Duration of Exposure to Elevated Temperature Affects Competitive Interactions in Juvenile Reef Fishes
Bibliographic record
Abstract
Climate change will affect key ecological processes that structure natural communities, but the outcome of interactions between individuals and species will depend on their thermal plasticity. We tested how short- and long-term exposure to projected future temperatures affects intraspecific and interspecific competitive interactions in two species of coral reef damselfishes. In conspecific contests, juvenile Ambon damselfish, Pomacentrus amboinensis, exhibited no change in aggressive interactions after 4d exposure to higher temperatures. However, after 90d of exposure, fish showed a nonadaptive reduction in aggression at elevated temperatures. Conversely, 4d exposure to higher temperature increased aggression towards conspecifics in the lemon damselfish, Pomacentrus moluccensis. 90d exposure began to reduce this pattern, but overall there was little effect of temperature. Aggression in interspecific contests increased with short-term exposure, but was significantly lower after long-term exposure indicative of acclimation. Our results show how the length of exposure to elevated temperature can affect the outcome of competitive interactions. Furthermore, we illustrate that results from intraspecific contests may not accurately predict interspecific interactions, which will challenge our ability to generalise the effects of warming on competitive interactions.
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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 | 0.000 |
| 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) | 0.001 | 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".