Evidence map›Paper›PMID 42032770›Full record

ArticleMovement ecology2026

Assessment of goose-beaked whale responses to mid-frequency active sonar using a hierarchical hidden Markov model.

Stacy L DeRuiter, Erin A Falcone, Shannon N Coates, Brenda K Rone, David A Sweeney, Karin A Dolan, Susan M Jarvis, Russel D Andrews, Stephanie L Watwood, Gregory S Schorr

Abstract read
In one paragraph

Article in Movement ecology, 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

10 authors.

Stacy L DeRuiterCalvin University, Grand Rapids, MI, USA. stacy.deruiter@calvin.edu.
Erin A FalconeFoundation for Marine Ecology and Telemetry Research, Seabeck, WA, USA.
Shannon N CoatesFoundation for Marine Ecology and Telemetry Research, Seabeck, WA, USA.
Brenda K RoneFoundation for Marine Ecology and Telemetry Research, Seabeck, WA, USA.
David A SweeneyFoundation for Marine Ecology and Telemetry Research, Seabeck, WA, USA.
Karin A DolanRanges, Engineering and Analysis Department, Naval Undersea Warfare Center, Newport, RI, USA.
Susan M JarvisRanges, Engineering and Analysis Department, Naval Undersea Warfare Center, Newport, RI, USA.
Russel D AndrewsNorth Pacific Wildlife Consulting, Seattle, WA, USA.
Stephanie L WatwoodRanges, Engineering and Analysis Department, Naval Undersea Warfare Center, Newport, RI, USA.
Gregory S SchorrFoundation for Marine Ecology and Telemetry Research, Seabeck, WA, USA.

Funding

Calvin University SabbaticalU.S. Navy Office of Naval Research SMRT Tag DevelopmentU.S. Navy Pacific Fleet contract No's. N66604-14-Q-0145, N66604-18-P-2187, N62473-19-2-0025, and N66604-22-D-F200
6 · The paper itself

Abstract

backgroundAcoustic disturbance is increasingly recognized as an ecological concern, particularly at sea. In light of marine mammal stranding events associated with use of naval mid-frequency active sonar (MFAS), which often involve goose-beaked whales (Ziphius cavirostris), research has sought to detect and characterize Ziphius behavioral responses to MFAS.

methodsWe deployed Sound and Motion Recording and Telemetry (SMRT) tags on 13 Ziphius in the Southern California Bight. The study area includes the United States Navy’s Southern California Anti-submarine Warfare Range (SOAR), and deployments overlapped temporally with training exercises including MFAS use. We used hierarchical hidden Markov models (HHMMs), which provided a framework to model whale behavior and putative responses to MFAS at two relevant time scales: foraging dive cycles, which may last hours, and five-minute intervals, where finer-scale changes in movement and behavior are evident.

resultsWe analyzed 70.7 days of tag data (1697.6 h) representing 361 Ziphius foraging dive cycles, 52 with MFAS detections. Cumulative MFAS sound energy level (cSEL) per dive cycle was 69.9–160.3 dB re 1µPa2s (median 121.3, inter-quartile range (IQR) 21.7). We identified two dive-cycle states: “Typical” and “Variant.” In Variant state, durations of dive cycles, foraging dives, non-foraging dives, and echolocation periods were shorter on average but more variable, while time spent near the surface and net distance traveled were longer and more variable. According to Akaike’s information criterion (AIC), MFAS cSEL (rather than per-sound maximum received level) increased the probability of switching from Typical to Variant dive-cycle state, with MFAS cSEL also modulating transition rates between four fine-scale behavior states during Variant dive cycles only. Whales transitioned to Variant state in 12 of 52 MFAS-exposed dive cycles (23.1%), with observed Variant state dwell times ranging from 1 to 72.4 h (median 9.0, IQR 9.2).

conclusionsThis work has relevance for conservation and management, enhancing our understanding of Ziphius behavioral responses to operational MFAS in an area where naval training activities occur regularly. These analysis tools may prove useful in assessing responses to other acoustic disturbance and understanding how shorter-term behavior changes translate to longer-term consequences.

Indexed as

Acoustic disturbanceBehavioral responseBiologgingHierarchical hidden Markov modelHigh-resolution biologgingMid-frequency active sonarZiphius cavirostris

Identifiers

PMID42032770
PMCPMC13107642

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.