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Cited by 34Animal Vocal Communication and BehaviorAvian ecology and behaviorWildlife Ecology and Conservation

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Automated auditory detection of a rare, secretive marsh bird with infrequent and acoustically indistinct vocalizations

Schroeder, Katie M., McRae, Susan B.·2020·Ibis
DOI 10.1111/ibi.12805
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Abstract

Autonomous recording units (ARUs) provide a non‐invasive and efficient method for acoustic detection of elusive species across large temporal and spatial scales. However, species with indistinct vocalization structures can be a considerable challenge for automated signal recognizers. We investigated the performance of ARUs and signal recognizers in identifying the broadband, short‐syllable, pulsed calls of a secretive, threatened marsh bird, the King Rail Rallus elegans. Other sympatric species in the same habitat also have repetitive calls within the same frequency range that can be difficult to distinguish. Following serial ARU deployments at specified sites in known breeding habitat, we conducted standardized callback surveys and nest searches to provide an independent measure of breeder density. To analyse recordings, we developed a signal recognizer based on user‐input training files to detect two common call types, kek and grunt. Detections that remained following manual review of recognizer output revealed a previously undescribed seasonal decline and crepuscular diel pattern in calling rate. The rate of the grunt call also predicted density. These patterns emerged despite the recognizer's low precision and high false‐positive rate, which were largely due to misclassification of other species' calls, although ambient noise and effective detection radius also limited the detectability of King Rail calls. We demonstrate that with informed ARU scheduling, improved ability to manipulate user‐specified parameters within signal detection software, and attention to quality control, even the simplest call structures can be located consistently in a diverse acoustic landscape. Our behavioural findings will inform improvements to auditory surveys and to management of King Rails across their range.

Overview

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Crossref · 10.1111/ibi.12805

Cites

42 of 42 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 6

  1. Butler C.J., 2015, Assessing black rail occupancy and vocalisations along the Texas Gulf Coast
  2. Charif R.A., 2010, Raven Pro 1.4 User’s Manual
  3. Cooper T.R., 2008, King Rail Conservation Plan, Version 1, 121
  4. Cornell Lab of Ornithology, 2011, Raven Pro: Interactive Sound Analysis Software
  5. Duke E.C., 2013, Testing the efficacy of autonomous recording units for monitoring secretive marsh birds
  6. Skaug H., 2016, Generalized Linear Mixed Models using ‘AD Model Building’. R package version 0.8.5

Cited by

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Metadata

Authors
Schroeder, K. M., & McRae, S. B.
Affiliations
East Carolina University (US)
Corresponding author
Schroeder, Katie M.
ORCID
Venue
Ibis
Publisher
Wiley
Published
2020-01-10
Volume
162
Issue
3
Pages
1033-1046
ISSN
  • 0019-1019 (ISSN-L)
  • 1474-919X
DOI
10.1111/ibi.12805
OpenAlex
W2993137105
Cited by
34
References
42
Citations by year
2026: 5 · 2025: 7 · 2024: 3 · 2023: 3 · 2022: 8 · 2021: 5 · 2020: 3
FWCI
3.72
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
Ibis (publishedVersion)
Funders
  • U.S. Fish and Wildlife Service
  • East Carolina University
Topics
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Vocal Communication and Behavior
  • Physical Sciences › Environmental Science › Ecology › Avian ecology and behavior
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
Keywords
King Rail, Rallus elegans, autonomous recording units, call recognition, pulsed calls, breeding density, false positive detection, marsh birds, threatened species management

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.