Predator activity, proactive anti-predator strategies, and nesting phenology produce a dynamic landscape of risk to tundra goose reproduction
Bibliographic record
Abstract
Birds generally rely on proactive anti-predator strategies when selecting nest sites, as they have limited options to adapt to changing levels of risk once incubation begins. Arctic waterfowl often nest colonially as an anti-predator strategy but dispersed-breeding species may use other proactive strategies, such as nesting in less risky areas. However, empirical links between spatial patterns of predation risk and nest habitat selection or success are needed to better understand how predator activity shapes Arctic waterfowl reproduction. Using activity data from the main cursorial nest predators, Arctic and red foxes (Vulpes lagopus, V. vulpes), and activity data from aerial predators, we evaluate the influence predator activity has on Canada goose (Branta canadensis interior) nest habitat selection and success, and how nesting phenology mediates these effects. By comparing habitat selection models fit to i) goose nest locations and ii) fox locations obtained from GPS-collars, we show that geese and foxes display nearly opposite patterns of selection for the same landscape traits. Geese selected sites that appeared to minimize their probability of encountering foxes while also maximizing their ability to detect foxes. Spatial predictions of fox activity revealed nests located in areas with higher probability of fox use had lower nest success, indicating fox space-use patterns reflect predation risk. Nests initiated earlier had a greater probability of nest success, but these patterns strongly depended on location; the benefits of nesting early were largely lost in areas of high fox activity. We also found a negative relationship between clutch size and incubation date, suggesting nests incubated later had higher rates of failure likely due, in part, to geese being in poorer body condition. Although nest success was lower in areas with high aerial predator activity, fox activity likely has a greater influence on goose nest spatial patterns due largely to movement constraints imposed by tundra landscape features on foxes but not aerial predators. Our study demonstrates the mechanisms by which nesting phenology and predator activity interact to shape bird reproduction, and provides an empirical demonstration of the value of incorporating temporal dynamics into the “landscape of fear” framework.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".