Using Breeding Bird Survey and eBird data to improve marsh bird monitoring abundance indices and trends
Bibliographic record
Abstract
The elusive nature of many marsh-breeding birds presents a challenge for effective population monitoring. The Great Lakes Marsh Monitoring Program (GLMMP), delivered by Birds Canada, addressed these challenges by concentrating survey efforts in marsh bird habitats and by using survey protocols aimed at maximizing marsh bird detections. GLMMP data suggest that numerous marsh bird species are declining. Here we consider the value of other avian monitoring programs to support our understanding of marsh bird population trends. Our goal was to compare the GLMMP, North American Breeding Bird Survey (BBS), and eBird with each other and with a combined survey, by evaluating frequency of detection, annual indices of abundance, and trend estimates. Using 23 years (1997–2019) of GLMMP, BBS, and eBird data, we calculated annual indices of abundance and trends for each survey for 18 marsh-breeding species across southern Ontario, Canada. We found that the GLMMP had more frequent detections, greater counts, and/or more precise trends for 8 species that breed almost exclusively in marshes, whereas 10 species with more variable habitat preferences had more frequent detections, greater counts, and/or more precise trends based on eBird and/or BBS. We found that combining counts from the GLMMP, BBS, and eBird increased the precision around trend estimates for 11/18 (61%) species; however, trend estimates for combined data tended to be positively biased relative to GLMMP trends for species that also frequent non-marsh habitats. We, therefore, provide evidence that combining citizen science data from multiple sources could increase the power to detect changes in marsh-dependent bird populations. Integrated datasets thus provide a promising avenue for future marsh bird conservation and management.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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".