Temporal variation in body composition (C : N) helps explain seasonal patterns of zooplankton <i>δ</i><sup>13</sup>C
Bibliographic record
Abstract
Summary 1. The stable carbon isotope ratio δ13C is a useful tracer of energy flow in lake food webs, and the zooplankton signature is commonly used to establish a baseline for the pelagic habitat. However, sources of temporal variability in the δ13C of different zooplankton taxa are rarely considered. 2. Here, we investigate to what extent temporal variation in the δ13C of particulate organic matter (POM) (<41 μm) and the C : N of zooplankton can explain the temporal variability in δ13C of freshwater zooplankton. We compare temporal patterns of δ13C and C : N for Daphnia, Hesperodiaptomus franciscanus and Leptodiaptomus tyrelli over a 6‐month period at four sites in two oligotrophic lakes. 3. In all three taxa, seasonal variation in zooplankton C : N explained more of the variation in zooplankton δ13C than did the δ13C of POM. This suggests that variation in the lipid content of zooplankton can strongly influence temporal variation of δ13C in zooplankton. 4. Using these data, we evaluate procedures that estimate the δ13C of only the non‐lipid component of zooplankton. If zooplankton lipids are primarily dietary in origin, than extracting lipids or ‘normalising’δ13C based on C : N will exclude a major dietary source, and therefore may be inappropriate. 5. We conclude that temporal variation in body composition (C : N) of zooplankton can significantly influence the temporal variation of zooplankton δ13C signatures.
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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 | 0.002 |
| 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) | 0.002 | 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".