The effect of prey and predator densities on wolf predation

• Main Authors: Vucetich, John A., Peterson, Rolf O., Schaefer, Carrie C.
• Format: Text
• Language: unknown
• Published: Digital Commons @ Michigan Tech 2002
• Subjects: Alces alces; Canis lupus; Functional response; Isle Royale National Park (Michigan; USA); Kill ratio; per capita; Moose-wolf system; Predation; prey-dependent vs. ratio-dependent; Predator kill rates; Predator-prey dynamics
• Online Access: https://digitalcommons.mtu.edu/michigantech-p/13695; https://doi.org/10.1890/0012-9658(2002)083[3003:TEOPAP]2.0.CO;2

Predator kills rate (i.e., kills per predator per time) is routinely presupposed to depend exclusively on prey density: However, per capita rates of killing may typically depend on the density of both prey and predator. Unfortunately, our perception of many ecological phenomena may be limited by the inappropriate assumption that kill rates do not depend on predator density. One of many ways to represent the influence of predator density is ratio-dependent predation, where kill rate depends on the ratio of prey to predator rather than the actual numbers of prey and predator. Determining the role of ratio dependency in predation theory has been contentious. Assessments of the influence of predator density on kill rate have been primarily limited to theoretical considerations, indirect evidence, and simplified laboratory demonstrations. We directly observed the influence of both prey and predator density on kill rates in an unmanipulated terrestrial system of large mammals wolves (Canis lupus) and moose (Alces alces). Predator density explained more variation in kill rate than did prey density (R2 = 0.36 vs. R2 = 0.17, respectively). Moreoven the ratio-dependent model greatly outperformed the prey-dependent model. Nevertheless, the ratio-dependent model failed to explain most of the variation in kill rate (i.e., R2 = 0.34). The ratio-dependent-prey-dependent controversy may dissipate with greater appreciation and acknowledgment that both models may be overly simplistic, both have value, and neither deserves primacy.