A novel statistical method for behaviour sequence analysis and its application to birdsong.
Complex vocal signals, such as birdsong, contain acoustic elements that differ in both order and duration. These elements may convey socially relevant meaning, both independently and through their interactions, yet statistical methods that combine order and duration data to extract meaning have not, to our knowledge, been fully developed. Here we design novel semi-Markov methods, Bayesian estimation and classification trees to extract order and duration information from behavioural sequences and apply these methods to songs produced by male European starlings,Sturnus vulgaris, in two social contexts in which the function of song differs: a spring (breeding) and autumn (nonbreeding) context. Additionally, previous data indicate that damage to the medial preoptic nucleus (POM), a brain area known to regulate male sexually motivated behaviour, affects structural aspects of starling song such that males in a sexually relevant context (i.e. spring) sing shorter songs than appropriate for this context. We further test the utility of our statistical approach by comparing attributes of song structure in POM-lesioned males to song produced by control spring and autumn males. Spring and autumn songs were statistically separable based on the duration and order of phrase types. Males produced more structurally complex aspects of song in spring than in autumn. Spring song was also longer and more stereotyped than autumn song, both attributes used by females to select mates. Songs produced by POM-lesioned males in some cases fell between measures of spring and autumn songs but differed most from songs produced by autumn males. Overall, these statistical methods can effectively extract biologically meaningful information contained in many behavioural sequences given sufficient sample sizes and replication numbers.
Department of Biology, University of Wisconsin-Stevens Point, WI, U.S.A.; Department of Zoology, University of Wisconsin-Madison, WI, U.S.A.; Department of Statistics, University of Wisconsin-Madison, WI, U.S.A.
Sarah J
SJ
Alger
Department of Statistics, University of Wisconsin-Madison, WI, U.S.A.; Department of Botany, University of Wisconsin-Madison, WI, U.S.A.
Bret R
BR
Larget
Department of Zoology, University of Wisconsin-Madison, WI, U.S.A.
Lauren V
LV
Riters
Bayesian estimation; birdsong; classification tree; communication; context; medial preoptic nucleus; semi-Markov method; social behaviour; songbird; vocal control
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