Warmer and more seasonal climates reduce the effect of top‐down population control: An example with aphids and ladybirds
Bibliographic record
Abstract
Abstract Thermal performance within predator–prey systems may have profound effects on species interactions under climate change. However, how the thermal response of predators and prey to climate change affects their interactions is still understudied. To examine the responses of a predator–prey system to climate change, we constructed a biologically detailed stage‐structured population dynamic model using aphids (prey) and ladybirds (predator) as a model system. We explore the system's dynamics across the entire feasible parameter space of annual mean temperature and seasonality. Within this space, we explore all qualitatively possible scenarios of thermal performance mismatches to gain insight into how these affect the interacting species' responses to climatic change. We find that, generally, warmer and less seasonal climates are the most favourable climate conditions for both species. Our results also indicate that predation always has a stronger effect on aphid abundance than the climate in tropical and subtropical regions for all the thermal performance mismatch scenarios. Furthermore, predation's (biotic) effect on prey abundance will generally decrease relative to the effect of climate (abiotic) when future climates become warmer and more seasonal. Our research highlights that the effects of increasing seasonality are consistent with climate having a proportionally larger impact on species pairs with different thermal performances than predation. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".