The landscape of fear conceptual framework: definition and review of current applications and misuses
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Abstract
Landscapes of Fear (LOF), the spatially explicit distribution of perceived predation risk as seen by a population, is increasingly cited in ecological literature and has become a frequently used “buzz-word”. With the increase in popularity, it became necessary to clarify the definition for the term, suggest boundaries and propose a common framework for its use. The LOF, as a progeny of the “ecology of fear” conceptual framework, defines fear as the strategic manifest of the cost-benefit analysis of food and safety tradeoffs. In addition to direct predation risk, the LOF is affected by individuals’ energetic-state, inter- and intra-specific competition and is constrained by the evolutionary history of each species. Herein, based on current applications of the LOF conceptual framework, I suggest the future research in this framework will be directed towards: (1) finding applied management uses as a trait defining a population’s habitat-use and habitat-suitability; (2) studying multi-dimensional distribution of risk-assessment through time and space; (3) studying variability between individuals within a population; and (4) measuring eco-neurological implications of risk as a feature of environmental heterogeneity.
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2017. The landscape of fear conceptual framework: definition and review of current applications and misuses. PeerJ Preprints 5:e2840v4 https://doi.org/10.7287/peerj.preprints.2840v4note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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This version contains edits past minor revision in this manuscript.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Sonny S Bleicher conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
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This is a review manuscript. The research in this article did not generate, collect or analyse any raw data or code.
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The authors received no funding for this work.
