The effects of predator removal on mallard production and population change in northeastern North Dakota
Bibliographic record
Abstract
Abstract In 1994, Delta Waterfowl Foundation began trapping mammalian meso‐predators in North Dakota during the breeding season in an attempt to increase waterfowl nest success and enhance recruitment into the fall flight and subsequent breeding population. Multiple studies on these sites demonstrated that removing predators results in near doubling of nest success, which previous simulation modeling suggests is the most influential vital rate influencing the population growth rate of mid‐continent mallards (Anas platyrhynchos). We present an assessment of the impact of predator removal on mallard production using population models. We conducted this study on 9 township‐sized (93.2 km2) sites (4–8 sites annually per vital rate) in northeastern North Dakota from 2006–2008. Trappers removed mammalian meso‐predators on 5 sites and the other 4 served as unmanaged reference sites. To estimate recruitment, we used derived estimates and process variance of pair numbers, hen success (nest survival corrected for renesting), initial brood size, pre‐fledging survival, and post‐fledging survival, along with previously published estimates of breeding propensity and adult female survival rates. Trapped sites had greater hen success (H = 0.69, ${\rm {\hat {\sigma }}}$ = 0.03) than reference sites (H = 0.53, ${\rm {\hat {\sigma }}}$ = 0.06), but similar indicated breeding pairs, initial brood size, and pre‐fledging survival. We estimated that females on trapped sites added 140 more mallards of both sexes to the fall flight than females on reference sites, at an approximate cost of $74.29 per incremental mallard. Additionally, trapping predators provided a marginal increase (0.04) in finite population growth. We found that predator removal targeted at mammalian nest predators did not produce as many incremental mallards as previously thought and may not be a viable strategy for increasing mallard productivity under conditions similar to those observed during this study. We conducted a sensitivity analysis and determined that pre‐fledging survival was the most influential factor regulating mallard population growth. Although hen success increased as a result of trapping, duckling survival became a limiting factor. We suggest that waterfowl managers assess multiple vital rates to determine the likelihood that management actions focused on a single parameter, such as nest success, will yield desired population level effects. © 2012 The Wildlife Society.
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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 | 0.001 |
| 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".