Influence of resource selection on nonbreeding season mortality of mallards
Bibliographic record
Abstract
Abstract Relationships between individual resource selection strategies and fitness are difficult to quantify at large spatial scales. These links are important for understanding the potential effects of management on population‐level processes. We modeled the degree to which selection of specific landscape features altered mortality risk of female mallards (Anas platyrhynchos) during the non‐breeding season. We used individual resource selection estimates from adult female mallards equipped with Global Positioning System (GPS) backpack transmitters (n = 56) in the Lake St. Clair region of southwestern Ontario, Canada, in August of 2014 and 2015. We determined the fate of individuals between August and January and used time‐to‐event analyses to model survival over 158 days. Furthermore, we investigated how diurnal and nocturnal resource selection and year were related to mortality risk. The survival rate for the adult female mallards was 0.57 (95% CI = 0.42–0.77). Resource types were combinations of land class types (e.g., water, marsh, flooded agriculture, supplemental feeding areas, and dry agriculture) important to mallards and varying levels of risk from anthropogenic disturbance ranging from inviolate refuges to publicly accessed areas where we predicted mortality risk to be greatest. Our results suggest that water that the public can access (i.e., public water) influenced mortality risk during multiple seasons. Specifically, selection of public water by female mallards reduced mortality risk diurnally during the non‐hunting period (hazard ratio = 0.68, 95% CI = 0.48–0.96) but increased mortality risk during the first half of the hunting period (hazard ratio = 1.54, 95% CI = 1.08–2.20). Our research highlights that individual selection strategies by ducks within this landscape can influence mortality risk.
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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".