Can singing rate be used to predict male breeding status of forest songbirds? A comparison of three calibration models

Abstract

AbstractFor male songbirds, song rate varies throughout the breeding season and is correlated with breeding cycle stages. Although these patterns have been well documented, this relationship has not been used to predict a bird's breeding status from acoustic monitoring. This challenge of using a response (i.e., behavior) to indirectly measure an underlying biological state is common in ecology, but correctly addressing the associated statistical challenge of calibration is rare. The objective of this study was to determine whether variation in song rate can be used to predict the breeding status of the Olive‐sided Flycatcher (Contopus cooperi). In 2016, song rates from 28 male Olive‐sided Flycatchers were collected from human observers (n = 545 five‐minute counts) and breeding status (i.e., single, paired, and feeding young) was monitored throughout the breeding season. The predictive ability of three modeling approaches—regression, hierarchical, and a classification tree—was evaluated using sensitivity and specificity to determine the best modeling approach. The hierarchical model was the best at predicting all three breeding status classes, with a mean sensitivity of 69%, compared with 54% and 50% from the regression and machine learning models, respectively. Our results suggest that song rate can be used as an indirect measurement of breeding status in the Olive‐sided Flycatcher when using a hierarchical modeling approach to calibrate the breeding status–song rate relationship. This novel modeling approach provides a cost‐effective tool to collect much needed demographic information over large spatial extents and inform species status assessments, recovery strategies, and management plans for species of conservation concern.