Sixty-years of community-science data suggest earlier fall migration and short-stopping of waterfowl in North America
Bibliographic record
Abstract
Abstract Worldwide, migratory phenology and movement of many bird species is shifting in response to anthropogenic climate and habitat changes. However, due to variation among species and a shortage of analyses, changes in waterfowl migration, particularly in the fall, are not well understood. Fall migration phenology and movement patterns dictate waterfowl hunting success and satisfaction, with cascading implications on economies and support for habitat management and securement. Using 60 years of band recovery data for waterfowl banded in the Canadian Prairie Pothole Region (PPR), we evaluated whether fall migration timing and/or distribution changed in Mallard (Anas platyrhynchos), Northern Pintail (A. acuta), and Blue-winged Teal (Spatula discors) between 1960 and 2019. We found that in the Midcontinent Flyways, Mallards and Blue-winged Teal migrated faster in more recent time periods, whereas Northern Pintail began fall migration earlier. In the Pacific Flyway, Mallards began fall migration earlier. Both Mallards and Northern Pintails showed evidence of short-stopping in the Midcontinent Flyways. Indeed, the Mallard and Northern Pintail distribution of band recovery data shifted 180 and 226 km north, respectively, from 1960 to 2019. Conversely, Blue-winged Teal recovery distributions were consistent across years. Mallards and Northern Pintails also exhibited an increased proportion of band recoveries in the Pacific Flyway in recent decades. We provide clear evidence that the timing and routes of fall migration have shifted over the past 6 decades, but these phenological and spatial shifts differ among species. We suggest that using community-science data collected by hunters themselves to explain one of the group’s major concerns (changes in duck abundance at traditional hunting grounds), within the environmental lens of climate change, may help lead to further engagement and two-way dialogue to support effective waterfowl management for these culturally and ecologically important species.
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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 | 0.000 | 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) | 0.001 | 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".