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Pair-Specific Scents in African Wild Dogs, Lycaon pictus, and an Example of a Potential Method to Identify Signals Within Complex Mixtures

  • Neil R. Jordan
  • Peter J. Apps
  • Krystyna A. Golabek
  • J. Weldon McNutt

Abstract

Identifying specific signaling components within complex mixtures is a common problem in semiochemistry. Both glandular secretions and excretory products contain components of semiochemical importance, but identifying these signals is problematic because they are usually parts of mixtures with several 100 components, of which only a subset may be involved with signaling. In contrast to waste and metabolic byproducts—which can be expected to vary both between and within individuals according to extrinsic factors—signaling compounds are expected to be uniform among animals sending the same signal and stable over time. In group-living territorial species we would expect there to be a degree of group-specificity in signals that advertise territory residence. As part of an ongoing study investigating and manipulating scent-marking and territorial behavior in African wild dogs, several 100 volatile components have been located from their urine. How many and which, if any, of these have active roles in semiochemical communication of territory residence is currently unknown. Observations of scent marking behaviors of African wild dogs strongly suggest that dominant urine overmarks (DUOs)—where one member of a pair deposits urine on the urine of its partner—are the most likely source of such signals. We used multivariate statistics to investigate >990 separated chemical components (some of which could be multiple compounds) found in these DUOs, and found as few as 10 chemical components that together enabled statistical discrimination of specific dominant pairs. We suggest that this method may be broadly applied across communication systems to locate components of signals within complex “mixtures.”

Keywords

Principal Component Analysis Discriminant Function Analysis Scent Mark Discriminant Function Analysis Multivariate Statistical Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank the Paul G. Allen Family Foundation for funding this work. SpectralWorks (www.spectralworks.com/analyzerpro.html) kindly donated the AnalyzerPro software. All work was carried out under a permit from the Botswana Ministry of Environment Wildlife and Tourism, Department of Wildlife and National Parks. Reena H. Walker kindly assisted with data extraction. Briana Abrahms, Geoffrey Gilfillan and Jessica Vitale assisted in collecting the samples used in this study.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Neil R. Jordan
    • 1
    • 2
    • 3
  • Peter J. Apps
    • 4
  • Krystyna A. Golabek
    • 1
    • 5
  • J. Weldon McNutt
    • 1
  1. 1.Botswana Predator Conservation TrustMaunBotswana
  2. 2.Centre for Ecosystem Science, School of Biological, Earth and Environmental SciencesUniversity of New South WalesSydneyAustralia
  3. 3.Taronga Conservation Society Australia, Taronga Western Plains ZooWildlife Reproduction CentreDubboAustralia
  4. 4.Paul G. Allen Family Foundation Laboratory for Wildlife ChemistryBotswana Predator Conservation TrustMaunBotswana
  5. 5.Department of Zoology, Wildlife Conservation Research UnitUniversity of OxfordOxfordUK

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