Food web rewiring in a changing world
Bibliographic record
Abstract
Climate change is asymmetrically altering environmental conditions in space, from local to global scales, creating novel heterogeneity. Here, we argue that this novel heterogeneity will drive mobile generalist consumer species to rapidly respond through their behavior in ways that broadly and predictably reorganize—or rewire—food webs. We use existing theory and data from diverse ecosystems to show that the rapid behavioral responses of generalists to climate change rewire food webs in two distinct and critical ways. Firstly, mobile generalist species are redistributing into systems where they were previously absent and foraging on new prey, resulting in topological rewiring—a change in the patterning of food webs due to the addition or loss of connections. Secondly, mobile generalist species, which navigate between habitats and ecosystems to forage, will shift their relative use of differentially altered habitats and ecosystems, causing interaction strength rewiring—changes that reroute energy and carbon flows through existing food web connections and alter the food web’s interaction strengths. We then show that many species with shared traits can exhibit unified aggregate behavioral responses to climate change, which may allow us to understand the rewiring of whole food webs. We end by arguing that generalists’ responses present a powerful and underutilized approach to understand and predict the consequences of climate change and may serve as much-needed early warning signals for monitoring the looming impacts of global climate change on entire ecosystems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".