Abstract
Ruffed grouse (Bonasa umbellus) populations are declining throughout their range, which has prompted efforts to understand drivers of the decline. Ruffed grouse monitoring efforts often rely on acoustic drumming surveys, in which a surveyor listens for the distinctive drumming sound that male ruffed grouse produce during the breeding season. Field-based drumming surveys can fail to detect ruffed grouse when the birds drum infrequently or irregularly, making this species an excellent candidate for remote acoustic sensing with automated recording units (ARUs). An accurate automated recognition method for ruffed grouse drumming could enable effective and efficient use of ARU data for monitoring efforts; however, no such tool is currently available. Here we develop an automated method for detecting ruffed grouse drumming in audio recordings. Our detector uses a signal processing pipeline designed to recognize the accelerating pattern of drumming. We show that the automated recognition method accurately and efficiently detects drumming events in a set of labeled ARU field recordings. In a case study with 56 locations in Central Pennsylvania, we compared detections of ruffed grouse from 4 survey methods: field-based acoustic drumming surveys, surveys conducted by humans listening to ARU recordings, and automated recognition for both a 1-day and a 28-day period. Field-based surveys detected drumming at 9 of 56 locations (16%), while surveys conducted by humans listening to ARU recordings detected drumming at 8 locations (14%). Using automated recognition, the 1-day recording period produced detections at 17 locations (30%) and the 28-day recording period produced detections at 34 locations (61%). Our case study supports the idea that automated recognition can unlock the value of ARU datasets by temporally expanding the survey period. We provide an open-source Python implementation of the recognition method to support further use in ruffed grouse monitoring efforts.
| Original language | English |
|---|---|
| Article number | e1395 |
| Journal | Wildlife Society Bulletin |
| Volume | 47 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 2023 |
Bibliographical note
Publisher Copyright:© 2022 The Authors. Wildlife Society Bulletin published by Wiley Periodicals LLC on behalf of The Wildlife Society.
Funding
We thank T. Rhinehart, L. Freeland-Haynes, R. P. Lyon, L. Katsis, and L. Chronister for comments on earlier drafts of this manuscript. This material is based upon work supported by the National Science Foundation under Grants 1935507 and 2120084. This research was also supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. Finally, we thank the Forestry and Land Management staff of the Pennsylvania Game Commission's Northcentral Region for allowing access to study sites and assisting with project logistics (PGC permit 36850). Our work was financially supported by the Department of Biological Sciences at the University of Pittsburgh, the Gordon and Betty Moore Foundation, and the National Fish and Wildlife Foundation's Central Appalachia Habitat Stewardship Program. This research was funded by Natural Resource Conservation Service's Conservation Effects Assessment Project (No. 68-7482-15-501), National Fish and Wildlife Foundation (No. 0407.18.059680), and Indiana University of Pennsylvania's School of Graduate Studies and Research. We thank D. Dahlgren (Associate Editor), A. Knipps (Editorial Assistant), A. Tunstall (Copy Editor) and J. Levengood (Content Editor) and 2 anonymous reviewers for constructive comments that improved our manuscript. We thank T. Rhinehart, L. Freeland‐Haynes, R. P. Lyon, L. Katsis, and L. Chronister for comments on earlier drafts of this manuscript. This material is based upon work supported by the National Science Foundation under Grants 1935507 and 2120084. This research was also supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. Finally, we thank the Forestry and Land Management staff of the Pennsylvania Game Commission's Northcentral Region for allowing access to study sites and assisting with project logistics (PGC permit 36850). Our work was financially supported by the Department of Biological Sciences at the University of Pittsburgh, the Gordon and Betty Moore Foundation, and the National Fish and Wildlife Foundation's Central Appalachia Habitat Stewardship Program. This research was funded by Natural Resource Conservation Service's Conservation Effects Assessment Project (No. 68‐7482‐15‐501), National Fish and Wildlife Foundation (No. 0407.18.059680), and Indiana University of Pennsylvania's School of Graduate Studies and Research. We thank D. Dahlgren (Associate Editor), A. Knipps (Editorial Assistant), A. Tunstall (Copy Editor) and J. Levengood (Content Editor) and 2 anonymous reviewers for constructive comments that improved our manuscript.
| Funders | Funder number |
|---|---|
| Department of Biological Sciences | |
| Indiana University of Pennsylvania's School of Graduate Studies and Research | |
| USDA Natural Resource Conservation Service's Conservation Effects Assessment Project | 0407.18.059680, 68‐7482‐15‐501 |
| National Science Foundation Arctic Social Science Program | 2120084, 1935507 |
| Gordon and Betty Moore Foundation | |
| National Fish and Wildlife Foundation | |
| University of Pittsburgh Medical Center, Children's Hospital of Pittsburgh | 36850 |
Keywords
- ARU
- Bonasa umbellus
- acoustic monitoring
- automated recognition
- drumming
- machine learning
- ruffed grouse
- signal processing
ASJC Scopus subject areas
- Nature and Landscape Conservation