A link between water availability and nesting success mediated by predator–prey interactions in the Arctic
Bibliographic record
Abstract
Although water availability is primarily seen as a factor affecting food availability (a bottom-up process), we examined its effect on predator-prey interactions through an influence on prey behavior (a top-down process). We documented a link between water availability, predation risk, and reproductive success in a goose species (Chen caerulescens atlantica) inhabiting an Arctic environment where water is not considered a limited commodity. To reach water sources during incubation recesses, geese nesting in mesic tundra (low water availability) must move almost four times as far from their nest than those nesting in wetlands, which reduced their ability to defend their nest against predators and led to a higher predation rate. Nesting success was improved in high rainfall years due to increased water availability, and more so for geese nesting in the low water availability habitat. Likewise, nesting success was improved in years where the potential for evaporative water loss (measured by the atmospheric water vapor pressure) was low, presumably because females had to leave their nest less often to drink. Females from water-supplemented nests traveled a shorter distance to drink, and their nesting success was enhanced by 20% compared to the control. This shows that water availability and rainfall can have a strong effect on predator-prey dynamics and that changes in precipitation brought by climate change could have an impact on some Arctic species through a top-down effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".