A statistical approach to identify candidate cues for nestmate recognition

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The ability of social insects to discriminate nestmates (NMs) from non-nestmates(nNMs) is mainly achieved through chemical communication. To ultimately understandthis recognition and its decision rules, identification of the recognition cues is essential.Although recognition cues are most likely cuticular hydrocarbons (CHCs), identifying theexact cues for specific species has remained a daunting task, partly due to the sheernumber of odor compounds. Perhaps unsurprisingly, one of the few species wherethe recognition cues have been identified, Formica exsecta, has only around ten majorhydrocarbons on its cuticle. In this study we use previous results of this species to searchfor nestmate recognition cues (NMR cues) in two other species of ants, Camponotusaethiops, and Monomorium pharaonis. Employing chemical distances and observedaggression between colonies, we first ask which type of data normalization, centroid,and distance calculation is most diagnostic to discriminate between NMR cues andother compounds. We find that using a “global centroid” instead of a “colony centroid”significantly improves the analysis. One reason may be that this new approach, unlikeprevious ones, provides a biologically meaningful way to quantify the chemical distancesbetween NMs, allowing for within-colony variation in recognition cues. Next,we ask whichsubset of hydrocarbons most likely represents the cues that the ants use for nestmaterecognition, which shows less clear results for C. aethiops and M. pharaonis than forF. exsecta, possibly due to less than ideal datasets. Nonetheless, some compound setsperformed better than others, showing that this approach can be used to identify candidatecompounds to be tested in bio-assays, and eventually crack the sophisticated code thatgoverns nestmate recognition.
Original languageEnglish
Article number73
JournalFrontiers in Ecology and Evolution
Volume2
Number of pages11
ISSN2296-701X
DOIs
Publication statusPublished - 2014

ID: 130109468