Evidence map›Paper›PMID 31223482›Full record

ArticleMovement ecology2019

Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat.

Edward Hurme, Eliezer Gurarie, Stefan Greif, L Gerardo Herrera M, José Juan Flores-Martínez, Gerald S Wilkinson, Yossi Yovel

Open access · goldAbstract read
In one paragraph

Article in Movement ecology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
8.6field-weighted citation impact, top 3% 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

15 citing papers in PubMed, 42 citations in OpenAlex.

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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

7 authors at 3 institutions in 3 countries.

Edward Hurme1Department of Biology, University of Maryland, College Park, MD 20742 USA.ORCID 0000-0002-2936-2449
Eliezer Gurarie1Department of Biology, University of Maryland, College Park, MD 20742 USA.
Stefan Greif2School of Zoology, Faculty of Life Sciences, Tel-Aviv University, 6997801 Tel-Aviv, Israel.
L Gerardo Herrera M4Estación de Biología de Chamela, Instituto de Biología, Universidad Nacional Autónoma de México, 48980 San Patricio, Mexico.
José Juan Flores-Martínez5Laboratorio de Sistemas de Información Geográfica, Departamento de Zoología, Instituto de Biología, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico.
Gerald S Wilkinson *1Department of Biology, University of Maryland, College Park, MD 20742 USA.
Yossi Yovel *2School of Zoology, Faculty of Life Sciences, Tel-Aviv University, 6997801 Tel-Aviv, Israel.
University of Maryland, College Park · USTel Aviv University · ILUniversidad Nacional Autónoma de México · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultiple methods have been developed to infer behavioral states from animal movement data, but rarely has their accuracy been assessed from independent evidence, especially for location data sampled with high temporal resolution. Here we evaluate the performance of behavioral segmentation methods using acoustic recordings that monitor prey capture attempts.

methodsWe recorded GPS locations and ultrasonic audio during the foraging trips of 11 Mexican fish-eating bats,

resultsWhile the five methods differed in the median percentage of buzzes occurring during predicted foraging events, or true positive rate (44-75%), a two-state hidden Markov model had the highest median balanced accuracy (67%). Hidden Markov models and first-passage time predicted foraging flight speeds and turn angles similar to those measured at locations with feeding buzzes and did not differ in the number or duration of predicted foraging events.

conclusionThe hidden Markov model method performed best at identifying fish-eating bat foraging segments; however, first-passage time was not significantly different and gave similar parameter estimates. This is the first attempt to evaluate segmentation methodologies in echolocating bats and provides an evaluation framework that can be used on other species.

Indexed as

Behavioral change point analysisCorrelated velocity movementExpectation maximization and binary clusteringFirst-passage timeForagingGPS telemetryHidden Markov modelsK-meansPath segmentation

Identifiers

PMID31223482
PMCPMC6567457
OpenAlexW2952490641

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.