A synthesis of tissue-preservation effects on carbon and nitrogen stable isotope signatures
Bibliographic record
Abstract
Stable-isotope analysis is a powerful method for characterizing flows of mass and energy through ecosystems. Long-term food-web studies using stable isotopes are valuable but rare because the required samples are not readily available. We examine the feasibility of using preserved specimens from natural-history collections as a source of long-term data for food-web studies and test whether chemical preservation affects the stable-isotope signature of tissues. We experimentally determined the effects of tissue preservation and fixation with 75% ethanol and 10% formalin, respectively, on δ13C and δ15N of three aquatic consumers: Sacramento sucker, Catostomus occidentalis, Asian clam, Corbicula fluminea, and a caddisfly, Hydropsyche sp. Using both our results and previously published literature results, we characterize preservation effects across many different consumer taxa including invertebrates, fish, and birds. Overall, only formalin fixation systematically affected isotope signature, causing an average depletion of 1.65 in δ13C, a bias that can easily be corrected for prior to interpreting data. Preservation affected mean δ15N values with far lower frequency and magnitude, although variability increased with preservation for some taxa but not others. These findings suggest that preserved specimens may be used for stable-isotope analysis and open up the possibility of using archived collections to reconstruct food webs and biogeochemical changes at scales of tens to hundreds of years.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".