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Metrics of predation: perils of predator-prey ratios

Predator-prey ratios

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Abstract

We developed an original modeling approach using program Stella® to investigate the usefulness of predator–prey ratios (PPRs) for interpreting top-down and bottom-up forcing on moose Alces alces. We included density-dependent feedbacks for the moose population, allowed K to vary based on amount and quality of available forage for moose, integrated effects of compensatory mortality, and added time lags in wolves Canis lupus tracking the moose population. Modeling scenarios we developed included bottom-up and top-down regulation as predetermined outcomes. We then evaluated whether PPRs would reflect the various combinations of trajectories of predator and prey populations under top-down versus bottom-up regulation. The resulting patterns of PPRs were impossible to disentangle from one another, and did not provide reliable insights into whether top-down or bottom-forcing occurred, especially over short time spans where critical decisions related to management of moose and wolves might be necessary. Only under top-down regulation did PPRs reflect the degree of predation experienced by moose, but in that instance, knowledge of top-down regulation must be known a priori to correctly interpret PPRs. Potential problems with interpreting PPRs include their double-variable nature, which resulted in the failure to reflect patterns of increase and decrease for predators and prey. We suggest that confidence intervals for PPRs be calculated from a binomial, similar to that proposed for sex and age ratios, which should help discourage the inappropriate use of this metric. We caution that the temptation to use PPRs often is irresistible, but their reliability is highly questionable. We provide an alternative method to using PPRs or other predation metrics for determining whether top-down or bottom-up forcing is occurring by adopting an approach based on the physical condition and life-history characteristics of prey.

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Acknowledgments

This study was funded, in part, by the Department of Biological Sciences at Idaho State University, the Wyoming Cooperative Fish and Wildlife Research Unit at University of Wyoming, and the Alaska Department of Fish and Game. We thank V.C. Bleich, K.M. Stewart, J.C. Whiting, and R.A. Long, and two anonymous reviewers for their advice in preparing our manuscript.

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  1. Kevin L. Monteith

    Present address: Cooperative Fish and Wildlife Research Unit, University of Wyoming, Box 3166, 1000 East University Avenue, Laramie, WY, 82071, USA

Authors and Affiliations

  1. Department of Biological Sciences, Idaho State University, 921 South 8th Avenue, Stop 8007, Pocatello, ID, 83209, USA

    R. Terry Bowyer, John G. Kie & Kevin L. Monteith

  2. Division of Wildlife Conservation, Alaska Department of Fish and Game, 2030 Sea Level Drive, Ketchikan, AK, 99901, USA

    David K. Person

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  1. R. Terry Bowyer
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Correspondence to R. Terry Bowyer.

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Communicated by: Kris Hundertmark

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Appendix 1

STELLA® flow chart used in building models in Figs. 3, 4 and 5. A illustrates a flow between two stocks, B is flow from a source into a sink, C is flow from a stock into a sink, and D is a converter and connector determining rate of flow (PDF 107 kb)

Appendix 2

Equations used in building the STELLA® models with three initial stocks including the moose population, predator population, and browse biomass followed by modifiers (i.e., converters) (DOC 39 kb)

Appendix 3

Rate of mortality related to malnutrition and recruitment as a function of the fraction of forage needs met by moose, which served as a proxy to nutritional condition in STELLA® models (JPEG 26 kb)

High Resolution Image (TIFF 149 kb)

Appendix 4

The functional (a) and numeric (b) responses of wolves to abundance of moose under the modeling scenario where wolves track their primary prey, but moose were regulated by bottom-up forcing (JPEG 46 kb)

High Resolution Image (TIFF 287 kb)

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Bowyer, R.T., Kie, J.G., Person, D.K. et al. Metrics of predation: perils of predator-prey ratios. Acta Theriol 58, 329–340 (2013). https://doi.org/10.1007/s13364-013-0133-1

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