Influence of landscape, habitat, and species co-occurrence on occupancy dynamics of Canada Warblers
Bibliographic record
Abstract
The Canada Warbler (Cardellina canadensis) is a species of high conservation importance because of its low overall density and long-term widespread population declines on the breeding grounds. Results of previous research on the species suggest that its breeding habitat preferences vary across its range. However, the underlying processes associated with habitat use are unknown. Using a 20 yr dataset, we developed occupancy models for Canada Warblers to determine the influence of habitat characteristics (e.g., understory vegetation, canopy cover), landscape context (e.g., edge, forest patch size), and species co-occurrence on occupancy, colonization, and local extinction parameters. Our results show that multiple habitats are used by Canada Warblers on the breeding grounds; common variables associated with large-scale, long-term occupancy dynamics are forest age, landscape composition at the 100 m and 500 m scales, and mean patch size. Overall, Canada Warblers were nearly twice as persistent in mature forest stands (>80 yr) and large, mixed forest stands. Further, models indicated that species co-occurrence was an important predictor of Canada Warbler occupancy in some cover types. The results of this study increase our understanding of population processes over large, dynamic landscapes and provide essential conservation information to improve habitat and landscape management for the Canada Warbler.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".