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Are innovative species ecological generalists? A test in North American birds

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  • Sarah E. Overington
  • Andrea S. Griffin
  • Daniel Sol
  • Louis Lefebvre

Abstract

Foraging innovation occurs when animals exploit novel food sources or invent new foraging techniques. Species vary widely in their rates of innovation, and these differences can be quantified using counts of novel behavior observed in the wild. One of the assumed benefits of innovativeness is that it allows species to exploit a wider variety of habitats and foods, enhancing survival when resources are in shortage or when individuals invade new environments. However, the relationship between innovation propensity and ecological generalism lacks firm empirical support. Moreover, innovativeness does not only imply benefits but may also lead to higher risks incurred in the wide array of habitats exploited. In this study, we test whether innovative species exploit a wider variety of habitats and food types as well as face the potential risk of more predators as a consequence of their ecological generalism. Using data for 193 North American bird species in a phylogenetically informed analysis, we find a significant positive relationship between innovation rate and habitat generalism, but not diet breadth. Although habitat generalism is also associated with exposure to a wider variety of predators, there is no direct relationship between innovation rate and predation. Our results suggest that although innovators use a wider variety of habitats, they are not necessarily diet generalists, challenging the classic view that feeding generalism is equivalent to feeding flexibility. Copyright 2011, Oxford University Press.

Suggested Citation

  • Sarah E. Overington & Andrea S. Griffin & Daniel Sol & Louis Lefebvre, 2011. "Are innovative species ecological generalists? A test in North American birds," Behavioral Ecology, International Society for Behavioral Ecology, vol. 22(6), pages 1286-1293.
  • Handle: RePEc:oup:beheco:v:22:y:2011:i:6:p:1286-1293
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    File URL: http://hdl.handle.net/10.1093/beheco/arr130
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    1. Bates, Douglas & M├Ąchler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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