Establishing isotopic turnover rates and trophic discrimination factors in tiger beetle (Coleoptera: Cicindelidae) larvae through a diet switch experiment
Bibliographic record
Abstract
Stable isotope ratios give insight into food web interactions, but interpretation can be clouded by the timing of isotopic change associated with changes in diet and the difference in isotope ratios between consumers and their diets at equilibrium. The 15N/14N, deemed δ15N, increases with each trophic transfer as 15N becomes enriched, whereas the 13C/12C ratio, denoted as δ13C, remains relatively constant with each trophic transfer but can be influenced by lipid content. This study reports the trophic discrimination factors and isotopic half-lives in tiger beetles (Coleoptera: Cicindelidae). Wild-caught tiger beetle larvae were reared in a laboratory setting, subjected to a diet switch experiment, and sampled over time. Quadratic plateau models were used to characterize the change in δ15N, δ13C, and δ13Ccorr over time. Trophic discrimination factors were calculated by subtracting the mean prey δ15N, δ13C, and δ13Ccorr from that of the tiger beetle asymptotic δ15N, δ13C and δ13Ccorr values, respectively. The tiger beetle trophic discrimination factor for δ15N was 1.7 ± 0.2‰ with a half-life of 11.4 days. For δ13C, the trophic discrimination factor and half-life were –0.6 ± 0.2‰ and 3.9 days, respectively. After correcting for lipids (δ13Ccorr), the trophic discrimination factor was –0.2 ± 0.2‰ with a half-life of 4.7 days. Isotopic turnover was fast with short half-lives, but factors that influence metabolic rates, such as ambient temperatures and life stage, should be considered when applying these estimates to wild tiger beetles. Despite this, the trophic discrimination factors and turnover rates calculated in this article are suitable estimates which can be applied to future studies.
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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 | 0.001 |
| 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".