Recent national interest in golden eagle (Aquila chrysaetos) conservation and wind energy development prompted us to investigate golden eagle home range and resource use in the Columbia Plateau Ecoregion (CPE) in Washington and Oregon. From 2004 to 2013, we deployed satellite transmitters on adult eagles (n = 17) and monitored their movements for up to 7 years. We used the Brownian bridge movement model (BBMM) to estimate range characteristics from global position system (GPS) fixes and flight paths of 10 eagles, and modeled resource selection probability functions (RSPFs). Multi‐year home ranges of resident eagles were large (99% volume contour; x¯=245.7 knormalm2, SD = 370.2 km2) but were one‐third the size (x¯=82.3 knormalm2, SD = 94.6 km2) and contained half as many contours when defined by 95% isopleths. Annual ranges accounted for 66% of multi‐year range size. During the breeding season (16 Jan–15 Aug), eagles occupied ranges that were less fragmented, about half as large, and largely contained within ranges they used outside the breeding season (truex¯ overlap = 82.5%, SD = 19.0). Eagles selected upper slopes, rugged terrain, and ridge tops that appear to reflect underlying influences of prey, deflective wind currents, and proximity to nests. Fix distribution predicted by our resource selection model and that of 4 eagles monitored independently in the CPE were highly correlated (rs = 0.992). Our findings suggest conservative landscape management strategies addressing development in lower‐elevation montane and shrub‐steppe/grassland ecosystems can best define golden eagle ranges using exclusive 12.8‐km buffers around nests. Less conservative strategies based on 9.6‐km buffers must include identification and management of upper slopes, ridge‐tops, and areas of varied terrain defined by predictive models or GPS telemetry. For both strategies, high, year‐round intensity of eagle flight and perch use within 50% volume contours (average 3.2 km from nests) due to nest centricity may dramatically increase the probability of eagle conflict with wind turbines in core areas as evidenced by eagle turbine strikes that studies have documented within and beyond this zone. © 2014 The Wildlife Society.

No abstract

The ability to accurately predict the potential occurrence of species of management concern is useful for wildlife managers, particularly for those whose management activities involve large areas where sampling is difficult due to logistical or financial constraints. During the summers of 2002 and 2003, we used mist nets to capture bats (Myotis yumanensis, M. californicus, M. evotis, M. thysanodes, Eptesicus fuscus, Lasionycteris noctivagans, Tadarida brasiliensis, Antrozous pallidus, Lasiurus borealis, and Lasiurus cinereus) in Whiskeytown National Recreation Area in north-central California, USA. We used landscape-scale variables, logistic regression, and Akaike's Information Criterion (AIC c ) to model species distributions and produce spatially discerning predictive occurrence maps. We developed a priori models that we used to determine which landscape-scale variables best discriminated between capture sites and non-capture sites. The odds of capturing a bat were 3.3 greater when total edge increased by 10,000 m, whereas for Yuma myotis (Myotis yumanensis), the odds of predicting presence were 0.2 greater when distance to lakes and ponds decreased by 2,000 m. Elevation was important in predicting the distribution of silver-haired bats (Lasionycteris noctivagans) and big brown bats (Eptesicus fuscus). Increasing elevation by 400 m decreased the odds of capturing a silver-haired bat by 0.1 and a big brown bat by 0.4. Classification accuracy for our models ranged from 80.9% for all bat species combined to 72.3% for Yuma myotis and silver-haired bats. Predictive occurrence models can be valuable to bat conservation efforts because they provide spatial data important for evaluating the effects of management activities on species distributions. (JOURNAL OF WILDLIFE MANAGEMENT 71 (3):693-700; 2007)

No abstract