Spatial drivers of breeding habitat selection in Eastern Population sandhill cranes
Bibliographic record
Abstract
Canada’s eastern boreal forest provides important breeding and foraging habitat for many wildlife species, including sandhill cranes ( Antigone canadensis (Linnaeus, 1758)), a species of conservation concern. Following significant population declines in the early 20th century, sandhill crane populations have since rebounded and rapidly expanded their breeding ranges into the boreal forest, yet habitat selection in these newly occupied areas remains poorly understood. Using high-resolution satellite telemetry, we developed resource selection functions to assess how land cover and land use influence breeding habitat selection of 42 sandhill cranes in Ontario and Quebec, Canada. Within the home range (95% minimum convex polygon), sandhill cranes exhibited the strongest selection for wetlands, which were selected approximately twice as often as croplands, forest disturbances, and open water. Intact forests and urban areas were consistently avoided. These findings highlight wetlands as key predictors of habitat use, emphasing the need for targeted management strategies that prioritise wetland conservation. While current forestry practices do not appear to negatively impact breeding habitat selection in the boreal forest, their long-term effects on breeding success remain unknown, requiring further research to explore the detailed impacts of forestry development, industrial development, and urban expansion on breeding behaviour and success.
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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 | 0.001 |
| 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".