Modelling inducible defences in predator-prey interactions: assumptions and dynamical consequences of three distinct approaches.

Ecol Lett

Department of Ecology and Ecosystem Modelling, Institute of Biochemistry and Biology, University of Potsdam, Am Neuen Palais 10, Potsdam, 14469, Germany.

Published: February 2019

AI Article Synopsis

  • Inducible defenses are adaptations in prey that help manage the costs of being preyed upon, especially when threats vary over time or location.
  • The choice of modeling approach—Switching Function, Fitness Gradient, and Optimal Trait—significantly influences how these defenses impact ecological dynamics like predator-prey interactions and stability.
  • Each model has unique outcomes; for instance, Switching Function models often stabilize populations, while Fitness Gradient models require careful interpretation due to their differences from evolutionary dynamics.

Article Abstract

Inducible defences against predation are widespread in the natural world, allowing prey to economise on the costs of defence when predation risk varies over time or is spatially structured. Through interspecific interactions, inducible defences have major impacts on ecological dynamics, particularly predator-prey stability and phase lag. Researchers have developed multiple distinct approaches, each reflecting assumptions appropriate for particular ecological communities. Yet, the impact of inducible defences on ecological dynamics can be highly sensitive to the modelling approach used, making the choice of model a critical decision that affects interpretation of the dynamical consequences of inducible defences. Here, we review three existing approaches to modelling inducible defences: Switching Function, Fitness Gradient and Optimal Trait. We assess when and how the dynamical outcomes of these approaches differ from each other, from classic predator-prey dynamics and from commonly observed eco-evolutionary dynamics with evolving, but non-inducible, prey defences. We point out that the Switching Function models tend to stabilise population dynamics, and the Fitness Gradient models should be carefully used, as the difference with evolutionary dynamics is important. We discuss advantages of each approach for applications to ecological systems with particular features, with the goal of providing guidelines for future researchers to build on.

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Source
http://dx.doi.org/10.1111/ele.13183DOI Listing

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