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Record W3122778046 · doi:10.1101/2021.01.14.426682

Local ecological knowledge, catch characteristics and evidence of elasmobranch depletions in Western Ghana

2021· preprint· en· W3122778046 on OpenAlexaff
Issah Seidu, Lawrence Kwabena Brobbey, Emmanuel Danquah, Samuel Oppong, David van Beuningen, Nicholas K. Dulvy

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsSimon Fraser University
FundersSave Our Seas Foundation
KeywordsOverfishingFishingFisheryGeographyFisheries managementStock assessmentOverexploitationEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Local Ecological Knowledge has the potential to improve fishery management by providing new data on the fishing efforts, behavior, and abundance trends of fish and other aquatic animals. Here, we relied on local knowledge of fishers to investigate ecological factors that affect elasmobranch fishers‟ operations and the changes in stock status of sharks and rays from 1980 to 2020 in five coastal communities in Ghana. Data were gathered from fishers using participant observation, interviews, focus group discussions, and participatory rural appraisal techniques. The results revealed fisher‟s understanding of six main ecological variables, which are mostly applied to enhance their fishing operations: season and weather conditions, lunar phase, bait type, presence of seabirds and fish movement, color of seawater, and sea current. These ecological features have been applied over the years to enhance fishing operations as well as maximize fisher catch. Fishers reported a profound decline in shark and ray catch from 1980 to 2020 and attributed the decline in size, number, and composition of their catch to overfishing and Illegal, Unreported and Unregulated (IUU) fishing operations. In general, most shark and ray species were abundant in 1980 but have been severely depleted as of 2020, with the exception of Blue Shark ( Prionace glauca) and Devil rays ( Mobula spp), which were reported to be common by the interviewed fishers. The first species depleted were the Thresher sharks (Alopiidae), Tiger Shark ( Galeocerdo cuvier ), Blackchin Guitarfish ( Glaucostegus cemiculus ), and Lemon Shark ( Negaprion brevirostris ), which were depleted early in the 2000s. The next depletions of Hammerhead sharks (Sphyrnidae), Bull Shark ( Carcharhinus leucas ), Sand Tiger Shark ( Carcharias taurus ), Stingrays ( Fontitrygon spp), and Spineback Guitarfish ( Rhinobatos irvinei ) occurred in the 2010s. We found Local Ecological Knowledge of fishers to be surprisingly consistent with scholarly knowledge and call for their inclusion in research, decision-making and management interventions by biologists and policy makers.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.237
Teacher spread0.222 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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