Direct and indirect effects of regional and local climatic factors on trophic interactions in the Arctic tundra
Bibliographic record
Abstract
Climate change can impact ecosystems by reshaping the dynamics of resource exploitation for predators and their prey. Alterations of these pathways could be especially intense in ecosystems characterized by a simple trophic structure and rapid warming trends, such as in the Arctic. However, quantifying the multiple direct and indirect pathways through which climate change is likely to alter trophic interactions and their relative strength remains a challenge. Here, we aim to identify direct and indirect causal mechanisms driven by climate affecting predator-prey interactions of species sharing a tundra food web. We based our study on relationships between one Arctic predator (Arctic fox) and its two main prey - lemmings (preferred prey) and snow geese (alternate prey) - which are exposed to variable local and regional climatic factors across years. We used a combination of models mapping multiple causal links among key variables derived from a long-term dataset (21 years). We obtained several possible scenarios linking regional climate factors (Arctic oscillations) and local temperature and precipitation to the breeding of species. Our results suggest that both regional and local climate factors have direct and indirect impacts on the breeding of foxes and geese. Local climate showed a positive causal link with goose nesting success, while both regional and local climate displayed contrasted effects on the proportion of fox breeding. We found no impact of climate on lemming abundance. We observed positive relationships between lemming, fox and goose reproduction highlighting numerical and functional responses of fox to the variability of lemming abundance. Our study measures causal links and strength of interactions in a food web, quantifying both numerical response of a predator and apparent interactions between its two main prey. These results improve our understanding of the complex effects of climate on predator-prey interactions and our capacity to anticipate food web response to ongoing climate change.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".