Oral Presentations
Effectiveness of AudioMoth acoustic recording devices in detecting Black-billed Cuckoos over varying distances
Presentation Type
Presentation
Faculty Mentor’s Full Name
Erim Gomez
Faculty Mentor’s Department
Wildlife Biology Department
Abstract / Artist's Statement
Black-billed cuckoos (Coccyzus erythropthalmus) are classified as a species of concern in Montana and have seen declines due to habitat loss and fragmentation. However, we lack data on the current population of Black-billed cuckoos in Montana. They are a cryptic species, occupying dense riparian vegetation, and not moving or calling frequently. Thus, they are difficult to detect, making research and management of these birds difficult and often inefficient, which can be costly with insufficient reward. Autonomous acoustic survey methods offer the potential to be more effective and efficient than traditional avian survey methods. Autonomous recording units (ARUs) are small, programmable, relatively inexpensive acoustic detectors, and have been used in several other studies on a variety of species. However, because cuckoos nest and perch in dense riparian vegetation, the detection capabilities of ARUs is potentially limited. To learn what degree of limitation is present, we investigated the detection distance of ARUs and how it changes with varying vegetation density. We set up thirteen 200-meter transects in locations in Western Montana. Each site had varying vegetation density, from open landscape to dense vegetation. We mounted an ARU to a 6-foot PVC pipe at one end of the transect, then played Black-billed cuckoo calls from a speaker at intervals of 50 meters. We then analyzed how well the ARU detected the calls at each distance and examined how that changed with increased vegetation cover. As predicted, detection capability decreased as distance increased. The influences of vegetation density are still under investigation but is predicted to further decrease detection distance. We also found that increased levels of ambient noise further decreased detection distance both with and without dense vegetation. Our work will help researchers to maximize detection probability by modifying the number of ARUs, and the distance between each ARU.
Category
Life Sciences
Effectiveness of AudioMoth acoustic recording devices in detecting Black-billed Cuckoos over varying distances
UC 326
Black-billed cuckoos (Coccyzus erythropthalmus) are classified as a species of concern in Montana and have seen declines due to habitat loss and fragmentation. However, we lack data on the current population of Black-billed cuckoos in Montana. They are a cryptic species, occupying dense riparian vegetation, and not moving or calling frequently. Thus, they are difficult to detect, making research and management of these birds difficult and often inefficient, which can be costly with insufficient reward. Autonomous acoustic survey methods offer the potential to be more effective and efficient than traditional avian survey methods. Autonomous recording units (ARUs) are small, programmable, relatively inexpensive acoustic detectors, and have been used in several other studies on a variety of species. However, because cuckoos nest and perch in dense riparian vegetation, the detection capabilities of ARUs is potentially limited. To learn what degree of limitation is present, we investigated the detection distance of ARUs and how it changes with varying vegetation density. We set up thirteen 200-meter transects in locations in Western Montana. Each site had varying vegetation density, from open landscape to dense vegetation. We mounted an ARU to a 6-foot PVC pipe at one end of the transect, then played Black-billed cuckoo calls from a speaker at intervals of 50 meters. We then analyzed how well the ARU detected the calls at each distance and examined how that changed with increased vegetation cover. As predicted, detection capability decreased as distance increased. The influences of vegetation density are still under investigation but is predicted to further decrease detection distance. We also found that increased levels of ambient noise further decreased detection distance both with and without dense vegetation. Our work will help researchers to maximize detection probability by modifying the number of ARUs, and the distance between each ARU.