Legal Harvest of Shorebirds and Resident Game Birds on a Caribbean island: A Martinique Case Study
Bibliographic record
Abstract
Abstract Legal hunting practiced under regulatory frameworks is not always assessed for its sustainability. The lack of harvest data is of particular concern in the case of birds occurring on islands. Resident species on islands often exhibit limited geographic ranges and inherently small population sizes, rendering them particularly susceptible to environmental and anthropogenic threats. Islands also support seasonally fluctuating populations of migratory species, providing essential habitats for stopovers and non-breeding periods. Within this context, the sustainability of gamebird harvest practiced on islands within a regulatory framework needs to be evaluated to inform management and prevent negative impacts on populations. Here, we present a case study of an island-wide assessment of bird hunting estimates, based on data available from a legal hunting program, in Martinique, a tropical oceanic island part of the Caribbean biodiversity hotspot. Our objectives were to determine the magnitude of gamebird harvest, track changes in harvest levels over time, and assess harvest sustainability. Our assessment, which is based on hunting logbooks and hunter surveys between the 2012-2013 and 2021-2022 hunting seasons, provides harvest estimates for two groups of migratory species, shorebirds ( Charadriidae, Scolopacidae ) and teals ( Anatidae) , and two groups of resident species ( Columbidae and Mimidae ). In Martinique, harvest management measures, combined with increased hunter’s engagement and enforcement of regulations, has led more stringent harvest regulations, improved compliance from hunters, and a reduction of Martinique’s national shorebird harvest. However, considering continued and rapid shorebirds population declines across the Western Atlantic flyway, shorebird harvest assessment at the flyway level remains a priority.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".