Fitness and body size in mature odonates
Bibliographic record
Abstract
Summary The relationship between body size and fitness components in odonates was examined using a meta‐analysis of 33 published studies. There was a positive and significant overall effect of body size on mating rate and lifetime mating success among males. There was also a weaker but still significant positive effect of body size on survivorship of males. The relationship between body size, mating rate, longevity, and lifetime mating success differed significantly between males of territorial and nonterritorial species. The effect of body size was significant for all fitness components in territorial species but significant only for longevity and lifetime mating success in nonterritorial species. Effect sizes appeared to be strongest on longevity in both sexes, and on male mating rate in territorial species. Other effect sizes, even when significant, were small. Despite a much smaller data set, female fitness also increased significantly with body size. Both clutch size and longevity showed a significant positive relationship with body size. These results suggest that there is a general fitness benefit to large size in odonates. Nevertheless, significant heterogeneity is apparent in this effect, which can be attributed to sex, mating system, and fitness component. Finally, these analyses point to inadequacies in the current data that need further study before the potentially rich patterns in size effects on fitness can be explored more thoroughly.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".