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Record W4393236998 · doi:10.1111/ele.14418

Rarity mediates species‐specific responses of tropical reef fishes to protection

2024· article· en· W4393236998 on OpenAlexaff
Loïc Sanchez, Nicolas Loiseau, Graham J. Edgar, Cyril Hautecoeur, Fabien Leprieur, Stéphanie Manel, Matthew McLean, Rick D. Stuart‐Smith, Laure Velez, David Mouillot

Bibliographic record

VenueEcology Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsDalhousie University
FundersDirectorate for Biological SciencesRoyal Society
KeywordsMarine protected areaTrophic levelEcologyCoral reefTrophic cascadeCoral reef fishBiodiversityHabitatContext (archaeology)BiologyHerbivoreApex predatorMesopredator release hypothesisPredationFisheryGeographyFood web

Abstract

fetched live from OpenAlex

Marine protected areas (MPAs) are the most widely applied tool for marine biodiversity conservation, yet many gaps remain in our understanding of their species-specific effects, partly because the socio-environmental context and spatial autocorrelation may blur and bias perceived conservation outcomes. Based on a large data set of nearly 3000 marine fish surveys spanning all tropical regions of the world, we build spatially explicit models for 658 fish species to estimate species-specific responses to protection while controlling for the environmental, habitat and socio-economic contexts experienced across their geographic ranges. We show that the species responses are highly variable, with ~40% of fishes not benefitting from protection. When investigating how traits influence species' responses, we find that rare top-predators and small herbivores benefit the most from MPAs while mid-trophic level species benefit to a lesser extent, and rare large herbivores experience adverse effects, indicating potential trophic cascades.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score1.000

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)0.0010.001

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 head0.019
GPT teacher head0.215
Teacher spread0.196 · 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.

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

Citations9
Published2024
Admission routes1
Has abstractyes

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