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Spatial ecology of perceived predation risk and vigilance behavior in white-faced capuchins

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  • Fernando A. Campos
  • Linda M. Fedigan

Abstract

Although predation has likely played a central role in the evolution of primate socioecology, we currently lack a thorough understanding of how fine-scale variation in perceived predation risk affects primates’ short-term space use patterns and predator avoidance strategies. We examined the spatial and ecological characteristics of predator encounters, as well as behavioral responses to perceived predation risk, in 5 groups of wild white-faced capuchins (Cebus capucinus) in Costa Rica over a 1.5-year period. Alarm-calling bouts directed at birds were more likely to originate in high forest strata, whereas alarm-calling bouts at snakes and terrestrial quadrupeds were more likely to originate near the ground. Relative risk maps based on the locations of predator encounters revealed that high-risk areas for birds and for all guilds combined consisted of more mature forest, whereas low-risk areas for these predators consisted of relatively younger forest. The animals were most vigilant near the ground, which may reflect greater perceived exposure to snakes and terrestrial predators in lower vertical levels. Incorporating the combined risk function into a predictive model of vigilance behavior improved prediction relative to null models of uniform risk or habitat-specific risk. Our results suggest that capuchin monkeys in this study system perceive reduced predation risk in the high and middle forest layers, and they adjust their vigilance behavior to small-scale spatial variation in perceived risk.

Suggested Citation

  • Fernando A. Campos & Linda M. Fedigan, 2014. "Spatial ecology of perceived predation risk and vigilance behavior in white-faced capuchins," Behavioral Ecology, International Society for Behavioral Ecology, vol. 25(3), pages 477-486.
  • Handle: RePEc:oup:beheco:v:25:y:2014:i:3:p:477-486.
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    1. Ben T. Hirsch & Lesley J. Morrell, 2011. "Measuring marginal predation in animal groups," Behavioral Ecology, International Society for Behavioral Ecology, vol. 22(3), pages 648-656.
    2. Davies, Tilman M. & Hazelton, Martin L. & Marshall, Jonathan. C, 2011. "sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i01).
    3. Roper Roy E. & Goodzey C., 2004. "Ecology of Fear," Journal of Homeland Security and Emergency Management, De Gruyter, vol. 1(2), pages 1-7, January.
    4. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
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