Data and R code for: 'Predator activity, proactive anti-predator strategies, and nesting phenology produce a dynamic landscape of risk to tundra goose reproduction'
Bibliographic record
Abstract
This repository contains all of the data and R code needed to reproduce the analyses for: "Predator activity, proactive anti-predator strategies, and nesting phenology produce a dynamic landscape of risk to tundra goose reproduction" by Johnson-Bice et al., Journal of Animal Ecology. Refer to the main text for details on the methods used in this study.Abstract1. 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 areas perceived to be safer. 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.2. Using activity data from the main cursorial nest predators, Arctic and red foxes (Vulpes lagopus, V. vulpes), and 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.3. We compared habitat selection models fit to i) goose nest locations and ii) fox locations obtained from GPS collars and found that geese and foxes displayed nearly opposite patterns of selection for the same landscape traits. Geese selected sites that minimized 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.4. Landscape heterogeneity influenced both fox and goose nest habitat selection patterns and, consequently, spatial variation in predation risk and reproductive success. Aerial predators appeared to have a lesser effect on spatial patterns of goose nest habitat selection and success.5. Spatial patterns of nest success were, however, strongly influenced by nesting phenology. Nests initiated earlier had a greater probability of successfully hatching, but these patterns depended on location; the benefits of nesting early decreased in areas of high fox activity.6. Our study demonstrates the mechanisms by which nesting phenology, predator activity, and landscape heterogeneity interact to shape bird reproduction, and provides an empirical demonstration of how our understanding of predation risk is enhanced by integrating both spatial and temporal dynamics.
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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 | 0.008 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.427 | 0.249 |
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".