Evidence map›Paper›PMID 42604061›Full record

ArticleEcology and evolution2026

Detection Range of Acoustic Monitoring Devices in an Open Grassland Ecosystem.

Grace E Schuster, Leroy J Walston, Andrew R Little

Abstract read
In one paragraph

Article in Ecology and evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Grace E SchusterSchool of Natural Resources University of Nebraska-Lincoln Lincoln Nebraska USA.ORCID https://orcid.org/0009-0000-2599-4935
Leroy J WalstonEnvironmental Science Division Argonne National Laboratory Lemont Illinois USA.ORCID https://orcid.org/0000-0002-6569-1223
Andrew R LittleSchool of Natural Resources University of Nebraska-Lincoln Lincoln Nebraska USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Passive Acoustic Monitoring (PAM) is a novel method for estimating avian biodiversity, producing results comparable to traditional methods, such as point count surveys. However, uncertainty in the detection space around acoustic recording units (ARUs) can complicate monitoring designs and potentially lead to biased results. Therefore, understanding ARU detection space in different environments is essential to advancing the reliability of PAM for avian population monitoring. In this study, we used a distance-based broadcasting method at seven distances from ARUs in grassland systems in eastern Nebraska, USA, to quantify the detection space for six focal species common in this landscape. We found that detection space was species-specific but, overall, most confirmed detections (detection probability ≥ 0.50) occurred within 100 m (m) of the ARU. Detectability was positively influenced by amplitude, while the effect of vocalization frequency varied, producing species-specific differences in detectability. Detection space is a highly variable yet essential consideration in the design of large-scale PAM systems, and its estimation should be a key first step in any PAM study. We demonstrate a replicable method for assessing detection space that can be applied in other ecosystems and for other bird species. Although more labor-intensive than other approaches, this method provides reliable estimates of detection space, which are important considerations for the ongoing application of PAM technologies for biodiversity conservation.

Indexed as

acoustic recording units (ARUs)detection spacegrasslandspassive acoustic monitoring (PAM)

Identifiers

PMID42604061
PMCPMC13476758

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.