Automated recognition of ruffed grouse drumming in field recordings
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 ma...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-03-01
|
| Series: | Wildlife Society Bulletin |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/wsb.1395 |
| _version_ | 1797739822749581312 |
|---|---|
| author | Samuel Lapp Jeffery L. Larkin Halie A. Parker Jeffery T. Larkin Dakotah R. Shaffer Carolyn Tett Darin J. McNeil Cameron J. Fiss Justin Kitzes |
| author_facet | Samuel Lapp Jeffery L. Larkin Halie A. Parker Jeffery T. Larkin Dakotah R. Shaffer Carolyn Tett Darin J. McNeil Cameron J. Fiss Justin Kitzes |
| author_sort | Samuel Lapp |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T14:02:43Z |
| format | Article |
| id | doaj.art-61c77b9089e64fda83cd9d82daf8071c |
| institution | Directory Open Access Journal |
| issn | 2328-5540 |
| language | English |
| last_indexed | 2024-03-12T14:02:43Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Wildlife Society Bulletin |
| spelling | doaj.art-61c77b9089e64fda83cd9d82daf8071c2023-08-21T21:45:16ZengWileyWildlife Society Bulletin2328-55402023-03-01471n/an/a10.1002/wsb.1395Automated recognition of ruffed grouse drumming in field recordingsSamuel Lapp0Jeffery L. Larkin1Halie A. Parker2Jeffery T. Larkin3Dakotah R. Shaffer4Carolyn Tett5Darin J. McNeil6Cameron J. Fiss7Justin Kitzes8University of Pittsburgh 103 Clapp Hall, Fifth and Ruskin Avenues Pittsburgh PA 15260 USADepartment of Biology Indiana University of Pennsylvania 1011 South Drive, Indiana, PA 15701 USADepartment of Biology Indiana University of Pennsylvania 1011 South Drive, Indiana, PA 15701 USADepartment of Environmental Conservation University of Massachusetts‐Amherst 160 Holdsworth Way Amherst MA 01003‐9285 USADepartment of Biology Indiana University of Pennsylvania 1011 South Drive, Indiana, PA 15701 USAUniversity of Pittsburgh 103 Clapp Hall, Fifth and Ruskin Avenues Pittsburgh PA 15260 USADepartment of Forestry and Natural Resources University of Kentucky Lexington KY 40546 USADepartment of Environmental Biology State University of New York College of Environmental Science and Forestry 1 Forestry Dr. Syracuse NY 13210 USAUniversity of Pittsburgh 103 Clapp Hall, Fifth and Ruskin Avenues Pittsburgh PA 15260 USAAbstract 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.https://doi.org/10.1002/wsb.1395acoustic monitoringARUautomated recognitionBonasa umbellusdrummingmachine learning |
| spellingShingle | Samuel Lapp Jeffery L. Larkin Halie A. Parker Jeffery T. Larkin Dakotah R. Shaffer Carolyn Tett Darin J. McNeil Cameron J. Fiss Justin Kitzes Automated recognition of ruffed grouse drumming in field recordings Wildlife Society Bulletin acoustic monitoring ARU automated recognition Bonasa umbellus drumming machine learning |
| title | Automated recognition of ruffed grouse drumming in field recordings |
| title_full | Automated recognition of ruffed grouse drumming in field recordings |
| title_fullStr | Automated recognition of ruffed grouse drumming in field recordings |
| title_full_unstemmed | Automated recognition of ruffed grouse drumming in field recordings |
| title_short | Automated recognition of ruffed grouse drumming in field recordings |
| title_sort | automated recognition of ruffed grouse drumming in field recordings |
| topic | acoustic monitoring ARU automated recognition Bonasa umbellus drumming machine learning |
| url | https://doi.org/10.1002/wsb.1395 |
| work_keys_str_mv | AT samuellapp automatedrecognitionofruffedgrousedrumminginfieldrecordings AT jefferyllarkin automatedrecognitionofruffedgrousedrumminginfieldrecordings AT halieaparker automatedrecognitionofruffedgrousedrumminginfieldrecordings AT jefferytlarkin automatedrecognitionofruffedgrousedrumminginfieldrecordings AT dakotahrshaffer automatedrecognitionofruffedgrousedrumminginfieldrecordings AT carolyntett automatedrecognitionofruffedgrousedrumminginfieldrecordings AT darinjmcneil automatedrecognitionofruffedgrousedrumminginfieldrecordings AT cameronjfiss automatedrecognitionofruffedgrousedrumminginfieldrecordings AT justinkitzes automatedrecognitionofruffedgrousedrumminginfieldrecordings |