Eric C. Soehren | Search USGS

USGS2023

Winter distribution of golden eagles in the Eastern USA

Golden Eagles ( Aquila chrysaetos ) have a Holarctic distribution, but some details of that overall distribution are poorly understood, including parts of the range in eastern North America. Recent studies in the region suggest that Golden Eagles may be more widely distributed than previously recognized. For species specific conservation efforts to be effective, an understanding of the distribution of the species is essential. Thus, the goal of this study was to map the winter distribution of Golden Eagles in the eastern half of the USA. To accomplish this, we reviewed and compiled 11,981 Golden Eagle records from eight data sources, including literature and ornithology records, community science data, survey data, and telemetry data. We found that Golden Eagles were observed in winter in each of the 31 states that lie completely east of the 100th meridian and in 1244 of the 2045 counties (61%) in those states. The proportion of counties with records varied by physiographic province, with higher proportions in physiographic provinces with more rugged terrain and greater forest cover. Our study shows that Golden Eagles are more widely distributed during winter in eastern USA states than was previously recognized. This work provides an important foundation for future management and research at a time when threats to this species are expanding rapidly on the landscape.

Journal of Raptor Research↗

USGS2023

Conservation plan for golden eagles in eastern North America

No abstract available.

Report↗

USGS2021

Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distribution

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k -fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

Ecography↗