MétaCan
Menu
Back to cohort
Record W3166033106 · doi:10.1093/conphys/coab035

Walking sharks cannot beat the heat

2021· article· en· W3166033106 on OpenAlexaff
Ian A. Bouyoucos

Bibliographic record

VenueConservation Physiology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBiologyBeat (acoustics)FisheryAcoustics

Abstract

fetched live from OpenAlex

Ocean warming is a driving force in marine conservation physiology research. Yet, scientists only began to investigate this threat in sharks in the second decade of the 21st century. Since then, researchers around the world have considered the nearly 1200 species of sharks and their kin and have been working to describe biological traits that can be used to predict ‘winners and losers’ of climate change. The epaulette shark (Hemiscyllium ocellatum), for example, is renowned for its ability to live in harsh environments and thought to be a clear winner; however, a recent study has singled out ocean warming as this species’—and possibly many others’—kryptonite. Carolyn Wheeler and her colleagues from the Anderson Cabott Centre for Ocean Life and the Australian Research Council Centre of Excellence for Coral Reef Studies targeted the epaulette shark as a bioindicator—a so-called canary in the coal mine—of ocean warming for sharks. Wheeler and colleagues wondered, if epaulette sharks cannot beat the heat, how will less-tolerant species fare? The epaulette shark is an egg-laying species (along with ~40% of all sharks and their kin) only found in Australia’s Great Barrier Reef and is thought to be highly resilient to environmental stress. This species is known for its ability to walk out of water between tide pools, its resilience to ocean acidification conditions and its ability to survive for hours without oxygen! However, as an egg-laying species, epaulette shark embryos develop in eggs deposited on the ocean floor that cannot move if water temperatures prove unfavourable. This makes embryos of epaulette sharks—and other egg-laying species—a potential bottleneck in the tolerance of shark populations to ocean warming. Wheeler and colleagues reared 27 epaulette shark embryos until hatching. Previous research singled out 32°C as `too hot’, where embryos fail to hatch. Knowing this information, the team cleverly tested epaulette sharks at their average summer water temperature (27°C) as well as mid-of-century (29°C) and end-of-century (31°C) predictions with ocean warming to find their pejus (Latin for ‘getting worse’) temperature. The team tracked the embryos’ growth, development and metabolic costs until hatching, and they continued monitoring newly hatched sharks. Almost every trait that the team measured decreased under end-of-century (31°C) relative to present-day (27°C) temperatures. Notably, embryos reared at 31°C hatched faster, but were smaller and needed to eat sooner than embryos reared at 27°C. Newly hatched epaulette sharks also took twice as long to recover from exercise challenges at 31°C when compared to 27°C. These data led Wheeler and colleagues to conclude the pejus temperature for epaulette shark development and metabolic performance is somewhere between 29°C and 31°C. Wheeler and colleagues’ findings suggest that epaulette sharks may be living at the tipping point of their temperature tolerance. As a bioindicator species, it may be that ecologically similar species—particularly other tropical, egg-laying shark species—may also be similarly vulnerable to ocean warming. If such species are unable to find cooler habitats or adapt to warmer water, ocean warming could have detrimental impacts on the growth and development of their embryos, which may hold negative consequences for the health of the ecosystems that those species support. Illustrations: Erin Walsh, ewalsh.sci@gmail.com Editor: Jodie L. Rummer

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.228
Teacher spread0.206 · 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

Citations0
Published2021
Admission routes1
Has abstractno

Explore more

Same venueConservation PhysiologySame topicPhysiological and biochemical adaptationsFrench-language works237,207