Detecting trophic‐level variation in consumer assemblages
Bibliographic record
Abstract
Summary 1. Stable isotopes of nitrogen are useful for quantifying the trophic structure of food webs, but only if the variation in trophic enrichment (Δ N ), which is the difference in δ 15 N between a consumer and its food, is small relative to the value of Δ N itself. 2. We examined the sources of variation in zooplankton Δ N by measuring the trophic enrichment (Δ N ) of seven species of freshwater cladocerans, and by testing for an effect of age and temperature on the Δ N of Daphnia pulicaria . 3. We found that Δ N was similar among Cladocera and was not correlated with body size. Overall, the Δ N for D. pulicaria was 1.4‰ (SE = 0.69, n = 57), as was expected for the detritus diet that we used in our experiments. We found no effect of temperature (15–25 °C) on Δ N , but found that Δ N of D. pulicaria increased with increasing age (10–30 days). 4. We developed a new method to test for trophic‐level variation in a group of consumers that explicitly accounts for the uncertainty in Δ N . Using this approach, we confirmed that natural assemblages of zooplankton feed at several trophic levels in lake food webs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".