In 2009, the United States Fish and Wildlife Service promulgated permit regulations for the unintentional lethal take (anthropogenic mortality) and disturbance of golden eagles (Aquila chrysaetos). Accurate population trend and size information for golden eagles are needed so agency biologists can make informed decisions when eagle take permits are requested. To address this need with available data, we used a log‐linear hierarchical model to average data from a late‐summer aerial‐line‐transect distance‐sampling survey (WGES) of golden eagles in the United States portions of Bird Conservation Region (BCR) 9 (Great Basin), BCR 10 (Northern Rockies), BCR 16 (Southern Rockies/Colorado Plateau), and BCR 17 (Badlands and Prairies) from 2006 to 2010 with late‐spring, early summer Breeding Bird Survey (BBS) data for the same BCRs and years to estimate summer golden eagle population size and trends in these BCRs. We used the ratio of the density estimates from the WGES to the BBS index to calculate a BCR‐specific adjustment factor that scaled the BBS index (i.e., birds per route) to a density estimate. Our results indicated golden eagle populations were generally stable from 2006 to 2010 in the 4 BCRs, with an estimated average rate of population change of −0.41% (95% credible interval [CI]: −4.17% to 3.40%) per year. For the 4 BCRs and years, we estimated annual golden eagle population size to range from 28,220 (95% CI: 23,250–35,110) in 2007 to 26,490 (95% CI: 21,760–32,680) in 2008. We found a general correspondence in trends between WGES and BBS data for these 4 BCRs, which suggested BBS data were providing useful trend information. We used the overall adjustment factor calculated from the 4 BCRs and years to scale BBS golden eagle counts from 1968 to 2005 for the 4 BCRs and for 1968 to 2010 for the 8 other BCRs (without WGES data) to estimate golden eagle population size and trends across the western United States for the period 1968 to 2010. In general, we noted slightly declining trends in southern BCRs and slightly increasing trends in northern BCRs. However, we estimated the average rate of golden eagle population change across all 12 BCRs for the period 1968–2010 as +0.40% per year (95% CI = −0.27% to 1.00%), suggesting a stable population. We also estimated the average rate of population change for the period 1990–2010 was +0.5% per year (95% CI = −0.33% to 1.3%). Our annual estimates of population size for the most recent decade range from 31,370 (95% CI: 25,450–39,310) in 2004 to 33,460 (95% CI: 27,380–41,710) in 2007. Our results clarify that golden eagles are not declining widely in the western United States. © 2013 The Wildlife Society.

Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.

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The development and installation of renewable energy comes with environmental cost, including the death of wildlife. These costs occur locally, and seem small compared to the global loss of biodiversity. However, failure to acknowledge uncertainties around these costs affects local conservation, and may lead to the loss of populations or species. Working with these uncertainties can result in adaptive management plans designed to benefit renewable energy development and conservation. An example is the U.S. government's policy for managing bald (Haliaeetus leucocephalus) and golden (Aquila chrysaetos) eagle deaths at terrestrial wind facilities. Using records from 422 U.S. wind facilities we improved the precision of estimates of exposure (8.79 eagle minutes hr À1 km À3, SD: 13.64) and collision probability (0.0058 birds per minute of exposure, SD: 0.0038) currently used in U.S. policy.The new estimates for bald (exposure: 3.19 eagle minutes hr À1 km À3 , SD: 2.583; collision probability: 0.007025 eagles per minute of exposure, SD: 0.004379) and golden (exposure: 1.21 eagle minutes hr À1 km À3 , SD: 0.352; collision probability: 0.005648 birds per minute of exposure, SD: 0.004413) eagles had a smaller mean and standard deviation. Thus, their implementation within the government's adaptive management framework could help refine the balance between energy consumption and conservation. K E Y W O R D Sadaptive management, bald eagle, Bayesian analysis, golden eagle, renewable energy, risk, wind-wildlife interactions | INTRODUCTIONEnergy consumption has increased due to the proliferation of technology, smaller household size and urban sprawl (Liu, Daily, Ehrlich, & Luck, 2013). Nonrenewable energy sources can meet demand, but their finite nature and concern over a changing climate has led to increasing use of renewable energy generated from hydroelectric, solar, and wind, amongst others (e.g., Twidell & Weir, 2015). These sources of energy diminish harmful byproducts, such as greenhouse gas emissions, and are described as "green energy." However, renewable energy