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Abstract Monitoring wildlife abundance across space and time is an essential task to study their population dynamics and inform effective management. Acoustic recording units are a promising technology for efficiently monitoring bird populations and communities. While current acoustic data models provide information on the presence/absence of individual species, new approaches are needed to monitor population abundance, ideally across large spatio‐temporal regions.We present an integrated modelling framework that combines high‐quality but temporally sparse bird point count survey data with acoustic recordings. Our models account for imperfect detection in both data types and false positive errors in the acoustic data. Using simulations, we compare the accuracy and precision of abundance estimates using differing amounts of acoustic vocalizations obtained from a clustering algorithm, point count data, and a subset of manually validated acoustic vocalizations. We also use our modelling framework in a case study to estimate abundance of the Eastern Wood‐Pewee (Contopus virens) in Vermont, USA.The simulation study reveals that combining acoustic and point count data via an integrated model improves accuracy and precision of abundance estimates compared with models informed by either acoustic or point count data alone. Improved estimates are obtained across a wide range of scenarios, with the largest gains occurring when detection probability for the point count data is low. Combining acoustic data with only a small number of point count surveys yields estimates of abundance without the need for validating any of the identified vocalizations from the acoustic data. Within our case study, the integrated models provided moderate support for a decline of the Eastern Wood‐Pewee in this region.Our integrated modelling approach combines dense acoustic data with few point count surveys to deliver reliable estimates of species abundance without the need for manual identification of acoustic vocalizations or a prohibitively expensive large number of repeated point count surveys. Our proposed approach offers an efficient monitoring alternative for large spatio‐temporal regions when point count data are difficult to obtain or when monitoring is focused on rare species with low detection probability.
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Breeding bird counts around Toolik Field Station, Alaska (2010-2022)
The Toolik Field Station Bird Monitoring Program was established in 2008 to monitor long term trends in arrival, departure, status and abundance of all birds breeding within the Toolik-centric region. Transects through the commonly occurring habitats were established. The overall protocol was adopted from the Alaska Landbird Monitoring Survey (http://www.absc.usgs.gov/research/bpif/Monitor/alms2.html). These data are from the bird count surveys in 2010-2022. The point count locations are situated 500 m apart, ten minutes are spent at each point count location and all birds seen and heard are recorded. The number of points within each transect was determined based on the natural features of the landscape. The data collected from these transects will be used to determine status and abundance of birds that breed in the Toolik area. The survey points are to be walked starting from the field station and moving away from the camp pad. Transects are marked on the map. The distances recorded from 2010-2011 were intervals; from 2012 onward, the distances recorded are actual distances.
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- Award ID(s):
- 2221133
- PAR ID:
- 10507546
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- Toolik Arctic Breeding birds Toolik Field Station Alaska
- Format(s):
- Medium: X
- Location:
- Toolik Field Station
- Sponsoring Org:
- National Science Foundation
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