The Acadian Flycatcher is a habitat specialist, and it shows
Bibliographic record
Abstract
Declines in North American bird populations are being driven by a suite of threats, and it can be difficult to disentangle drivers of decline for any single species, especially those with large ranges or that experience different threats in different parts of their range. Community science platforms offer new and rapidly expanding datasets to observe species across space and time. Here, we use community science databases to analyze the habitat characteristics of the Acadian Flycatcher (Empidonax virescens), a Neotropical migrant songbird that is threatened across portions of its range. The Acadian Flycatcher is often described as a habitat specialist, though often only where it is considered at risk. We use Acadian Flycatcher observations sourced from one of the biggest community science platforms, eBird, and assess habitat land cover and co-occurrence of species associated with habitat quality for each of these observations. We use publicly available land use data to assess habitat cover and assess co-occurring species using observations sourced from multiple community science platforms. Our results show the Acadian Flycatcher is largely observed in high-quality landscapes of preferred habitat (31% deciduous forest and 11% wetland cover) and without invasive vegetation more than bird observation sites where the Acadian Flycatcher was not detected (25% more likely to be near preferred trees, 77% less likely to be near invasive vegetation, p < 0.001). Our results also show an overrepresentation of urban land cover, potentially highlighting a bias in some community science data due to observer behavior. Overall, our results support land management strategies that maintain patches of native land cover and manage invasive species and highlight how community science databases can provide important information about species’ presence over space and time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".