@article {58049,
	title = {Can singing rate be used to predict male breeding status of forest songbirds? A comparison of three calibration models},
	year = {2020},
	abstract = {<p>For 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\&rsquo;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 address- ing 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\&mdash;regres- sion, hierarchical, and a classification tree\&mdash;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\&ndash;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 assess- ments, recovery strategies, and management plans for species of conservation concern.</p>
},
	keywords = {bioacoustics, breeding status, classification and regression trees (CART), forest birds, hierarchical model, multinomial logistic regression, Olive-sided Flycatcher, song rate},
	doi = {10.1002/ecs2.v11.1},
	url = {https://onlinelibrary.wiley.com/toc/21508925/11/1},
	author = {Upham-Mills, Emily J. and Reimer, Jody R. and Hach{\'e}, Samuel and Lele, Subhash R. and Erin M. Bayne}
}
@article {47729,
	title = {Autonomous recording units in avian ecological research: current use and future applications},
	journal = {Avian Conservation and Ecology},
	volume = {12},
	year = {2017},
	month = {Jan-01-2017},
	abstract = {Acoustic surveys are a widely used sampling tool in ecological research and monitoring. They are used to monitor populations and ecosystems and to study various aspects of animal behavior. Autonomous recording units (ARUs) can record sound in most environments and are increasingly used by researchers to conduct acoustic surveys for birds. In this review, we summarize the use of ARUs in avian ecological research and synthesize current knowledge of the benefits and drawbacks of this technology. ARUs enable researchers to do more repeat visits with less time spent in the field, with the added benefits of a permanent record of the data collected and reduced observer bias. They are useful in remote locations and for targeting rare species. ARUs are mostly comparable to human observers in terms of species richness, but in some cases, they detect fewer species and at shorter distances. Drawbacks of ARUs include the cost of equipment, storage of recordings, loss of data if units fail, and potential sampling trade-offs in spatial vs. temporal coverage. ARUs generate large data sets of audio recordings, but advances in automated species recognition and acoustic processing techniques are contributing to make the processing time manageable. Future applications of ARUs include biodiversity monitoring and studying habitat use, animal movement, and various behavioral ecology questions based on vocalization activity. ARUs have the potential to make significant advances in avian ecological research and to be used in more innovative ways than simply as a substitute for a human observer in the field.
},
	keywords = {acoustic surveys, Biodiversity monitoring, noninvasive sampling, passive acoustic monitoring, point counts, vocal communication},
	issn = {1712-6568},
	doi = {10.5751/ACE-00974-120114},
	url = {http://www.ace-eco.org/vol12/iss1/art14/},
	author = {Julia Shonfield and Erin M. Bayne}
}
