COS 167-4 - The many faces of fear: A synthesis of the methodological variation in characterizing predation risk

Thursday, August 10, 2017: 2:30 PM
D139, Oregon Convention Center
Remington J. Moll1, Kyle M. Redilla1, Tutilo Mudumba1, Arthur B. Muneza1, Steven M. Gray1, Leandro Abade2, Matt W. Hayward3, Joshua J. Millspaugh4 and Robert A. Montgomery1, (1)Michigan State University, (2)University of Oxford, (3)Bangor University, (4)University of Montana

Predators affect prey by killing them directly (lethal effects) and by inducing costly antipredator behaviors in living prey (risk effects). Risk effects can strongly influence prey populations and cascade through trophic systems. A prerequisite for assessing risk effects is characterizing the spatiotemporal variation in predation risk. Risk effects research has experienced rapid growth in the last several decades. However, preliminary assessments of the resultant literature suggest that researchers characterize predation risk using a variety of techniques. The implications of this methodological variation for inference and comparability among studies have not been well-recognized or formally synthesized. We couple a literature survey with a hierarchical framework, developed from established theory, to quantify the methodological variation in characterizing risk using carnivore-ungulate systems as a case study.


We documented 244 metrics of risk from 141 studies falling into at least 13 distinct subcategories within 3 broader categories. Both empirical and theoretical work suggest risk and its effects on prey constitute a complex, multi-dimensional process with expressions varying by spatiotemporal scale. Our survey suggests this multi-scale complexity is reflected in the literature as a whole but often underappreciated in any given study, which complicates comparability among studies and leads to an overemphasis on documenting the presence of risk effects rather than their mechanisms or scale of influence. We suggest risk metrics be placed in a more concrete conceptual framework to clarify inference surrounding risk effects and their cascading effects throughout ecosystems. We recommend studies 1) take a multi-scale approach to characterizing risk, 2) measure “true” predation risk (probability of predation per unit time), and 3) use risk metrics that facilitate comparison among studies and the evaluation of multiple competing hypotheses. Addressing the pressing questions in risk effects research, including how, to what extent, and on what scale they occur, requires leveraging the advantages of the many methods available to characterize risk while minimizing the confusion caused by variability in their application.