Comparison of δ13C and δ15N of ecologically relevant amino acids among beluga whale tissues
Bibliographic record
Abstract
Abstract Ecological applications of compound-specific stable isotope analysis (CSIA) of amino acids (AAs) include 1) tracking carbon pathways in food webs using essential AA (AAESS) δ13C values, and 2) estimating consumer trophic position (TP) by comparing relative differences of ‘trophic’ and ‘source’ AA δ15N values. Despite the significance of these applications, few studies have examined AA-specific SI patterns among tissues with different AA compositions and metabolism/turnover rates, which could cause differential drawdown of body AA pools and impart tissue-specific isotopic fractionation. To address this knowledge gap, especially in the absence of controlled diet studies examining this issue in captive marine mammals, we used a paired-sample design to compare δ13C and δ15N values of 11 AAs in commonly sampled tissues (skin, muscle, and dentine) from wild beluga whales (Delphinapterus leucas). δ13C of two AAs, glutamic acid/glutamine (Glx, a non-essential AA) and, notably, threonine (an essential AA), differed between skin and muscle. Furthermore, δ15N of three AAs (alanine, glycine, and proline) differed significantly among the three tissues, with glycine δ15N differences of approximately 10 ‰ among tissues supporting recent findings it is unsuitable as a source AA. Significant δ15N differences in AAs such as proline, a trophic AA used as an alternative to Glx in TP estimation, highlight tissue selection as a potential source of error in ecological applications of CSIA-AA. Amino acids that differed among tissues play key roles in metabolic pathways (e.g., ketogenic and gluconeogenic AAs), pointing to potential physiological applications of CSIA-AA in studies of free-ranging animals. These findings underscore the complexity of isotopic dynamics within tissues and emphasize the need for a nuanced approach when applying CSIA-AA in ecological research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".