To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon‐Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under‐ or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

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Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case-by-case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding

The U.S. supply of renewable electricity, having grown at a remarkable pace over the past decade, is substantially reducing our dependence on coal-burning power plants that harm public health and destabilize our climate. Today, renewable energy technologies-such as wind, solar, geothermal, and biopower-are supplying affordable, reliable, and pollution-free power to the equivalent of some 16 million typical American homes. Renewable electricity generation, which grew from less than 2 percent of U.S. capacity in 2007 to more than 5 percent in 2012, was the largest source of new-capacity growth in 2012 (Bloomberg 2013). The nation's growing commitment to renewable energy also means increasing opportunities for economic development. Renewable energy industries now support hundreds of thousands of clean energy jobs, promote billions of dollars in new investment, and constitute an important source of revenue for states and local communities. May 2013How Renewable Electricity Standards Deliver Economic Benefits RENEWABLES: ENERGY YOU CAN COUNT ON In Brief © NRELWorkers install PV modules on an Englewood, CO, home. Jobs are just one of the economic benefits that come from the increased investment in renewable energy spurred by state renewable electricity standards.

We used structured decision‐making to develop a 2‐part framework to assist managers in the proactive management of disease outbreaks in Montana, USA. The first part of the framework is a model to estimate the probability of disease outbreak given field observations available to managers. The second part of the framework is decision analysis that evaluates likely outcomes of management alternatives based on the estimated probability of disease outbreak, and applies managers' values for different objectives to indicate a preferred management strategy. We used pneumonia in bighorn sheep (Ovis canadensis) as a case study for our approach, applying it to 2 populations in Montana that differed in their likelihood of a pneumonia outbreak. The framework provided credible predictions of both probability of disease outbreaks, as well as biological and monetary consequences of management actions. The structured decision‐making approach to this problem was valuable for defining the challenges of disease management in a decentralized agency where decisions are generally made at the local level in cooperation with stakeholders. Our approach provides local managers with the ability to tailor management planning for disease outbreaks to local conditions. Further work is needed to refine our disease risk models and decision analysis, including robust prediction of disease outbreaks and improved assessment of management alternatives. © The Wildlife Society, 2012