Leveraging eBird data products to inform regional bird conservation priorities and objectives
Bibliographic record
Abstract
Abstract As migratory bird populations continue to decline at alarming rates worldwide, there is an urgent need for comprehensive information on their distributions, abundances, and population trajectories to help pinpoint causes of declines and develop strategies for recovery. To address these needs, North American Migratory Bird Joint Ventures (JVs) are increasingly leveraging eBird Status and Trends data products—freely available, scalable information on bird populations—to fill key information gaps and inform on-the-ground habitat conservation planning and delivery. We present 6 empirical case studies demonstrating how JVs are applying eBird data products to address regional conservation priorities and meet common information needs encountered by conservation organizations: (1) estimating region-specific proportions of global populations to support priority species selection; (2) assessing temporal abundance patterns (ie, migration chronologies) to inform full annual cycle conservation; (3) identifying areas of multi-species habitat use (ie, hotspots) across seasons; and (4) monitoring spatially explicit population trends to detect and respond to localized changes. Together, these examples offer a transferrable framework for how conservation practitioners can integrate information from eBird into decision-making—enabling more targeted, data-informed conservation delivery in the face of escalating biodiversity loss and limited resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".