IDEAS home Printed from https://ideas.repec.org/a/oup/beheco/v24y2013i5p1108-1113..html
   My bibliography  Save this article

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

Author

Listed:
  • 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.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/beheco/art037
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Benjamin R. Saville & Amy H. Herring, 2009. "Testing Random Effects in the Linear Mixed Model Using Approximate Bayes Factors," Biometrics, The International Biometric Society, vol. 65(2), pages 369-376, June.
    2. Long Qu & Tobias Guennel & Scott L. Marshall, 2013. "Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 883-892, December.
    3. Zhenzhen Zhang & Thomas M. Braun & Karen E. Peterson & Howard Hu & Martha M. Téllez-Rojo & Brisa N. Sánchez, 2018. "Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 634-650, December.
    4. Chen, Haiqiang & Fang, Ying & Li, Yingxing, 2015. "Estimation And Inference For Varying-Coefficient Models With Nonstationary Regressors Using Penalized Splines," Econometric Theory, Cambridge University Press, vol. 31(4), pages 753-777, August.
    5. Ferreira, Marco A.R. & Porter, Erica M. & Franck, Christopher T., 2021. "Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    6. Philip T. Reiss & R. Todd Ogden, 2010. "Functional Generalized Linear Models with Images as Predictors," Biometrics, The International Biometric Society, vol. 66(1), pages 61-69, March.
    7. Braun, Julia & Sabanés Bové, Daniel & Held, Leonhard, 2014. "Choice of generalized linear mixed models using predictive crossvalidation," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 190-202.
    8. Andrada Ivanescu & Ana-Maria Staicu & Fabian Scheipl & Sonja Greven, 2015. "Penalized function-on-function regression," Computational Statistics, Springer, vol. 30(2), pages 539-568, June.
    9. Tang, Min & Slud, Eric V. & Pfeiffer, Ruth M., 2014. "Goodness of fit tests for linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 176-193.
    10. Sonja Greven & Fabian Scheipl, 2016. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1568-1573, October.
    11. Kruse, René-Marcel & Silbersdorff, Alexander & Säfken, Benjamin, 2022. "Model averaging for linear mixed models via augmented Lagrangian," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    12. Takumi Saegusa & Chongzhi Di & Ying Qing Chen, 2014. "Hypothesis testing for an extended cox model with time-varying coefficients," Biometrics, The International Biometric Society, vol. 70(3), pages 619-628, September.
    13. Sumanta Adhya & Tathagata Banerjee & Gaurangadeb Chattopadhyay, 2012. "Inference on finite population categorical response: nonparametric regression-based predictive approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 69-98, January.
    14. Sweeney Elizabeth & Crainiceanu Ciprian & Gertheiss Jan, 2016. "Testing differentially expressed genes in dose-response studies and with ordinal phenotypes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(3), pages 213-235, June.
    15. Xuhua Liu & Xingzhong Xu, 2016. "Confidence distribution inferences in one-way random effects model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 59-74, March.
    16. Baey, Charlotte & Cournède, Paul-Henry & Kuhn, Estelle, 2019. "Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 107-122.
    17. Satkartar K. Kinney & David B. Dunson, 2007. "Fixed and Random Effects Selection in Linear and Logistic Models," Biometrics, The International Biometric Society, vol. 63(3), pages 690-698, September.
    18. Gumedze, Freedom N. & Welham, Sue J. & Gogel, Beverley J. & Thompson, Robin, 2010. "A variance shift model for detection of outliers in the linear mixed model," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2128-2144, September.
    19. repec:jss:jstsof:43:c01 is not listed on IDEAS
    20. Merve Yasemin Tekbudak & Marcela Alfaro-Córdoba & Arnab Maity & Ana-Maria Staicu, 2019. "A comparison of testing methods in scalar-on-function regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 411-436, September.
    21. Nicholas T. Longford, 2015. "On the inefficiency of the restricted maximum likelihood," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(2), pages 171-196, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:beheco:v:24:y:2013:i:5:p:1108-1113.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/beheco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.