RELATING GROUSE NEST SUCCESS AND CORVID DENSITY TO HABITAT: A MULTI-SCALE APPROACH
Bibliographic record
Abstract
Abstract Predators are the major cause of nest failure for prairie grouse, and corvids are widespread generalist predators that exploit land altered by humans where grouse are found. We studied how human-caused habitat change affected predator and prey by using habitat variables to model nest selection, corvid density, and nest success for sharp-tailed grouse (Tympanuchus phasianellus) in Alberta, Canada, 1999–2001. Habitat was quantified over a range of extents (radius of observation) from 2 to 2,265 m. We predicted that habitat features associated with corvid density at broad extents would also relate to grouse nest success, and that nesting cover and the presence of avian predator perch sites would be important at smaller extents. Corvid density was higher in landscapes with higher proportions of crop and sparse grassland (1,600-m extent). Conversely, nest success was markedly higher (≥ 4 times) in landscapes with < 10% crop and < 35% crop and sparse grassland (aggregated) at broad extents (1,600 m). Moreover, nests were 8 times more likely to succeed in landscapes with lower relative corvid densities (< 3 vs. ≥ 3 corvids/km2). At smaller scales, nests were more likely to succeed with greater heights of concealment cover within 50-m of nests. Land managers can likely improve nest success for grouse in grassland systems by targeting concealment cover heights of at least 13 cm measured over a 50-m extent, and focusing efforts in landscapes with < 10% crop and < 35% crop and sparse grassland (1,600-m extent).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".