Same view through a different lens: Comparing population trends for North American birds using eBird and the Breeding Bird Survey
Bibliographic record
Abstract
Abstract Confidently estimating population trends is of vital importance for a wide range of ecological, conservation, and management applications. North America has 2 major data sources for estimating population trends of breeding birds—the North American Breeding Bird Survey (BBS) and the global participatory science project eBird. Because the surveys differ in protocols, coverage, and data analysis, their trend estimates are expected to vary in magnitude, direction, and/or precision for at least some species and regions. Here, we compare independently derived estimates of population change between 2012 and 2022 for every combination of species and bird conservation region (BCR) covered by both surveys (n = 5,577 combinations) as well as aggregated across entire ranges or within the U.S. or Canada. Uncertainty was substantial for both surveys, though more prevalent for BBS (81% of credibility intervals for estimates included zero) than eBird (34% of confidence intervals overlapped zero). We found agreement of trend directions between the 2 surveys. Only 1.3% of estimated trends were significant in opposite directions between the 2 surveys for all species/BCR combinations, with the median difference in trend magnitude being –0.02% (BBS minus eBird trend). Correlations between the 2 were strongest for estimates that were graded as being high credibility compared to estimates judged to have medium or low credibility. Both surveys were subject to species, taxonomic, and regional effects that influenced agreement. Overall, we show where trend estimates derived from BBS and eBird agree, explore where they diverge, present several comparisons to assist in interpreting results from both surveys, and inform efforts to integrate information from each.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".