Upland nesting waterfowl population responses to predator reduction in North Dakota
Bibliographic record
Abstract
Population growth for mallards (Anas platyrhynchos), and presumably other upland nesting ducks, in the Prairie Pothole Region is most sensitive to nest success, and nest success is most strongly influenced by predation. I evaluated the efficacy of reducing predator populations to improve nest success and increase local breeding populations of upland nesting ducks on township-sized (93.2 km2) management units in eastern North Dakota, USA, during 2005−2008. I also examined potential territorial limitations on local population growth for mallards. Trappers annually removed an average of 245 predators per trapped site. I monitored 7,489 nests on 7 trapped and 5 nontrapped sites, and I found nest success to be 1.4−1.9 times greater on trapped sites, depending on year. I surveyed an average of 621 wetlands twice annually and observed 3,674 blue-winged teal (A. discors), 3,227 mallard, 2,287 gadwall (A. strepera), 1,539 shoveler (A. clypeata), and 679 pintail (A. acuta) breeding pairs. I found little evidence that local breeding populations of upland nesting ducks increased following predator reduction. Defense of territories, which may limit local population growth, was most frequent during settling and declined as greater portions of local mallard populations commenced nesting. Territorial defense was strongly correlated to the ratio of breeding pairs to available wetland habitat, such that sites with higher pair densities had greater frequencies of territorial behavior. Hence, defense of territories may function to limit local breeding populations. Though predator reduction provides managers with an effective tool to improve nest success at large spatial scales, they should not rely on the practice to increase local breeding populations.
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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 | 0.000 | 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".