Evidence map›Paper›PMID 38655208›Full record

ArticleF1000Research2023

Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests.

Shih-Hung Wu, Jerome Chie-Jen Ko, Ruey-Shing Lin, Chia-Hao Chang-Yang, Hsueh-Wen Chang

Open access · goldAbstract read
In one paragraph

Article in F1000Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 26% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

5 authors at 1 institution in 1 country.

Shih-Hung WuDepartment of Biological Sciences, National Sun Yat-sen University, Kaohsiung, 804, Taiwan.ORCID https://orcid.org/0000-0003-3619-0646
Jerome Chie-Jen KoDepartment of Biological Sciences, National Sun Yat-sen University, Kaohsiung, 804, Taiwan.ORCID https://orcid.org/0000-0002-8343-604X
Ruey-Shing LinDepartment of Biological Sciences, National Sun Yat-sen University, Kaohsiung, 804, Taiwan.ORCID https://orcid.org/0000-0001-7705-9770
Chia-Hao Chang-YangTaiwan Biodiversity Research Institute, Nantou, 552, Taiwan.
Hsueh-Wen ChangTaiwan Biodiversity Research Institute, Nantou, 552, Taiwan.
National Sun Yat-sen University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: From passive acoustic monitoring (PAM) recordings, the vocal activity rate (VAR), vocalizations per unit of time, can be calculated and is essential for assessing bird population abundance. However, VAR is subject to influences from a range of factors, including species and environmental conditions. Identifying the optimal sampling design to obtain representative acoustic data for VAR estimation is crucial for research objectives. PAM commonly uses temporal sampling strategies to decrease the volume of recordings and the resources needed for audio data management. Yet, the comprehensive impact of this sampling approach on VAR estimation remains insufficiently explored. Methods: In this study, we used vocalizations extracted from recordings of 12 bird species, taken at 14 PAM stations situated in subtropical montane forests over a four-month period, to assess the impact of temporal sampling on VAR across three distinct scales: short-term periodic, diel, and hourly. For short-term periodic sampling analysis, we employed hierarchical clustering analysis (HCA) and the coefficient of variation (CV). Generalized additive models (GAMs) were utilized for diel sampling analysis, and we determined the average difference in VAR values per minute for the hourly sampling analysis. Results: We identified significant day and species-specific VAR fluctuations. The survey season was divided into five segments; the earliest two showed high variability and are best avoided for surveys. Data from days with heavy rain and strong winds showed reduced VAR values and should be excluded from analysis. Continuous recordings spanning at least seven days, extending to 14 days is optimal for minimizing sampling variance. Morning chorus recordings effectively capture the majority of bird vocalizations, and hourly sampling with frequent, shorter intervals aligns closely with continuous recording outcomes. Conclusions: While our findings are context-specific, they highlight the significance of strategic sampling in avian monitoring, optimizing resource utilization and enhancing the breadth of monitoring efforts.

Indexed as

AcousticsBirdsForestsVocalization, AnimalAnimalsEnvironmental MonitoringAvespassive acoustic monitoringtemporal samplingvocal activity rate

Identifiers

PMID38655208
PMCPMC11036034
OpenAlexW4391136158

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.