Variation in δ<sup>15</sup>N and δ<sup>13</sup>C trophic fractionation: Implications for aquatic food web studies
Bibliographic record
Abstract
Use of stable isotope techniques to quantify food web relationships requires a priori estimates of the enrichment or depletion in δ 15 N and δ 13 C values between prey and predator (known as trophic fractionation; hereafter Δδ 15 N and Δδ 13 C). We conducted a broad‐scale analysis of Δδ 15 N and Δδ 13 C from aquatic systems, including three new field estimates. Carnivores had significantly higher Δδ 15 N values than herbivores. Furthermore, carnivores, invertebrates, and lab‐derived estimates were significantly more variable than their counterparts ( f‐test, p < 0.00001). Δδ 13 C was higher for carnivores than for herbivores ( p = 0.001), while variances did not differ significantly. Excluding herbivores, the average Δδ 15 N and Δδ 13 C were 3.4‰ and 0.8‰, respectively. But even with unbiased fractionation estimates, there is variation in isotopic fractionation that contributes to error in quantitative isotope model outputs. We simulated the error variance in δ 15 N‐based estimates of trophic position and two‐source δ 13 C diet mixing models, explicitly considering the observed variation in Δδ 15 N and Δδ 13 C, along with the other potential error sources. The resultant error in trophic position and mixing model outputs was generally minor, provided that primary consumers were used as baseline indicators for estimating trophic position and that end member d13C values in dietary mixing models were sufficiently distinct.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".