Unseen Experts of the Sea: Fishers' Local Ecological Knowledge Reveals Elasmobranch Hotspot Decline Around Curaçao, Dutch Caribbean
Bibliographic record
Abstract
ABSTRACT Fishing pressure is the primary threat to coastal elasmobranch populations, and understanding its impact requires long‐term regional data—often lacking in complex, small‐scale fishery settings. This is the case for Curaçao, a southern Dutch Caribbean island with an unmonitored artisanal fishery where, according to anecdotes, elasmobranchs have severely declined but continue to be landed. In such data‐limited regions, fishers' local ecological knowledge (FLEK) is a valuable tool for reconstructing historical baselines. Using FLEK from 21 surveys, we quantified historical and current elasmobranch diversity around Curaçao. Participatory mapping identified spatial distributional changes of 14 elasmobranch species, comparing the time of the surveys with fishers' career beginnings. Temporal trends were analysed alongside shifts in fishing efforts, socioeconomic contexts and perceptions of fishery management. Between 1957 and 2009, we identified 36 spatial hotspots of elasmobranch richness, which declined to 14 hotspots from 2010 to 2022, with a 4.3‐fold greater likelihood of hotspots occurring in the past. Species richness in these areas significantly decreased from 7.44 ± 1.00 (mean ± s.e.) to 3.00 ± 1.18 species, while the number of fishers increased from 2.86 ± 0.23 fishers to 5.14 ± 0.49 per hotspot. Although not targeted, incidental elasmobranch catches are commonly retained. Most fishers expressed a desire for increased inclusion in fishery management but viewed elasmobranch‐specific measures as unnecessary, perceiving local populations as healthy. We thus provide critical spatial baseline data for evidence‐based conservation of elasmobranchs around Curaçao while emphasising the benefits and importance of engaging small‐scale fishers in managing elasmobranch populations.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".