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Forewing pigmentation predicts migration distance in wild-caught migratory monarch butterflies

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  • Daniel Hanley
  • Nathan G. Miller
  • D.T. Tyler Flockhart
  • D. Ryan Norris

Abstract

Surprisingly, little is known about how the environment influences the production of the iconic orange coloration of the monarch butterfly (Danaus plexippus). Previous research under controlled laboratory conditions has shown that the temperature during larval development influences the color of monarch wings, where females raised in warm conditions had a greater proportion of melanization, whereas males raised in warm conditions had a lower proportion of melanization. These melanin-based colors have been found to increase flying ability in Lepidoptera, and recent experiments have found that monarchs with redder forewings flew greater distances than monarchs with less intense coloration. We examined whether wild-caught monarchs captured in the Great Lakes region exhibited geographic polyphenism by using stable isotopes to estimate natal origin, and hence rearing temperature, spectrophotometry to measure forewing coloration, and image analysis to estimate shape. We found that monarchs from the Gulf Coast were more melanized than monarchs from the Great Lakes, and southern male monarchs were more saturated than northern male monarchs. This supports previous research suggesting that colors that absorb more solar energy allow for greater flying ability but contradicts the patterns we expected based on natal temperature. Interestingly, this effect of color on migration distance was independent of wing shape. We provide the first evidence that the coloration of wild monarchs influences their migration ability over a continental scale, and we suggest that these differences in color may benefit the cohort of monarchs destined for long-distance migration to their wintering ground.

Suggested Citation

  • Daniel Hanley & Nathan G. Miller & D.T. Tyler Flockhart & D. Ryan Norris, 2013. "Forewing pigmentation predicts migration distance in wild-caught migratory monarch butterflies," Behavioral Ecology, International Society for Behavioral Ecology, vol. 24(5), pages 1108-1113.
  • Handle: RePEc:oup:beheco:v:24:y:2013:i:5:p:1108-1113.
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    File URL: http://hdl.handle.net/10.1093/beheco/art037
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    1. Ciprian M. Crainiceanu & David Ruppert, 2004. "Likelihood ratio tests in linear mixed models with one variance component," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 165-185, February.
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