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Record W2763163916 · doi:10.1242/jeb.170399

Fastest whales and dolphins reinforce diaphragms

2017· article· en· W2763163916 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsWhaleWhalingShoreAnatomyFisheryGeologyBiology

Abstract

fetched live from OpenAlex

Every diving animal that depends on air to breathe faces the challenge of crushing pressures compressing every tissue in their bodies, including the lungs, as they descend. Elastic tissues, such as arteries, which expand to accommodate pressure surges above the surface, become vulnerable to compression as soon as an air breather leaves the surface. ‘A few years ago, we discovered that the mechanical properties of fin whale arteries were different from those of typical terrestrial animals’, says Margo Lillie, who – with colleagues, Robert Shadwick and Wayne Vogl from the University of British Columbia (UBC) Canada, and others – discovered that the arteries are remarkably resistant to collapse, which may protect them during a dive. However, the trio also realised that the distinctive bobbing swimming style of whales and dolphins – where they beat their tails up and down – might also place arteries under increasing pressure if the animals compress their abdomen with each downward tail beat. Intrigued by the possibility that the abdomens of these diving mammals may be under more pressure than had been realised, the team wondered whether the divers’ diaphragms might be reinforced in some way to withstand the additional abdominal pressure generated during each downward tail beat.Together, the team collected over 20 whale and dolphin diaphragms over a 5 year period. ‘We relied heavily on a network of people who collect the carcasses of animals that stranded on the shore and we also got diaphragms collected as part of a Canadian biological sampling programme and a commercial whaling operation in Iceland’, says Lillie. Although it was possible to ship most of the organs back to Vancouver, Lillie, Shadwick, Vogl and Stephen Raverty from the Animal Health Centre, Canada, had to travel to Iceland to investigate the colossal 1.5 m-wide fin whale diaphragms.‘Compared to the diaphragms of terrestrial animals, the cetacean diaphragms looked so complex and they vary from one species to another, so it took me a while to start seeing patterns’, admits Lillie. However, it eventually became clear that the animals’ diaphragms were reinforced with a network of stiff collagen fibres. ‘There is a lot of variation in the amount of collagen on the surface of the diaphragm’, says Lillie; ‘There was hardly any on the beluga and minke diaphragms, while the Dall's porpoise diaphragm was covered with it’, she adds. Suspecting that the amount of reinforcement was related to the strength of the species’ tail beats – and therefore their swimming speed – Lillie was pleased when she realised that the diaphragms of the fastest swimming species – the Dall's and harbour porpoises – were covered in the highest proportion of collagen (almost 60%), while the slowest species – the belugas, and fin and minke whales – had the lowest proportion of collagen (∼10%).‘We were surprised how closely the amount of collagen correlated with speed. It means that collagen deposited on the diaphragm could be necessary to withstand increased abdominal pressures associated with swimming’, says Lillie. And when they compared the whale and dolphin's diaphragm structures with those of other diving mammals (seals and sea lions), which probably do not compress their abdomens when beating their tails from side to side, there was no evidence of the extensive collagen reinforcement. Reflecting on the discovery, Lillie says, ‘The evolutionary decision to swim by moving the flukes up and down instead of side to side appears to have altered the design of the diaphragm’, and now she and Shadwick are keen to find out how the increase in abdominal pressure has affected the return flow of blood through veins to these animals’ hearts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.301
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
Has abstractyes

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