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Record W4394958887 · doi:10.3389/fmars.2024.1368756

Fine-scale hunting strategies in Australian fur seals

2024· article· en· W4394958887 on OpenAlexaff
Perla Salzeri, Sebastián P. Luque, John P. Y. Arnould

Bibliographic record

VenueFrontiers in Marine Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
FundersParks Victoria
KeywordsScale (ratio)FisheryFur tradeGeographyOceanographyEcologyBiologyEnvironmental ethicsGeologyCartographyPhilosophy

Abstract

fetched live from OpenAlex

Introduction Knowledge of the hunting strategies of top predators can provide insights into the cost-benefit trade-offs of their foraging activities. Air-breathing marine predators are constrained in their foraging activities due to their metabolic expenditure at depth being supported by limited body oxygen stores. Understanding how these species adapt their behaviours to maximise foraging success is of importance in view of the anticipated alterations to marine ecosystems in response to global change. The Australian fur seal (Arctocephalus pusillus doriferus), the largest fur seal species, has a distribution restricted to south-eastern Australia, which is one of the fastest warming oceanic regions and where the abundance, distribution and diversity of prey species is expected to change in coming decades. Methods In the present study, combined IMU (acceleration, magnetometer, gyroscope), depth and GPS data logger information was used to reconstruct 3-dimensional tracks during diving, assess energy expenditure and quantify prey capture events in adult female Australian fur seals during benthic foraging. Results The results revealed that individuals ascended at steeper pitches (to reduce transit time), remained for shorter durations and travelled shorter distances at the surface, and then descended at steeper pitches on subsequent dives after predatory events on the seafloor. Higher travel speeds and more directional changes during searching for prey along the seafloor, while requiring greater energy expenditure, were associated with more prey captures. Interestingly, individuals did not display conventional Area Restricted Search, with the heading between dives not influenced by prey encounters. Discussion Together, these results suggest Australian fur seals undertake rapid searching along the seafloor to surprise cryptic prey and, if prey is encountered, undertake rapid surfacing (to reload body oxygen stores) and return to nearby seafloor habitat with a similar but undisturbed prey field.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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