Can Nocturnal Flight Calls of the Migrating Songbird, American Redstart, Encode Sexual Dimorphism and Individual Identity?

<div><p>Bird species often use flight calls to engage in social behavior, for instance maintain group cohesion and to signal individual identity, kin or social associations, or breeding status of the caller. Additional uses also exist, in particular among migrating songbirds for communication during nocturnal migration. However, our understanding of the information that these vocalizations convey is incomplete, especially in nocturnal scenarios. To examine whether information about signaler traits could be encoded in flight calls we quantified several acoustic characteristics from calls of a nocturnally migrating songbird, the American Redstart. We recorded calls from temporarily captured wild specimens during mist-netting at the Powdermill Avian Research Center in Rector, PA. We measured call similarity among and within individuals, genders, and age groups. Calls from the same individual were significantly more similar to one another than to the calls of other individuals, and calls were significantly more similar among individuals of the same sex than between sexes. Flight calls from hatching-year and after hatching-year individuals were not significantly different. Our results suggest that American Redstart flight calls may carry identifiers of gender and individual identity. To our knowledge, this is the first evidence of individuality or sexual dimorphism in the flight calls of a migratory songbird. Furthermore, our results suggest that flight calls may have more explicit functions beyond simple group contact and cohesion. Nocturnal migration may require coordination among numerous individuals, and the use of flight calls to transmit information among intra- and conspecifics could be advantageous. Applying approaches that account for such individual and gender information may enable more advanced research using acoustic monitoring.</p></div

2-D Principal Coordinate Analysis (PCoA) plot based on extracted feature measurements showing multivariate homogeneity of group dispersions between all calls (N = 180),with 99% confidence ellipse based on the standard errors of the axis score averages.

<p>Calls from the same sex are plotted in relation to their centroids (M = Male, F = Female). Differences between sexes were shown to be statistically significant (MANOVA; <i>F</i> = 12.46, R² = 0.059, <i>p</i> < 0.001). Two orthogonal axes summarize the variability in the data set.</p

2-D Principal Coordinate Analysis (PCoA) plot based on extracted feature measurements showing multivariate homogeneity of group dispersions between all calls (N = 180),with 99% confidence ellipse based on the standard errors of the axis score averages.

<p>Calls from the same sex are plotted in relation to their centroids (M = Male, F = Female). Differences between sexes were shown to be statistically significant (MANOVA; <i>F</i> = 12.46, R² = 0.059, <i>p</i> < 0.001). Two orthogonal axes summarize the variability in the data set.</p

Similarity matrix generated by calculating random forest distance between quantitative features measured from flight calls.

<p>Birds are labeled by the bird band assigned given to the bird when the recording was made at the Powdermill bird banding station (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156578#pone.0156578.s008" target="_blank">S4 Table</a>). The individual pixels in the matrix represent the pairwise similarity values between the 180 flight calls, and the dark grid lines between pixels separate the calls from different individuals (N = 36 individuals, with 5 calls from each). Darker pixels indicate higher pairwise similarity.</p

2-D Principal Coordinate Analysis (PCoA) plot based on extracted feature measurements showing multivariate homogeneity of group dispersions between all calls (N = 180), with 99% confidence ellipse based on the standard errors of the axis score averages.

<p>Calls from the same variant class are plotted in relation to their centroids. Differences between classes were shown to be statistically significant (MANOVA; <i>F</i> = 4.51, R² = 0.086, <i>p</i> < 0.001). Two orthogonal axes summarize the variability in the data set. Note: The M centroid is behind the V centroid, but has the large confidence ellipse.</p

Number of birds recorded in both age and gender categories.

<p>Number of birds recorded in both age and gender categories.</p

Flight behavior of 5 free-ranging golden eagles interpreted from acceleration data.

<p>Plots show percentage of time spent in flapping or soaring flight at different times of a day (a,b) and flight behavior as a function of flight altitude (c,d). Behavior was classified with (a, c) a random forest model and (b, d) a K-nearest neighbor model.</p

Out of Bag (OOB) errors versus number of predictors, by node, from random forest classification of accelerometer data collected from a trained golden eagle.

<p>Number of nodes (mtry) ranged from 0–28 and number of trees (ntree) from 500 to 5000. We classified data to (a) three behavioral classes: flapping, sitting and soaring and (b) five behavioral classes: flapping banking, flapping straight, sitting, soaring banking and soaring straight. Boxes identify combinations of mtry and ntree values resulting in the lowest OOB error.</p

Ten-fold cross validation error rates for classification of accelerometer data collected from a trained golden eagle.

<p>Models tested were random forest (RF) and K-nearest neighbor (KNN).</p

Accuracy of behavioral classification accuracy when sampling acceleration data from a trained golden eagle from 5 to 40Hz.

<p>Data were classified to three behavioral classes (flapping, sitting and soaring) and modeled with (a) a random forest classification model and (b) a K-nearest neighbor model.</p