The adoption of market-based instruments for resource management: Three case studies

Market-based instruments (MBIs) for resource management create financial incentives for people and businesses to use resources more efficiently, within a regulatory context designed to ensure that ecological, social and cultural objectives are also met. Three case studies were done to identify factors influencing the adoption or rejection of market-based instruments in New Zealand. Case studies included Individual Transferable Quota (ITQ) for New Zealand's inshore fisheries, Transferable Water Permits (TWPs) in Tasman District and Waikato Region, and charges for occupation of coastal space at both the national and regional levels in New Zealand. This paper provides a summary of findings from these case studies. These include: MBIs are difficult to implement if they threaten the position of existing users. It is important to have clear objectives. Norms and values can be an obstacle to MBIs, especially where they help to protect the interests of key stakeholders, but value-based opposition can be overcome if practical concerns are addressed.market-based instruments, ITQ, transferable water permits, coastal occupation charges, Agribusiness, Agricultural and Food Policy, Consumer/Household Economics, Crop Production/Industries, Environmental Economics and Policy, Farm Management,

Examining the Invasion of a Bush Honeysuckle using Climate Analysis

Within the study of invasive plants, particular importance is placed on elucidating the mechanisms by which these plants proliferate and dominate within their introduced ranges. Several theories have been advanced to explain these invasions, each with different implications for the predicted range of invasive plants. Recent studies have provided support for the application of several invasion theories to Lonicera maackii, or what is more commonly referred to as bush honeysuckle. This species provides a unique opportunity to examine the efficacy of these theories in explaining the range expansion of invasive plants. L. maackii is endemic to eastern Asia, but it has invaded much of the eastern United States, posing a serious threat to the health of forests and other natural areas. To evaluate the application of biotic and abiotic theories of invasion for L. maackii, we modeled the climatic niche space of L. maackii in both its native and invasive ranges. We visually inspected and verified 1,046 L. maackii localities and 126 L. subsessilis localities, the sister taxon to L. maackii. After associating these localities with 19 climatic variables (BIOCLIM), we performed a principal component analysis (PCA) and observed a clear separation between the climatic conditions of the native East Asian L. maackii points and the invasive North American points. The climate niches of each population group (native L. maackii, invasive L. maackii, and L. subsessilis) were significantly different, suggesting that the North American population of L. maackii occupies a different climate niche than in its native East Asian range. This separation was consistent with the predicted verses observed probable occurrence maps of North America and East Asia which we built using Maxent. This change in L. maackii’s climatic niche lends support for ecological theories of invasion that feature biotic constraints on range expansion (like the Enemy Release and Novel Weapon Hypotheses) over theories relying on abiotic climatic constraints (like the Environmental Filtering Hypothesis)

Belief Space Scheduling

This thesis develops the belief space scheduling framework for scheduling under uncertainty in Stochastic Collection and Replenishment (SCAR) scenarios. SCAR scenarios involve the transportation of a resource such as fuel to agents operating in the field. Key characteristics of this scenario are persistent operation of the agents, and consideration of uncertainty. Belief space scheduling performs optimisation on probability distributions describing the state of the system. It consists of three major components---estimation of the current system state given uncertain sensor readings, prediction of the future state given a schedule of tasks, and optimisation of the schedule of the replenishing agents. The state estimation problem is complicated by a number of constraints that act on the state. A novel extension of the truncated Kalman Filter is developed for soft constraints that have uncertainty described by a Gaussian distribution. This is shown to outperform existing estimation methods, striking a balance between the high uncertainty of methods that ignore the constraints and the overconfidence of methods that ignore the uncertainty of the constraints. To predict the future state of the system, a novel analytical, continuous-time framework is proposed. This framework uses multiple Gaussian approximations to propagate the probability distributions describing the system state into the future. It is compared with a Monte Carlo framework and is shown to provide similar discrimination performance while computing, in most cases, orders of magnitude faster. Finally, several branch and bound tree search methods are developed for the optimisation problem. These methods focus optimisation efforts on earlier tasks within a model predictive control-like framework. Combined with the estimation and prediction methods, these are shown to outperform existing approaches