Exploring fall migratory patterns of American black ducks using eight decades of band‐recovery data
Bibliographic record
Abstract
ABSTRACT As regions of habitat used by migratory waterfowl are subjected to rising anthropogenic pressures, establishing patterns of landscape use during migratory cycles is becoming increasingly important for managing and maintaining populations. Although data collection strategies such as global positioning system (GPS) telemetry promise high‐resolution insight on geographic use of contemporary populations, decades of available band recovery records on many species can provide a low‐cost, multi‐generational alternative for defining broad, historically informed patterns of landscape use. We used nearly a century of band‐recovery data to reconstruct patterns of fall migratory landscape use for American black ducks in the Mississippi and Atlantic flyways. We partitioned band recovery positions by month from September to February and spatially analyzed positions using kernel density estimates (KDEs) to delineate geographic regions used by American black ducks and track changes in landscape use during migration. Additionally, we considered the appropriateness of current management strategies, which treat American black ducks as a single population, by testing for differences in month‐specific landscape use between ducks banded in the Mississippi and Atlantic flyways, and between international management units (ducks banded in Canada or the U.S.). We found that geographic distributions during peak migration faithfully recovered regions previously hypothesized as migratory corridors. Furthermore, regardless of banding origin, we found highly similar distributions and strong flyway fidelity in Atlantic flyway American black ducks. Conversely, American black ducks banded in the Mississippi flyway displayed differences in landscape use between Canadian and U.S. populations, with Canadian ducks significantly more likely to winter in areas within the Atlantic flyway (i.e., migrate between flyways). Flyway‐ and population‐specific patterns of black duck landscape use indicate populations behave more independently than currently treated by management models (including the Adaptive Harvest Management plan), and that population maintenance may be advanced by managing stocks separately. © 2014 The Wildlife Society.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".