The effect of habitat on the breeding season survival of Mallards (<i>Anas platyrhynchos</i>) in the Great Lakes region
Bibliographic record
Abstract
Modeling the effect of habitat on animal survival is critical for understanding population dynamics and developing effective habitat management strategies. Despite the importance of this information, knowledge of survival–habitat associations are often lacking, particularly for waterfowl species. Here we evaluated female Mallard (Anas platyrhynchos Linnaeus, 1758) survival during the breeding season in relation to habitat conditions within each individual’s home range. We implanted telemetry transmitters and tracked 283 female Mallards across nine study sites in the Great Lakes region. For each Mallard, we quantified core breeding season home ranges via the creation of utilization distributions (UDs). We then fit known-fate models in the program MARK to predict breeding season survival as a function of the proximity of core home ranges to various habitat types, the proportion of habitat types within the core areas, number of core areas, and home range size. We found that breeding season survival decreased as the proportion of forestland habitat within core home ranges increased (β = −1.740, SE = 0.787). No additional upland or wetland habitat types significantly affected breeding season survival. Managers striving to increase breeding season survival for Mallards should focus their efforts on restoring habitats in areas with low proportions of forestland habitat to mitigate the risk of predation.
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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".