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Record W2026693181 · doi:10.1111/ecog.00793

Increasing rate of species discovery in sharks coincides with sharp population declines: implications for biodiversity

2014· article· en· W2026693181 on OpenAlexaff
H. S. Randhawa, Robert Poulin, Martin Krkošek

Bibliographic record

VenueEcography · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsApex predatorBiodiversityEcologyMesopredator release hypothesisTrophic levelBiologyExtinction (optical mineralogy)PopulationOverfishingTrophic cascadeGeographyFishingFood webPaleontology

Abstract

fetched live from OpenAlex

The global biodiversity of some taxonomic groups is poorly described, but thought to be decreasing rapidly. Surprisingly, this holds for a group of the world's most iconic large‐bodied animals: sharks. Our analysis shows rapid and steep contemporary population declines in sharks coinciding with an increasing rate in species discovery. Larger sharks occupying lower trophic positions with wide geographic distributions (latitudinal ranges) found in shallow waters tend to be discovered first. In light of this increasing trend in species discovery and a cumulative description record far from reaching an asymptote, models cannot predict the global number of sharks. Our results highlight that while our knowledge of shark diversity improves at an accelerating rate, this diversity is under threat and declining rapidly; most shark species are vulnerable to declines, especially smaller‐bodied sharks. This surprising finding may relate to mesopredator declines following periods of rapid expansion due to the demise of large sharks (apex predators). Furthermore, shark population declines are structured by phylogeny and, to a lesser extent, geography. Decline in sharks are likely to influence other species as well, e.g. via trophic cascades. The net result may be a greater loss of biodiversity in the oceans and could potentially explain why fewer extinction events are observed than predicted by models. Likewise, it is not inconceivable that species may be lost prior to their discovery.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.003
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.217
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 teacher head, 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

Citations27
Published2014
Admission routes1
Has abstractyes

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