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 (RSF), 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 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.

All data collection and analytical methods and detailed variable descriptions can be found in the manuscript cited above. Code can be found in the supplemental material associated with the manuscript.

Data was collected between 2013-2020. Telemetry locations were aggregated to a 5km x 5km grid cell. 
CRS for grid cell centroids are defined in R as CRS("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0")

VARIABLES:

id.month: concatenation of eagle identification number (ID) and the month of the year (month)

x: x coordinate in meters (see above for CRS)


y: y coordinate in meters (see above for CRS)


month: three letter abbreviation for month


count: (0/1) 1 - presence of an individual eagle in the associated grid cell for that month, 0 - randomly chosen available location representing a grid cell where the particular eagle was not found


ID: individual eagle

ID 
temp: mean minimum monthly temperature in Celsius associated with the grid cell


open_prop: proportion of open habitat associated with the grid cell


for_prop: proportion of forested habitat associated with the grid cell


demRange: range in elevation associated with the grid cell


w: weight (1 - for all presence locations & 5000 - for all available locations)


month2: month as a numerical variable (1-12)

For questions please contact Jennifer D. McCabe (mccabe.jennifer@peregrinefund.org).