Evidence map›Paper›PMID 38845039›Full record

ArticleMovement ecology2024

Predicting suitable habitats for foraging and migration in Eastern Indian Ocean pygmy blue whales from satellite tracking data.

Luciana C Ferreira, Curt Jenner, Micheline Jenner, Vinay Udyawer, Ben Radford, Andrew Davenport, Luciana Moller, Virginia Andrews-Goff, Mike Double, Michele Thums

Abstract read
In one paragraph

Article in Movement ecology, 2024. 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
–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

1 citing paper in PubMed.

  1. Incorporating fine-scale behaviors into habitat suitability modeling: A case study for sea turtles.Ecological applications : a publication of the Ecological Society of America · 2025
    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

10 authors.

Luciana C FerreiraAustralian Institute of Marine Science, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, WA, Australia. l.ferreira@aims.gov.au.
Curt JennerCentre for Whale Research (WA) Inc., Fremantle, WA, Australia.
Micheline JennerCentre for Whale Research (WA) Inc., Fremantle, WA, Australia.
Vinay UdyawerAustralian Institute of Marine Science, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, WA, Australia.
Ben RadfordAustralian Institute of Marine Science, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, WA, Australia.
Andrew DavenportCentre for Whale Research (WA) Inc., Fremantle, WA, Australia.
Luciana MollerCetacean Ecology, Behaviour and Evolution Lab, College of Science and Engineering, Flinders University, Bedford Park, SA, Australia.
Virginia Andrews-GoffAustralian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, Kingston, TAS, Australia.
Mike DoubleAustralian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, Kingston, TAS, Australia.
Michele ThumsAustralian Institute of Marine Science, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, WA, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate predictions of animal occurrence in time and space are crucial for informing and implementing science-based management strategies for threatened species.

methodsWe compiled known, available satellite tracking data for pygmy blue whales in the Eastern Indian Ocean (n = 38), applied movement models to define low (foraging and reproduction) and high (migratory) move persistence underlying location estimates and matched these with environmental data. We then used machine learning models to identify the relationship between whale occurrence and environment, and predict foraging and migration habitat suitability in Australia and Southeast Asia.

resultsOur model predictions were validated by producing spatially varying accuracy metrics. We identified the shelf off the Bonney Coast, Great Australian Bight, and southern Western Australia as well as the slope off the Western Australian coast as suitable habitat for migration, with predicted foraging/reproduction suitable habitat in Southeast Asia region occurring on slope and in deep ocean waters. Suitable foraging habitat occurred primarily on slope and shelf break throughout most of Australia, with use of the continental shelf also occurring, predominanly in South West and Southern Australia. Depth of the water column (bathymetry) was consistently a top predictor of suitable habitat for most regions, however, dynamic environmental variables (sea surface temperature, surface height anomaly) influenced the probability of whale occurrence.

conclusionsOur results indicate suitable habitat is related to dynamic, localised oceanic processes that may occur at fine temporal scales or seasonally. An increase in the sample size of tagged whales is required to move towards developing more dynamic distribution models at seasonal and monthly temporal scales. Our validation metrics also indicated areas where further data collection is needed to improve model accuracy. This is of particular importance for pygmy blue whale management, since threats (e.g., shipping, underwater noise and artificial structures) from the offshore energy and shipping industries will persist or may increase with the onset of an offshore renewable energy sector in Australia.

Indexed as

Habitat suitabilityMachine learningMovementSatellite trackingSpatial accuracySpatial predictionSpecies distribution modellingSpecies managementThreatened species

Identifiers

PMID38845039
PMCPMC11157879

What OpenQuestion holds

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LicenceCC BY
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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.