Global change risks a threatened species due to alteration of predator–prey dynamics
Bibliographic record
Abstract
Abstract Although global change can reshape ecosystems by triggering cascading effects on food webs, indirect interactions remain largely overlooked. Climate‐ and land‐use‐induced changes in landscape cause shifts in vegetation composition, which affect entire food webs. We used simulations of forest dynamics and movements of interacting species, parameterized by empirical observations, to predict the outcomes of global change on a large‐mammal food web in the boreal forest. We demonstrate that climate‐ and land‐use‐induced changes in forest landscapes exacerbate asymmetrical apparent competition between moose and threatened caribou populations through wolf predation. Although increased prey mortalities came from both behavioral and numerical responses, indirect effects from numerical responses had an overwhelming effect. The increase in caribou mortalities was exacerbated by the cumulating effects of land use over the short term and climate change impacts over the long term, with higher impact of land use. Indirect trophic interactions will be key to understanding community dynamics under global change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".