Population Dynamics of Breeding Mallards in the Great Lakes States
Bibliographic record
Abstract
Abstract: Mallard ( Anas platyrhynchos ) populations in the United States portion of the Great Lakes region increased through the 1990s but have since declined. To promote sustainable growth of this population, managers need to understand how perturbation of vital rates will affect annual population growth rate (Λ). We developed a stage‐based model representing the female mallard population in the Great Lakes using vital rates generated from a landscape‐level study documenting reproductive parameters from 2001 to 2003. We conducted perturbation analyses (i.e., sensitivity analyses) to identify vital rates that most influence Λ and variance decomposition analyses to determine the proportion of variation in Λ explained by variation in each vital rate. Perturbation analyses indicated that Λ was most sensitive to changes in nonbreeding survival, duckling survival, and nest success. Therefore, changes in these vital rates would be expected to result in the greatest ΔΛ. Process variation in breeding season parameters accounted for 63% of variation in Λ. Breeding season parameters explaining the most variation were duckling survival (32%) and nest success (16%). Survival of adult females outside the breeding season accounted for 36% of variation in Λ. Harvest derivation, high harvest, and high sensitivity of Λ to nonbreeding survival for Great Lakes female mallards suggests there is a strong potential for managing the Great Lakes mallard population via harvest management. Because Λ was highly sensitive to changes in duckling survival, we suggest programs that emphasize wetland protection, enhancement, and restoration as a management strategy to improve population growth for breeding mallards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".