Accelerating declines of North America’s shorebirds signal the need for urgent conservation action
Bibliographic record
Abstract
Abstract Shorebirds are declining to a greater extent than many other avian taxa around the world. In North America, shorebirds, along with aerial insectivores and grassland birds, have some of the highest proportions of declining species of any group. Here, we apply a new hierarchical Bayesian model to analyze shorebird migration monitoring data from across North America, from 1980 to 2019, and present the most recent available estimates of trends for 28 species. Point estimates for survey-wide trends in abundance were negative for 26 of 28 species (93%). Despite challenges with low precision associated with migration count data, trends for 19 species had 95% credible intervals that were entirely negative. More than half of the species were estimated to have lost >50% of their abundance. Furthermore, estimated rates of decline have accelerated during the last three generations for most species. Point estimates of trend were more negative for 18 species (64%) during the most recent three-generation period in comparison to the previous three-generation period. Many species now exceed international criteria for threatened species listing. The analytic approach used here allows us to model regional variation in trends, although survey coverage and strength of inference were greatest in the eastern portions of North America (east of 100°W). We found the greatest declines at staging sites along the Atlantic Coast from North Carolina to Nova Scotia, and lesser declines along the Gulf Coast and in the midcontinental United States. The declines in shorebird populations reported here are worrisome and signal the urgent need for conservation action. In addition, it would be beneficial to validate these results through the collection and analysis of complementary data, and to initiate demographic studies throughout the annual cycle to determine where and when declines are most likely to originate. This improved information will allow for the development of more targeted efforts to reverse declines through conservation action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".