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Record W4410286809 · doi:10.1016/j.biocon.2025.111211

Spatial scale and conservation options for carpet sharks

2025· article· en· W4410286809 on OpenAlexafffund
Maryam Nakhostin, Nicholas K. Dulvy

Bibliographic record

VenueBiological Conservation · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsSimon Fraser UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsScale (ratio)GeographyFisheryCartographyBiology

Abstract

fetched live from OpenAlex

Although there is considerable momentum for expanding protected area coverage, a key criticism is the lack of connection to species-specific conservation outcomes. Conservation action needs to be tailored to species characteristics, such as behaviour, life-history traits, migratory range, as well as the scale and intensity of existing threats. Due to their small average size (<10 km 2 ), Marine Protected Areas (MPAs) are likely to be more beneficial for species with smaller geographic ranges than for larger pelagic migratory species. We developed a conservation classification scheme for the 28 Critically Endangered ( n = 1), Endangered ( n = 4), Vulnerable ( n = 12), Near Threatened ( n = 6) and Data Deficient ( n = 5) carpet sharks (Order: Orectolobiformes) based on their distribution, biology, and threats. Carpet sharks exhibit large diversity in their life histories and ecology; ranging from the Halmahera Epaulette Shark that reaches a 70 cm (total length, TL) with a geographic range of 14,446 km 2 to the Whale Shark that reaches 21 m TL and geographic range of 171,000,000 km 2 . We find that none of these carpet sharks would benefit exclusively from a single site-scale MPA protection; instead MPA networks or a combination of MPAs with broad-scale threat mitigation measures are needed for over half of the species due to the broad geographic ranges and widespread overfishing. The choice of measures would depend on (a) fraction of the population moving between sites, (b) how diffuse are the inter-site movements, and (c) the presence or otherwise of significant barriers or threats to connectivity. Hence, MPAs on their own will not solve widespread overfishing, and instead diagnosis and a tailored treatment is likely to be more effective than a single cure-all. Fishing mortality will also need to be reduced around, as well as eliminated within, MPAs to ensure maximum conservation benefit. • Single MPAs are currently unlikely to be large enough to benefit Carpet Sharks. • MPAs networks or in combination with broad-scale conservation is likely to benefit half of at-risk carpet sharks. • Tackling broad-scale threats, particularly overfishing, is an unavoidable need alongside the MPA growth agenda

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 score0.070
Threshold uncertainty score0.373

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 head0.026
GPT teacher head0.261
Teacher spread0.235 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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