Competitive interactions modify the temperature dependence of damselfly growth rates
Bibliographic record
Abstract
Individual growth rates and survival are major determinants of individual fitness, population size structure, and community dynamics. The relationships between growth rate, survival, and temperature may thus be important for predicting biological responses to climate change. Although it is well known that growth rates and survival are affected by competition and predation in addition to temperature, the combined effect of these factors on growth rates, survival, and size structure has rarely been investigated simultaneously in the same ecological system. To address this question, we conducted experiments on the larvae of two species of damselflies and determined the temperature dependence of growth rate, survival, and cohort size structure under three scenarios of increasing ecological complexity: no competition, intraspecific competition, and interspecific competition. In one species, the relationship between growth rate and temperature became steeper in the presence of competitors, whereas that of survival remained unchanged. In the other species, the relationship between growth rate and temperature was unaffected by competitive interactions, but survival was greatly reduced at high temperatures in the presence of interspecific competitors. The combined effect of competitive interactions and temperature on cohort size structure differed from the effects of these factors in isolation. Together, these findings suggest that it will be challenging to scale up information from single-species laboratory studies to the population and community level.
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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 | 0.000 |
| 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".