Breeding home range selection of Eastern Population sandhill cranes across the boreal forest
Bibliographic record
Abstract
Abstract Understanding the distribution and selection of breeding habitat is important for effective conservation and management of wildlife species. Advances in global positioning system (GPS) tracking technology allow for the collection of high‐resolution location data and analysis of habitat selection in remote areas. We investigated home range selection of breeding sandhill cranes ( Antigone canadensis ) in the boreal forests of Ontario and Quebec, Canada, using high‐resolution GPS tracking data across a gradient of land cover and land use classes. We quantified breeding home range selection using resource selection functions and developed spatial maps to predict the distribution of breeding home ranges across the boreal forest landscape. Sandhill cranes arrived at their breeding home ranges in late April and departed in late August, remaining on breeding grounds for approximately 4 months. The size of breeding home ranges varied considerably among individuals, with an average size of 14.12 ± 21.70 (SD) km 2 . Our models revealed selection for home ranges containing greater proportions of cropland, forest disturbance, and wetland. Management efforts should focus on enhancing the quality and connectivity of selected cover types, particularly wetlands, to support sandhill crane conservation in the boreal forest ecosystem. These findings highlight the importance of integrating landscape‐level analyses with detailed patterns of habitat selection to inform the development of effective management strategies that support the long‐term conservation of breeding sandhill cranes.
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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".