Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance

Abstract

1. 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.2. 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.3. 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. 4. 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. | 1041Methods in Ecology and Evoluঞon DOSER Et al.

Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources

“…While our case study used three data sources arising from the same method (i.e., point count surveys), the ICOM can incorporate detection-nondetection data from different data collection approaches (e.g., camera traps, autonomous recording units, citizen science checklists) in an analogous manner. Similar integrated models could be developed to estimate alternative ecological processes such as abundance of multiple species within a community by extending single species integrated models that use distance sampling (Farr et al, 2021), acoustic recordings (Doser et al, 2021a), or capture-recapture data (Chandler and Clark, 2014). If regional species pools differ between data source locations, the ICOM could be adapted to a multiregion framework (Sutherland et al, 2016).…”

Acoustic and Genetic Data Can Reduce Uncertainty Regarding Populations of Migratory Tree-Roosting Bats Impacted by Wind Energy

“…The response variables for mobile transects are detection/non-detection for each species and the number of bat passes per species along the transect; thus, an index of relative abundance can be derived from the number of passes [ 39 ] to estimate the superpopulation or the total number of individuals estimated to use each grid cell, summed across the modeled range during the survey period. Using analytical methods documented in Doser et al [ 48 ] and Kéry and Royle [ 49 ], it is possible to estimate absolute abundance at the grid cell, but because individuals can and are expected to use more than one cell, the estimate is not representative of census population size.…”

spOccupancy: An R package for single-species, multi-species, and integrated spatial occupancy models

“…Integrated occupancy models (IOMs) simultaneously analyze multiple detection-nondetection data sources to better estimate the latent process of interest and sources of uncertainty. These models are particularly attractive as there is an increasing number of approaches used to collect detection-nondetection data sources across potentially vast spatial regions, such as autonomous recording units (Doser et al, 2021a), camera traps (Kays et al, 2020), and citizen science checklists (Kéry et al, 2010).…”