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Record W4389827580 · doi:10.1101/2023.12.15.571896

Downscaling global reference points to assess the sustainability of local fisheries

2023· preprint· en· W4389827580 on OpenAlexaff
Jessica Zamborain‐Mason, Sean R. Connolly, M. Aaron MacNeil, Michele L. Barnes, Andrew G. Bauman, David A. Feary, Víctor Huertas, Fraser A. Januchowski‐Hartley, Jacqueline Lau, Michalis Mihalitsis, Joshua E. Cinner

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsDalhousie University
FundersAkershus Universitetssykehus
KeywordsOverfishingFisheryFishingCoral reefCoral reef fishReefDownscalingMarine protected areaSustainabilityContext (archaeology)Environmental scienceEnvironmental resource managementGeographyClimate changeEcologyHabitatBiology

Abstract

fetched live from OpenAlex

Abstract Multispecies coral reef fisheries are typically managed by local communities who often lack research and monitoring capacity, which prevents estimation of well-defined sustainable reference points to perform locally relevant fishery assessments. Recent global advances in modelling coral reef fisheries have developed pathways to use environmental indicators to estimate multispecies sustainable reference points. These global reference points are a promising tool for assessing data-poor reef fisheries but need to be downscaled to be relevant to resource practitioners. Here, using a small-scale multispecies reef fishery from Papua New Guinea, we estimate sustainable reference points and assess the sustainability of the fishery by integrating global-scale analyses with local-scale environmental conditions, fish catch, reef area, standing biomass estimates, and fishers’ perceptions. We found that assessment results from global models applied to the local context of our study location provided results consistent with local fishers’ perceptions. Specifically, our downscaled results suggest that the fishing community is overfishing their reef fish stocks (i.e., catching more than can be sustained) and stocks are below B MMSY (i.e., below biomass levels that maximize production), making the overall reef fishery unsustainable. These results were consistent with fisher perceptions that reef fish stocks were declining in abundance and mean fish length, and that they had to spend more time finding fish. Our downscaled site-level assessment reveals severe local resource exploitation, whose dynamics are masked in national-scale assessments, emphasizing the importance of matching assessments to the scale of management. More broadly, our study shows how global reference points can be applied locally when long-term data are not available, providing baseline assessments for sustainably managing previously un-assessed multispecies reef fisheries around the globe.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.246
Teacher spread0.216 · 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 designSimulation or modeling
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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicCoral and Marine Ecosystems Studies→French-language works237,207→