3 research outputs found
Effectiveness of AudioMoth acoustic recording devices in detecting Black-billed Cuckoos over varying distances
No full textBlack-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
Evaluation of autonomous recording unit performance in detecting Black-billed Cuckoos
No full textBlack-billed Cuckoos (Coccyzus erythropthalmus) are classified as a species of concern in Montana and have declined due to habitat loss and fragmentation. Cuckoos are challenging to study, as they do not move or call frequently in the presence of people. This makes researching and managing these birds difficult. Autonomous acoustic survey methods offer the potential to be more effective than traditional avian survey methods. Autonomous recording units (ARUs) are small, programmable, relatively inexpensive acoustic detectors, and have been used in several other studies. Cuckoos, however, nest and perch in dense riparian vegetation, potentially limiting the detection capabilities of ARUs. I investigated the detection distance of ARUs and how that changes in the presence of varying levels of vegetation density. I set up thirteen 200-meter transects in locations in western Montana. Each site had different levels of vegetation density, from open landscape to dense foliage. We mounted an ARU to a 2 m PVC pipe at one end of the transect, then played Black-billed Cuckoo calls from a speaker at 50 m intervals. I then analyzed how well the ARU detected the calls at each distance interval and examined how that changed with increased vegetation density. Detection capability decreased as distance increased, then detection decreased further as vegetation increased. Detection also varied depending on the various call types used by cuckoos. These results suggest an ARU can detect a coo call, the most common call type, within 47 m, a kowlp call within 125 m, and a long call within 131 m at a detectability score of 3 in areas with no vegetation. In moderate vegetation, the ARU could detect a coo call within 83 m, a kowlp call within 89 m, and a long call within 92 m. In dense vegetation, the ARU could detect a coo call at about 45 m, a kowlp call at about 31 m, and a long call at about 35 m. These results demonstrate the detection capabilities of ARUs across distances and with varying vegetation densities. I recommend that researchers employ a pilot study on ARUs to evaluate the efficacy of call detection across different distances and environments for their target species
Effectiveness of Audiomoth Acoustic Recording Devices in Detecting Black-Billed Cuckoo Calls over Varying Distances (Poster)
No full textBlack-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 and do not often call in the presence of people. Thus, they are difficult to detect, making research and management of them difficult. 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. Cuckoos, however, nest and perch in dense riparian vegetation, potentially limiting the detection capabilities of ARUs. We investigated the detection distance of ARUs and the change in effectiveness within varying levels of vegetation cover. We set up thirteen 200 meter transects in locations in Western Montana. Each site had varying levels of vegetation cover, 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 interval and how that changed with increased vegetation cover. Detection capability decreased as distance increased, then decreased further as vegetation increased. 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
