Invasive elodea threatens remote ecosystem services in Alaska: a spatially-explicit bioeconomic risk analysis

Thesis (Ph.D.) University of Alaska Fairbanks, 2017This dissertation links human and ecological systems research to analyze resource management decisions for elodea, Alaska's first submerged aquatic invasive plant. The plant likely made it to Alaska through the aquarium trade. It was first discovered in urban parts of the state but is being introduced to remote water bodies by floatplanes and other pathways. Once introduced, elodea changes freshwater systems in ways that can threaten salmon and make floatplane destinations inaccessible. The analysis integrates multiple social and ecological data to estimate the potential future economic loss associated with its introduction to salmon fisheries and floatplane pilots. For estimating the effects on commercial sockeye fisheries, multiple methods of expert elicitation are used to quantify and validate expert opinion about elodea's ecological effects on salmon. These effects are believed to most likely be negative, but can in some instances be positive. Combined with market-based economic valuation, the approach accounts for the full range of potential ecological and economic effects. For analyzing the lost trip values to floatplane pilots, the analysis uses contingent valuation to estimate recreation demand for landing spots. A spatially-explicit model consisting of seven regions simulates elodea's spread across Alaska and its erratic population dynamics. This simulation model accounts for the change in region-specific colonization rates as elodea populations are eradicated. The most probable economic loss to commercial fisheries and recreational floatplane pilots is $97 million per year, with a 5$456 million annually. The analysis describes how loss varies among stakeholders and regions, with more than half of statewide loss accruing to commercial sockeye salmon fisheries in Bristol Bay. Upfront management of all existing invasions is found to be the optimal management strategy for minimizing long-term loss. Even though the range of future economic loss is large, the certainty of long-term damage favors investments to eradicate current invasions and prevent new arrivals. The study serves as a step toward risk management aimed at protecting productive ecosystems of national and global significance.General introduction -- Chapter 1. Quantifying Expert Knowledge Using a Discrete Choice Model: Persistence of Salmonids in Habitat Invaded by Elodea -- Chapter 2. Aquatic Invasive Species Change Ecosystem Services from the World's Largest Sockeye Salmon Fisheries in Alaska -- Chapter 3. Aquatic Invasive Plants Alter Recreation Access for Alaska's Floatplane Pilots: an Application of Stated Geographic Preferences to Economic Valuation -- Chapter 4. Aquatic Invasive Species from Urban Source Lakes Threaten Remote Ecosystem Services in Alaska: Linking Floatplane Pathway Dynamics with Bioeconomic Risk Analysis -- General Conclusion -- References -- Appendix

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse