DOI: 10.5061/dryad.9p8cz8wh3
title: Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions

description: 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.
description: 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).