The Potential for Less Invasive Inference of Resource Use: Covariation in Stable Isotope Composition between Females and Their Eggs in Bluegill
Bibliographic record
Abstract
Abstract Stable isotope analysis is frequently used to examine resource use in wild populations, but it often involves invasive or lethal methods of collecting tissue samples. The development of less invasive or nonlethal sampling techniques will expand the possible uses of stable isotopes. We examined whether fish eggs meet three basic requirements for inferring female resource use from them: (1) the isotope composition of the eggs is correlated with that of other maternal tissues for which isotope composition is known to be related to diet; (2) the isotope composition remains constant over the egg development period; and (3) dietary inferences using eggs are similar to those for other maternal tissues. Using artificial crosses, we tested the relationship between eggs and two commonly sampled maternal tissues (white muscle and liver) in wild‐caught Bluegills Lepomis macrochirus. We found that egg isotope composition was strongly correlated with that of other maternal tissues, particularly liver, and remained constant from prefertilization to the day of hatch, with no change in 13C and an increase of only 0.3‰ in 15N. Furthermore, the results of SIAR (Stable Isotope Analysis in R) mixing models indicated a large degree of overlap in diet estimates between eggs and the other maternal tissues. Overall, eggs can be reliably used to infer the prebreeding foraging ecology of female Bluegills throughout the egg development period. Received July 10, 2014; accepted October 28, 2014
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".