Effects of predator reduction on nest success of upland nesting ducks in low-grassland density landscapes in eastern North Dakota
Bibliographic record
Abstract
Nest success of upland nesting ducks is the primary driver of duck population growth in the Prairie Pothole Region. Nest success is greatly influenced by nest predation and the amount of available nesting cover on the landscape. The decline in acres enrolled in the Conservation Reserve Program (CRP) in this region has negatively impacted the amount of available nesting cover, making nesting cover sparse and confined to small patches where predation rates are potentially elevated. I evaluated the efficacy of seasonal predator reduction on increasing nest success on low-grassland density (>10% grassland cover), 93 km2 landscapes in two different habitat types used by nesting birds, large fields and roadside ditches. Ditches were sampled because they are a major cover source in low-grassland density landscapes. I monitored 1,899 nests during the 2010-2012 breeding seasons. Predator reduction had a significant influence in large fields as nest success was 1.6 times greater in large fields on trapped sites (44% nest success) than on control sites (27% nest success). Predator reduction, however, did not significantly increase nest success in roadside ditches (13% nest success on trapped sites, 12% nest success on control sites). A large majority of monitored nests were located in large fields resulting in the overall effect of predator reduction significantly increasing nest success by 13%. These results indicate that predator reduction is an effective intensive management technique in low-grassland density landscapes and can be used as a management tool in a post-CRP era. Future research should evaluate different trapping techniques in efforts to increase nest success in the roadside ditches.
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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 | 0.000 | 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".