Vegetation influences patch occupancy but not settlement and dispersal decisions in a declining migratory songbird
Bibliographic record
Abstract
Territorial clustering within larger, continuous patches of seemingly appropriate habitat could indicate that a species has additional, finer scale habitat requirements. Studying fine-scale (e.g., territory-level) habitat selection using methods that elucidate individual preferences may allow us to identify resources that influence species distributions. We examined breeding territory selection in the sagebrush Brewer’s Sparrow ( Spizella breweri Cassin, 1856) at the northern extent of its range to test for influences on fine-scale habitat selection. We used an information–theoretic approach to evaluate models relating a suite of vegetation characteristics to breeding habitat selection. We employed two methods: (1) assessment of patch occupancy at a territory scale and (2) examination of individual decisions relating to settlement and dispersal. We found that patch occupancy was most consistently predicted by models that included the cover of big sagebrush ( Artemisia tridentata Nutt.) with the greatest likelihood of occupancy at 20%–25% cover. However, assessment of settlement and dispersal decisions did not identify additional fine-scale preferences for other vegetation characteristics. Vegetation cover also did not influence breeding success, indicating that within the vegetation range found in Brewer’s Sparrow territory clusters, there is little benefit in basing individual settlement or dispersal decisions on vegetation cover.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".