Although spatial heterogeneity of prey and landscapes are known to contribute to variation around predator-prey functional response models, few studies have quantified these effects. We illustrate a new approach using data from winter movement paths of GPS-collared wolves in the Rocky Mountains of Canada and time-to-event models with competing risks for measuring the effect of prey and landscape characteristics on the time-to-kill, which is the reciprocal of attack rate (aN) in a Holling's functional response. We evaluated 13 a priori models representing hypothesized mechanisms influencing attack rates in a heterogeneous landscape with two prey types. Models ranged from variants on Holling's disc equation, including search rate and prey density, to a full model including prey density and patchiness, search rates, satiation, and landscape features, which were measured along the wolf's movement path. Movement rates of wolves while searching explained more of the variation in time-to-kill than prey densities. Wolves did not compensate for low prey density by increasing movement rates and there was little evidence that spatial aggregation of prey influenced attack rates in this multi-prey system. The top model for predicting time-to-kill included only search rate and landscape features. Wolves killed prey more quickly in flat terrain, likely due to increased vulnerability from accumulated snow, whereas attack rates were lower when wolves hunted near human-made features presumably due to human disturbance. Understanding the sources of variation in attack rates provides refinements to functional response models that can lead to more effective predator–prey management in human-dominated landscapes.