An understanding of individuality in animal vocalizations can assist in tracking individuals spatially and temporally, and is particularly useful for species of conservation concern. We determined whether fitz bew vocalizations of the endangered Southwestern Willow Flycatcher (Empidonax traillii extimus) showed vocal individuality, assessed the differences in vocal individuality among three populations, and tested the ability of predictive vocalization models to reidentify individuals. Fitz bew vocalizations were recorded from two populations in Arizona (Roosevelt Lake and San Pedro River) and one in California (Kern River). Individuality was determined using discriminant function analysis (DFA) and trained artificial neural networks (ANN). Southwestern Willow Flycatchers showed individuality in the fitz bew, with the models correctly classifying 81–86% of the vocalizations. We also found population-level variations: vocal structure differed substantially between Roosevelt Lake and Kern River, but vocal structure in the San Pedro River population overlapped with that of the other two populations. These population differences could be related to dispersal patterns of adults or offspring, patterns of territory establishment, or habitat structure differences. The ANN vocalization models reidentified an average of 81% of the individuals and were able to identify novel individuals. On the basis of these results, we recommend that acoustic tracking should complement rather than replace current monitoring practices, given its logistical limitations. We provide a basic understanding of vocal parameters that can be used in the future to study different aspects related to vocal individuality (e.g., taxonomic, seasonal, yearly, and sex differences).
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Vol. 126 • No. 1