Geographic variation in evolutionary rescue in a predator-prey system under climate change: an example with aphids and ladybird beetles
Bibliographic record
Abstract
Under climate change, species can adapt to changing environments through phenotypic plasticity and natural selection, and this kind of evolutionary adaptation can vary geographically. Most species distribution models (SDMs) are built upon the “Niche conservatism” assumption. They often ignore the possibility of “evolutionary rescue” and underestimate species’ future range limits under climate change. Here, we select aphids and ladybirds as model species and develop an eco-evolutionary model to explore evolutionary rescue in a predator-prey system under climate change. We model the adaptive change of species thermal performance, accounting for biotic interactions of unique life-history trait. Our results show that there is geographic variation in evolutionary rescue for ladybirds (the predator) across different locations in the United States, with ladybirds being more likely to be rescued from extinction in southeastern locations. The possibility of rescue is primarily influenced by the change in seasonality. Our findings also indicate the additive genetic variance of predators has a stronger influence on the phenotype evolution and population dynamics of both prey and predators, compared to the additive genetic variance of the prey. Our research emphasizes the importance of incorporating evolutionary adaptation when predicting species range shift under climate change. The eco-evolutionary model framework can be applied to study the effect of evolution on interacting species’ population abundance and geographic distribution under 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".