Species interactions mediate thermal evolution
Bibliographic record
Abstract
Abstract Understanding whether populations and communities can evolve fast enough to keep up with ongoing climate change is one of the most pressing issues in biology today. A growing number of studies have documented rapid evolutionary responses to warming, suggesting that populations may be able to persist despite temperature increases. The challenge now is to better understand how species interactions, which are ubiquitous in nature, mediate these population responses to warming. Here, we use laboratory natural selection experiments in a freshwater community to test hypotheses related to how thermal evolution of Daphnia pulex to two selection temperatures (12 and 18°C) is mediated by rapid thermal evolution of its algal resource ( Scenedesmus obliquus ) or by the presence of the zooplankton predator Chaoborus americanus . We found that cold‐evolved algae (a high‐quality resource) facilitated the evolution of increased thermal plasticity in Daphnia populations selected at 12°C, for both body size and per capita growth rates ( r ). Conversely, warm‐evolved algae facilitated the evolution of increased r thermal plasticity for Daphnia selected at 18°C. Lastly, we found that the effect of selection temperature on evolved Daphnia body size was more pronounced when Daphnia were also reared with predators. These data demonstrate that trait evolution of a focal population to the thermal environment can be affected by both bottom‐up and top‐down species interactions and that rapid temperature evolution of a resource can have cascading effects on consumer thermal evolution. Our study highlights the importance of incorporating species interactions when estimating ecological and evolutionary responses of populations and communities to ongoing temperature warming.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".