Time‐to‐kill: measuring attack rates in a heterogenous landscape with multiple prey types

Abstract

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 multiprey 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.

Hunger mediates apex predator's risk avoidance response in wildland–urban interface

“…One alternative interpretation of the increased hunting success in higher housing densities is that the forager will require a higher feeding rate (Smith, Wang, & Wilmers, ), and thus higher hunting success rates (McPhee et al, ; Merrill et al., ) from risky over the safe patches (Brown & Kotler, ). Thus, our interpretation of increased housing density conveying a higher reward value would potentially be inaccurate.…”

Diel movement patterns influence daily variation in wolf kill rates on moose

“…Wolf movement rates were calculated using 2·5‐h fix intervals, and data were only included from wolves that killed at least one prey, disregarding data < 150 m from kills and < 1 km from dump sites, yielding 23 558 relocations from 39 collared individuals. These selection criteria were used to ensure that wolves were actually searching for new prey, rather than handling past kills or scavenging at a dump (DeCesare ; McPhee, Webb & Merrill ).…”

Individual Variability

“…personality type) within populations, something that is beginning to be explored (Pruitt et al, 2012). Importantly, if individual variability strongly structures variability in handling time and attack rate, current model assumptions are likely to be violated (McPhee et al, 2011). For example, if both attack rate and handling time are highly variable from one individual predator to the next, then population average predation rates are unlikely to be helpful in making mechanistic interpretations about predatorprey interactions, because multiple combinations of attack rate and handling time will result in the same functional response curve (Okuyama, 2012; see also Table A.2 in the Appendix).…”