Three grassland bird species’ responses to fire and habitat structure in southern Illinois, USA suggest broad benefits of grassland size and plant diversity
Bibliographic record
Abstract
Grassland birds are the most rapidly declining bird guild in North America, largely due to extensive habitat loss and fragmentation. Because many grassland bird species have different habitat preferences, managing grasslands to provide habitat for a range of species can be a challenge. We used four years of data from southern Illinois, USA grasslands to estimate the influence of prescribed fire and habitat structure on nest survival, nest density, and abundance of three grassland bird species with different habitat preferences: Dickcissel (Spiza americana), Field Sparrow (Spizella pusilla), and Common Yellowthroat (Geothlypis trichas). We found that Dickcissels exhibited the strongest response to prescribed fire, as nest density and nest survival both increased after previously undisturbed grasslands were burned. Fire may have also benefitted Common Yellowthroats and Field Sparrows by reducing woody cover and increasing bare ground, both of which were characteristics associated with nest survival for these birds. Dickcissel abundance was positively related to plant diversity within a grassland and agriculture in the surrounding landscape (within 400 m of a grassland patch), and negatively related to edge-interior ratio. Field Sparrows demonstrated a positive association with woody cover and proximity to forests. Common Yellowthroats were associated with tall vegetation and agriculture in the surrounding landscape. Both Field Sparrows and Common Yellowthroats associated positively with habitat characteristics that reduced nest survival, suggesting potential adaptive mismatches. Our results suggest that periodic prescribed fire, increased plant diversity, and larger patch size may simultaneously benefit a broad variety of grassland bird species with different habitat preferences.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".