting out the actions that need to be taken to achieve the step changes in the Ecological and Carbon Footprint of Northern Ireland. A range of policies and strategies were evaluated using the Resources and Energy Analysis Programme. The analysis provided the first regional evidence base that current sustainable development policy commitments would not lead to the necessary reductions in either the Ecological Footprint or carbon dioxide emissions. Building on previous applications of Ecological Footprint analysis in regional policy making, the research has demonstrated that there is a valuable role for Ecological and Carbon Footprint Analysis in policy appraisal. The use of Ecological and Carbon Footprint Analysis in regional policy making has been evaluated and recommendations made on ongoing methodological development. The authors hope that the research can provide insights for the ongoing use Ecological and Carbon Footprint Analysis in regional policy making and help set out the priorities for research to support this important policy area

The use of material flow analysis and the ecological footprint in regional policy-making: application and insights from Northern Ireland.

This paper aims to contribute to the ongoing debate on the use of resource accounting tools in regional policy making. The Northern Limits project applied Material Flow Analysis and Ecological Footprinting to regional policy making in Northern Ireland over a number of years. The early phase of the research informed the regions first sustainable development strategy which was published in 2006 with key targets relating to the Ecological Footprint and improving the resource efficiency of the economy. Phase II identified the next steps required to address data availability and quality and the use of MFA and EF in providing a measurement and monitoring framework for the strategy and in the development of the strategy implementation plan. The use of MFA and Ecological Footprinting in sustainable regional policy making and the monitoring of its implementation is an ongoing process which has raised a number of research issues which can inform the ongoing application and development of these and other resource accounting tools to within Northern Ireland, provide insights for their use in other regions and help set out the priorities for research to support this important policy area.