Harvesting Practices and Local Ecological Knowledge (LEK) of Bahamian Land Crabs: Bridging Gaps Between Traditional and Scientific Knowledge
Bibliographic record
Abstract
Three species of land crab occur in The Bahamas; these are an important source of protein and income for Bahamian islanders. The crab harvesters represent an important and largely untapped knowledge source. We conducted surveys on the Bahamian islands of Andros, New Providence, and Eleuthera to document crabbing practices and catalogue this local ecological knowledge (LEK) of land crabs. The survey primarily employed close-ended questions targeting land crab harvesters; we also recorded general feedback from open-ended questions. Crab collection was primarily for self-consumption. Catch rates varied among islands, and were the highest on Andros. There was a preference for white land crabs (Cardisoma guanhumi) on Andros, whereas on Eleuthera and New Providence, there was no preference for either white or black crabs (Gecarcinus ruricola). The majority of respondents reported a decline in white and black crab numbers, with land development and overharvesting being consistently cited factors. On Andros, forest fires were also reported to account for the loss of crab habitat, whereas on Eleuthera, invasive raccoons were blamed for the population decline. Respondents identified broadleaf forests as critical refuges and food sources for black crabs. Birds were the major predator, confirming findings for other land crab species. Land crabs were not merely a food resource but represented a complex nexus of ecological knowledge, economic systems, cultural traditions, and community practices within Bahamian society. We demonstrated a substantial overlap between traditional and scientific knowledge systems, providing valuable insights into land crab behaviour, habitat use, and ecology that complements formal scientific research.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".