Learning about a Moving Target in Resource Management: Optimal Bayesian Disease Control

Resource managers must often make difficult choices in the face of imperfectly observed and dynamically changing systems (e.g., livestock, fisheries, water, and invasive species). A rich set of techniques exists for identifying optimal choices when that uncertainty is assumed to be understood and irreducible. Standard optimization approaches, however, cannot address situations in which reducible uncertainty applies to either system behavior or environmental states. The adaptive management literature overcomes this limitation with tools for optimal learning, but has been limited to highly simplified models with state and action spaces that are discrete and small. We overcome this problem by using a recently developed extension of the Partially Observable Markov Decision Process (POMDP) framework to allow for learning about a continuous state. We illustrate this methodology by exploring optimal control of bovine tuberculosis in New Zealand cattle. Disease testing—the control variable—serves to identify herds for treatment and provides information on prevalence, which is both imperfectly observed and subject to change due to controllable and uncontrollable factors. We find substantial efficiency losses from both ignoring learning (standard stochastic optimization) and from simplifying system dynamics (to facilitate a typical, simple learning model), though the latter effect dominates in our setting. We also find that under an adaptive management approach, simplifying dynamics can lead to a belief trap in which information gathering ceases, beliefs become increasingly inaccurate, and losses abound

Water Management and the Valuation of Indirect Environmental Services

Comprehensive water basin and watershed planning and management require valuation of the intermediate ecological services provided to the water resources themselves. Valuation of forest cover in the augmentation of water resources is discussed in the context of aggregate economic planning, water-basin or sectoral planning, and conservation project evaluation. The importance of valuing intermediate non-market goods is illustrated for each planning tool in the context of an illustrative example of the Pearl Harbor/Ko'olau watershed in Hawaii. In the context of water allocation and investment in waterworks, considerations of full income valuation imply that the value of water should incorporate the risk of watershed degradation contingent on the expected conservation effort. What appear to be new objectives of economic planning, such as sustainable development, do not require new criteria but rather the augmentation of existing methods of income accounting and project valuation to include the values on non-market goods. We also show that measurement of non-market valuation does not necessarily require the use of contingent-valuation methods, even when the usual alternatives (hedonics, household production, etc.) are not directly applicable.

Invasion success of a global avian invader is explained by within-taxon niche structure and association with humans in the native range

Aim To mitigate the threat invasive species pose to ecosystem functioning, reli- able risk assessment is paramount. Spatially explicit predictions of invasion risk obtained through bioclimatic envelope models calibrated with native species distribution data can play a critical role in invasive species management. Fore- casts of invasion risk to novel environments, however, remain controversial. Here, we assess how species’ association with human-modified habitats in the native range and within-taxon niche structure shape the distribution of invasive populations at biogeographical scales and influence the reliability of predictions of invasion risk. Location Africa, Asia and Europe. Methods We use ~1200 native and invasive ring-necked parakeet (Psittacula krameri) occurrences and associated data on establishment success in combi- nation with mtDNA-based phylogeographic structure to assess niche dynam- ics during biological invasion and to generate predictions of invasion risk. Niche dynamics were quantified in a gridded environmental space while bioclimatic models were created using the biomod2 ensemble modelling framework. Results Ring-necked parakeets show considerable niche expansion into climates colder than their native range. Only when incorporating a measure of human modification of habitats within the native range do bioclimatic envelope mod- els yield credible predictions of invasion risk for parakeets across Europe. Inva- sion risk derived from models that account for differing niche requirements of phylogeographic lineages and those that do not achieve similar statistical accu- racy, but there are pronounced differences in areas predicted to be susceptible for invasion. Main conclusions Information on within-taxon niche structure and especially association with humans in the native range can substantially improve predic- tive models of invasion risk. To provide policymakers with robust predictions of invasion risk, including these factors into bioclimatic envelope models is recommended

Multimodal MRI-based Imputation of the Aβ+ in Early Mild Cognitive Impairment.

ObjectiveTo identify brain atrophy from structural-MRI and cerebral blood flow(CBF) patterns from arterial spin labeling perfusion-MRI that are best predictors of the Aβ-burden, measured as composite 18F-AV45-PET uptake, in individuals with early mild cognitive impairment(MCI). Furthermore, to assess the relative importance of imaging modalities in classification of Aβ+/Aβ- early mild cognitive impairment.MethodsSixty-seven ADNI-GO/2 participants with early-MCI were included. Voxel-wise anatomical shape variation measures were computed by estimating the initial diffeomorphic mapping momenta from an unbiased control template. CBF measures normalized to average motor cortex CBF were mapped onto the template space. Using partial least squares regression, we identified the structural and CBF signatures of Aβ after accounting for normal cofounding effects of age, sex, and education.Results18F-AV45-positive early-MCIs could be identified with 83% classification accuracy, 87% positive predictive value, and 84% negative predictive value by multidisciplinary classifiers combining demographics data, ApoE ε4-genotype, and a multimodal MRI-based Aβ score.InterpretationMultimodal-MRI can be used to predict the amyloid status of early-MCI individuals. MRI is a very attractive candidate for the identification of inexpensive and non-invasive surrogate biomarkers of Aβ deposition. Our approach is expected to have value for the identification of individuals likely to be Aβ+ in circumstances where cost or logistical problems prevent Aβ detection using cerebrospinal fluid analysis or Aβ-PET. This can also be used in clinical settings and clinical trials, aiding subject recruitment and evaluation of treatment efficacy. Imputation of the Aβ-positivity status could also complement Aβ-PET by identifying individuals who would benefit the most from this assessment

Harnessing enforcement leverage at the border to minimize biological risk from international live species trade

Allocating inspection resources over a diverse set of imports to prevent entry of plant pests and pathogens presents a substantial policy design challenge. We model inspections of live plant imports and producer responses to inspections using a “state-dependent” monitoring and enforcement model. We capture exporter abatement response to a set of feasible inspection policies from the regulator. Conditional on this behavioral response, we solve the regulator’s problem of selecting the parameters for the state-dependent monitoring regime to minimize entry of infested shipments. We account for exporter heterogeneity, fixed penalties for noncompliance, imperfect abatement control and imperfect inspections at the border. Overall, we estimate that state-dependent targeting (based on historical interceptions) cuts the rate of infested shipments that are accepted by one-fifth, relative to uniformly allocated inspections

Prioritizing Invasive Species Threats Under Uncertainty

Prioritizing exotic or invasive pest threats in terms of agricultural, environmental, or human health damages is an important resource allocation issue for programs charged with preventing or responding to the entry of such organisms. Under extreme uncertainty, program managers may decide to research the severity of threats, develop prevention or control actions, and estimate cost-effectiveness in order to provide better information and more options when making decisions to choose strategies for specific pests. We examine decision rules based on the minimax and relative cost criteria in order to express a cautious approach for decisions regarding severe, irreversible consequences, discuss the strengths and weaknesses of these rules, examine the roles of simple rules and sophisticated analyses in decision making, and apply a simple rule to develop a list of priority plant pests.invasive species, decision criteria, uncertainty, Resource /Energy Economics and Policy,

A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities

The measurement and provision of precise and upto-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic controls systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data such as velocity of individual vehicles as well as vehicle type information can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%

Bridging the Communication Gap Between Economists and Biological Scientists in the Management of Invasive Species

Resource /Energy Economics and Policy,