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The need to implement the landscape of fear within rodent pest management strategies

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Abstract Current reactive pest management methods have serious drawbacks such as the heavy reliance on chemicals, emerging genetic rodenticide resistance and high secondary exposure risks. Rodent control needs to be… Click to show full abstract

Abstract Current reactive pest management methods have serious drawbacks such as the heavy reliance on chemicals, emerging genetic rodenticide resistance and high secondary exposure risks. Rodent control needs to be based on pest species ecology and ethology to facilitate the development of ecologically based rodent management (EBRM). An important aspect of EBRM is a strong understanding of rodent pest species ecology, behaviour and spatiotemporal factors. Gaining insight into the behaviour of pest species is a key aspect of EBRM. The landscape of fear (LOF) is a mapping of the spatial variation in the foraging cost arising from the risk of predation, and reflects the levels of fear a prey species perceives at different locations within its home range. In practice, the LOF maps habitat use as a result of perceived fear, which shows where bait or traps are most likely to be encountered and used by rodents. Several studies have linked perceived predation risk of foraging animals with quitting‐harvest rates or giving‐up densities (GUDs). GUDs have been used to reflect foraging behaviour strategies of predator avoidance, but to our knowledge very few papers have directly used GUDs in relation to pest management strategies. An opportunity for rodent control strategies lies in the integration of the LOF of rodents in EBRM methodologies. Rodent management could be more efficient and effective by concentrating on those areas where rodents perceive the least levels of predation risk. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Keywords: management; rodent pest; pest management; landscape fear

Journal Title: Pest Management Science
Year Published: 2017

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