Optimizing detection of the bobwhite reproduction call using passive acoustic monitoring

Abstract

Northern bobwhite (Colinus virginianus) populations have declined across much of their range. In response to these declines, wildlife biologists and managers have increased survey efforts and tried to optimize detection and capitalize on technological advances to improve population estimates and cost‐effectiveness. Our objective was to determine how environmental conditions influence detection of the reproduction call, or whistle, of masked bobwhite (C. v. ridgwayi), an endangered subspecies of northern bobwhite, using autonomous recording units (ARUs). We estimated the call intensity of the masked bobwhite reproduction call as 112 ± 0.5 decibels (mean ± SE) at 10 cm. We then broadcasted 16,284 calls during 17 trials to compare manual and automated call detection in recordings collected with ARUs. We used these data to model detectability of a bobwhite reproduction call, for when the bird is present and available, as a function of distance and weather conditions using generalized linear mixed models with trial as a random effect. Regardless of detection type, one model structure was competitive and suggested detection probability was a function of distance, wind speed, and wind direction. Detectability decreased with increased distance and wind speed and was influenced by wind direction. We demonstrate the use of our results to predict the probability of detecting a reproduction call during ARU‐based monitoring efforts. By understanding the effects of environmental factors on the detection of a bobwhite reproductive call, bobwhite surveys can be improved.