Large-scale population trend analysis by integrating migration counts with breeding origin estimates from feather stable isotopes: a case study with Blackpoll Warbler ( Setophaga striata )
Bibliographic record
Abstract
Large portions of the boreal forest are inaccessible to breeding season surveys, leading to highly uncertain assessments of boreal bird populations. However, systematic monitoring of boreal-breeding bird populations during migration has the potential to inform trend estimates for these species. A network of bird observatories across North America have collected decades of standardized daily counts during fall and spring migration seasons with a goal of monitoring avian population dynamics, but statistical approaches to appropriately weight station-level trends in regional-scale analyses have been lacking. Here, we describe a statistical model that estimates population trends across a species’ breeding range by integrating migration count data with estimates of the proportions of migrants coming from separate breeding-ground strata based on stable hydrogen isotopes (δ²Hf) in feather samples of migrants. We applied this model to Blackpoll Warbler (Setophaga striata), a species of conservation concern, and compared migration-based population trend estimates to those from the North American Breeding Bird Survey (BBS). Migration-based and BBS-derived trend estimates were strongly negative for the portion of the species’ boreal breeding range east of the Great Lakes, where our analysis indicated populations have potentially declined by > 40% from 1998 to 2018. In contrast, migration analyses suggested that populations were stable or increasing in western Canada, though BBS suggested those populations likely declined, possibly owing to spatial biases in breeding season surveys in that region. Continental trend estimates depended strongly on the source of relative abundance estimates that were used to re-weight stratum trends at larger scales, emphasizing the critical need for improved breeding abundance estimates throughout the core of the boreal forest. Our approach yields trend estimates that are independent from other breeding season survey programs and can be integrated with breeding survey estimates to provide a weight-of-evidence approach when spatial biases in data collection are a major concern. Application of our method to other species inadequately monitored throughout their life cycle will be an important advance for North American landbird monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".