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Imperfect detection is ubiquitous among wildlife research and is therefore commonly included in abundance estimation. Yet, the factors that affect observation success are largely unknown for rare and elusive species, such as large carnivores. Here, we took advantage of intensive ground-based monitoring effort and an extensive GPS data set (2000–2018) and developed a winter sightability model for gray wolves (Canis lupus) in northern Yellowstone National Park, Wyoming, USA. Our resulting sightability model indicated that observation success was positively affected by the topographic nature of where wolves were in relation to observer locations (viewshed), areas being less forested (openness), and wolf group size, and negatively affected by distance from observer locations. Of these, viewshed had the strongest effect on the probability of observing a wolf, with the odds of observing a wolf being four times more likely when wolves were in the predicted viewshed. Openness was the next most influential covariate, and group size was the least influential. We also tested whether a wolf being harvested from a pack when they were outside of Yellowstone National Park had an effect on wolf sightability. We did not, however, find support for human-induced mortality affecting wolf sightability inside of Yellowstone National Park. Our results indicate that the ability to observe wolves was greatly affected by ecological and landscape-level factors, a finding that is likely to generally extend to other large carnivores. As such, our sightability model highlights the importance of considering landscape structure and variation in large carnivore use of the landscape when conducting observational-based studies. [ABSTRACT FROM AUTHOR]

Anthropogenic infrastructure can negatively affect wildlife through direct mortality and/or displacement behaviors. Some tetranoids (grouse spp.) species are particularly vulnerable to tall anthropogenic structures because they evolved in ecosystems void of vertical structures. In western North America, electric power transmission and distribution lines (power lines) occur in sagebrush (Artemisia spp.) landscapes within the range of the greater sage-grouse (Centrocercus urophasianus; sage-grouse). The U.S. Fish and Wildlife Service recommended using buffer zones near leks to mitigate the potential impacts of power lines on sage-grouse. However, recommended buffer distances are inconsistent across state and federal agencies because data are lacking. To address this, we evaluated the effects of power lines on sage-grouse breeding ecology within Utah, portions of southeastern Idaho, and southwestern Wyoming from 1998-2013. Overall, power lines negatively affected lek trends up to a distance of 2.7 and 2.8 km, respectively. Power lines died not affect lek persistence. Female sage-grouse avoided transmission lines during the nesting and brooding seasons at distances up to 1.1 and 0.8 km, respectively. Nest and brood success were negatively affected by transmission lines up to distances of 2.6 and 1.1 km, respectively. Distribution lines did not appear to affect sage-grouse habitat selection or reproductive fitness. Our analyses demonstrated the value of sagebrush cover in mitigating potential power line impacts. Managers can minimize the effects of new transmission power lines by placing them in existing anthropogenic corridors and/or incorporating buffers at least 2.8 km from active leks. Given the uncertainty we observed in our analyses regarding sage-grouse response to distribution lines coupled with their role in providing electric power service directly to individual consumers, we recommend that buffers for these power lines be considered on a case-by-case basis. Micrositing to avoid important habitats and habitat reclamation may reduce the potential impacts of new power line construction., Competing Interests: SH was employed by Rocky Mountain Power/Pacific Power, 1407 West North Temple, Salt Lake City, Utah, United States of America. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.