DOI: 10.5061/dryad.ns1rn8pth
title: Data from: Resource exploitation collapses the home range of an apex predator

description: Optimizing energy acquisition and expenditure is a fundamental trade-off
 for consumers, strikingly reflected in how mobile organisms use space.
 Several studies have established that home range size decreases as
 resource density increases, but the balance of costs and benefits
 associated with exploiting a given resource density is unclear. We
 evaluate how the ability of consumers to exploit their resources through
 movement (termed “resource exploitation”) interacts with resource density
 to influence home range size. We then contrast two hypotheses to evaluate
 how resource exploitation influences home range size across a vast
 gradient of productivity and density of human-created linear features
 (roads and seismic lines) that are known to facilitate animal movements.
 Under the Diffusion Facilitation Hypothesis, linear features are predicted
 to lead to more diffuse space use and larger home ranges. Under the
 Exploitation Efficiency Hypothesis, linear features are predicted to
 increase foraging efficiency, resulting in less space being required to
 meet energetic demands and therefore smaller home ranges. Using GPS
 telemetry data from 142 wolves (Canis lupus) distributed over more than
 500,000 km2, we found that wolf home range size was influenced by the
 interaction between resource density and exploitation efficiency. Home
 range size decreased as linear feature density increased, supporting the
 Exploitation Efficiency Hypothesis. However, the effect of linear features
 on home range size diminished in more productive areas, suggesting that
 exploitation efficiency is of greater importance when resource density is
 low. These results suggest that smaller home ranges will occur where both
 linear feature density and primary productivity are higher, thereby
 increasing regional wolf density.
description: GPS telemetry data were collected between 2011 and 2017 from 36
 individuals in 15 packs (BC), 34 individuals from 10 packs (AB N), 44
 individuals from 16 packs (AB NE), and 28 individuals from 19 packs (SK).
 All animals were captured and handled using approved Animal Care
 protocols. Collars were programmed to retrieve GPS locations at varying
 intervals, from 5 minutes to 3 hours. We visually checked for location
 errors, and rarified data to the longest fix rate of 3-hour locations. BWe
 divided data into two seasons, roughly corresponding to the snow and
 snow-free seasons. To quantify home range area we created 95%
 autocorrelated Kernel Density Estimators (aKDEs; Fleming et al., 2015;
 Noonan et al., 2019) for each individual in each season using the ctmm
 package in R (Calabrese et al., 2016). Prior to creating aKDEs, we removed
 any individual season combination with less than 50 locations and visually
 assessed and removed individual seasons with extra-territorial forays. As
 suggested by Calabrese et al (2016), we used Maximum Likelihood to fit
 independent identically distributed, Brownian motion, Ornstein–Uhlenbeck,
 integrated Ornstein–Uhlenbeck, and Ornstein–Uhlenbeck Foraging models, and
 selected the model with the lowest Akaike Information Criterion (AIC)
 score (Akaike, 1974). For additional details
 see https://github.com/MelanieDickie/WolfHomeRange.