Annual Moisture Levels Drive Population Dynamics Across the Breeding Range of a Declining Waterbird
Bibliographic record
Abstract
In order to understand causes of avian population declines and establish conservation strategies, we require knowledge of how changes to the environment are linked to changes in population abundance. Such linkages can be examined with large-scale, long-term databases, such as the North American Breeding Bird Survey (BBS). The BBS is generally under-utilized for waterbirds, in part due to the notion that it is unreliable; however, for some readily detectable species such as Black Terns (Chlidonias niger), this may be a valuable data source. We applied Bayesian hierarchical generalized additive models to BBS counts to examine population trends and drivers of abundance across the Black Tern range. We uncovered long-term declines and strong spatial variation in trends, with declines particularly prominent in peripheral areas at the edge of the species' range. There was a strong positive effect of spring moisture (measured via the Standardized Precipitation Evapotranspiration Index) on abundance, whereas moisture in the core of the species' range in the prairies had a negative effect on abundance in peripheral areas, suggesting birds are attracted to the core of the range in wet years, and vice versa. There was a weak and spatially varying effect of the North Atlantic Oscillation Index on annual abundance. Ongoing wetland loss coupled with climate extremes will become increasingly important for predicting population fluctuations in this and other waterbirds.
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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 | 0.001 | 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) | 0.000 | 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 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".