Shorebird subsistence harvest and indigenous knowledge in Alaska: Informing harvest management and engaging users in shorebird conservation
Bibliographic record
Abstract
Abstract Limited data on harvest and population parameters are impediments to assessing shorebird harvest sustainability. Because of sharp declines in shorebird populations, timely conservation efforts require approaches that account for uncertainty in harvest sustainability. We combined harvest assessment and ethnographic research to better understand shorebird conservation concerns related to subsistence harvest in Alaska and to support culturally sensible conservation actions. Our objectives were to (1) estimate the Alaska-wide shorebird subsistence harvest and (2) document shorebird indigenous knowledge on the Yukon-Kuskokwim Delta. Harvest estimates were based on surveys conducted in 1990–2015 (n = 775 community-years). Key respondent interviews conducted in 2017 (n = 72) documented shorebird ethnotaxonomy and ethnography. The Alaska-wide shorebird harvest was 2,783 birds per year, including 1,115 godwits per year—mostly Bar-tailed Godwits (Limosa lapponica baueri), whose population has low harvest potential. The egg harvest was 4,678 eggs per year, mostly small shorebird eggs. We documented 24 Yup’ik shorebird names and 10 main ethnotaxonomic categories. Children learning harvesting skills focused on small birds and adults also occasionally harvested shorebirds, but shorebirds were not primary food or cultural resources. Older generations associated shorebirds with a time when people were closer to nature and their cultural roots. Shorebirds connected people with the environment as well as with Yup’ik traditions and language. Our results can inform improvements to harvest assessment and management, as well as outreach and communication efforts to engage subsistence users in shorebird conservation.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".