@article {58311,
	title = {Does the presence of an observer affect a bird{\textquoteright}s occurrence rate or singing rate during a point count?},
	year = {2020},
	abstract = {<p>The approach or presence of an observer may affect the behavior of nearby birds, rendering them either more or less detectable than when no observer is present due to a change in singing rates. To test whether there are systematic detection biases associated with the presence of an observer during point count bird surveys, we compared the occurrence and singing rates of birds during a 10-min period immediately preceding the time when an observer arrived to conduct a count and during the formal count itself by extracting song information from autonomous sound recorders. We obtained recordings of 36 species of birds detected at \&ge;5 locations in one of three vegetation types, including burned conifer forest, green conifer/riparian streamside forest, and riparian bottomland/marshland. We found that species richness and both the probability of occurrence and singing rate for any of the species recorded were unaffected by the presence of an observer. In addition, the probability of occurrence did not differ significantly among four 2.5-min recording sessions during 10-min counts when an observer was present. Thus, the presence of an observer did not appear to introduce any detectable systematic bias that would make bird lists or unadjusted occurrence rates inaccurate on that basis alone. In addition, rates of bird occurrence across 2.5-min temporal subsets of a 10-min count did not vary in a systematic way that would violate the assumption of equal occupancy across adjacent time periods as sometimes used to build detection histories in occupancy modeling.</p>
},
	keywords = {bird survey, detectability, observer effect, occupancy, passive acousticsampling, point count, singing rate, utonomous recording unit},
	doi = {10.1111/jofo.12329},
	url = {http://doi.wiley.com/10.1111/jofo.12329},
	author = {Hutto, Richard L. and Hutto, Russell R.}
}
@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}
}
