Use of North American Breeding Bird Survey data in avian conservation assessments
Bibliographic record
Abstract
Conservation resources are limited, and prioritizing species based on their relative vulnerability and risk of extinction is a fundamental component of conservation planning. In North America, the conservation consortium Partners in Flight (PIF) has developed and implemented a data-driven species assessment process, at global and regional scales, based on quantitative vulnerability criteria. This species assessment process has formed the biological basis for PIF's continental and regional planning and has informed the ranking and legal listing of bird species for conservation protection by state, provincial, and national agencies in Canada, the U.S., and Mexico. Because of its long time series, extensive geographic and species coverage, standardized survey methods, and prompt availability of results, the North American Breeding Bird Survey (BBS) has been an invaluable source of data, allowing PIF to assign objective vulnerability scores calibrated across more than 460 landbird species. BBS data have been most valuable for assessing long-term population trends (PT score). PIF has also developed methods for estimating population size by extrapolating from BBS abundance indices, allowing the assignment of categorical population size (PS) scores for landbird species. At regional scales, BBS relative abundance indices have allowed PIF to assess the area importance (i.e. stewardship responsibility) of each Bird Conservation Region (BCR) for each species, using measures of both relative density and percent of total population in each BCR. Besides direct applicability to assessment scores, PIF has recently used BBS trend data to create new metrics of conservation urgency (e.g., ‘half-life'), as well as for setting population objectives for tracking progress toward meeting conservation goals. Future directions include integrating BBS data with other sources (e.g., eBird) to assess additional species and nonbreeding season measures, working closely with BBS coordinators to expand surveys into Mexico, and providing assessment scores at implementation-relevant scales, such as for migratory bird joint ventures.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".