Assessment of Compound-Specific Fatty Acid δ<sup>13</sup>C and δ<sup>2</sup>H Values to Track Fish Mobility in a Small Sub-alpine Catchment
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Methods for identifying origin, movement, and foraging areas of animals are essential for understanding ecosystem connectivity, nutrient flows, and other ecological processes. Telemetric methods can provide detailed spatial coverage but are limited to a minimum body size of specimen for tagging. In recent years, stable isotopes have been increasingly used to track animal migration by linking landscape isotope patterns into movement (isoscapes). However, compared to telemetric methods, the spatial resolution of bulk stable isotopes is low. Here, we examined a novel approach by evaluating the use of compound-specific hydrogen and carbon stable isotopes of fatty acids (δ 2 H FA and δ 13 C FA ) from fish liver, muscle, brain, and eye tissues for identifying site specificity in a 254 km 2 sub-alpine river catchment. We analyzed 208 fish (European bullhead, rainbow trout, and brown trout) collected in 2016 and 2018 at 15 different sites. δ 13 C FA values of these fish tissues correlated more among each other than those of δ 2 H FA values. Both δ 2 H FA and δ 13 C FA values showed tissue-dependent isotopic fractionation, while fish taxa had only small effects. The highest site specificity was for δ 13 C DHA values, while the δ 2 H isotopic difference between linoleic acid and alpha-linolenic acid resulted in the highest site specificity. Using linear discrimination analysis of FA isotope values, over 90% of fish could be assigned to their location of origin; however, the accuracy dropped to about 56% when isotope data from 2016 were used to predict the sites for samples collected in 2018, suggesting temporal shifts in site specificity of δ 2 H FA and δ 13 C FA . However, the predictive power of δ 2 H FA and δ 13 C FA over this time interval was still higher than site specificity of bulk tissue isotopes for a single time point. In summary, compound-specific isotope analysis of fatty acids may become a highly effective tool for assessing fine and large-scale movement and foraging areas of animals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".